Search results for: bitter apricot kernel oil

Authors: Fatemeh Nejati, Shahram Dokhani

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Bitterness is a flavor defect encountered in some cheeses, such as Iranian white brined cheese and is responsible for reducing acceptability of the cheeses. The objective of this study was to investigate the effect of an adjunct culture on removal of bitterness fro, Iranian white-brined cheese. The chemical and proteolysis characteristics of the cheese were also monitored. Bitter cheeses were made using overdose of clotting enzyme with and without L. helveticus CH-1 as an adjunct culture. Cheese made with normal doses of clotting enzyme was used as the control. Adjunct culture was applied in two different forms: attenuated and non-attenuated. Proteolysis was assessed by measuring the amount of water soluble nitrogen, 12% trichloroacetic acid soluble nitrogen and total free amino acids during ripening. A taste panel group also evaluated the cheeses at the end of ripening period. Results of the statistical analysis showed that the adjunct caused considerable proteolysis and the level of water soluble nitrogen and 12% soluble nitrogen fractions were found to be significantly higher in the treatment involving L. helveticus (respectively P < 0.05 and P < 0.01). Regarding to organoleptic evaluations, the non-shocked adjunct culture caused reduction in bitterness and enhancement of flavor in cheese.

Keywords: Bitterness, Iranian white brined Cheese, Lactobacillus helveticus, Ripening

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Marija Milašinović-Šeremešić, Valentina Semenčenko, Milica Radosavljević, Dušanka Terzić, Ljiljana Mojović, Ljubica Dokić

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Maize (Zea mays L.) is one of the most important cereal crops, and as such, one of the most significant naturally renewable carbohydrate raw materials for the production of energy and multitude of different products. The main goal of the present study was to investigate a suitability of selected maize hybrids of different genetic background produced in Maize Research Institute ‘Zemun Polje’, Belgrade, Serbia, for starch, bioethanol and animal feed production. All the hybrids are commercial and their detailed characterization is important for the expansion of their different uses. The starches were isolated by using a 100-g laboratory maize wet-milling procedure. Hydrolysis experiments were done in two steps (liquefaction with Termamyl SC, and saccharification with SAN Extra L). Starch hydrolysates obtained by the two-step hydrolysis of the corn flour starch were subjected to fermentation by S. cerevisiae var. ellipsoideus under semi-anaerobic conditions. The digestibility based on enzymatic solubility was performed by the Aufréré method. All investigated ZP maize hybrids had very different physical characteristics and chemical composition which could allow various possibilities of their use. The amount of hard (vitreous) and soft (floury) endosperm in kernel is considered one of the most important parameters that can influence the starch and bioethanol yields. Hybrids with a lower test weight and density and a greater proportion of soft endosperm fraction had a higher yield, recovery and purity of starch. Among the chemical composition parameters only starch content significantly affected the starch yield. Starch yields of studied maize hybrids ranged from 58.8% in ZP 633 to 69.0% in ZP 808. The lowest bioethanol yield of 7.25% w/w was obtained for hybrid ZP 611k and the highest by hybrid ZP 434 (8.96% w/w). A very significant correlation was determined between kernel starch content and the bioethanol yield, as well as volumetric productivity (48h) (r=0.66). Obtained results showed that the NDF, ADF and ADL contents in the whole maize plant of the observed ZP maize hybrids varied from 40.0% to 60.1%, 18.6% to 32.1%, and 1.4% to 3.1%, respectively. The difference in the digestibility of the dry matter of the whole plant among hybrids (ZP 735 and ZP 560) amounted to 18.1%. Moreover, the differences in the contents of the lignocelluloses fraction affected the differences in dry matter digestibility. From the results it can be concluded that genetic background of the selected maize hybrids plays an important part in estimation of the technological value of maize hybrids for various purposes. Obtained results are of an exceptional importance for the breeding programs and selection of potentially most suitable maize hybrids for starch, bioethanol and animal feed production.

Keywords: bioethanol, biomass quality, maize, starch

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Authors: Alexander P. Smirnov, Sergey A. Matveev, Dmitry A. Zheltkov, Eugene E. Tyrtyshnikov

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We propose a new computational technique for multidimensional (multicomponent) Smoluchowski coagulation equation. Using low-rank approximations in Tensor Train format of both the solution and the coagulation kernel, we accelerate the classical finite-difference Runge-Kutta scheme keeping its level of accuracy. The complexity of the taken finite-difference scheme is reduced from O(N^2d) to O(d^2 N log N ), where N is the number of grid nodes and d is a dimensionality of the problem. The efficiency and the accuracy of the new method are demonstrated on concrete problem with known analytical solution.

Keywords: tensor train decomposition, multicomponent Smoluchowski equation, runge-kutta scheme, convolution

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Authors: Hassan Eshkiki, Benjamin Mora

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The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

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Authors: Acacia A. Salomão, Willian L. Gomes da Silva, Gustavo G. Shimamoto, Matthieu Tubino

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Biodiesel physical and chemical properties depend on the raw material composition used in its synthesis. Saturated fatty acid esters confer high oxidative stability, while unsaturated fatty acid esters improve the cold flow properties. In this study, an alternative vegetal source - the macauba kernel oil - was used in the biodiesel synthesis instead of conventional sources. Macauba can be collected from native palm trees and is found in several regions in Brazil. Its oil is a promising source when compared to several other oils commonly obtained from food products, such as soybean, corn or canola oil, due to its specific characteristics. However, the usage of biodiesel made from macauba oil alone is not recommended due to the difficulty of producing macauba in large quantities. For this reason, this project proposes the usage of blends of the macauba oil with conventional oils. These blends were prepared by mixing the macauba biodiesel with biodiesels obtained from soybean, corn, and from residual frying oil, in the following proportions: 20:80, 50:50 e 80:20 (w/w). Three parameters were evaluated, using the standard methods, in order to check the quality of the produced biofuel and its blends: oxidative stability, cold filter plugging point (CFPP), and iodine value. The induction period (IP) expresses the oxidative stability of the biodiesel, the CFPP expresses the lowest temperature in which the biodiesel flows through a filter without plugging the system and the iodine value is a measure of the number of double bonds in a sample. The biodiesels obtained from soybean, residual frying oil and corn presented iodine values higher than 110 g/100 g, low oxidative stability and low CFPP. The IP values obtained from these biodiesels were lower than 8 h, which is below the recommended standard value. On the other hand, the CFPP value was found within the allowed limit (5 ºC is the maximum). Regarding the macauba biodiesel, a low iodine value was observed (31.6 g/100 g), which indicates the presence of high content of saturated fatty acid esters. The presence of saturated fatty acid esters should imply in a high oxidative stability (which was found accordingly, with IP = 64 h), and high CFPP, but curiously the latter was not observed (-3 ºC). This behavior can be explained by looking at the size of the carbon chains, as 65% of this biodiesel is composed by short chain saturated fatty acid esters (less than 14 carbons). The high oxidative stability and the low CFPP of macauba biodiesel are what make this biofuel a promising source. The soybean, corn and residual frying oil biodiesels also have low CFPP, but low oxidative stability. Therefore the blends proposed in this work, if compared to the common biodiesels, maintain the flow properties but present enhanced oxidative stability.

Keywords: biodiesel, blends, macauba kernel oil, stability oxidative

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Authors: Sylwia K. Naliwajko, Justyna Moskwa, Patryk Nowakowski, Renata Markiewicz-Zukowska, Krystyna Gromkowska-Kepka, Anna Puscion-Jakubik, Maria H. Borawska

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Amygdalin is found in many fruit seeds, including apricot, peach, quince, apples, and almonds. Amygdalin (also named vitamin B17), as well as its sources, are commonly used as an alternative therapy or prevention of cancer. The potential activity of amygdalin is related to its enzymatic degradation to the hydrogen cyanide. Hydrogen cyanide is a toxic substance that causes liver and nerves damage, fever, coma or even death. Amygdalin is much better tolerated after intravenous than oral administration. The aim of this study was to examine the influence of amygdalin on glioblastoma multiforme cell lines. Three glioblastoma multiforme cell lines – U87MG, T98, LN18 were incubated (48 h) with amygdalin in concentrations 100, 250, 500, 1000 and 2000 µg/mL. The MTT (Thiazolyl Blue Tetrazolium Bromide) test and DNA binding test by [3H]-thymidine incorporation were used to determine the anti-proliferative activity of amygdalin. The secretion of metalloproteinases (MMP2 and MMP-9) from U87MG cells was estimated by gelatin zymography. The statistical analysis was performed using Statistica v. 13.0 software. The data was presented as a % of control. Amygdalin did not show significant inhibition of viability of all the glioblastoma cells in concentrations 100, 250, 500, 1000 µg/mL. In 2000 µg/mL there were significant differences compared to the control, but inhibition of viability was less than 20% (more than 80% of control). The average viability of U87MG cells was 92,0±4%, T98G: 85,8±3% and LN18: 94,7±2% of the control. There was no dose-response viability, and IC50 value was not recognized. DNA binding in U87MG cells was not inhibited (109,0±3 % of control). After treatment with amygdalin, we observed significantly increased secretion of MMP2 and MMP9 in U87MG cells (130,3±14% and 112,0±5% of control, respectively). Our results suggest that amygdalin has no anticancer activity in glioblastoma cell lines.

Keywords: amygdalin, anticancer, cell line, glioblastoma

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Authors: Divya Choudhary, Hariprasad P., S. N. Naik

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This study examines a low-alcohol nutra-beverage made with shatavari, a plant commonly used in traditional medicine. During fermentation, the addition of a specific strain of yeast affected the beverage's properties, including its pH level, yeast count, ethanol content, and antioxidant, phenolic, and flavonoid levels. We also analyzed the beverage's storage and shelf life. Despite its bitter taste, the low alcohol content of the beverage made it enjoyable to drink and visually appealing. Our analysis showed that the optimal time for fermentation was between the 14th and 21st day when the beverage had ideal levels of sugar, organic acids, and vitamins. The final product contained fructose and citric acid but not succinic, pyruvic, lactic, or acetic acids. It also contained vitamins B2, B1, B12, and B9. During the shelf life analysis, we observed changes in the beverage's pH, TSS, and cfu levels, as well as its antioxidant activity. We also identified volatile (GC-MS) and non-volatile compounds (LC-MS/MS) in the fermented product, some of which were already present in the Shatavari root. The highest yield of product contained the maximum concentration of antioxidant compounds, which depended on both the pH and the microorganisms' physiological status. Overall, our study provides insight into the properties and potential health benefits of this Nutra-beverage.

Keywords: antioxidants, fermentation, volatile compounds, acetonin, 1-butanol, non-volatile compounds, Shatavarin V, IX, kaempferol

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Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Mohammad H. Fattahi

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Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: chaotic behavior, wavelet, noise reduction, river flow

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Authors: J. C. Iwuchukwu, K. C. Okwor

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In recent time and in the study area, yam farmers are moving into less laborious and more economical crops and very few yam farmers are growing trifoliate yam. In yam markets, little or no bitter yam is displayed or sold. The work was therefore designed to ascertain trend and causes of decline in trifoliate yam production in Enugu state. Three agricultural zones, six blocks, eighteen circles and one hundred and eight trifoliate yam farmers that were purposively selected constituted sample for the study. An interview schedule was used to collect data while percentage, mean score and standard deviation were used for data analysis. Findings of the study revealed that the respondents had no extension contact, Majority (90.7%) sourced information on trifoliate yam from neighbours/friends/relatives and produced mainly for consumption (67.6%) during rainy season (70.4%). Trifoliate yam was produced manually(71.3%) and organically (58.3%) in a mixture of other crops (87%) using indigenous/local varieties (73.1%). Mean size of land allocated to trifoliate yam production was relatively steady, mean cost of input and income were increasing while output was decreasing within the years under consideration (before 2001 to 2014). Poor/lack of finance(M=1.8) and drudgery associated with trifoliate yam product(M=1.72) were some of the causes of decline in trifoliate yam production in the area. The study recommended that more research and public enlightenment campaigns on the importance of trifoliate yam should be carried out to encourage and consolidate farmers and the masses effort in production and consumption of the crop so that it will not go extinct and then contribute to food security.

Keywords: causes, decline, trend, trifoliate yam

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Authors: Sue S. Feldman, Bradley Tipper, Benjamin Schooley

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Nearly every community in the US has been impacted by opioid related addictions/deaths; it is a national problem that is threatening our social and economic welfare. Most believe that tackling this problem from a prevention perspective advances can be made toward breaking the chain of addiction. One mechanism, community based participatory research, involves the community in the prevention approach. This project combines that approach with a design science approach to develop an integrated solution. Findings suggested accountable care communities, transpersonal psychology, and social exchange theory as product kernel theories. Evaluation was conducted on a prototype.

Keywords: substance use and abuse recovery, community resource centers, accountable care communities, community based participatory research

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Authors: Michael Graz, Andrew Shearer, Gareth Evans

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Manipulation of rumen bacteria is receiving increasing attention as a way of controlling greenhouse gas (GHG) emissions that are generated by the agricultural sector. Feed supplementation in particular is one of the ways in which this drive is being addressed, in particular with reference to livestock-generated GHG emissions. A blend of naturally occurring chemical extracts obtained from garlic and bitter orange extracts has been identified as a natural, sustainable and non-antibiotic based way of reducing methane production by ruminant livestock. In the current study, the acceptability and impact of this blend of natural extracts on feed rations of beef cattle was trialed in vivo on a commercial farm in Europe. Initial findings have demonstrated acceptable palatability, with all animals accepting the feed supplement into their ration both when it was mixed into the total daily ration and when used as a part of their high energy rations. Measurement of the impact of this feed supplement on productivity weight gain and milk quality is ongoing. In conclusion, this field study confirmed the palatability of the combination of garlic and citrus extracts and hence pointed to possibility of the extract blend to improve digestion, enhance body energy retention and limit CH4 formation in relation to feed intake.

Keywords: citrus, garlic, methane reduction, palatability, ruminants

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Authors: Tahra ElObeid, Noura Al-Wahiemed, Jawaher Al-shammari, Wedad Al-Asmar

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The aim of this study is to utilize Qatari raw materials in the production of a date juice high in antioxidants. The antioxidants were extracted from five Qatari herbs: Caspian manna, Tetraena mongolica, Capparis spinosa, Ziziphus Vulgaris and Lycium shawii. The date juice was prepared in the lab and was infused with the polyphenolic extracts from the 5 different Qatari herbs. The date juice was then infused with the antioxidant containing the highest antioxidant activity and was within the acceptable range in sensory evaluation scale. The phenolic content for Lycium shawii, Alhagi maurorum, Ziziphus Vulgaris, Capparis spinosa and Tetraena mongolica was 4294 ppm, 3843 ppm, 804.59 ppm, 189.14 ppm and 226 ppm respectively, whereas their antioxidant capacity of was 6.21 %, 45.27 %, 69.81 %, 2.96 % and 8.63 % respectively. The highest antioxidant capacity was found in Ziziphus Vulgaris 69.8 % and the highest phenolic content was found in Lycium shawii 4294 ppm. Alhagi maurorum, Tetraena mongolica and Lycium shawii showed good results in terms of taste and aroma however Ziziphus Vulgaris exhibited bitter flavor. Alhagi maurorum antioxidant extract was used to be added to the date juice due to its high phenolic content, high antioxidant capacity, good taste and aroma.

Keywords: Qatar, dates, herbs, antioxidants

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Authors: Arfan Ali, Idrees Ahmad Nasir

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Potato (Solanum tuberosum) is an important cash crop and popular vegetable in Pakistan and throughout the world. Cold storage of potatoes accelerates the conversion of starch into reduced sugars (glucose and fructose). This process causes dry mass and bitter taste in the potatoes that are not acceptable to end consumers. In the current study, the phosphofructokinase B gene was cloned into the pET-30 vector for protein expression and the pCambia-1301 vector for plant expression. Amplification of a 930bp product from an E. coli strain determined the successful isolation of the phosphofructokinase B gene. Restriction digestion using NcoI and BglII along with the amplification of the 930bp product using gene specific primers confirmed the successful cloning of the PfkB gene in both vectors. The protein was expressed as a His-PfkB fusion protein. Western blot analysis confirmed the presence of the 35 Kda PfkB protein when hybridized with anti-His antibodies. The construct Fani-01 was evaluated transiently using a histochemical gus assay. The appearance of blue color in the agroinfiltrated area of potato leaves confirmed the successful expression of construct Fani-01. Further, the area displaying gus expression was evaluated for PfkB expression using ELISA. Moreover, PfkB gene expression evaluated through transient expression determined successful gene expression and highlighted its potential utilization for stable expression in potato to reduce sweetening due to long-term storage.

Keywords: potato, Solanum tuberosum, transformation, PfkB, anti-sweetening

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Authors: S. Aizikovich, L. Krenev, Y. Tokovyy, Y. C. Wang

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An approximate analytical-numerical solution to the axisymmetric problem on thermo-mechanical indentation of a flat cylindrical punch into an arbitrarily non-homogeneous elastic half-space is constructed by making use of the bilateral asymptotic method. The key point of this method lies in evaluation of the ker¬nels in the obtained integral equations by making use of a numerical technique. Once the structure of the kernel is defined, it then is approximated by an analytical expression of special kind so that the solution of the integral equation can be achieved analytically. This fact allows for construction of the solution in an analytical form, which is convenient for analysis of the mechanical effects concerned with arbitrarily presumed non-homogeneity of the material.

Keywords: contact problem, circular punch, arbitrarily-nonhomogeneous halfspace

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Authors: Orogun Daniel Oghenekevhwe

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Africa being an agrarian continent has an abundance of fruits that are both nutritional and medicinal. Regardless of the abundance of these healing elements, Africa leads the statistics of poor healthcare globally. Among others, there are two noticeable challenges in the healthcare system which are ‘Poor access and high cost of medical healthcare’. The effects of both the access and economic implications are (1) Low responsiveness and (2) High mortality rate. While the United Nations and the global health community continue to work towards reduced mortality rates and poor responsiveness to healthcare and wellness, this paper investigates how some Africans use the spiritual symbols of African fruits as responsive catalysts to embrace naturopathy thereby reducing the effects and impacts of poor healthcare challenges in Africa. The main argument is whether there are links between spiritual symbols and fruits that influence Africans' response to naturopathy and low-cost healthcare. Following that is the question of how medical healthcare responds to such development. Bitter Kola (Garcinia) is the case study fruit, and Sunnyside in Pretoria, South Africa, has been spotted as one of the high-traffic selling points of herbal fruits. A mixed research method is applicable with an expected 20 Quantitative data respondents among sellers and nutritionists and 50 Qualitative Data respondents among consumers. Based on the results, it should be clear how spirituality contributes to alternative healthcare and how it can be further encouraged to bridge the gap between the high demand and low supply of healthcare in Africa and beyond.

Keywords: spiritual symbols, naturopathy, African fruits, spirituality, healthcare

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Authors: Khin Aye Myint, Mohd Rafii Yusop, Mohd Yusoff Abd Samad, Shairul Izan Ramlee, Mohd Din Amiruddin, Zulkifli Yaakub

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The narrow base of genetic is the main obstacle of breeding and genetic improvement in oil palm industry. In order to broaden the genetic bases, the Malaysian Palm Oil Board has been extensively collected wild germplasm from its original area of 11 African countries which are Nigeria, Senegal, Gambia, Guinea, Sierra Leone, Ghana, Cameroon, Zaire, Angola, Madagascar, and Tanzania. The germplasm collections were established and maintained as a field gene bank in Malaysian Palm Oil Board (MPOB) Research Station in Kluang, Johor, Malaysia to conserve a wide range of oil palm genetic resources for genetic improvement of Malaysian oil palm industry. Therefore, assessing the performance and genetic diversity of the wild materials is very important for understanding the genetic structure of natural oil palm population and to explore genetic resources. Principal component analysis (PCA) and Cluster analysis are very efficient multivariate tools in the evaluation of genetic variation of germplasm and have been applied in many crops. In this study, eight populations of MPOB-Senegal oil palm germplasm were studied to explore the genetic variation pattern using PCA and cluster analysis. A total of 20 yield and yield component traits were used to analyze PCA and Ward’s clustering using SAS 9.4 version software. The first four principal components which have eigenvalue >1 accounted for 93% of total variation with the value of 44%, 19%, 18% and 12% respectively for each principal component. PC1 showed highest positive correlation with fresh fruit bunch (0.315), bunch number (0.321), oil yield (0.317), kernel yield (0.326), total economic product (0.324), and total oil (0.324) while PC 2 has the largest positive association with oil to wet mesocarp (0.397) and oil to fruit (0.458). The oil palm population were grouped into four distinct clusters based on 20 evaluated traits, this imply that high genetic variation existed in among the germplasm. Cluster 1 contains two populations which are SEN 12 and SEN 10, while cluster 2 has only one population of SEN 3. Cluster 3 consists of three populations which are SEN 4, SEN 6, and SEN 7 while SEN 2 and SEN 5 were grouped in cluster 4. Cluster 4 showed the highest mean value of fresh fruit bunch, bunch number, oil yield, kernel yield, total economic product, and total oil and Cluster 1 was characterized by high oil to wet mesocarp, and oil to fruit. The desired traits that have the largest positive correlation on extracted PCs could be utilized for the improvement of oil palm breeding program. The populations from different clusters with the highest cluster means could be used for hybridization. The information from this study can be utilized for effective conservation and selection of the MPOB-Senegal oil palm germplasm for the future breeding program.

Keywords: cluster analysis, genetic variability, germplasm, oil palm, principal component analysis

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Authors: Eduardo C. Guardia, Jose W. M. Lima, Afonso H. M. Santos

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This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.

Keywords: bathtub curve, failure analysis, lifetime estimation, parameter estimation, Weibull distribution

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Authors: Shiuh-Jer Huang, Shin-Ham Lee, Sheam-Chyun Lin

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A two wheel inverted pendulum (TWIP) vehicle is built with two hub DC motors for motion control evaluation. Arduino Nano micro-processor is chosen as the control kernel for this electric test plant. Accelerometer and gyroscope sensors are built in to measure the tilt angle and angular velocity of the inverted pendulum vehicle. Since the TWIP has significantly hub motor dead zone and nonlinear system dynamics characteristics, the vehicle system is difficult to control by traditional model based controller. The intelligent model-free fuzzy sliding mode controller (FSMC) was employed as the main control algorithm. Then, intelligent controllers are designed for TWIP balance control, and two wheels synchronization control purposes.

Keywords: balance control, synchronization control, two-wheel inverted pendulum, TWIP

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Authors: Siavash Eftekharifar, Tohid Yousefi Rezaii, Mahdi Shamsi

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The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method.

Keywords: compressed sensing, ECG compression, Gaussian kernel, sparse representation

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Chidiebere C. Agoha, Armstong C. Awuzie, Chukwuebuka N. Onwubuariri, Joy O. Njoku

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Spatial analysis is a field of study that utilizes geographic or spatial information to understand and analyze patterns, relationships, and trends in data. It is characterized by the use of geographic or spatial information, which allows for the analysis of data in the context of its location and surroundings. It is different from non-spatial or aspatial techniques, which do not consider the geographic context and may not provide as complete of an understanding of the data. Spatial analysis is applied in a variety of fields, which includes urban planning, environmental science, geosciences, epidemiology, marketing, to gain insights and make decisions about complex spatial problems. This review paper explores definitions of spatial analysis from various sources, including examples of its application and different analysis techniques such as Buffer analysis, interpolation, and Kernel density analysis (multi-distance spatial cluster analysis). It also contrasts spatial analysis with non-spatial analysis.

Keywords: aspatial technique, buffer analysis, epidemiology, interpolation

Procedia PDF Downloads 271

Authors: H. Amellal, Y. Noui, A. Djouab, S. Benamara

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Mech-Degla, Degla-Beida, and Frezza are the date (Phoenix dactylifera L.) common varieties with a more or less good availability and feeble trade value. Some morphologic and physicochemical factors were determined. Results show that the whole date weight is significantly different (P= 95%) concerning Mech-Degla and Degla-Beida which are more commercialised than Frezza whereas the pulp/kernel ratio for this last is highest (above 7) since it represents almost the double of that found for the two other varieties. The water content for all fruits is below 15g/100g (wet basis) what confers a dried consistence for common date. Some other morphologic and chemical proprieties of the whole pulps and their two constitutive parts (brown or pigmented and white) are also investigated. The predominance of phenolics in Mech-Degla (4.01g/100g, w.b) and Frezza (4.96 g/100g, w.b) pulps brown part is the main result revealed in this study.

Keywords: common dates, phenolics, sugars, tissues

Procedia PDF Downloads 377

Authors: Olufunmilola A. Abiodun, Adegbola O. Dauda, Ayobami Ojo, Samson A. Oyeyinka

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Serendipity berry (Dioscoreophyllum cumminsii diel) is a tropical dioecious rainforest vine and native to tropical Africa. The vine grows during the raining season and is used mainly as sweetener. The sweetener in the berry is known as monellin which is sweeter than sucrose. The sweetener is extracted from the fruits and the seed is discarded. The discarded seeds contain bitter principles but had high yield of oil. Serendipity oil was extracted using three methods (N-hexane, expression and expression/n-hexane). Fatty acids and physicochemical properties of the oil obtained were determined. The oil obtained was clear, liquid and have odour similar to hydrocarbon. The percentage oil yield was 38.59, 12.34 and 49.57% for hexane, expression and expression-hexane method respectively. The seed contained high percentage of oil especially using combination of expression and hexane. Low percentage of oil was obtained using expression method. The refractive index values obtained were 1.443, 1.442 and 1.478 for hexane, expression and expression-hexane methods respectively. Peroxide value obtained for expression-hexane was higher than those for hexane and expression. The viscosities of the oil were 125.8, 128.76 and 126.87 cm³/s for hexane, expression and expression-hexane methods respectively which showed that the oil from expression method was more viscous than the other oils. The major fatty acids in serendipity seed oil were oleic acid (62.81%), linoleic acid (22.65%), linolenic (6.11%), palmitic acid (5.67%), stearic acid (2.21%) in decreasing order. Oleic acid which is monounsaturated fatty acid had the highest value. Total unsaturated fatty acids were 91.574, 92.256 and 90.426% for hexane, expression, and expression-hexane respectively. Combination of expression and hexane for extraction of serendipity oil produced high yield of oil. The oil could be refined for food and non-food application.

Keywords: serendipity seed oil, expression method, fatty acid, hexane

Procedia PDF Downloads 221

Authors: Mozhde Shafie, Nahid Naderi, Mandana Mangeli

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Is it true to say that Saadi was an idealized embodiment of his moral teachings in Gulistan and Boustan? The body of criticism on Saadi’s works suggests that his biography provides no clear and valid information to judge about the extent to which Saadi acted what he preached. His moral teachings depict a sort of individual and social morality which is defined in relation to power and falls in the category of political ethics. Political ethics appear as for the noble and the subordinate in Gulistan and Boustan. Ethics for the noble include all his teachings for governors and rulers in eulogies. On the other hand, ethics for the subordinate include all his suggestions for the public in relation to power position. Here, Saadi puts forward some conservative recommendations that trigger some contemporary critical commentaries. However, there are some cases where he takes up a third person narrative position to narrate the story of a king and a mendicant. In these stories, the mendicant is a witty man with bitter criticism on society, implying that one should relinquish earthly pleasures and advantages if he wants his criticism to be acceptable. First person narratives fall in two categories determinate and indeterminate narratives. Indirect speeches reflecting biographical facts are indeterminate narratives which give no information about the poet’s personality. Other narratives are more of an autobiography that report probable observations. These latter narratives demonstrate Saadi as a man quick at repartee that feels free to disclose his poverty and some cases of impiety. Therefore, they provide no idealized picture of the poet in terms of ethical principles.

Keywords: Saadi, ethics, Boustan, Gulistan, first-person narrative

Procedia PDF Downloads 372

Authors: R. Sabre

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This work focuses on the symmetric alpha stable process with continuous time frequently used in modeling the signal with indefinitely growing variance, often observed with an unknown additive error. The objective of this paper is to estimate this error from discrete observations of the signal. For that, we propose a method based on the smoothing of the observations via Jackson polynomial kernel and taking into account the width of the interval where the spectral density is non-zero. This technique allows avoiding the “Aliasing phenomenon” encountered when the estimation is made from the discrete observations of a process with continuous time. We have studied the convergence rate of the estimator and have shown that the convergence rate improves in the case where the spectral density is zero at the origin. Thus, we set up an estimator of the additive error that can be subtracted for approaching the original signal without error.

Keywords: spectral density, stable processes, aliasing, non parametric

Procedia PDF Downloads 102

Authors: Sachin Kumar

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Fuzzy fractional diffusion equation is widely useful to depict different physical processes arising in physics, biology, and hydrology. The motive of this article is to deal with the fuzzy fractional diffusion equation. We study a mathematical model of fuzzy space-time fractional diffusion equation in which unknown function, coefficients, and initial-boundary conditions are fuzzy numbers. First, we find out a fuzzy operational matrix of Legendre polynomial of Caputo type fuzzy fractional derivative having a non-singular Mittag-Leffler kernel. The main advantages of this method are that it reduces the fuzzy fractional partial differential equation (FFPDE) to a system of fuzzy algebraic equations from which we can find the solution of the problem. The feasibility of our approach is shown by some numerical examples. Hence, our method is suitable to deal with FFPDE and has good accuracy.

Keywords: fractional PDE, fuzzy valued function, diffusion equation, Legendre polynomial, spectral method

Procedia PDF Downloads 154

Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

Procedia PDF Downloads 429

Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

Procedia PDF Downloads 119