Search results for: palm kernel oil esters

Authors: Jiajun Duan, Yang Li, Jianan Gao, Runzi Cao, Enxiang Shang, Wen Zhang

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Reactive oxygen species (ROS) generation is considered as an important photoaging mechanism of microplastics (MPs) and nanoplastics (NPs). To elucidate the ROS-induced MP/NP aging processes in water under UV365 irradiation, we examined the effects of surface coatings, polymer types, and grain sizes on ROS generation and photoaging intermediates. Bare polystyrene (PS) NPs generated hydroxyl radicals (•OH) and singlet oxygen (¹O₂), while coated PS NPs (carboxyl-modified PS (PS-COOH), amino-modified PS (PS-NH₂)) and PS MPs generated fewer ROS due to coating scavenging or size effects. Polypropylene, polyethylene, polyvinyl chloride, polyethylene terephthalate, and polycarbonate MPs only generated •OH. For aromatic polymers, •OH addition preferentially occurred at benzene rings to form monohydroxy polymers. Excess •OH resulted in H abstraction, C-C scission, and phenyl ring opening to generate aliphatic ketones, esters, aldehydes, and aromatic ketones. For coated PS NPs, •OH preferentially attacked the surface coatings to result in decarboxylation and deamination reactions. For aliphatic polymers, •OH attack resulted in the formation of carbonyl groups from peracid, aldehyde, or ketone via H abstraction and C-C scission. Moreover, ¹O₂ might participate in phenyl ring opening for PS NPs and coating degradation for coated PS NPs. This study facilitates understanding the ROS-induced weathering process of NPs/MPs in water under UV irradiation.

Keywords: microplastics, nanoplastics, photoaging, reactive oxygen species, surface coating

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Authors: Yesim Özogul, Fethiye Takadaş, Mustafa Durmus, Yılmaz Ucar, Ali Rıza Köşker, Gulsun Özyurt, Fatih Özogul

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It has been well documented that polyunsaturated fatty acids (PUFAs), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have beneficial effects on health, regarding prevention of cardiovascular diseases, cancer and autoimmune disorders, development the brain and retina and treatment of major depressive disorder etc. Thus, an adequate intake of omega PUFA is essential and generally marine fish are the richest sources of PUFA in human diet. Thus, this study was conducted to evaluate the efficiency of different extraction methods (Bligh and Dyer, soxhlet, microwave and ultrasonics) on the fat content and fatty acid profiles of marine fish species (Mullus babatus, Upeneus moluccensis, Mullus surmuletus, Anguilla anguilla, Pagellus erythrinus and Saurida undosquamis). Fish species were caught by trawl in Mediterranean Sea and immediately iced. After that, fish were transported to laboratory in ice and stored at -18oC in a freezer until the day of analyses. After extracting lipid from fish by different methods, lipid samples were converted to their constituent fatty acid methyl esters. The fatty acid composition was analysed by a GC Clarus 500 with an autosampler (Perkin Elmer, Shelton, CT, USA) equipped with a flame ionization detector and a fused silica capillary SGE column (30 m x 0.32 mm ID x 0.25 mm BP20 0.25 UM, USA). The results showed that there were significant differences (P < 0.05) in fatty acids of all species and also extraction methods affected fat contents and fatty acid profiles of fish species.

Keywords: extraction methods, fatty acids, marine fish, PUFA

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Authors: David Gamboa, Bernardo Herrera, Karen Cacua

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Nanomaterials have been explored as alternatives to reduce particulate matter from diesel engines, which is one of the most common pollutants of the air in urban centers. However, the use of nanomaterials as additives for diesel has to overcome the instability of the dispersions to be considered viable for commercial use. In this work, functionalization of carbon nanotubes with amide groups was performed to improve the stability of these nanomaterials in a mix of 90% petroleum diesel and 10% palm oil biodiesel (B10) in concentrations of 50 and 100 ppm. The resulting nano fuel was used as the fuel for a stationary internal combustion engine, where the particulate matter, NOx, and CO were measured. The results showed that the use of amide groups significantly enhances the time for the carbon nanotubes to remain suspended in the fuel, and at the same time, these nanomaterials helped to reduce the particulate matter and NOx emissions. However, the CO emissions with nano fuel were higher than those ones with the combustion of B10. These results suggest that carbon nanotubes have thermal and catalytic effects on the combustion of B10.

Keywords: carbon nanotubes, diesel, internal combustion engine, particulate matter

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Authors: Araujo J. F. Marina, Araujo F. Marcus Vinicius, Mulinari R. Daniella

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Due to the resultant leachate from waste decomposition in landfills has polluter potential hundred times greater than domestic sewage, this is considered a problem related to the depreciation of environment requiring pre-disposal treatment. In seeking to improve this situation, this project proposes the treatment of landfill leachate using natural fibers intercropped with advanced oxidation processes. The selected natural fibers were palm, coconut and banana fiber. These materials give sustainability to the project because, besides having adsorbent capacity, are often part of waste discarded. The study was conducted in laboratory scale. In trials, the effluents were characterized as Chemical Oxygen Demand (COD), Turbidity and Color. The results indicate that is technically promising since that there were extremely oxidative conditions, the use of certain natural fibers in the reduction of pollutants in leachate have been obtained results of COD removals between 67.9% and 90.9%, Turbidity between 88.0% and 99.7% and Color between 67.4% and 90.4%. The expectation generated is to continue evaluating the association of efficiency of other natural fibers with other landfill leachate treatment processes.

Keywords: lndfill leachate, chemical treatment, natural fibers, advanced oxidation processes

Procedia PDF Downloads 335

Authors: Keri Alhadi Ighwela, Aziz Bin Ahmad, A. B. Abol-Munafi

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Three purified diets were formulated using fish meal, soya bean, wheat flour, palm oil, minerals and maltose. The carbohydrate in the diets was increased from 5 to 15% by changing the cellulose content to study the effect of dietary carbohydrate level on the growth parameters of Nile tilapia Oreochromis niloticus.The protein and the lipid contents were kept constant in all the diets. The results showed that, weight gain, protein efficiency ratio, net protein utilisation and hepatosomatic index of fish fed the diet containing 15% cellulose were the lowest among all groups. Addition, the fish fed the diet containing 5% cellulose had the best specific growth rate, and food conversion ratio. While, there was no effect of the dietary cellulose levels on condition factor and survival rate. These results indicate that Nile tilapia fingerlings are able to utilize dietary cellulose does not exceed 10% in their feed for optimum growth.

Keywords: dietary cellulose, growth parameters, oreochromis niloticus, purified diets

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Authors: Yawen Wang, Hongchang Chen, Shaomei Li, Chao Gao, Jiangpeng Zhang

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As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm.

Keywords: target tracking, PCA-SIFT, mean-shift, scale-adaptive

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Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

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This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

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Authors: Wassim Guermazi, Saoussan Boukhris, Neila Annabi Trabelsi, Tarek Rebai, Alya Sellami-Kamoun, Habib Ayadi

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This work investigates the protective effects of the microalga Halamphora sp. extract (H. Ext) as a natural product on lead-intoxicated liver and kidney human cells in vitro and in vivo on rats wistar. HepG2 cells line derived from human hepatocellular carcinoma and HEK293 cells line derived from human embryonic kidney were used for the in vitro study. The analysis of the fatty acids methyl esters of the extract was performed by a GC/MS. Four groups of rats, each of which was composed of six animals, were used for the in vivo experiment. The pretreatment of HepG2 and HEK293 cells line with the extract (100 µg mL-1) significantly (p < 0.05) protected against cytotoxicity induced by lead exposure. In vivo, the biochemical parameters in serum, namely malondialdehyde level (MDA), superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities, were measured in supernatants of organ homogenates. H. Ext was found to be rich in fatty acids, essentially palmitic and palmitoleic accounting respectively 29.46% and 42.07% of total fatty acids. Both in vitro and in vivo, the co-treatment with H. Ext allowed the protection of the liver and kidney cells structure, as well as the significant preservation of normal antioxidant and biochemical parameters in rats. Halamphora extract rich in fatty acids has been proven to be effective in protection against Pb-induced toxicity.

Keywords: microalga extract, human cells line, fatty acid, lead exposure, oxidative stress, rats

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Authors: Ellisha Iling, Dayang Siti Hazimmah Ali

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Experimental composite boards were fabricated using oil palm (Elaeis guineensis Jacq) fronds particles by applying hot press pressure of 5MPa, 6MPa and 7MPa respectively. Modulus of rupture (MOR) and internal bond strength (IB) of the composite boards made with target density of 0.80 g/cm³ were evaluated. Composite board fabricated under hot press pressure of 5MPa had MOR and IB values of 16.27 and 4.34 N/mm² respectively. Corresponding values for composite board fabricated under hot press pressure of 6MPa were 16.76 and 5.41 N/mm² respectively. Whereas, the MOR and IB values of composite board fabricated under hot press pressure of 7MPa were 17.24 and 6.19 N/mm² respectively. All composite boards met the MOR and IB requirement stated in Japanese Industrial Standard (JIS). Based on results of this work, the strength of mechanical properties of composite board increased with increase of hot press pressure. This study revealed that the selection of applied pressure during fabrication of composite board is important to improve mechanical properties of composite boards.

Keywords: composite board, Elaeis guineensis Jacq. Fronds, hot press pressure, mechanical properties

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Authors: Deepa S N, Srinivasan a D, Veeramanju K T

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The power transformer is a critical equipment in the transmission and distribution network that must be managed to ensure uninterrupted power service. The liquid insulation is essential for the proper functioning of the transformer, as it serves as both coolant and insulating medium, which influences the transformer’s durability. Further, the insulating state of a power transformer has a significant impact on its reliability. Mineral oil derived from petroleum crude oil has been employed as liquid dielectrics for decades due to its superior functional characteristics, however as a resource for the same are getting depleted over the years. Research is undertaken across the globe to identify a viable substitute for mineral oil. Further, alternate insulating oils are being investigated for better environmental impact, biodegradability and economics. Several combinations of vegetable oil derived natural esters are being inspected by researchers across the globe in these domains. In this work, mineral oil is blended with soyabean oil with various proportions and dielectric properties such as dielectric breakdown voltage, relative permittivity, dissipation factor, viscosity, flash and fire point have been investigated according to international standards. A quantitative comparison is made among various samples and is observed that the blended oil sample of equal proportion of mineral oil and soyabean oil, MO50+SO50 exhibits superior dielectric properties such as breakdown voltage of 65kV, dissipation factor of 0.0044, relative permittivity of 3.1680 that are closer to the range of values recommended for power transformer applications. Also, Breakdown voltage values of all the investigated oil samples obeyed the Weibull and Normal probability distribution.

Keywords: blended oil, dielectric breakdown, liquid insulation, power transformer

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Authors: Ching-Ting Hsu, Wei-Hua Ho, Yi-Chun Chang

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Video object tracking is one of the popular research topics in computer graphics area. The trajectory can be applied in security, traffic control, even the sports training. The trajectory for sports training can be utilized to analyze the athlete’s performance without traditional sensors. There are many relevant works which utilize mean shift algorithm with background subtraction. This kind of the schemes should select a kernel function which may affect the accuracy and performance. In this paper, we consider the motion information in the pre-coded bitstream. The proposed algorithm extracts the trajectory by composing the motion vectors from the pre-coded bitstream. We gather the motion vectors from the overlap area of the object and calculate mean of the overlapped motion vectors. We implement and simulate our proposed algorithm in H.264 video codec. The performance is better than relevant works and keeps the accuracy of the object trajectory. The experimental results show that the proposed trajectory extraction can extract trajectory form the pre-coded bitstream in high accuracy and achieve higher performance other relevant works.

Keywords: H.264, video bitstream, video object tracking, sports training

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Authors: Xiongxiong You, Zhanwen Niu

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Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method.

Keywords: adaptive selection, expensive optimization, rotor system, surrogates assisted evolutionary algorithms

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Authors: Ambreen Aslam, Tahira Aziz Mughal, Skye R. Thomas-Hall, Peer M. Schenk

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Biodiesel is an alternative to petroleum-derived fuel mainly composed of fatty acid from oleaginous microalgae feedstock. Microalgae produced fatty acid methyl esters (FAMEs) as they can store high levels of lipids without competing for food productivity. After lipid extraction and esterification, fatty acid profile from algae feedstock possessed the abundance of fatty acids with carbon chain length specifically C16 and C18. The qualitative analysis of FAME was done by cultivating mix microalgae consortia under three different CO₂ concentrations (1%, 3%, and 5.5%) from a coal fired flue gas. FAME content (280.3 µg/mL) and productivity (18.69 µg/mL/D) was higher under 1% CO₂ (flue gas) as compare to other treatments. Whereas, Mixed C. (F) supplemented with 5.5% CO₂ (50% flue gas) had higher SFA (36.28%) and UFA (63.72%) which improve the oxidative stability of biodiesel. Subsequently, low Iodine value (136.3 gI₂/100g) and higher Cetane number (52) of Mixed C.+P (F) were found to be in accordance with European (EN 14214) standard under 5.5% CO₂ along with 50mM phosphate buffer. Experimental results revealed that sufficient phosphate reduced FAME productivity but significantly enhance biodiesel quality. This research aimed to develop an integrated approach of utilizing flue gas (as CO₂ source) for significant improvement in biodiesel quality under surplus phosphorus. CO₂ sequestration from industrial flue gas not only reduce greenhouse gases (GHG) emissions but also ensure sustainability and eco-friendliness of the biodiesel production process through microalgae.

Keywords: biodiesel analysis, carbon dioxide, coal fired flue gas, FAME productivity, fatty acid profile, fuel properties, lipid content, mixed culture microalgae

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Sariah Abang, S. M. Anisuzzaman, Awang Bono, D. Krishnaiah, S. Rasmih

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Biodiesel is a potential renewable energy due to biodegradable and non-toxic. The challenge of its commercialization is associated with high production cost due to its feedstock also useful in various food products. Non-competitive feedstock such as waste cooking oils normally contains a large amount of free fatty acids (FFAs). Large amount of fatty acid degrades the alkaline catalyst in the biodiesel production, thereby decreasing the biodiesel production rate. Generally, biodiesel production processes including esterification and trans-esterification are conducting in a mixed system, in which the hydrodynamic effect on the reaction could not be completely defined. The aim of this study was to investigate the effect of variation rate constant and activation energy on energy consumption of biodiesel production. Usually, the changes of rate constant and activation energy depend on the operating temperature and the degradation of catalyst. By varying the activation energy and kinetic rate constant, the effects can be seen on the energy consumption of biodiesel production. The result showed that the energy consumption of biodiesel is dependent on the changes of rate constant and activation energy. Furthermore, this study was simulated using Aspen HYSYS.

Keywords: methanol, palm oil, simulation, transesterification, triolein

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Authors: Carolina V. Viegas, Monique Gonçalves, Gisel Chenard Diaz, Yordanka Reyes Cruz, Donato Alexandre Gomes Aranda

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To develop the massive cultivation of microalgae, it is necessary to isolate and characterize the species, improving genetic tools in search of specific characteristics. Therefore, the detection, identification and quantification of the compounds that compose the Scenedesmus sp. were prerequisites to verify the potential of these microalgae. The main objective of this work was to carry out the characterization of Scenedesmus sp. as to the content of ash, carbohydrates, proteins and lipids as well as the determination of the composition of their lipid classes and main fatty acids. The biomass of Scenedesmus sp, showed 15,29 ± 0,23 % of ash and CaO (36,17 %) was the main component of this fraction, The total protein and carbohydrate content of the biomass was 40,74 ± 1,01 % and 23,37 ± 0,95 %, respectively, proving to be a potential source of proteins as well as carbohydrates for the production of ethanol via fermentation, The lipid contents extracted via Bligh & Dyer and in situ saponification were 8,18 ± 0,13 % and 4,11 ± 0,11 %, respectively. In the lipid extracts obtained via Bligh & Dyer, approximately 50 % of the composition of this fraction consists of fatty compounds, while the other half is composed of an unsaponifiable fraction composed mainly of chlorophylls, phytosterols and carotenes. From the lowest yield, it was possible to obtain a selectivity of 92,14 % for fatty components (fatty acids and fatty esters) confirmed through the infrared spectroscopy technique. The presence of polyunsaturated acids (~45 %) in the lipid extracts indicated the potential of this fraction as a source of nutraceuticals. The results indicate that the biomass of Scenedesmus sp, can become a promising potential source for obtaining polyunsaturated fatty acids, carotenoids and proteins as well as the simultaneous obtainment of different compounds of high commercial value.

Keywords: microalgae, Desmodesmus, lipid classes, fatty acid profile, proteins, carbohydrates

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Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Serge B. Provost

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Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples.

Keywords: density estimation, log-density, polynomial adjustments, sample moments

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Shuba Anastasiia, Kuchmenko Tatiana, Umarkhanov Ruslan

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The development of new sensitive coatings for sensors is one of the key directions in the development of sensor technologies. Recently, there has been a trend towards the creation of multicomponent coatings for sensors, which make it possible to increase the sensitivity, and specificity, and improve the performance properties of sensors. When analyzing samples with a complex matrix of biological origin, the inclusion of micelles of bioactive substances (amino and nucleic acids, peptides, proteins) in the composition of the sensor coating can also increase useful analytical information. The purpose of this work is to evaluate the analytical characteristics of composite coatings of piezoelectric quartz sensors based on medium-molecular viscous sorbents with incorporated micellar casein concentrate during the sorption of vapors of volatile organic compounds. The sorption properties of the coatings were studied by piezoelectric quartz microbalance. Macromolecular compounds (dicyclohexyl-18-crown-6, triton X-100, lanolin, micellar casein concentrate) were used as sorbents. Highly volatile organic compounds of various classes (alcohols, acids, aldehydes, esters) and water were selected as test substances. It has been established that composite coatings of sensors with the inclusion of micellar casein are more stable and selective to vapors of highly volatile compounds than to water vapors. The method and technique of forming a composite coating using molecular viscous sorbents do not affect the kinetic features of VOC sorption. When casein micelles are used, the features of kinetic sorption depend on the matrix of the coating.

Keywords: piezoquartz sensor, viscous sorbents, micellar casein, coating, volatile compounds

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

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Authors: Xia Huo

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The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

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Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Ogbuagu Josephat Okechukwu, Okeke Abuchi Princewill, Arinze Rosemary Uche, Tabugbo Ifeyinwa Blessing, Ogbuagu Adaora Stellamaris

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Phthalates or Phthalate esters (PAEs), categorized as an endocrine disruptor and persistent organic pollutants, are known for their environmental contamination and toxicological effects. In this study, the concentration of selected phthalates was determined across the sampling site to investigate their occurrence and the ecological and health risk assessment they pose to the environment. Water and sediment samples were collected following standard procedures. Solid phase and ultrasonic methods were used to extract seven different PAEs, which were analyzed by Gas Chromatography with Mass Detector (GCMS). The analytical average recovery was found to be within the range of s83.4% ± 2.3%. The results showed that PAEs were detected in six out of seven samples with a high percentage of detection rate in water. Di-n-butyl phthalate (DPB) and diisobutyl phthalates (DiBP) showed greater detection rate compared to other PAEs monomers. The concentration of PEs was found to be higher in the sediment samples compared to the water samples due to the fact that sediments serve as a sink for most persistent organic pollutants. The concentrations of PAEs in water samples and sediments ranged from 0.03 ± 0.01 to 0.29 ± 0.002 ppm and 3.99 ± 0.43 to 6.04 ± 1.25 ppm, respectively. Ecological risk assessment using the risk quotient method (RQ) reveals that the estimated environmental risk caused by phthalates lies within the moderate level as RQ ranges from 0.1 to 1.0, whereas the health risk assessment caused by phthalates on estimating the average daily dose reveals that the ingestion of phthalates was found to be high which can cause serious carcinogenic occurrence in the human system with time due to excess accumulation.

Keywords: phthaletes, assessment, marine pollution, endocrine

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Authors: A. Monika, Manu Sharma, Hong Boo Lee, Richa Dhingra, Neelima Dhingra

Abstract:

Nuclear factor-κappa B serve as a molecular lynchpin that links persistent infections and chronic inflammation to increased cancer risk. Inflammation has been recognized as a hallmark and cause of cancer. Natural products present a privileged source of inspiration for chemical probe and drug design. Herbal remedies were the first medicines used by humans due to the many pharmacologically active secondary metabolites produced by plants. Some of the metabolites like Lantadene (pentacyclic triterpenoids) from the weed Lantana camara has been known to inhibit cell division and showed anti-antitumor potential. The C-3 aromatic esters of lantadenes were synthesized, characterized and evaluated for cytotoxicity and inhibitory potential against Tumor necrosis factor alpha-induced activation of Nuclear factor-κappa B in lung cancer cell line A549. The 3-methoxybenzoyloxy substituted lead analogue inhibited kinase activity of the inhibitor of nuclear factor-kappa B kinase in a single-digit micromolar concentration. At the same time, the lead compound showed promising cytotoxicity against A549 lung cancer cells with IC50 ( half maximal inhibitory concentration) of 0.98l µM. Further, molecular docking of 3-methoxybenzoyloxy substituted analogue against Inhibitor of nuclear factor-kappa B kinase (Protein data bank ID: 3QA8) showed hydrogen bonding interaction involving oxygen atom of 3-methoxybenzoyloxy with the Arginine-31 and Glutamine-110. Encouraging results indicate the Lantadene’s potential to be developed as anticancer agents.

Keywords: anticancer, lantadenes, pentacyclic triterpenoids, weed

Procedia PDF Downloads 137

Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

Abstract:

The spectral random field method is one of the widely used methods to obtain more reliable and accurate results in geotechnical problems involving material variability. Karhunen-Loeve (K-L) expansion method was applied to perform random field discretization of cross-correlated creep parameters. Karhunen-Loeve expansion method is based on eigenfunctions and eigenvalues of covariance function adopting Kernel integral solution. In this paper, the accuracy of Karhunen-Loeve expansion was investigated to predict long-term settlement of soft soils adopting elastic visco-plastic creep model. For this purpose, a parametric study was carried to evaluate the effect of K-L expansion terms and spatial correlation length on the reliability of results. The results indicate that small values of spatial correlation length require more K-L expansion terms. Moreover, by increasing spatial correlation length, the coefficient of variation (COV) of creep settlement increases, confirming more conservative and safer prediction.

Keywords: Karhunen-Loeve expansion, long-term settlement, reliability analysis, spatial correlation length

Procedia PDF Downloads 136

Authors: A. Bahrani

Abstract:

In order to study the effect of restricted irrigation systems and different potassium fertilizer on water use efficiency and yield of rapeseed (Brassica napus L.), an experiment was conducted in an arid area in Khuzestan, Iran in 2013. The main plots consisted of three irrigation methods: FI (full irrigation), alternate furrow irrigation (AFI) and fixed furrow irrigation (FFI). Each subplot received three rates of K fertiliser application: 0, 150 or 300 kg ha-1. The results showed that the plots receiving the full irrigation resulted in significantly higher grain yields, 1000-kernel weight and grain number per pod than both alternate treatments. However, the highest WUE were obtained in alternate furrow irrigation and 300 kg K ha-1 and the lowest one was found in the FI treatment and 0 kg K ha-1. Potassium application increased RWC in alternate furrow irrigation and fixed furrow irrigation than FI treatment. Maximum oil content was observed in those treatments where full irrigation was applied while minimum oil content was produced in FFI irrigated treatments. Potassium fertilizer also increased grain oil by 15 % than control. Deficit irrigation reduced oleic acid and erucic acid. However, oleic acid and linoleic acid increased with increasing of potassium.

Keywords: erucic acid, irrigation methods, linoleic acid, oil percent, oleic acid

Procedia PDF Downloads 257

Authors: Sharifah Nabihah Syed Jaafar, Umar Adli Amran, Rasidi Roslan, Chia Chin Hua, Sarani Zakaria

Abstract:

Bio-composites derived from plant fiber and bio-derived polymer, are likely more ecofriendly and demonstrate competitive performance with petroleum based. In this research, the green phenolic resin was used as a matrix and oil palm empty fruit bunch fiber (EFB) was used as filler. The matrix was synthesized from soda lignin, phenol and hydrochloric acid as a catalyst. The phenolic resin was synthesized via liquefaction and condensation to enhance the combination of phenol during the process. Later, the phenolic resin was mixed with EFB by using mechanical stirrer and was molded with hot press at 180 oC. In this research, the composites were prepared with EFB content of 5%, 10%, 15% and 20%. The samples that viewed under scanning electron microscopy (SEM) showed that the EFB filler remained embedded in the resin. From impact and hardness testing, samples 10% of EFB showed the optimum properties meanwhile sample 15% showed the optimum properties for flexural testing. Thermal stability of the composites was investigated using thermogravimetric (TGA) analysis and found that the weight loss and the activation energy (Ea) of the composites samples were decreased as the filler content increased.

Keywords: EFB, liquefaction, phenol formaldehyde, lignin

Procedia PDF Downloads 558