Search results for: neural activity

Authors: Farhad Azadi, Mohammad Mahdi Mohammadi, Mohsen Vahedi, Zahra Mahdiin

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Context: The aging population is growing all over the world and maintaining physical activity is essential for healthy aging. However, fear of falling is a major obstacle to physical activity among the elderly. The aim of this study is to investigate and compare the relationship between fear of falling and physical activity in Iranian urban and rural elderly. Research Aim: The main aim of this cross-sectional analytical study is to investigate and compare the relationship between fear of falling and physical activity in Iranian rural and urban elderly. Methodology: The study used simple non-probability sampling to select 350 participants aged 60 years and older from rural and urban areas of Konarak, Sistan and Baluchistan provinces in Iran. The Persian versions of the Falls Efficacy Scale - International, Rapid Physical Activity Assessment, Activities of Daily Living, and Instrumental Activities of Daily Living questionnaires were used to assess fear of falling and physical activity. The data were analyzed using Pearson correlation tests. Findings: The study found a statistically significant negative correlation between fear of falling and physical activity, as measured by ADL, IADL, and RAPA1(aerobic ), in all elderly and rural and urban elderly (p<0.001). Fear of falling was higher in rural areas, while physical activity levels measured by ADL and RAPA1 were higher in urban areas. No significant difference was found between the two groups in IADL and RAPA2 (strength and flexibility) scores. Theoretical Importance: This study highlights the importance of considering the fear of falling as a significant obstacle to proper physical activity, especially among the elderly living in rural areas. Furthermore, the study provides insight into the difference between rural and urban elderly people in terms of fear of falling and physical activity. Data Collection and Analysis Procedures: Data was collected through questionnaires and analyzed using Pearson correlation tests. Questions Addressed: The study attempted to answer the following questions: Is there a relationship between fear of falling and physical activity in Iranian urban and rural elderly people? Is there a difference in fear of falling and physical activity between rural and urban elderly? Conclusion: Fear of falling is a major obstacle to physical activity among the elderly, especially in rural areas. The study found a significant negative correlation between fear of falling and physical activity in all elderly and rural and urban elderly. In addition, urban and rural elderly have differences in aerobic activity levels, but they do not differ in terms of flexibility and strength. Therefore, proper interventions are required to ensure that the elderly can maintain physical activity, especially in rural and deprived areas.

Keywords: aged, fear of falling, physical activity, urban population, rural population

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Authors: Ahmed M. Ashteyat

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Artificial Neural Network (ANN) models have been widely used in material modeling, inter-correlations, as well as behavior and trend predictions when the nonlinear relationship between system parameters cannot be quantified explicitly and mathematically. In this paper, ANN was used to predict the residual modulus of elasticity (RME) of self compacted concrete (SCC) damaged by heat. The ANN model was built, trained, tested and validated using a total of 112 experimental data sets, gathered from available literature. The data used in model development included temperature, relative humidity conditions, mix proportions, filler types, and fiber type. The result of ANN training, testing, and validation indicated that the RME of SCC, exposed to different temperature and relative humidity levels, could be predicted accurately with ANN techniques. The reliability between the predicated outputs and the actual experimental data was 99%. This show that ANN has strong potential as a feasible tool for predicting residual elastic modulus of SCC damaged by heat within the range of input parameter. The ANN model could be used to estimate the RME of SCC, as a rapid inexpensive substitute for the much more complicated and time consuming direct measurement of the RME of SCC.

Keywords: residual modulus of elasticity, artificial neural networks, self compacted-concrete, material modeling

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Authors: Nassim Belkacem, Amina Azzam, Dalila Haouchine, Kahina Bennacer, Samira Soufit

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Objective: To evaluate the antioxidant activity of the methanolic extract along with the antimicrobial activity of the essential oil of the aerial parts of Rosmarinus officinalis L. collected in the region of Bejaia (northern center of Algeria). Materials and methods: The polyphenols and flavonoids contents of the methanolic extract were measured. The antioxidant activity was evaluated using two methods: the ABTS method and DPPH assay. The antimicrobial activity was studied by the agar diffusion method against five bacterial strains (Three Gram positive strains and two Gram negative strains) and one fungus. Results: The total polyphenol and flavonoid content was about 43.8 mg gallic acid equivalent per gram (GA Eq/g) and 7.04 mg quercetin equivalent per gram (Q Eq/g), respectively. In the ABTS assay, the rosemary extract has shown an inhibition of 98.02% at the concentration of 500ug/ml with a half maximal inhibitory concentration value (IC50) of 194.92ug/ml. The results of DPPH assay have shown that the rosemary extract has an inhibition of 94.67 % with an IC50 value of 17.87ug/ml, which is lower than that of Butylhydroxyanisol (BHA) about 6.03ug/ml and ascorbic acid about 1.24μg/ml. The yield in essential oil of rosemary obtained by hydrodistillation was 1.42%. Based on the determination of the diameter of inhibition, different antimicrobial activity of the essential oil was revealed against the six tested microbes. Escherichia coli from the University Hospital (UH), Streptococcus aureus (UH) and Pseudomonas aeruginosa ATCC have a minimum inhibitory concentration value (MIC) of 62.5µl/ml. However, Bacillus sp (UH) and Staphylococcus aureus ATCC have an MIC value of 125μl/ml. The inhibition zone against Candida sp was about 24 mm. The aromatograms showed that the essential oil of rosemary exercises an antifungal activity more important than the antibacterial one.

Keywords: Rosmarinus officinalis L., maceration, essential oil, antioxidant, antimicrobial activity

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Linda Chularojmontri, Seewaboon Sireeratawong, Suvara Wattanapitayakul

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Terminalia arjuna Roxb. (family Combretaceae) is commonly known as ‘Sa maw thet’ in Thai. The fruit is used in traditional medicine as natural mild laxatives, carminative and expectorant. Aim of the study: This research aims to study the anti-inflammatory, analgesic and antipyretic activities of Terminalia arjuna extract by using animal models in comparison to the reference drugs. Materials and Methods: The anti-inflammatory study was conducted by two experimental animal models namely ethyl phenylpropionate (EPP)-induced ear edema and carrageenan-induced paw edema. The study of analgesic activity used two methods of pain induction including acetic acid and heat-induced pain. In addition, the antipyretic activity study was performed by induced hyperthermia with yeast. Results: The results showed that the oral administration of Terminalia arjuna extract possessed acute anti-inflammatory effect in carrageenan-induced paw edema. Terminalia arjuna extract showed the analgesic activity in acetic acid-induced writhing response and heat-induced pain. This indicates its peripheral effect by inhibiting the biosynthesis and/or release of some pain mediators and some mechanism through Central nervous system. Moreover, Terminalia arjuna extract at the dose of 1000 and 1500 mg/kg body weight showed the antipyretic activity, which might be because of the inhibition of prostaglandins. Conclusion: The findings of this study indicated that the Terminalia arjuna extract possesses the anti-inflammatory, analgesic and antipyretic activities in animals.

Keywords: analgesic activity, anti-inflammatory activity, antipyretic activity, Terminalia arjuna extract

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Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Redhouane Ghernaout, Amira Fredj, Boualem Remini

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Suspended sediment transport is a serious problem worldwide, but it is much more worrying in certain regions of the world, as is the case in the Maghreb and more particularly in Algeria. It continues to take disturbing proportions in Northern Algeria due to the variability of rains in time and in space and constant deterioration of vegetation. Its prediction is essential in order to identify its intensity and define the necessary actions for its reduction. The purpose of this study is to analyze the concentration data of suspended sediment measured at Wadi El-Abtal Hydrometric Station. It also aims to find and highlight regressive power relationships, which can explain the suspended solid flow by the measured liquid flow. The study strives to find models of artificial neural networks linking the flow, month and precipitation parameters with solid flow. The obtained results show that the power function of the solid transport rating curve and the models of artificial neural networks are appropriate methods for analysing and estimating suspended sediment transport in Wadi Mina at Wadi El-Abtal Hydrometric Station. They made it possible to identify in a fairly conclusive manner the model of neural networks with four input parameters: the liquid flow Q, the month and the daily precipitation measured at the representative stations (Frenda 013002 and Ain El-Hadid 013004 ) of the watershed. The model thus obtained makes it possible to estimate the daily solid flows (interpolate and extrapolate) even beyond the period of observation of solid flows (1985/86 to 1999/00), given the availability of the average daily liquid flows and daily precipitation since 1953/1954.

Keywords: suspended sediment, concentration, regression, liquid flow, solid flow, artificial neural network, modeling, mina, algeria

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

Procedia PDF Downloads 195

Authors: Dipanshu Sur, Ratnabali Chakravorty, Rimi Pal, Siddhartha Chatterjee, Joyshree Chaterjee, Amal Mallik

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Background: Women with polycystic ovary disease (PCOD) frequently suffer from metabolic disturbances. PCOD is a common ovulatory disorder in young women, which affects 5-10% of the population and results in infertility due to anovulation. Importantly, aromatase in ovarian granulosa and luteinized granulosa cells plays an important role for women of reproductive age. Generation and metabolism of androgen is directly related to aromatase activity. The E2/T ratio provides important information about aromatase activity because conversion of androgens to estrogens is mediated by CYP19, suggesting that the E2/T ratio may be a direct marker of aromatase activity. The nature of the interaction between ovarian aromatase activity and PCOD in women has been controversial, and the impact of weight gain on aromatase activity as well as E2 levels is unknown. Aim: The objective of this study was to investigate the association and relation between aromatase activity and levels of body mass index (BMI) from a reproductive hormone perspective in a group of women with or without PCOD. Methods: We designed a cohort study which included 200 individuals. It enrolled 100 cases of PCOD based on 2006 Rotterdam criteria and 100 ovulatory normal- non PCOD, healthy, age-matched controls. Plasma sex hormones viz. estradiol (E2), testosterone (T), follicle stimulating hormone (FSH), and luteinizing hormone (LH) were measured by ELISA on the second day of the menstrual cycle, together with BMI and E2/T were calculated. Aromatase activity in PCOD patients with different BMI, T and E2 levels were compared. Results: PCOD patients showed significantly increased levels of BMI, E2 (P=0.004), T and LH, while their E2/T (P= <0.001), FSH and FSH/LH values were decreased compared with the control group. Higher E2 levels correlated with a relatively enhanced E2/T as well as T and LH levels but reduced BMI, FSH and FSH/LH levels in women with PCOD. Hyperandrogenic PCOD patients had increased E2 levels but their aromatase activity was markedly inhibited independent of their BMI values. Conclusions: We found a significant decrease of ovarian aromatase activity in women with PCOD as compared to controls. Our study showed that ovarian aromatase activity in PCOD was decreased which was independent of BMI. Enhancing aromatase activity may become an optimized strategy for developing therapies for PCOD women, especially those with obesity.

Keywords: aromatase activity, polycystic ovary disease, obesity, body mass index

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Authors: Hulya Tuba Kiyan, Nilgun Ozturk

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Gentiana, a member of Gentianaceae, is represented by approximately 400 species in the world and 12 species in Turkey. Flavonoids, iridoids, triterpenoids and also xanthones are the major compounds of this genus, have been previously reported to have antiinflammatory, antimicrobial, antioxidant, hepatoprotective, hypotensive, hypoglycaemic, DNA repair and immunomodulatory properties. The methanolic extract of the aerial parts of Gentiana brachyphylla Vill. from Turkey was evaluated for its biological activities and its total phenolic content in the present study. According to the antioxidant activity results, G. brachyphylla methanolic extract showed very strong anti-DNA damage antioxidant activity with an inhibition of 81.82%. It showed weak ferric-reducing power with a EC50 value of 0.65 when compared to BHT (EC50 = 0.2). Also, at 0.5 mg/ml concentration, the methanolic extract inhibited ABTS radical cation activity with an inhibition of 20.13% when compared to Trolox (79.01%). Chelating ability of G. brachyphylla was 44.71% whereas EDTA showed 78.87% chelating activity at 0.2 mg/ml. Also G. brachyphylla showed weak 27.21% AChE, 20.23% BChE, strong 67.86% MAO-A and moderate 50.06% MAO-B, weak 19.14% COX-1, 29.11% COX-2 inhibitory activities at 0.25 mg/ml. The total phenolic content of G. brachyphylla was 156.23 ± 2.73 mg gallic acid equivalent/100 g extract.

Keywords: antioxidant activity, cholinesterase inhibitory activity, Gentiana brachyphylla Vill., total phenolic content

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Authors: Richa Bhardwaj

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Tecomella undulata (Sm.) Seem is a monotypic genus belonging to family Bignoniaceae. The plant holds tremendous potential of medicinal value and has been traditionally used in various ailments like syphilis, leukoderma, blood disorders to name a few. The plant has gained prominence due to the presence of some prominent secondary metabolites. The present study focuses on the GC-MS analysis of leaf extracts of T. undulata which revealed the presence of certain bioactive compounds like stigmasterol, sitosterol, thiazoline, phytol, pthalic acid, methyl alpha ketopalmitate and so forth. A total of about 20 bioactive compounds were identified from the leaf extract spectra. Antimicrobial activity of the leaf extract was assayed against pathogenic bacteria and fungi. The alkaloids from leaf extracts showed antimicrobial activity against E.coli and B.subtilis. The flavonoids from leaves showed positive activity against Penicillium species and Candida albicans. The study thus infers that the presence of bioactive components may be the principle behind the antimicrobial property of different plant parts and therefore Tecomella forms a potential plant for herbal drug formulation.

Keywords: Tecomella undulata, bioactive compounds, GC-MS, antimicrobial activity

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Authors: L. L. Tundisi, A. Pessoa Jr., E. B. Tambourgi, E. Silveira, P. G. Mazzola

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L-asparaginase (L-ASNase) is the gold standard treatment for acute lymphoblastic leukemia that mainly affects pediatric patients; treatment increases survival from 20% to 90%. The characterization of other L-Asparaginases, apart from the most used from Escherichia coli and Erwinia chrysanthemi, has been reported, but the choice of the most appropriate is still under debate. This choice should be based on its pharmacokinetics, immune hypersensitivity, doses, prices, pharmacodynamics. The main factors influencing the antileukemic activity of ASNase are enzymatic activity, Km, glutaminase activity, clearance of the enzyme and development of resistance. However, most of the commercialized enzyme present an intrinsic glutaminase activity, which is responsible for some side effects. In this study, glutaminase free asparaginase produced from Aspergillus oryzae was precipitated in different percentages of ethanol (0–80%), until optimum ethanol concentration of 60% (w/w) was found. Following, precipitation of crude L-ASNase was performed in a single step, using 60% (w/w) ethanol, under constant agitation and temperature. It presented activity of 135.45 U/mg and after gel filtration chromatography with Sephadex G-the enzymatic activity was 322.02 U/mg. The apparent molecular mass of the purified L-ASNase fraction was estimated by 10% SDS-PAGE. Proteins were stained with Coomassie Brilliant Blue R-250. The molar mass range was from 10 kDa to 250 kDa. L-ASNase from Aspergillus oryzae was characterized aiming possible therapeutic use. Four different buffers (phosphate-citrate buffer pH 2.6 to 5.8; phosphate buffer pH 5.8 to 7.4; Tris - HCl pH 7.4 to 9.0; and carbonate buffer pH 9.8 to 10.6) were used to measure the optimum pH for L-ASNase activity. The optimum temperature for enzyme activity was measured at optimal pH conditions (Tris-HCl and phosphate buffer, pH 7.4) at different temperatures ranging from 5 to 55°C. All activities were calculated by quantifying the free ammonia, using the Nessler reagent. The kinetic parameters calculation, e.g. Michaelis-Menten constant (Km), maximum velocity (Vmax) and Hills coefficient (n), were performed by incubating the enzyme in different concentrations of the substrate at optimum conditions of pH and fitted on Hill’s equation. This glutaminase free asparaginase showed a low Km (3.39 mM and 3.81 mM) and enzymatic activity of 135.45 U/mg after precipitation with ethanol. After gel filtration chromatography it rose to 322.02 U/mg. Optimum activity was found between pH 5.8 - 9.0, best activity results with phosphate buffer pH 7.4 and Tris-HCl pH 7.4 and showed activity from 5°C to 55°C. These results indicate that L-ASNase from A. oryzae has the potential for human use.

Keywords: biopharmaceuticals, bioprocessing, bioproducts, biotechnology, enzyme activity, ethanol precipitation

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Asmau Mukhtar Ahmed, Olga Duran

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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

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Authors: Ritesh Kumar, Gitanjali Chhetri, Mandira Bhatia, Mohit Mishra, Abhijith Bailur, Abhinav

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The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data.

Keywords: EEG signal, auditory evoked potential, biometrics, multilayer perceptron neural network, back propagation rule, Gaussian random weight initialization

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Authors: Ibrahim M. Labouta, Marwa H. El-Wakil, Hayam M. Ashour, Ahmed M. Hassan, Manal N. Saudi

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The receptor tyrosine kinase c-Met is an attractive target for therapeutic treatment of cancers nowadays. Among the wide variety of heterocycles that have been explored for developing c-Met kinase inhibitors, the 1,2,4-triazines have been rarely investigated, although they are well known in the literature to possess antitumor activities. Herein we describe the design and synthesis of a novel series of 1,2,4-triazine derivatives possessing N-acylarylhydrazone moiety and another series combining the 1,2,4-triazine scaffold to the well-known anticancer drug 6-MP in order to explore their “double-drug” effect. The synthesized compounds were evaluated for their in vitro antitumor activity against three c-Met addicted cancer cell lines (A549, HT-29 and MKN-45). Most compounds showed moderate to excellent antiproliferative activity and four compounds showed potent inhibitory activity more than the reference drug Foretinib against one or more cancer cell lines. The obtained results revealed that the potent compounds are highly selective to A549 (lung adenocarcinoma) cancer cell line. The c-Met kinase inhibitory activity of the potent derivatives is still under investigation. The present study clearly demonstrates that the 1,2,4-triazine core ring exhibits promising antitumor activity with potential c-Met kinase inhibitory activity.

Keywords: 1, 2, 4-triazine, antitumor, c-Met inhibitor, double-drug

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Authors: Brwa Abdulrahman Abubaker

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Recently, there has been a lot of interest in the difficult task of applying reinforcement learning to autonomous mobile robots. Conventional reinforcement learning (TRL) techniques have many drawbacks, such as lengthy computation times, intricate control frameworks, a great deal of trial and error searching, and sluggish convergence. In this paper, a modified Spiking Neural Network (SNN) is used to offer a distinct method for autonomous mobile robot learning and control in unexpected surroundings. As a learning algorithm, the suggested model combines dopamine modulation with spike-timing-dependent plasticity (STDP). In order to create more computationally efficient, biologically inspired control systems that are adaptable to changing settings, this work uses the effective and physiologically credible Izhikevich neuron model. This study is primarily focused on creating an algorithm for target tracking in the presence of obstacles. Results show that the SNN trained with three obstacles yielded an impressive 96% success rate for our proposal, with collisions happening in about 4% of the 214 simulated seconds.

Keywords: spiking neural network, spike-timing-dependent plasticity, dopamine modulation, reinforcement learning

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Authors: Mikel Alonso López

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This paper explores, in depth, the idea that emotions are present in all consumer decision making processes, meaning that purchase decisions have never been purely cognitive or as they traditionally have been defined, rational. Human beings, in all kinds of decisions, has "always" used neural systems related to emotions along with neural systems related to cognition, regardless of the type of purchase or the product or service in question. Therefore, all purchase decisions are, at the same time, cognitive and emotional. This paper presents an analysis of the main contributions of researchers in this regard.

Keywords: emotions, decision making, consumer behaviour, emotional behaviour

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Martyn Queen, Diane Crone, Andrew Parker, Saul Bloxham

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Rationale: There is a growing body of evidence that supports the use of physical activity during and after cancer treatment. However, activity levels for patients remain low. As more cancer patients are treated successfully, and treatment costs continue to escalate, physical activity may be a promising adjunct to a person-centred healthcare approach to recovery. Aim: The aim was to further understand how physical activity may enhance the recovery process for a group of mixed-site cancer patients. Objectives: The research investigated longitudinal changes in physical activity and perceived the quality of life between two and six month’s post-exercise interventions. It also investigated support systems that enabled patients to sustain these perceived changes. Method: The respondent cohort comprised 14 mixed-site cancer patients aged 43-70 (11 women, 3 men), who participated in a two-phase physical activity intervention that took place at a university in the South West of England. Phase 1 consisted of an eight-week structured physical activity programme; Phase 2 consisted of four months of non-supervised physical activity. Semi-structured interviews took place three times over six months with each participant. Grounded theory informed the data collection and analysis which, in turn, facilitated theoretical development. Findings: Our findings propose three theories on the impact of physical activity for recovering cancer patients: 1) Knowledge gained through a structured exercise programme can enable recovering cancer patients to independently sustain physical activity to four-month follow-up. 2) Sustaining physical activity for six months promotes positive changes in the quality of life indicators of chronic fatigue, self-efficacy, the ability to self-manage and energy levels. 3) Peer support from patients facilitates adherence to a structured exercise programme and support from a spouse, or life partner facilitates independently sustained physical activity to four-month follow-up. Conclusions: This study demonstrates that qualitative research can provide an evidence base that could be used to support future care plans for cancer patients. Findings also demonstrate that a physical activity intervention can be effective at helping cancer patients recover from the side effects of their treatment, and recommends that physical activity should become an adjunct therapy alongside traditional cancer treatments.

Keywords: physical activity, health, cancer recovery, quality of life, support systems, qualitative, grounded theory, person-centred healthcare

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Authors: Rajesh A. Rane, Shital S. Naphade, Rajshekhar Karpoormath

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Thiozolidin-4-one, a mimic of thiazolobenzimidazole (TBZ) has drawn many attentions due to its potent and selective inhibition against the HIV-1 and low toxicity by binding to the allosteric site of the reverse transcriptase (RT) as a non-nucleoside RT inhibitor (NNRTI). Similarly, marine bromopyrrole alkaloids are well known for their diverse array of anti-infective properties. Hence, we have reported synthesis and in vitro HIV-1 RT inhibitory activity of a series of 4-thiazolidinone-bromopyrrole alkaloid hybrids tethered with amide linker. The results of in vitro HIV-1 RT kit assay showed that some of the compounds, such as 4c, 4d, and 4i could effectively inhibit RT activity. Among them, compounds 4c having 4-chlorophenyl substituted 4-thiazolidione ring was the best one with the IC50 value of 0.26 µM. The sturdy emerges with key structure-activity relationship that pyrrole-NH-free core benefited inhibition against HIV-1 RT inhibition. This study identified conjugate 4c with potent activity and selectivity as promising compound for further drug development to HIV.

Keywords: antiviral drugs, bromopyrrole alkaloids, HIV-1 RT inhibition, 4-thiazolidinone

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Authors: Aline Giardin

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Introduction: This review addresses the relationship between physical education and mental health and its main objective is to discuss the meanings that circulate in Psychiatric Hospitalization Units and Psychosocial Care Centers (CAPS) about the presence of physical education teachers and the practices developed by Them within these services. Material and methods: It is based on the theoretical contribution of the Psychiatric Reform and is methodologically inspired by the Bibliographic Review. Objectives: The objective of this review was to identify the main scientific evidence on the effects of physical activity on the main psychological aspects associated with mental health during the hospitalization process. Results: It was observed that physical activity has beneficial effects in the psychological, social and cognitive aspects, being thus a fundamental aspect of the lifestyle in promoting a healthy and successful treatment. In studies evaluating the effects of physical activity on mental health, the most frequently evaluated outcomes include anxiety, depression, and health-related quality of life (eg, self-esteem and self-efficacy). Evidence from epistemological studies indicates that the level of physical activity is positively associated with good mental health, when mental health is defined as good mood, general well-being and decreased symptoms. Conclusion: It is necessary to intervene and a greater interest of the professionals of physical education in the treatment with the people with mental disorders so that the negative symptoms are modified, through the aid of the physical activity, by better quality of life, physical condition, nutritional state and A healthy emotional appearance.

Keywords: health mental, physical activity, benefits, treatment

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Robert Dutkiewicz, Monika Szpringer, Mariola Wojciechowska

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Physical activity is one of the factors greatly influencing healthy lifestyle. The recent research into physical activity of the Polish society reveals that contribution of physical culture to healthy lifestyle is insufficient. Students, regardless of age, spend most of free-time in front of a TV or computer. The research attempted to identify the level of physical activity and healthy lifestyle among students of medical sciences and other students doing their teaching degrees. The findings of physical activity research conducted in 2014, which covered 364 students of medical sciences and future teachers from the University of Jan Kochanowski in Kielce were analysed. The research involved the method of diagnostic survey based on a questionnaire. It attempted to establish to what extent such factors as the field of studies, the place of residence and BMI affect students’ physical activity. Empirical material was analysed by means of SPSS/PC, the leading statistical software. The field of study significantly influences physical activity of the respondents. The students of physiotherapy and public health tend to be more physically active than students of biology and geography: 46.8% students of geography and 51.8 % biology students seldom take up physical activity. Obesity and overweight are currently serious problems of university students: 6.6% of them are obese and 19% overweight. It is alarming that these students are not willing to find ways to be more physically active. Most of the obese and overweight respondents study biology or geography and live in a rural area. Unequal chances in terms of youth physical culture are determined by the differences between rural and urban environments. Young people living in rural areas are less physically active, particularly in terms of the frequency and the amount of time devoted to physical activity. This is caused by poor infrastructure to perform physical activity, the lack of or limited number of sports clubs and centres. It is thought-provoking that most of the students claim that they do not have enough time to do sports or other activities, but at the same time they spend a lot of time at a computer or watching TV.

Keywords: BMI, healthy lifestyle, sports activity, students

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Authors: Kheira A. Bekhechi, Belkacem Khiat

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The goal of our study is to know if there is an influence of the regular sporting physical-activity on the school performances of Algerian children. An experimental group composed of 55 sporting pupils and a reference group of 55 non-sporting pupils between 6 to10 years old (boys and girls) of the primary schools in Oran (Algeria) were followed during 15 months (Five terms). The socio-demographic data was collected from a survey given to pupils of the two groups and the school results from the administration at the end of each term. The sporting pupils have a general school average significantly higher than those of the non- sporting pupils (p < 0.05). The practice of physical activity and regular sporting by the children would deserve to be largely encouraged based on the beneficial effects not only on health but also on the academic performance. The parents, teachers and health professionals should be strongly aware.

Keywords: cognitive capacities, physical activity and sport, school children, school performances

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Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: Dina Helmy El-Ghonemy, Thanaa Hamed Ali

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The aim of this study was to purify and characterize a keratinolytic enzyme produced by Aspergillus sp. DHE7 cultured in basal medium containing chicken feather as substrate. The enzyme was purified through ammonium sulfate saturation of 60%, followed by gel filtration chromatography in Sephadex G-100, with a 16.4-purification fold and recovery yield of 52.2%. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed that the purified enzyme is a monomeric enzyme with an apparent molecular mass of 30 kDa — the purified keratinase of Aspergillus sp. DHE7 exhibited activity in a broad range of pH (7- 9) and temperature (40℃-60℃) profiles with an optimal activity at pH eight and 50℃. The keratinolytic activity was inhibited by protease inhibitors such as phenylmethylsulfonyl fluoride and ethylenediaminetetraacetate, while no reduction of activity was detected by the addition of dimethyl sulfoxide (DMSO). Bivalent cations, Ca²⁺ and Mn²⁺, were able to greatly enhance the activity of keratinase by 125.7% and 194.8%, respectively, when used at one mM final concentration. On the other hand, Cu²⁺ and Hg²⁺ inhibited the enzyme activity, which might be indicative of essential vicinal sulfhydryl groups of the enzyme for productive catalysis. Furthermore, the purified keratinase showed significant stability and compatibility against the tested commercial detergents at 37ºC. Therefore, these results suggested that the purified keratinase from Aspergillus sp. DHE7 may have potential use in the detergent industry and should be of interest in the processing of poultry feather waste.

Keywords: Aspergillus sp. DHE7, biochemical characterization, keratinase, purification, waste management

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