Search results for: coconut kernel

Authors: Mohammed Idaan Hassan Al-Majidi

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An analysis of twenty one assorted brands of vegetable oils in Babylon Iraq, reveals varying levels of cholesterol content. Cholesterol was found to be present in most of the oil brands sampled using three standard methods. Cholesterol was detected in seventeen of the vegetable oil brands with concentration of less than 1 mg/ml while seven of the oil brands had cholesterol concentrations ranging between 1-4 mg/ml. Low iodine values were obtained in four of the vegetable oil brands and three of them had high acid values. High performance liquid chromatography (HPLC) confirmed the presence of cholesterol at varying concentrations in all the oil brands and gave the lowest detectable cholesterol values in all the oil brands. The Laser brand made from rapeseed had the highest cholesterol concentration of 3.2 mg/ml while Grand brand made from groundnuts had the least concentration (0.12 mg/ml) of cholesterol using HPLC analysis. Leibermann-Burchard method showed that Gino brand from palm kernel had the least concentration of cholesterol (3.86 mg/ml ±0.032) and the highest concentration of 3.996 mg/ml ±0.0404 was obtained in Sesame seed oil brand. This report is important in view of health implications of cholesterol in our diets. Consequently, we have been able to show that there is no cholesterol free oil in the market as shown on the vegetable oil brand labels. Therefore, companies producing and marketing vegetable oils are enjoined to desist from misleading the public by labeling their products as “cholesterol free”. They should indicate the amount of cholesterol present in the vegetable oil, no matter how small the quantity may be.

Keywords: vegetable oils, heart diseases, leibermann-burchard, cholesterol

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Authors: Amirul Al-Ashraf Abdullah, Hema Ramachandran, Iszatty Ismail

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Oleochemical industry in Malaysia has been diversifying significantly due to the abundant supply of both palm and kernel oils as raw materials as well as the high demand for downstream products such as fatty acids, fatty alcohols and glycerine. However, environmental awareness is growing rapidly in Malaysia because oleochemical industry is one of the palm-oil based industries that possess risk to the environment. Glycerine pitch is one of the scheduled wastes generated from the fatty acid plants in Malaysia and its discharge may cause a serious environmental problem. Therefore, it is imperative to find alternative applications for this waste glycerine. Consequently, the aim of this research is to explore the application of glycerine pitch as direct fermentation substrate in the biosynthesis of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) [P(3HB-co-4HB)] copolymer, aiming to contribute toward the sustainable production of biopolymer in the world. Utilization of glycerine pitch (10 g/l) together with 1,4-butanediol (5 g/l) had resulted in the achievement of 40 mol% 4HB monomer with the highest PHA concentration of 2.91 g/l. Synthesis of yellow pigment which exhibited antimicrobial properties occurred simultaneously with the production of P(3HB-co-4HB) through the use of glycerine pitch as renewable carbon resource. Utilization of glycerine pitch in the biosynthesis of P(3HB-co-4HB) will not only contribute to reducing society’s dependence on non-renewable resources but also will promote the development of cost efficiency microbial fermentation towards biosustainability and green technology.

Keywords: biopolymer, glycerine pitch, natural pigment, P(3HB-co-4HB)

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Jorge Gonzalez Camus, Carlos Lizama

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In this work is proved the existence of at least one globally attractive mild solution to the Cauchy problem, for fractional evolution equation of neutral type, involving the fractional derivate in Caputo sense. An almost sectorial operator on a Banach space X and a kernel belonging to a large class appears in the equation, which covers many relevant cases from physics applications, in particular, the important case of time - fractional evolution equations of neutral type. The main tool used in this work was the Hausdorff measure of noncompactness and fixed point theorems, specifically Darbo-type. Initially, the equation is a Cauchy problem, involving a fractional derivate in Caputo sense. Then, is formulated the equivalent integral version, and defining a convenient functional, using the analytic integral resolvent operator, and verifying the hypothesis of the fixed point theorem of Darbo type, give us the existence of mild solution for the initial problem. Furthermore, each mild solution is globally attractive, a property that is desired in asymptotic behavior for that solution.

Keywords: attractive mild solutions, integral Volterra equations, neutral type equations, non-local in time equations

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Authors: S. Saju, G. Thirugnanam

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In this paper, a medical image watermarking algorithm based on contourlet is proposed. Medical image watermarking is a special subcategory of image watermarking in the sense that images have special requirements. Watermarked medical images should not differ perceptually from their original counterparts because clinical reading of images must not be affected. Watermarking techniques based on wavelet transform are reported in many literatures but robustness and security using contourlet are better when compared to wavelet transform. The main challenge in exploring geometry in images comes from the discrete nature of the data. In this paper, original image is decomposed to two level using contourlet and the watermark is embedded in the resultant sub-bands. Sub-band selection is based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. To extract the watermark, Kernel ICA is used and it has a novel characteristic is that it does not require the transformation process to extract the watermark. Simulation results show that proposed scheme is robust against attacks such as Salt and Pepper noise, Median filtering and rotation. The performance measures like PSNR and Similarity measure are evaluated and compared with Discrete Wavelet Transform (DWT) to prove the robustness of the scheme. Simulations are carried out using Matlab Software.

Keywords: digital watermarking, independent component analysis, wavelet transform, contourlet

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Authors: Shafiullah Soomro

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Active contour is one of the image segmentation techniques and its goal is to capture required object boundaries within an image. In this paper, we propose a novel image segmentation method by using an active contour method based on anisotropic diffusion feature enhancement technique. The traditional active contour methods use only pixel information to perform segmentation, which produces inaccurate results when an image has some noise or complex background. We use Perona and Malik diffusion scheme for feature enhancement, which sharpens the object boundaries and blurs the background variations. Our main contribution is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. By minimizing an energy function using partial differential framework the proposed method captures semantically meaningful boundaries instead of catching uninterested regions. Finally, we use a Gaussian kernel which eliminates the problem of reinitialization in level set function. We use several synthetic and real images from different modalities to validate the performance of the proposed method. In the experimental section, we have found the proposed method performance is better qualitatively and quantitatively and yield results with higher accuracy compared to other state-of-the-art methods.

Keywords: active contours, anisotropic diffusion, level-set, partial differential equations

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Authors: Joseph Thomas Eghwerido, Julian I. Mbegbu

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In modelling environment processes, we apply multidisciplinary knowledge to explain, explore and predict the Earth's response to natural human-induced environmental changes. Thus, the analysis of spatial-time ecological and environmental studies, the spatial parameters of interest are always heterogeneous. This often negates the assumption of stationarity. Hence, the dispersion of the transportation of atmospheric pollutants, landscape or topographic effect, weather patterns depends on a good estimate of spatial covariance. The generalized linear mixed model, although linear in the expected value parameters, its likelihood varies nonlinearly as a function of the covariance parameters. As a consequence, computing estimates for a linear mixed model requires the iterative solution of a system of simultaneous nonlinear equations. In other to predict the variables at unsampled locations, we need to know the estimate of the present sampled variables. The geostatistical methods for solving this spatial problem assume covariance stationarity (locally defined covariance) and uniform in space; which is not apparently valid because spatial processes often exhibit nonstationary covariance. Hence, they have globally defined covariance. We shall consider different existing methods of solutions of spatial covariance of a space-time processes at unsampled locations. This stationary covariance changes with locations for multiple time set with some asymptotic properties.

Keywords: parametric, nonstationary, Kernel, Kriging

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Authors: Aftab Khan, Ashfaq Khan

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The research paper focuses on an interesting challenge faced in Blind Image Deblurring (BID). It relates to the estimation of arbitrarily shaped or non-parametric Point Spread Functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring in this case requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on Genetic Algorithm (GA) and utilises the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other single image motion deblurring schemes as benchmark. Validation has been carried out on various blurred images. Results of both benchmark and real images are presented. Non-reference image quality measures were used to quantify the deblurring results. For benchmark images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions.

Keywords: blind deconvolution, blind image deblurring, genetic algorithm, image restoration, image quality measures

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Esma Birisci, Ronald McGarvey

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One of the common problems in food service industry is demand uncertainty. This research presents a multi-criteria optimization approach to identify the efficient frontier of points lying between the minimum-waste and minimum-shortfall solutions within uncertain demand environment. It also addresses correlation across demands for items (e.g., hamburgers are often demanded with french fries). Reducing overproduction food waste (and its corresponding environmental impacts) and an aversion to shortfalls (leave some customer hungry) need to consider as two contradictory objectives in an all-you-care-to-eat environment food service operation. We identify optimal production adjustments relative to demand forecasts, demand thresholds for utilization of leftovers, and percentages of demand to be satisfied by leftovers, considering two alternative metrics for overproduction waste: mass; and greenhouse gas emissions. Demand uncertainty and demand correlations are addressed using a kernel density estimation approach. A statistical analysis of the changes in decision variable values across each of the efficient frontiers can then be performed to identify the key variables that could be modified to reduce the amount of wasted food at minimal increase in shortfalls. We illustrate our approach with an application to empirical data from Campus Dining Services operations at the University of Missouri.

Keywords: environmental studies, food waste, production planning, uncertain and correlated demand

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Zahrul Faizi M. S., Ali A., Norhuda A. M.

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Emission of greenhouse gases (GHG) from solid waste treatment and dependency on fossil fuel to produce electricity are the major concern in Malaysia as well as global. Innovation in downdraft gasification with combined heat and power (CHP) systems has the potential to minimize solid waste and reduce the emission of anthropogenic GHG from conventional fossil fuel power plants. However, the efficiency and capability of downdraft gasification to generate electricity from various alternative fuels, for instance, agriculture residues (i.e., woodchip, coconut shell) and municipal solid waste (MSW), are still controversial, on top of the toxicity level from the produced bottom ash. Thus this study evaluates the adaptability and reliability of the 20 kW downdraft gasification system to generate electricity (while considering environmental sustainability from the bottom ash) using flexible local feedstock at 20, 40, and 60% mixed ratio of MSW: agriculture residues. Feedstock properties such as feed particle size, moisture, and ash contents are also analyzed to identify optimal characteristics for the combination of feedstock (feedstock flexibility) to obtain maximum energy generation. Results show that the gasification system is capable to flexibly accommodate different feedstock compositions subjected to specific particle size (less than 2 inches) at a moisture content between 15 to 20%. These values exhibit enhance gasifier performance and provide a significant effect to the syngas composition utilizes by the internal combustion engine, which reflects energy production. The result obtained in this study is able to provide a new perspective on the transition of the conventional gasification system to a future reliable carbon-negative energy technology. Subsequently, promoting commercial scale-up of the downdraft gasification system.

Keywords: carbon-negative energy, feedstock flexibility, gasification, renewable energy

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Authors: Ratmawati Malaka, Effendi Abustam, Kusumandari Indah Prahesti, Sudirman Baco

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Dangke is natural cheese from Enrekang South Sulawesi, Indonesia produced through aglutination buffalo milk, cow, goat or sheep using the sap of papaya (Carica papaya). Dangke has been widely known in South Sulawesi but this soft cheese product diversification by using passion fruit juice as milk clotting agents has not been used. Passion fruit juice has a high acidity with a pH of around 4 - 4.5 and has a proteolytic enzyme, so that it can be used to agglutinate milk. The purpose of this study was to investigate the nature Dangke using passion fruit juice as coagulate milk. Dangke made by 10 lt of raw milk by heating at a temperature of 73oC with coagulant passion fruit juice (7.5% and 10%), and added 1% salt. Curd clot and then be formed using a coconut shell, is then pressed until the cheese is compact. The cheese is then observed for 28 days ripening at a temperature of about 5 ° C. Dangke then studied to violence, pH, fat levels and microstructure. Hardness is determined using CD-shear Force, pH is measured using a pH meter Hanna, and fat concentrations were analyzed with methods of proximate. Microstructure viewed using a light microscope with magnification 1000 x. The results showed that the levels of clotting material very significant influence on hardness, pH, and lipid levels. Maturation increase the hardness but lower the pH, the level of fat soft cheese with an average Dangke respectively 21.4% and 30.5% on 7.5% addition of passion fruit juice and 10%. Dangke violence is increasing with the increasing maturation time (1.38 to 3.73 kg / cm), but Dangke pH was decreased by the increase in storage maturation (5.34 to 4.1). Microktrukture cheeses coagulated with 10% of the passion fruit are very firmer and compact with a full globular fat of 7.5%. But the sensory properties of the soft cheese similar in both treatment. The manufacturing process with the addition of coagulant passion fruit juice on making Dangke affect hardness, pH, fat content and microstructure during storage at 5 ° C for 1 d - 28 d.

Keywords: dangke, passion fruits, microstructure, cheese

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Indri Mahadiraka Rumamby, R. R. Dwinanti Rika Marthanty, Jessica Sjah

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Smoothed particle hydrodynamics (SPH) was originally created to simulate nonaxisymmetric phenomena in astrophysics. However, this method still has several shortcomings, namely the high computational cost required to model values with high resolution and problems with boundary conditions. The difficulty of modeling boundary conditions occurs because the SPH method is influenced by particle deficiency due to the integral of the kernel function being truncated by boundary conditions. This research aims to answer if SPH modeling with a focus on boundary layer interactions and continuous flow can produce quantifiably accurate values with low computational cost. This research will combine algorithms and coding in the main program of meandering river, continuous flow algorithm, and solid-fluid algorithm with the aim of obtaining quantitatively accurate results on solid-fluid interactions with the continuous flow on a meandering channel using the SPH method. This study uses the Fortran programming language for modeling the SPH (Smoothed Particle Hydrodynamics) numerical method; the model is conducted in the form of a U-shaped meandering open channel in 3D, where the channel walls are soil particles and uses a continuous flow with a limited number of particles.

Keywords: smoothed particle hydrodynamics, computational fluid dynamics, numerical simulation, fluid mechanics

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Saheed Biodun Qaasim-Badmusi

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A lot has been written on peaceful co-existence with non-Muslims in Islam, but little attention is paid to the conflict between Ahadith relating to dissociation from non-Muslims as a kernel of Islamic faith, and the one indicating peaceful co-existence with them. Undoubtedly, proper understanding of seemingly contradictory prophetic traditions is an antidote to the bane of pervasive extremism in our society. This is what calls for need to shed light on ‘Harmonization of Conflict Ahadith between Dissociation and Peaceful Co-existence with Non-Muslims. It is in view of the above that efforts are made in this paper to collate Ahadith pertaining to dissociation from non-Muslims as well as co-existence with them. Consequently, a critical study of their authenticity is briefly explained before proceeding to analysis of their linguistic and contextual meanings. To arrive at the accurate interpretation, harmonization is graphically applied. The result shows that dissociation from non –Muslims as a bedrock of Islamic faith could be explained in Sunnah by prohibition of participating or getting satisfaction from their religious matters, and anti-Islamic activities. Also, freedom of apostasy, ignoring da`wah with wisdom and seeking non-Muslims support against Muslims are frowned upon in Sunnah as phenomenon of dissociation from non –Muslims. All the aforementioned are strictly prohibited in Sunnah whether under the pretext of enhancing peaceful co-existence with non-Muslims or not. While peaceful co-existence with non-Muslims is evidenced in Sunnah by permissibility of visiting the sick among them, exchange of gift with them, forgiving the wrong among them, having good relationship with non-Muslim neighbours, ties of non-Muslim kinship, legal business transaction with them and the like. Finally, the degree of peaceful co-existence with non-Muslims is determined by their attitude towards Islam and Muslims.

Keywords: Ahadith, conflict, co-existence, non-Muslims

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Authors: G. H. B. Dilhari, A. A. D. T. Saparamadu

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Sri Lanka is one of the prominent countries for the coir production. The coir is one of the by-products of the coconut and the coir industry is considered to be one of the traditional industries in Sri Lanka. Because of the inherent nature of the coir industry, labourers play a significant role in the coir production process. The study has analyzed the determinants of the income of the household level coir yarn labourers. The study was conducted in the Kumarakanda Grama Niladhari division. Simple random sampling was used to generate a sample of 100 household level coir yarn labourers and structured questionnaire, personal interviews, and discussion were performed to gather the required data. The obtained data were statistically analyzed by using Statistical Package for Social Science (SPSS) software. Mann-Whitney U and Kruskal-Wallis test were performed for mean comparison. The findings revealed that the household level coir yarn industry is dominated by the female workers and it was identified that fewer numbers of workers have engaged in this industry as the main occupation. In addition to that, elderly participation in the industry is higher than the younger participation and most of them have engaged in the industry as a source of extra income. Level of education, the methods of engagement, satisfaction, engagement in the industry by the next generation, support from the government, method of government support, working hours per day, employed as a main job, number of completed units per day, suffering from job related diseases and type of the diseases were related with income level of household level coir yarn laboures. The recommendations as to flourish in future includes, technological transformation for coir yarn production, strengthening the raw material base and regulating the raw material supply, introduction of new technologies, markets and training programmes, the establishment of the labourers’ association, the initiation of micro credit schemes and better consideration about the job oriented diseases.

Keywords: coir yarn, determinants, income, Sri Lanka

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Authors: Sunita Singh, Rajani Srivastava

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For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands.

Keywords: GNDVI, NDRE, NDVI, rapid eye, vegetation indices

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Authors: N. Piwpuan, J. Tosalee, N. Phonkerd

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Malachite green (MG), a synthetic dye, is used in industries and aquaculture and also disposed in the effluent. Use of wastewater in irrigation increases due to water shortage. However, wastewater containing dyes, MG, are toxic to biological systems. Therefore, effects of MG on growth of vegetables were evaluated in order to utilize dye-contaminated wastewater for irrigation. In this study, Pak choy (Brassica chinensis) and Chinese convolvulus (Ipomoea aquatica) were grown in growing material (mixture of soil, coconut fiber, and compost) for four weeks and afterward kept watering with 200 ml of tap water containing MG at the concentrations of 0 (control), 1, 2, 10, and 20 mg/L. At harvest, number of leaf and shoot and root dry weight of the treated plants were measured and compared with control. For both species, their biomass values were similar among treatments and did not differ from the control plants (dry weight were 0.6-1.0 and 1.1-1.7 g/plant for B. chinensis and I. aquatica, respectively). B. chinensis treated with 2, 10, and 20 mg/L of MG produced lower number of new leaf and had smaller and shorter leaf compared to control and treatment of 1 mg/L. These results indicate the different responses between plant species, which B. chinensis is more sensitive to contaminant compared to I. aquatica. There was no sign of MG and leucomalachite green (LMG) detected in root and shoot tissues of plants treated with MG at 20 mg/L, tested by thin layer chromatography. After plant harvest, toxicity of the growing material from all treatments was tested on mung beans. Percent germination (83-97%), seedling fresh weight (0.3-0.5 g/plant), and shoot length (11-12.5 cm) were similar to the control. These indicated that contaminant in growing material did not pose detrimental effect on mung beans. Based on these results, the water contaminated with low concentration of MG, such as discharge from aquaculture, may serve as ferti-irrigation water to compensate water shortage.

Keywords: ferti-irrigation, soil toxicity, triphenylmethane dye, wastewater reuse

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Aka S. Koffi, N’Goran Yao, Philippe Bastide, Denis Bruneau, Diby Kadjo

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Cocoa beans (Theobroma cocoa L.) are the main components for chocolate manufacturing. The beans must be correctly fermented at first. Traditional process to perform the first fermentation (lactic fermentation) often consists in confining cacao beans using banana leaves or a fermentation basket, both of them leading to a poor product thermal insulation and to an inability to mix the product. Box fermenter reduces this loss by using a wood with large thickness (e>3cm), but mixing to homogenize the product is still hard to perform. Automatic fermenters are not rentable for most of producers. Heat (T>45°C) and acidity produced during the fermentation by microbiology activity of yeasts and bacteria are enabling the emergence of potential flavor and taste of future chocolate. In this study, a cylindro-rotative fermenter (FCR-V1) has been built and coconut fibers were used in its structure to confine heat. An axis of rotation (360°) has been integrated to facilitate the turning and homogenization of beans in the fermenter. This axis permits to put fermenter in a vertical position during the anaerobic alcoholic phase of fermentation, and horizontally during acetic phase to take advantage of the mid height filling. For circulation of air flow during turning in acetic phase, two woven rattan with grid have been made, one for the top and second for the bottom of the fermenter. In order to reduce air flow during acetic phase, two airtight covers are put on each grid cover. The efficiency of the turning by this kind of rotation, coupled with homogenization of the temperature, caused by the horizontal position in the acetic phase of the fermenter, contribute to having a good proportion of well-fermented beans (83.23%). In addition, beans’pH values ranged between 4.5 and 5.5. These values are ideal for enzymatic activity in the production of the aromatic compounds inside beans. The regularity of mass loss during all fermentation makes it possible to predict the drying surface corresponding to the amount being fermented.

Keywords: cocoa fermentation, fermenter, microbial activity, temperature, turning

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Authors: G. H. B. Dilhari, A. A. D. T. Saparamadu

Abstract:

The coir is one of the by-products of the coconut and the coir industry can be identified as one of the traditional industries in Sri Lanka. Sri Lanka is one of the prominent countries for the coir production. Due to the labour insensitiveness, the labourers are the significant factor in the coir production process. The study has analyzed the determinants and its impact on income of the household level coir yarn labourers. The study was conducted in the Kumarakanda Grama Niladhari division, Galle, Sri Lanka. Simple random sampling was used to generate the sample of 100 household level coir yarn labourers and structured questionnaire, personal interviews and discussion were performed to gather the required data. The obtained data were statistically analyzed by using Statistical Package for Social Science (SPSS) software. Mann-Whitney U and Kruskal-Wallis test were carried out. The findings revealed that the household level coir yarn industry is dominated by the female workers and fewer amounts of workers have engaged this industry as the main occupation. In addition to that, elderly participation of the industry is greater than younger participation and most of them engaged as an extra income source. Level of education, the methods of engagement, satisfaction, labour’s children employment in the coir industry, support from the government, method of government support, working hours per day, employed as a main job, no of completed units per day, suffering any job related diseases and type of the diseases were related with income level of household level coir yarn labourers. The recommendations were formulated in respect to these problems including technological transformation for coir yarn production, strengthening of the raw material base and regulating the raw material supply, introduction of new technologies, markets and training programs, the establishment of the labourers association, the initiation of micro credit schemes, better consideration about the job oriented diseases.

Keywords: coir, coir yarn labourers, income, Galle

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

Procedia PDF Downloads 666

Authors: M. Israil Hossain, T. P. Tiwari, Ashrafuzzaman Gulandaz, Nusrat Jahan

Abstract:

Two-wheel tractor (power tiller) is a common tillage tool in Bangladesh agriculture for easy access in fragmented land with affordable price of small farmers. Traditional maize sheller needs to be carried from place to place by hooking with two-wheel tractor (2WT) and set up again for shelling operation which takes longer time for preparation of maize shelling. The mobile maize sheller eliminates the transportation problem and can start shelling operation instantly any place as it is attached together with 2WT. It is counterclockwise rotating cylinder, axial flow type sheller, and grain separated with a frictional force between spike tooth and concave. The maize sheller is attached with nuts and bolts in front of the engine base of 2WT. The operating power of the sheller comes from the fly wheel of the engine of the tractor through ‘V” belt pulley arrangement. The average shelling capacity of the mobile sheller is 2.0 t/hr, broken kernel 2.2%, and shelling efficiency 97%. The average maize shelling cost is Tk. 0.22/kg and traditional custom hire rate is Tk.1.0/kg, respectively (1 US$=Tk.78.0). The service provider of the 2WT can transport the mobile maize sheller long distance in operator’s seating position. The manufacturers started the fabrication of mobile maize sheller. This mobile maize sheller is also compatible for the other countries where 2WT is available for farming operation.

Keywords: cost effective, mobile maize sheller, maize shelling capacity, small farmers, two wheel tractor

Procedia PDF Downloads 184

Authors: M. Javanmard

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This study was aimed at analyzing the effects of packaging (MAP) and preservation conditions on the packaged fresh walnut kernel quality. The central composite plan was used for evaluating the effect of oxygen (0–10%), carbon dioxide (0-10%), and temperature (4-26 °C) on qualitative characteristics of walnut kernels. Also, the response level technique was used to find the optimal conditions for interactive effects of factors, as well as estimating the best conditions of process using least amount of testing. Measured qualitative parameters were: peroxide index, color, decreased weight, mould and yeast counting test, and sensory evaluation. The results showed that the defined model for peroxide index, color, weight loss, and sensory evaluation is significant (p < 0.001), so that increase of temperature causes the peroxide value, color variation, and weight loss to increase and it reduces the overall acceptability of walnut kernels. An increase in oxygen percentage caused the color variation level and peroxide value to increase and resulted in lower overall acceptability of the walnuts. An increase in CO₂ percentage caused the peroxide value to decrease, but did not significantly affect other indices (p ≥ 0.05). Mould and yeast were not found in any samples. Optimal packaging conditions to achieve maximum quality of walnuts include: 1.46% oxygen, 10% carbon dioxide, and temperature of 4 °C.

Keywords: shelled walnut, MAP, quality, storage temperature

Procedia PDF Downloads 389

Authors: Cristine P. Canlas, Crislene Mae L. Gever, Patricia Bea R. Rosialda, Ma. Nina Regina M. Quibod, Perry Archival C. Buenavente, Normandy M. Barbecho, Cynthia Adeline A. Layusa, Michael Day

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Invasive plants have an impact on global biodiversity and ecosystem function, and their management is a complex and formidable task. Two of these invasive plant species, Lantana camara and Chromolaena odorata, are found in the Philippines. Lantana camara has the ability to suppress the growth of and outcompete neighboring plants. Chromolaena odorata causes serious agricultural and economical damage and causes fire hazards during dry season. In addition, both species has been reported to poison livestock. One of the known global management strategies to control invasive plants is the introduction of biological control agents. These natural enemies of the invasive plants reduce population density and impacts of the invasive plants, resulting in the balance of the nature in their invasion. Through secondary data sources, interviews, and field validation (e.g. microhabitat searches, sweep netting, opportunistic sampling, photo-documentation), we investigated whether the biocontrol agents previously released by the Philippine Coconut Authority (PCA) in their Davao Research Center to control these invasive plants are still present and are affecting their respective host weeds. We confirm the presence of the biocontrol agent of L. camara, Uroplata girardi, which was introduced in 1985, and Cecidochares connexa, a biocontrol agent of C. odorata released in 2003. Four other biocontrol agents were found to affect L. camara. Signs of damage (e.g. stem galls in C. odorata, and leaf mines in L. camara) signify that these biocontrol agents have successfully established outside of their release site in Davao. Further investigating the extent of the spread of these biocontrol agents in the Philippines and their damage to the two weeds will contribute to the management of invasive plant species in the country.

Keywords: invasive alien species, biological control agent, entomology, worst weeds

Procedia PDF Downloads 374

Authors: Anjela Koblischka-Veneva, Michael R. Koblischka

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In this contribution, the Transmission-Kikuchi Diffraction (TKD, or sometimes called t-EBSD) is applied to bulk, melt-grown YBa₂Cu₃O₇ (YBCO) superconductors prepared by the MTMG (melt-textured melt-grown) technique and the infiltration growth (IG) technique. TEM slices required for the analysis were prepared by means of Focused Ion-Beam (FIB) milling using mechanically polished sample surfaces, which enable a proper selection of the interesting regions for investigations. The required optical transparency was reached by an additional polishing step of the resulting surfaces using FIB-Ga-ion and Ar-ion milling. The improved spatial resolution of TKD enabled the investigation of the tiny YBa₂Cu₃O₅ (Y-211) particles having a diameter of about 50-100 nm embedded within the YBCO matrix and of other added secondary phase particles. With the TKD technique, the microstructural properties of the YBCO matrix are studied in detail. It is observed that the matrix shows the effects of stress/strain, depending on the size and distribution of the embedded particles, which are important for providing additional flux pinning centers in such superconducting bulk samples. Using the Kernel Average Misorientation (KAM) maps, the strain induced in the superconducting matrix around the particles, which increases the flux pinning effectivity, can be clearly revealed. This type of analysis of the EBSD/TKD data is, therefore, also important for other material systems, where nanoparticles are embedded in a matrix.

Keywords: transmission Kikuchi diffraction, EBSD, TKD, embedded particles, superconductors YBa₂Cu₃O₇

Procedia PDF Downloads 135

Authors: Angga Pratama Herman, Muhammad Shahbaz, Suzana Yusup

Abstract:

Bottom ash is a solid waste from thermal power plant and it is usually disposed of into landfills and ash ponds. These disposal methods are not sustainable since new lands need to be acquired as the landfills and ash ponds are fill to its capacity. Bottom ash also classified as hazardous material that makes the disposal methods may have contributed to the environmental effect to the area. Hence, more research needs to be done to explore the potential of recycling the bottom ash as more useful product. The objective of this research is to explore the potential of utilizing bottom ash as catalyst in biomass steam gasification. In this research, bottom ash was used as catalyst in gasification of Palm Kernel Shell (PKS) using Thermo Gravimetric Analyzer coupled with mass spectrometry (TGA/MS). The effects of temperature (650 – 750 °C), particle size (0.5 – 1.0 mm) and bottom ash percentage (2 % - 10 %) were studied with and without steam. The experimental arrays were designed using expert method of Central Composite Design (CCD). Results show maximum yield of hydrogen gas was 34.3 mole % for gasification without steam and 61.4 Mole % with steam. Similar trend was observed for syngas production. The maximum syngas yield was 59.5 mole % for without steam and it reached up to 81.5 mole% with the use of steam. The optimal condition for both product gases was temperature 700 °C, particle size 0.75 mm and cool bottom ash % 0.06. In conclusion, the use of bottom ash as catalyst is possible for biomass steam gasification and the product gases composition are comparable with previous researches, however the results need to be validated for bench or pilot scale study.

Keywords: bottom ash, biomass steam gasification, catalyst, lab scale

Procedia PDF Downloads 300