Search results for: neural activity

Authors: Zylfi Shehu, Rozeta Shatku

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The aim of this study is the identification of the goods and the consequences it brings up the mountain climbing to the youth, how mountain climbing influences in physical activity and the health of young people. Taken to study 37 young people aged 18-30 years, 25 males and 12 females. The selection was made at random and voluntary. Subjects were not professionals but amateurs climbing in the mountain. They were informed and instructed for the test to be carried out. The ascent was made in January 2016 in the Mount of Gjallica in Kukës, Albania, the height of the mountain is 2489 m above sea level. Backpack for each subject weighing 32 kg. Time of ascent, attitude and descent was 6 days. In 22 males, 2 of them did not afford the ascent on the first day and went back. Of the 12 women, 5 of them withdrew on the first day. During the descent on day six, 20 males 7 of them had minor injuries, three with serious injuries. While a total of 7 women, 4 of them had minor injuries and one with serious injuries. Most of the men and women who deal with physical activity throughout life faced the light and were not injured, and the rest that were not dealt with physical activity were more injured. Lack of experience and knowledge was one of the causes of injuries. The subjects had anxiety all the time, uncertainty and fear of avalanches of snow and difficult terrain.

Keywords: climbing, physical activity, young people

Procedia PDF Downloads 340

Authors: Yuchien Lin

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This study attempted to investigate the changes in electroencephalography (EEG) and emotion regulation following eight-week inward-attention meditation training program. The subjects were 24 adults without meditation experiences divided into meditation and control groups. The quantitatively analyzed changes in psychophysiological parameters during inward-attention meditation, and evaluated the emotion scores assessed by the State-Trait Anxiety Inventory (STAI), the Positive and Negative Affect Schedule (PANAS), and the Emotion Regulation Scale (ERS). The results were found: (1) During meditation, significant EEG increased for theta-band activity in the frontal and the bilateral temporal areas, for alpha-band activity in the left and central frontal areas, and for gamma-band activity in the left frontal and the left temporal areas. (2) The meditation group had significantly higher positive affect in posttest than in pretest. (3) There was no significant difference in the changes of EEG spectral characteristics and emotion scores in posttest and pretest for the control group. In the present study, a unique meditative concentration task with a constant level of moderate mental effort focusing on the center of brain was used, so as to enhance frontal midline theta, alpha, and gamma-band activity. These results suggest that this mental training allows individual reach a specific mental state of relaxed but focused awareness. The gamma-band activity, in particular, enhanced over left frontoparietal area may suggest that inward-attention meditation training involves temporal integrative mechanisms and may induce short-term and long-term emotion regulation abilities.

Keywords: meditation, EEG, emotion regulation, gamma activity

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

Procedia PDF Downloads 159

Authors: Paballo M. Moshupya, Tamiru A. Abiye, Ian Korir

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Mining and processing of naturally occurring radioactive materials could result in elevated levels of natural radionuclides in the environment. The aim of this study was to evaluate the radioactivity levels on a large scale in the West Rand District in South Africa, which is dominated by abandoned gold mine tailings and the consequential radiological exposures to members of the public. The activity concentrations of ²³⁸U, ²³²Th and 40K in mine tailings, soil and rocks were assessed using the BGO Super-Spec (RS-230) gamma spectrometer. The measured activity concentrations for ²³⁸U, ²³²Th and 40K in the studied mine tailings were found to range from 209.95 to 2578.68 Bq/kg, 19.49 to 108.00 Bq/kg and 31.30 to 626.00 Bq/kg, respectively. In surface soils, the overall average activity concentrations were found to be 59.15 Bq/kg, 34.91 and 245.64 Bq/kg for 238U, ²³²Th and 40K, respectively. For the rock samples analyzed, the mean activity concentrations were 32.97 Bq/kg, 32.26 Bq/kg and 351.52 Bg/kg for ²³⁸U, ²³²Th and 40K, respectively. High radioactivity levels were found in mine tailings, with ²³⁸U contributing significantly to the overall activity concentration. The external gamma radiation received from surface soil in the area is generally low, with an average of 0.07 mSv/y. The highest annual effective doses were estimated from the tailings dams and the levels varied between 0.14 mSv/y and 1.09 mSv/y, with an average of 0.51 mSv/y. In certain locations, the recommended dose constraint of 0.25 mSv/y from a single source to the average member of the public within the exposed population was exceeded, indicating the need for further monitoring and regulatory control measures specific to these areas to ensure the protection of resident members of the public.

Keywords: activity concentration, gold mine tailings, in-situ gamma spectrometry, radiological exposures

Procedia PDF Downloads 122

Authors: L. Bendifallah, F. Acheuk, M. Djouabi, M. Oukili, R. Ghezraoui, W. Lakhdari, R. Allouane

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Nigella sativa L. (Renunculaceae) native to the Mediterranean region and Western Asia, Black cumin is grown to India, through Sudan and Ethiopia. It is widely cultivated in Egypt, the Middle East, Saudi Arabia, Turkey, Sudan, Afghanistan and Europe. It is among the most important medicinal plants in Algeria that is known for its antifungal and antimicrobial properties. Despite its plethora of uses for treating various diseases, it has garnered very little scientific interest so far, particularly in Algeria. For this study, the seeds of Algerian Nigella sativa L cultivated in the area of Magra (M’sila) in northern Algeria, were collected in summer. In such a propitious context, the aim of this study was to enhance Nigella sativa as a medicinal herb. The phytochemical screening methods are used. For their antimicrobial activity, extracts of tannin and polyphenols were screened against four pathogenic bacterial strains and two pathogenic yeast strains. The phytochemical analysis results showed a remarkable combination of chemical components including a high content in tannins, in flavonoïds, and in alkaloids. The tannins and the polyphenols have strong antimicrobial activity against all the species. The maximum zone of inhibition was noted for polyphenol and tannin extracts against Escerichia coli (14 mm, 12.33 mm) and an antifungic activity against Aspergillus niger (11.66 mm, 9 mm). These results indicate to some benefits of Nigella sativa seeds which can use to treatment the microbial infection.

Keywords: Nigella sativa, phytochemistry, antimicrobial activity, Algeria

Procedia PDF Downloads 321

Authors: L. Bendifallah, F.Acheuk, M. Djouabi, M. Oukili, R. Ghezraoui, W. Lakhdari, R. Allouane

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Nigella sativa L. (Renunculaceae) native to the Mediterranean region and Western Asia, Black cumin is grown to India, through Sudan and Ethiopia. It is widely cultivated in Egypt, the Middle East, Saudi Arabia, Turkey, Sudan, Afghanistan and Europe. It is among the most important medicinal plants in Algeria that is known for its antifungal and antimicrobial properties. Despite its plethora of uses for treating various diseases, it has garnered very little scientific interest so far, particularly in Algeria. For this study, the seeds of Algerian Nigella sativa L cultivated in the area of Magra (M’sila) in northern Algeria, were collected in summer. In such a propitious context, the aim of this study was to enhance Nigella sativa as a medicinal herb. The phytochemical screening methods are used. For their antimicrobial activity, extracts of tannin and polyphenols were screened against four pathogenic bacterial strains and two pathogenic yeast strains. The phytochemical analysis results showed a remarkable combination of chemical components including a high content in tannins, in flavonoïds, and in alkaloids. The tannins and the polyphenols have strong antimicrobial activity against all the species. The maximum zone of inhibition was noted for polyphenol and tannin extracts against Escerichia coli (14 mm, 12.33 mm) and an antifungic activity against Aspergillus niger (11.66 mm, 9 mm). These results indicate to some benefits of Nigella sativa seeds which can use to treatment the microbial infection.

Keywords: Algeria, antimicrobial activity, Nigella sativa, phytochemistry

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Authors: Justin M. Swanson, Anna Nelson, Daniel Handysides, Patti Herring, Christopher Hill

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Not only may social media use have a psychological impact, but increased use may be tied to decreases in physical activity and influencing sedentary behaviors. Social media can be used to share physically active lifestyles and possibly influence others to participate. In contrast, social media use may have adverse effects by decreasing participation in exercise. This study used a qualitative design to examine the relationship between social media use and exercise patterns. Participants were asked questions about their social media habits and how it might impact their physical activity behaviors. Self-reported exercise seemed to increase after viewing others engage in relatable activities or viewing someone that has overcame challenges. To increase the likelihood of engaging in exercise, exercise related posts should be low in difficulty, require few materials, or displayed progress from the individual posting.

Keywords: social media, exercise, physical activity, adults

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Authors: Rama Shanker, Anil Kumar Sachan

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Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

Procedia PDF Downloads 386

Authors: Joanne Hardman

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South Africa’s crisis in mathematics attainment is well documented. To meet the need to develop students’ mathematical performance in schools the government has launched various initiatives using computers to impact on mathematical attainment. While it is clear that computers can change pedagogical practices, there is a dearth of qualitative studies indicating exactly how pedagogy is transformed with Information Communication Technologies (ICTs) in a teaching activity. Consequently, this paper addresses the following question: how, along which dimensions in an activity, does pedagogy alter with the use of computer drill and practice software in four disadvantaged grade 6 mathematics classrooms in the Western Cape province of South Africa? The paper draws on Cultural Historical Activity Theory (CHAT) to develop a view of pedagogy as socially situated. Four ideal pedagogical types are identified: Reinforcement pedagogy, which has the reinforcement of specialised knowledge as its object; Collaborative pedagogy, which has the development of metacognitive engagement with specialised knowledge as its object; Directive pedagogy, which has the development of technical task skills as its object, and finally, Defensive pedagogy, which has student regulation as its object. Face-to-face lessons were characterised as predominantly Reinforcement and Collaborative pedagogy and most computer lessons were characterised as mainly either Defensive or Directive.

Keywords: computers, cultural historical activity theory, mathematics, pedagogy

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Authors: Katharigatta N. Venugopala, Melendhran Pillay, Bander E. Al-Dhubiab

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Tuberculosis (TB) infection is caused mainly by Mycobacterium tuberculosis (MTB) and it is one of the most threatening and wide spread infectious diseases in the world. Benzothiazole derivatives are found to have diverse chemical reactivity and broad spectrum of pharmacological activity. Some of the important pharmacological activities shown by the benzothiazole analogues are antitumor, anti-inflammatory, antimicrobial, anti-tubercular, anti-leishmanial, anticonvulsant and anti-HIV properties. Keeping all these facts in mind in the present investigation it was envisaged to synthesize a series of novel {2-(benzo[d]-thiazol-2-yl-methoxy)-substitutedaryl}-(substitutedaryl)-methanones (4a-f) and characterize by IR, NMR (1H and 13C), HRMS and single crystal x-ray studies. The title compounds are investigated for in vitro anti-tubercular activity against two TB strains such as H37Rv (ATCC 25177) and MDR-MTB (multi drug resistant MTB resistant to Isoniazid, Rifampicin and Ethambutol) by agar diffusion method. Among the synthesized compounds in the series, test compound {2-(benzo[d]thiazol-2-yl-methoxy)-5-fluorophenyl}-(4-chlorophenyl)-methanone (2c) was found to exhibit significant activity with MICs of 1 µg/mL and 2 µg/mL against H37Rv and MDR-MTB, respectively when compared to standard drugs. Single crystal x-ray studies was used to study intra and intermolecular interactions, including polymorphism behavior of the test compounds, but none of the compounds exhibited polymorphism behavior.

Keywords: benzothiazole analogues, characterization, crystallography, anti-TB activity

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 108

Authors: Samrah Saeed

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Introduction: This study considers a measure of positive body image and the associations between body appreciation, beauty ideals internalization, dietary habits, and physical activity in young adults. Positive body image is assessed by Body Appreciation Scale 2. It is used to assess a person's acceptance of the body, the degree of positivity, and respect for the body.Regular physical activity and healthy eating arebasically important for the body, and they play an important role in creating a positive image of the body. Objectives: To identify the associations between body appreciation and beauty ideals internalization. To compare body appreciation and body ideals internalization among students of different physical activity. To explore the associations between dietary habits (unhealthy, healthy), body appreciation and body ideals internalization. Research methods and organization: Study participants were young adult students, aged 18-35, both male and female.The research questionnaire consisted of four areas: body appreciation, beauty ideals internalization, dietary habits, and physical activity.The questionnaire was created in Google Forms online survey platform.The questionnaire was filled out anonymously Result and Discussion: Physical dissatisfaction, diet, eating disorders and exercise disorders are found in young adults all over the world.Thorough nutrition helps people understand who they are by reassuring them that they are okay without judging or accepting themselves. Social media can positively influence body image in many ways.A healthy body image is important because it affect self-esteem, self-acceptance, and your attitude towards food and exercise.

Keywords: pysical activity, dietary habits, body image, beauty ideals internalization, body appreciation

Procedia PDF Downloads 92

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

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Authors: Anjali Verma, Mahesh Pal, Veena Pande, Dalip Kumar Upreti

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The present study is carried out to explore the anti-hyperglycemic activity of Leucas cephalotes plant parts. A fruit, leaves, stems, and roots part of the Leucas cephalotes has been extracted in ethanol and have been evaluated for anti-hyperglycemic activity. The present study indicated that, ethanolic extract of fruit and leaves have shown significant α- amylase inhibitory activity with IC50 value of 92.86 ± 0.89 μg/mL and 98.09 ± 0.69 μg/mL respectively. Two known compounds β-sitosterol and lupeol were isolated from ethanolic extract of L. cephalotes leaves and were subjected to anti-hyperglycemic activity. Lupeol shows the best activity with IC50 55.73 ± 0.47 μg/mL and the results were verified by docking study of these compounds with mammalian α-amylase was carried out on its active site. It was concluded from the study that β-sitosterol and lupeol form one H-bond interactions with the active site residues either Asp212 or Thr21. The estimated free energy binding of β-sitosterol was found to be -9.47 kcal mol-1 with an estimated inhibition constant (Ki) of 558.94 nmol whereas the estimated free energy binding of lupeol was -11.73 kcal mol-1 with an estimated inhibition constant (Ki) of 476.71pmmol. The present study clearly showed that lupeol is more potent in comparison to β-sitosterol. The study indicates that L. cephalotes have significant potential to inhibit α-amylase enzyme.

Keywords: alpha-amylase, beta-sitosterol, hyperglycemia, lupeol

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Authors: Soumeya Krimat, Tahar Dob, Mohamed Toumi, Aicha Kesouri, Hafidha Metidji, Chelghoum Chabane

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Lavandula antineae Maire is an endemic medicinal plant of Algeria which is traditionally used for the treatment of chills, bruises, oedema and rheumatism. The present study was designed to investigate the phytochemical screening, total phenolic and antioxidant activity of Lavandula antineae Maire for the first time. Phytochemical screening revealed the presence of different kind of chemical groups (anthraquinones, terpenes, saponins, flavonoids, tannins, O-heterosides, C-heterosides, phenolic acids). The amounts of total phenolics in the extracts (hydromethanolic and ethyl acetate extract) were determined spectrometrically. From the analyses, ethyl acetate extract had the highest total phenolic content (262.35 mg GA/g extract) and antioxidant activity (IC50=7.10 µg/ml) using DPPH method. The ethyl acetate extract was also more potent on reducing power compared to hydromethanolic extract. The results suggested that L. antineae could be considered as a new potential source of natural antioxidant for pharmaceuticals and food preservation.

Keywords: Lavandula antineae, antioxidant activity, phytochemical screening, total phenolics

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Authors: Delgado-Meza M., Minor-Pérez H.

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Protease is the generic name of enzymes that hydrolyze proteins. These are classified in the subgroup EC3.4.11-99X of the classification enzymes. In food technology the proteolysis is used to modify functional and nutritional properties of food, and in some cases this proteolysis may cause food spoilage. In general, seafood and rainbow trout have accelerated decomposition process once it has done its capture, due to various factors such as the endogenous enzymatic activity that can result in loss of structure, shape and firmness, besides the release of amino acid precursors of biogenic amines. Some studies suggest the use of protease inhibitors from legume as biological regulators of proteolytic activity. The enzyme inhibitors are any substance that reduces the rate of a reaction catalyzed by an enzyme. The objective of this study was to evaluate the reduction of the proteolytic activity of enzymes in extracts of rainbow trout with protease inhibitors obtained from chickpea flour. Different proportions of rainbow trout enzyme extract (75%, 50% and 25%) and extract chickpea enzyme inhibitors were evaluated. Chickpea inhibitors were obtained by mixing 5 g of flour in 30 mL of pH 7.0 phosphate buffer. The sample was centrifuged at 8000 rpm for 10 min. The supernatant was stored at -15°C. Likewise, 20 g of rainbow trout were ground in 20 mL of phosphate buffer solution at pH 7.0 and the mixture was centrifuged at 5000 rpm for 20 min. The supernatant was used for the study. In each treatment was determined the specific enzymatic activity with the technique of Kunitz, using hemoglobin as substrate for the enzymes acid fraction and casein for basic enzymes. Also biuret protein was quantified for each treatment. The results showed for fraction of basic enzymes in the treatments evaluated, that were inhibition of endogenous enzymatic activity. Inhibition values compared to control were 51.05%, 56.59% and 59.29% when the proportions of endogenous enzymes extract rainbow trout were 75%, 50% and 25% and the remaining volume used was extract with inhibitors. Treatments with acid enzymes showed no reduction in enzyme activity. In conclusion chickpea flour reduced the endogenous enzymatic activity of rainbow trout, which may favor its application to increase the half-life of this food. The authors acknowledge the funding provided by the CONACYT for the project 131998.

Keywords: rainbouw trout, enzyme inhibitors, proteolysis, enzyme activity

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Taras Agryzkov, Almudena Nolasco-Cirugeda, Jose L. Oliver, Leticia Serrano-Estrada, Leandro Tortosa, Jose F. Vicent

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This paper aims to represent the commercial activity of a city taking as source data the social network Foursquare. The city of Murcia is selected as case study, and the location-based social network Foursquare is the main source of information. After carrying out a reorganisation of the user-generated data extracted from Foursquare, it is possible to graphically display on a map the various city spaces and venues –especially those related to commercial, food and entertainment sector businesses. The obtained visualisation provides information about activity patterns in the city of Murcia according to the people`s interests and preferences and, moreover, interesting facts about certain characteristics of the town itself.

Keywords: social networks, spatial analysis, data visualization, geocomputation, Foursquare

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Authors: A. A. Dehpour, B. Eslami, S. Rezaie, S. F. Hashemian, F. Shafie, M. Kiaie

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The aims of study were investigation on chemical composition essential oil and the effect of extract of Coronilla varia on antimicrobial and cytotoxicity activity. The essential oils of Coronilla varia is obtained by hydrodistillation and analyzed by (GC/MS) for determining their chemical composition and identification of their components. Antibacterial activity of plant extract was determined by disc diffusion method. The effect of hydroalcolic extracts from Cornilla varia investigated on MCF7 cancer cell line by MTT assay. The major components were Caryophyllene Oxide (60.19%), Alphacadinol (4.13%) and Homoadantaneca Robexylic Acid (3.31%). The extracts from Coronilla varia had interesting activity against Proteus mirabilis in the concentration of 700 µg/disc and did not show any activity against Staphylococus aureus, Bacillus subtillis, Klebsiella pneumonia and Entrobacter cloacae. The positive control, Ampicillin, Chloramphenicol and Cenphalothin had shown zone of inhibition resistant all bacteria. Corohilla varia ethanol extract could inhibit the proliferation of MCF7 cell line in RPMI 1640 medium. IC50 5(mg/ml) was the optimum concentration of extract from Coronilla varia inhibition of cell line growth. The MCF7 cancer cell line and Proteus mirabilis were more sensitive to Coronilla varia ethanol extract.

Keywords: Coronilla varia, essential oil, antibacterial, anticancer, hela cell line

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Authors: Tsige Reda

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Essential oil of Thymus vulgaris was extracted by means of hydro-distillation. This study was done to investigate the chemical composition, antibacterial and antioxidant activities. The chemical composition of the essential oils was determined using gas chromatography coupled to mass spectroscopy (GC-MS). Using disc diffusion assay the antibacterial activity was assessed on one Gram-positive bacteria and one Gram-negative bacteria. The percentage oil yield of the essential oil was found to be 0.97 ± 0.08% (w/w) with yellow color. The physicochemical constants of the oil were also noted. The phytochemical screening of the plant extract revealed the presence of tannins, saponins, phenol, flavonoids, terpenoids, steroids and alkaloids. A total of 18 chemical constituents were identified by Gas Chromatography-Mass Spectroscopy analysis representing 100% of the total essential oil of Thymus vulgaris, with thymol (31.977%), o-cymene (29.992%), and carvacrol (14.541%). Previous studies have revealed that the thymol, o-cymen and carvacrol components of Thymus vulgaris are responsible for their biological activities. Thymus vulgaris have been used traditionally to treat a wide variety of infections. Based on the extensive use and lack of scientific evidence, a study was embarked upon to determine its bioactivity. The essential oil of Thymus vulgaris leaves exhibited higher activity towards the Gram-positive bacteria (Staphylococcus aurous) than the Gram-negative bacteria (Escherichia coli) and also has good antioxidant activity, and can be used medicinal and therapeutic applications. This activity may be due to the high amount of thymol, o-cymen and carvacrol.

Keywords: hydro-distillation, Thymus vulgaris, essential oil composition, phytochemical screening, physicochemical constants, antioxidant activity, antibacterial activity

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Authors: Lyudmila V. Ignatova, Yelena V. Brazhnikova, Togzhan D. Mukasheva, Ramza Zh. Berzhanova, Anel A. Omirbekova

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Endophytic microorganisms are presented in plants of different families growing in the foothills and piedmont plains of Trans-Ili Alatau. It was found that the maximum number of endophytic micromycetes is typical to the Fabaceae family. The number of microscopic fungi in the roots reached (145.9±5.9)×103 CFU/g of plant tissue; yeasts - (79.8±3.5)×102 CFU/g of plant tissue. Basically, endophytic microscopic fungi are typical for underground parts of plants. In contrast, yeasts more infected aboveground parts of plants. Small amount of micromycetes is typical to inflorescence and fruits. Antagonistic activity of selected micromycetes against Fusarium graminearum, Cladosporium sp., Phytophtora infestans and Botrytis cinerea phytopathogens was detected. Strains with a broad, narrow and limited range of action were identified. For further investigations Rh2 and T7 strains were selected, they are characterized by a broad spectrum of fungicidal activity and they formed the large inhibition zones against phytopathogens. Active antagonists are attributed to the Rhodotorula mucilaginosa and Beauveria bassiana species.

Keywords: endophytic micromycetes, fungicidal activity, prevalence, plants

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Authors: K. Senthil Kumar, A. Vasumalaikannan

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In this paper, teleoperation of the quadrotor using Internet with Force feedback is addressed. Teleoperation with Force feedback is the ability to remotely control a robot, where contact (obstacle) or environment (wind gust etc) information (force feedback) is communicated from the quadrotor to the master joystick and thus giving the operator a sense of telepresence. The stability and performance of such a teleoperator is highly dependent on the amount of time delay present in the control loop. This problem is further complicated given the fact that for network based communication the time delay is itself time varying and highly non deterministic. In this paper, a novel method using Neural based Smith Predictor at the master side the stability is achieved. The performance of the system even during worst case scenario is within acceptable.

Keywords: teleoperation, quadrotor, neural smith predictor, time delay

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Authors: Elitsa Kolentsova, Dimitar Dimitrov, Vasko Idakiev, Tatyana Tabakova, Krasimir Ivanov

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Formaldehyde production by selective oxidation of methanol is an important industrial process. The main by-products in the waste gas are CO and dimethyl ether (DME). The idea of this study is to combine the advantages of both Cu-Mn and Cu-Co catalytic systems by obtaining a new mixed Cu-Mn-Co catalyst with high activity and selectivity at the simultaneous oxidation of CO, methanol, and DME. Two basic Cu-Mn samples with high activity were selected for further investigation: (i) manganese-rich Cu-Mn/γ–Al2O3 catalyst with Cu/Mn molar ratio 1:5 and (ii) copper-rich Cu-Mn/γ-Al2O3 catalyst with Cu/Mn molar ratio 2:1. Manganese in these samples was replaced by cobalt in the whole concentration region, and catalytic properties were determined. The results show a general trend of decreasing the activity toward DME oxidation and increasing the activity toward CO and methanol oxidation with the increase of cobalt up to 60% for both groups of catalyst. This general trend, however, contains specific features, depending on the composition of the catalyst and the nature of the oxidized gas. The catalytic activity of the sample with Cu/(Mn+Co) molar ratio of 2:1 is gradually changed with increasing the cobalt content. The activity of the sample with Cu/(Mn+Co) molar ratio of 1: 5 passes through a maximum at 60% manganese replacement by cobalt, probably due to the formation of highly dispersed Co-based spinel structures (Co3O4 and/or MnCo2O4). In conclusion, the present study demonstrates that the Cu-Mn-Co/γ–alumina supported catalysts have enhanced activity toward CO, methanol and DME oxidation. Cu/(Mn+Co) molar ratio 1:5 and Co/Mn molar ratio 1.5 in the active component can ensure successful oxidation of CO, CH3OH and DME. The active component of the mixed Cu-Mn-Co/γ–alumina catalysts consists of at least six compounds - CuO, Co3O4, MnO2, Cu1.5Mn1.5O4, MnCo2O4 and CuCo2O4, depending on the Cu/Mn/Co molar ratio. Chemical composition strongly influences catalytic properties, this effect being quite variable with regards to the different processes.

Keywords: Cu-Mn-Co catalysts, oxidation, carbon oxide, VOCs

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Elham Ahmadi

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This study is conducted with the aim of investigating the effect of moderate physical activity (60% of maximal heart rate-MHR) on blood pressure in an elderly female with hypertension. Hypertension is considered a modifiable risk factor for cardiovascular disease through physical activity. The purpose and significance of this study were to investigate the role of exercise as an alternative therapy since some patients exhibit sensitivity/intolerance to some drugs. Initially, 65 hypertensive females (average age = 49.7 years) (systolic blood pressure, SBP >140 mmHg and/or diastolic blood pressure, DBP>85 mmHg) and 25 hypertensive females as a control group (average age = 50.3 years and systolic blood pressure, SBP >140 mmHg and/or diastolic blood pressure, DBP>85 mmHg) were selected. The subjects were divided based on their age, duration of disease, physical activity, and drug consumption. Then, blood pressure and heart rate (HR) were measured in all of the patients using a sphygmomanometer (pre-test). The exercise sessions consisted of warm-up, aerobic activity, and cooling down (total duration of 20 minutes for the first session up to 55 minutes in the last session). At the end of the 12th session (mid-test) and final session (24th session), blood pressure was measured for the last time (post-test). The control group was without any exercise during the study. The results were analyzed using a t-test. Our results indicated that moderate physical activity was effective in lowering blood pressure by 6.4/5.6–mm Hg for SBP and 2.4/4.3mm Hg for DBP in hypertensive patients, irrespective of age, duration of disease, and drug consumption ( P<.005). The control group indicates no changes in BP. Physical activity programs with moderate intensity (approximately at 60% MHR), three days per week, can be used not only as a preventive measure for diastolic hypertension (DBP>90 mmHg high blood pressure) but also as an alternative to drug therapy in the treatment of hypertension, as well.

Keywords: endurance exercise, elderly female, hypertension, physical activity

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Authors: Jutharat Sunprasert, Ekapong Hirunsirisawat, Narongrit Waraporn, Somporn Peansukmanee

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Metal Ship and Robotic Car is one of the hands-on activities in the course, the Fundamental of Engineering that can be divided into three parts. The first part, the metal ships, was made by using engineering drawings, physics and mathematics knowledge. The second part is where the students learned how to construct a robotic car and control it using computer programming. In the last part, the students had to combine the workings of these two objects in the final testing. This aim of study was to investigate the effectiveness of hands-on activity by integrating Science, Technology, Engineering and Mathematics (STEM) concepts to develop scientific and engineering skills. The results showed that the majority of students felt this hands-on activity lead to an increased confidence level in the integration of STEM. Moreover, 48% of all students engaged well with the STEM concepts. Students could obtain the knowledge of STEM through hands-on activities with the topics science and mathematics, engineering drawing, engineering workshop and computer programming; most students agree and strongly agree with this learning process. This indicated that the hands-on activity: “Metal Ship and Robotic Car” is a useful tool to integrate each aspect of STEM. Furthermore, hands-on activities positively influence a student’s interest which leads to increased learning achievement and also in developing scientific and engineering skills.

Keywords: hands-on activity, STEM education, computer programming, metal work

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: Harish Rajak, Preeti Patel

Abstract:

HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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