Search results for: color models

Authors: Ali S. Gargoum

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Graphical modeling plays an important role in providing efficient probability calculations in high dimensional problems (computational efficiency). In this paper, we address one of such problems where we discuss fragmenting puff models and some distributional assumptions concerning models for the instantaneous, emission readings and for the fragmenting process. A graphical representation in terms of a junction tree of the conditional probability breakdown of puffs and puff fragments is proposed.

Keywords: graphical models, influence diagrams, junction trees, Bayesian nets

Procedia PDF Downloads 396

Authors: Valdir Moura, FranciléIa De Oliveira E. Silva, Erivelto Mercante, Ranieli Dos Anjos De Souza, Jerry Adriani Johann

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Several colonization projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among all of these colonization projects, the most prominent were those with the Fishbone and Topographic models. Within this scope, the projects of settlements known as Anari and Machadinho were created, which stood out because they are contiguous areas with different models and structure of occupation and colonization. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonization models, implanted in the State of Rondonia in the 1980s. The Fishbone and Topographic models were implanted in the Anari and Machadinho settlements respectively. The understanding of these two forms of occupation will help in future colonization programs of the Brazilian Legal Amazon. These settlements are contiguous areas with different occupancy structures. A 32-year Landsat time series (1984-2016) was used to evaluate the rates and trends in the LULC process in the different colonization models. In the different occupation models analyzed, the results showed a rapid loss of primary and secondary forests (deforestation), mainly due to the dynamics of use, established by the Agriculture/Pasture (A/P) relation and, with heavy dependence due to road construction.

Keywords: land-cover, deforestation, rate fragments, remote sensing, secondary succession

Procedia PDF Downloads 136

Authors: Raquel Zydeck, Karina Azzolin, Luis Kosteski, Alisson Milani

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This work presents numerical models in plane deformations (2D), using the Discrete Element Method formedbybars (LDEM) andtheFiniteElementMethod (FEM), in structuralmasonrywallswith horizontal chasesof 20%, 30%, and 50% deep, located in the central part and 1/3 oftheupperpartofthewall, withcenteredandeccentricloading. Differentcombinationsofboundaryconditionsandinteractionsbetweenthemethodswerestudied.

Keywords: chases in structural masonry walls, discrete element method formed by bars, finite element method, numerical models, boundary condition

Procedia PDF Downloads 169

Authors: Nurudeen O. Lasisi, Sirajo Abdulrahman, Abdulkareem A. Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with the virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of the modeling of the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. The comparison of Vaccination, linear incident rate and novel quarantine-adjusted incident rate in the models are discussed. The dynamics of the models yield disease-free and endemic equilibrium states.The effective reproduction numbers of the models are computed in order to measure the relative impact of an individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models and we found that the stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, Endemic state, Mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

Procedia PDF Downloads 123

Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Ifeoma Maryjane Iloamaeke, Kelvin Obazie, Mmesoma Offornze, Chiamaka Marysilvia Ifeaghalu, Cecilia Aduaka, Ugomma Chibuzo Onyeije, Claudine Ifunanaya Ogu, Ngozi Anastesia Okonkwo

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This work investigated the effects of pH, settling time, and coagulant dosages on the removal of color, turbidity, and heavy metals from paint industrial wastewater using the seed of Artocarpus heterophyllus (AH) by the coagulation-flocculation process. The paint effluent was physicochemically characterized, while AH coagulant was instrumentally characterized by Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR), and X-ray diffraction (XRD). A Jar test experiment was used for the coagulation-flocculation process. The result showed that paint effluent was polluted with color, turbidity (36000 NTU), mercury (1.392 mg/L), lead (0.252 mg/L), arsenic (1.236 mg/L), TSS (63.40mg/L), and COD (121.70 mg/L). The maximum color removal efficiency was 94.33% at the dosage of 0.2 g/L, pH 2 at a constant time of 50 mins, and 74.67% at constant pH 2, coagulant dosage of 0.2 g/L and 50 mins. The highest turbidity removal efficiency was 99.94% at 0.2 g/L and 50 mins at constant pH 2 and 96.66% at pH 2 and 0.2 g/L at constant time of 50 mins. The mercury removal efficiency of 99.29% was achieved at the optimal condition of 0.8 g/L coagulant dosage, pH 8, and constant time of 50 mins and 99.57% at coagulant dosage of 0.8 g/L, time of 50 mins constant pH 8. The highest lead removal efficiency was 99.76% at a coagulant dosage of 10 g/L, time of 40 mins at constant pH 10, and 96.53% at pH 10, coagulant dosage of 10 g/L and constant time of 40 mins. For arsenic, the removal efficiency is 75.24 % at 0.8 g/L coagulant dosage, time of 40 mins, and constant pH of 8. XRD imaging before treatment showed that Artocarpus heterophyllus coagulant was crystalline and changed to amorphous after treatment. The SEM and FTIR results of the AH coagulant and sludge suggested there were changes in the surface morphology and functional groups before and after treatment. The reaction kinetics were modeled best in the second order.

Keywords: Artocarpus heterophyllus, coagulation-flocculation, coagulant dosages, setting time, paint effluent

Procedia PDF Downloads 100

Authors: H. M. Wang, C. J. Chang

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As the threat of global climate change is more seriously, "net zero emissions by 2050" has become a common global goal. In order to reduce the consumption of petrochemical raw materials and reduce carbon emissions, low-carbon fiber materials have become key materials in the future global textile supply chain. This project uses polyolefin raw materials to modify through synthesis and amination to develop low-temperature dyeable polypropylene fibers, endow them with low-temperature dyeability and high color fastness that can be combined with acid dyes, and improve the problem of low coloring strength. The color fastness to washing can reach the requirement of commerce with 3.5 level or more. Therefore, we realize the entry of polypropylene fiber into the clothing textile supply chain, replace existing fiber raw materials, solve the problem of domestic chemical fiber, textile, and clothing industry's plight of no low-carbon alternative new material sources, and provide the textile industry with a solution to achieve the goal of net zero emissions in 2050.

Keywords: acid dyes, dyeing, low-temperature, polypropylene fiber

Procedia PDF Downloads 88

Authors: Saad M. Algharib

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The major concern of planners when placing fire stations is finding their optimal locations such that the fire companies can reach fire locations within reasonable response time or distance. Planners are also concerned with the numbers of fire stations that are needed to cover all service areas and the fires, as demands, with standard response time or distance. One of the tools for such analysis is location-allocation models. Location-allocation models enable planners to determine the optimal locations of facilities in an area in order to serve regional demands in the most efficient way. The purpose of this study is to examine the geographic distribution of the existing fire stations in Kuwait City. This study utilized location-allocation models within the Geographic Information System (GIS) environment and a number of statistical functions to assess the current locations of fire stations in Kuwait City. Further, this study investigated how well all service areas are covered and how many and where additional fire stations are needed. Four different location-allocation models were compared to find which models cover more demands than the others, given the same number of fire stations. This study tests many ways to combine variables instead of using one variable at a time when applying these models in order to create a new measurement that influences the optimal locations for locating fire stations. This study also tests how location-allocation models are sensitive to different levels of spatial dependency. The results indicate that there are some districts in Kuwait City that are not covered by the existing fire stations. These uncovered districts are clustered together. This study also identifies where to locate the new fire stations. This study provides users of these models a new variable that can assist them to select the best locations for fire stations. The results include information about how the location-allocation models behave in response to different levels of spatial dependency of demands. The results show that these models perform better with clustered demands. From the additional analysis carried out in this study, it can be concluded that these models applied differently at different spatial patterns.

Keywords: geographic information science, GIS, location-allocation models, geography

Procedia PDF Downloads 178

Authors: Muna F. Al-Sammaraie

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The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform.

Keywords: image enhancement, multi-spectral, RGB, histogram

Procedia PDF Downloads 328

Authors: Fazıl Gökgöz, Fahrettin Filiz

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

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

Procedia PDF Downloads 294

Authors: Pibool Waijittragum

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This paper has its objectives to reveal contents and personality suitable to women’s newspapers. The research methodology employed in this study is the questionnaire which is derived from a literature review related to newspapers, graphic elements method for print media design and 12 sample sizes of different daily newspapers. In order to acquire an in-depth understanding and comprehensible view of desirable for a women’s newspaper design, graphic elements that related to that personality as well as other preferable elements for a women’s newspaper, including seven editorial Many Thai newspapers were offer a women’s documentary and column space. With its feminine looks, most of them appeared with warm tones and friendly mood through their headlines, contents, illustrations and graphics. The study found that most desirable personalities for a women’s newspaper design in Thailand are: Modern, Chic and Natural. Each personality has significant graphic elements as follows: 1. Modern: significant elements of modern personality comprises of the composition with graduation pattern which creates attractiveness by using an anomalous alignment layout grid and outstanding structure to create focal points and dynamic movement. Dark to black color that has narrowed, limited hue coupled with bright color tones. The round shape of the Thai font style was suitable for this concept. Such Thai fonts have harmonious proportion and consistent stroke with the urban-polite look. 2. Chic: significant elements of chic personality comprises of the proper composition with distinctive scale, using rhythmic repetition and a contrast of scale to draw in reader attention. Vivid and bright color tones with extensive hues coupled with similar color tones and round shape of the Thai font style with a light stroke and consistent line. 3. Natural: significant elements of natural personality comprises of the proper composition using rhythmic repetition that creates a focal point through striking images and harmonious perspective. Warm color tones with restricted hues that appear to look natural. Duo tone color was suitable through the gradually increasing gradient. The Thai style with hand writing font was suitable through the inconsistent stroke. There are 10 types of daily content that were revealed to be the most desirable for Thai women readers, these are: Daily News, Economics News, Education News, Entertainment News, International news, Political News, Public Health News, Scientific News, Social News and Sports News. As well, there are 16 topics identified as very desirable for Thai women readers, such as: Art and Culture, Automobile, Classified, Special Scoop, Editorial, Advertisement, Entertainment, Health and Quality of Life, History, Horoscope, Lifestyle and Fashion, Literature, Nature - Environment and Tourism, Night Life, Stars and Jet Set Gossip, Women’s Issue.

Keywords: women behaviors, feminine looks, newspaper design, news content

Procedia PDF Downloads 174

Authors: Rabia Almas

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Today’s world is demanding natural and biological colorants on priority bases as an alternative to toxic and unsustainable synthetic dyes. Sustainable natural colors from plants and/or living organisms such as bacteria's and fungi attracted the world research scholars and textile industries recently due to the excitement and opportunities they covered. So, in the present study, natural colors from food waste, such as orange peels and peanuts, were extracted and applied to cotton fabric. The dyeing recipes were optimized in terms of dye concentration, processing temperature and time for higher color strength. The characterization of the dyes and fabric, such as Fourier transform infrared spectroscopy, Scanning Electron Microscopy, and fastness properties were measured for the identification of the chemical groups involved for a better understanding of the dyeing behavior. The results revealed that proper mordanting and concentration of dye on cotton fabric could give high color strength and good fastness to wash and light and these natural dyes can be used as an alternative to synthetic toxic colorants.

Keywords: textile, textile dyes, natural dyes, bio colors

Procedia PDF Downloads 86

Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

Procedia PDF Downloads 191

Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

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Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

Procedia PDF Downloads 358

Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro

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Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

Procedia PDF Downloads 244

Authors: Shang-Yu Chiang, Yu-Shan Tsai, Shih-Hsien Sung, Chung-Ming Lo

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Mitral regurgitation (MR) is a heart disorder which the mitral valve does not close properly when the heart pumps out blood. MR is the most common form of valvular heart disease in the adult population. The diagnostic echocardiographic finding of MR is straightforward due to the well-known clinical evidence. In the determination of MR severity, quantification of sonographic findings would be useful for clinical decision making. Clinically, the vena contracta is a standard for MR evaluation. Vena contracta is the point in a blood stream where the diameter of the stream is the least, and the velocity is the maximum. The quantification of vena contracta, i.e. the vena contracta width (VCW) at mitral valve, can be a numeric measurement for severity assessment. However, manually delineating the VCW may not accurate enough. The result highly depends on the operator experience. Therefore, this study proposed an automatic method to quantify VCW to evaluate MR severity. Based on color Doppler ultrasound, VCW can be observed from the blood flows to the probe as the appearance of red or yellow area. The corresponding brightness represents the value of the flow rate. In the experiment, colors were firstly transformed into HSV (hue, saturation and value) to be closely align with the way human vision perceives red and yellow. Using ellipse to fit the high flow rate area in left atrium, the angle between the mitral valve and the ultrasound probe was calculated to get the vertical shortest diameter as the VCW. Taking the manual measurement as the standard, the method achieved only 0.02 (0.38 vs. 0.36) to 0.03 (0.42 vs. 0.45) cm differences. The result showed that the proposed automatic VCW extraction can be efficient and accurate for clinical use. The process also has the potential to reduce intra- or inter-observer variability at measuring subtle distances.

Keywords: mitral regurgitation, vena contracta, color doppler, image processing

Procedia PDF Downloads 371

Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Jyoti Kumari, Pavuluri Srinivasa Rao

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Jackfruit (ArtocarpusheterophyllusL.) is an underutilized yet highly nutritious fruit with unique flavour, known for its therapeutic and culinary properties. Fresh jackfruit bulb has a very short shelf life due to high moisture and sugar content leading to microbial and enzymatic browning, hindering its consumer acceptability and marketability. An attempt has been made for the preservation of the ripe jackfruit bulbs, by the application of high pressure (HP) over a range of 200-500 MPa at ambient temperature for dwell times ranging from 5 to 20 min. The physicochemical properties of jackfruit bulbs such as the pH, TSS, and titrable acidity were not affected by the pressurization process. The ripening index of the fruit bulb also decreased following HP treatment. While the ascorbic acid and antioxidant activity of jackfruit bulb were well retained by high pressure processing (HPP), the total phenols and carotenoids showed a slight increase. The HPP significantly affected the colour and textural properties of jackfruit bulb. High pressure processing was highly effective in reducing the browning index of jackfruit bulbs in comparison to untreated bulbs. The firmness of the bulbs improved upon the pressure treatment with longer dwelling time. The polyphenol oxidase has been identified as the most prominent oxidative enzyme in the jackfruit bulb. The enzymatic activity of polyphenol oxidase and peroxidase were significantly reduced by up to 40% following treatment at 400 MPa/15 min. HPP of jackfruit bulbs at ambient temperatures is shown to be highly beneficial in improving the shelf stability, retaining its nutrient profile, color, and appearance while ensuring the maximum inactivation of the spoilage enzymes.

Keywords: antioxidant capacity, ascorbic acid, carotenoids, color, HPP-high pressure processing, jackfruit bulbs, polyphenol oxidase, peroxidase, total phenolic content

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Authors: Reza Sadeghi

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In this study, microwave heating was employed for controlling Oryzaephilus mercator. adults infesting stored Iranian dried fig. For this purpose, the dried fig samples were artificially infested with O. mercator and then heated in a microwave oven (2450 MHz) at the power outputs of 450, 720, and 900 W for 10, 20, 30, and 40 s, respectively. Subsequently, changes in the colors of the product samples under the effects of the varied microwave applications were investigated in terms of lightness (ΔL*), redness (Δa*), and yellowness (Δb*) using an image processing technique. The results revealed that both parameters of microwave power and exposure time had significant impacts on the pest mortality rates (p<0.01). In fact, a direct positive relationship was obtained between the mortality rate and microwave irradiation power. Complete mortality was achieved for the pest at the power of 900 W and exposure time of 40 s. The dried fig samples experienced fewer changes in their color parameters. Considering the successful pest control and acceptable changes in the product quality, microwave irradiation can be introduced as an appropriate alternative to chemical fumigants.

Keywords: colorimetric assay, microwave heating, Oryzaephilus mercator, mortality

Procedia PDF Downloads 89

Authors: Ana M. T. Mata, Alexiane Ligneul

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Textile effluents are usually heavily loaded with organic carbon and color compounds, the latter being azo dyes in an estimated 70% of the case effluent posing a major challenge in environmental protection. In this study, the ecotoxicity of simulated textile effluent after biological treatment with anaerobic and aerobic phase (aerobic granular sludge, AGS) and after advanced oxidation processes (AOP) namely ozonation and UV irradiation as post-treatment, were tested to evaluate the fitness of this treatments for ecotoxicity abatement. AGS treatment achieved an 80% removal in both COD and color. AOP was applied with the intention to mineralize the metabolites resulting from biodecolorization of the azo dye Acid Red 14, especially the stable aromatic amine (4-amino-1-naphthalenesulfonic acid, 4A1NS). The ecotoxicity evaluation was based on growth inhibition of the algae Pseudokirchneriella subcapitata following OECD TG 201 except regarding the medium, MBL medium was used instead. Five replicate control cultures and samples were performed with an average STD of 2.7% regarding specific algae growth rate determination. It was found that untreated textile effluent holds an inhibition of specific growth rate of 82%. AGS treatment by itself is able to lower ecotoxicity to 53%. This is probably due to the high color removal of the treatment. AOP post-treatment with Ozone and UV irradiation improves the ecotoxicity abatment to 49 and 43% inhibition respectively, less significantly than previously thought. Since over 85% of 4A1NS was removed by either of the AOP (followed by HPLC), an individual ecotoxicity test of 4A1NS was performed showing that 4A1NS does not inhibit algae growth (0% inhibition). It was concluded that AGS treatment is able by itself to achieve a significant ecotoxicity abatement of textile effluent. The cost-benefit of AOP as a post-treatment have to be better accessed since their application resulted in an improvement of only 10% regarding ecotoxicity effluent removal. It was also found that the 4A1NS amine had no apparent effect on ecotoxicity. Further studies will be conducted to study where ecotoxicity is coming from after AGS biological treatment and how to eliminate it.

Keywords: textile wastewate, ecotoxicity, aerobic granular sludge, AOP

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Authors: Huma Balouch

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Menochilus sexmaculatus commonly known as six spotted zig zag ladybird, is an aphidophagus and the most misidentified Coccinellids due to the occurrence of numerous color variants. The correct identification of Menochilus sexmaculatus and its strains is necessary to implement the use of biological control. In the present study phenotypic and genotypic polymorphism was investigated in Menochilus sexmaculatus collected from Punjab, NWFP and Sindh provinces of Pakistan. Six different morphs of the species were distinguished by analyzing its Elytral color and spot pattern and then Polymerase Chain Reaction was used to generate random amplification of polymorphic DNA (RAPD) from six different types of Menochilus sexmaculatus. Forty primers (OPA & OPC Kit) were used to perform RAPD PCR on six different types of Menochilus sexmaculatus of which, seven primers revealed different patterns related to the Menochilus sexmaculatus types. These seven primers (OPA-04, OPA-09, OPA-18, OPC-04, OPC-12, OPC-15 and OPC-18) produced 111 clear polymorphic bands and 6 scorable strain specific markers. The cluster analysis applied to RAPD data showed high polymorphism among six types and it can be concluded that these six types are six polymorphic strains of the same species.

Keywords: Menochilus sexmaculatus, aphidophagus, coccinellids, phenotypic and genotypic polymorphism, RAPD-PCR, strain specific markers

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Authors: Ouchemoukh Salim, Amessis-Ouchemoukh Nadia, Guenaoui Nawel, Moumeni Lynda, Zaidi Hicham, Otmani Amar, Sadou Dyhia

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Honey is a hive food rich in carbohydrates and water and it also has a lot of nutrients (enzymes, minerals, organic acids, phytochemicals...). It is used in different nutritional and therapeutic fields. Algerian honey was studied for its physicochemical parameters, nutritional values (moisture, brix, pH, electrical conductivity, and amounts of HMF, proteins, proline, total phenolic compounds and flavonoids) and some biological activities (antioxidant, anti-inflammatory and enzymatic anti-browning). The antioxidant activities of the samples were estimated using different methods (ABTS, DPPH free radicals scavenging, reducing power, and chelating ferrous activity). All honeys were acidic (3.45≤pH≤4.65). The color varied from mimosa yellow to dark brown. The specific rotation was levorotatory in most honey samples, and the electrical conductivity, hydroxymethylfurfural, and proline values agreed with the international honey requirements. For anti-inflammatory activity, the results showed that the inhibiting capacity of the denaturation of the BSA of the honey analyzed varied from 15 to 75 % with a maximum of activity at the concentration of 0,5 mg/ml. All honey exhibited enzymatic anti-browning on different slices of fruits. In fact, the results showed that the controls have the greatest browning unit compared to the honeys studied and PPO and POD enzymes had the lowest enzyme activity. High significant correlations were found between the color of honey, its antioxidant content and its biological activities (antioxidant, anti-inflammatory and enzymatic anti-browning). The dark color of honey is a good indicator of the best biological properties, therefore, the best nutritional and therapeutic values.

Keywords: honey, physico-chemical parameters, bioactive compounds, biological properties

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Authors: Nurudeen Oluwasola Lasisi, Abdulkareem Afolabi Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of modeling the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. We do a comparison of Vaccination, linear incident rate, and novel quarantine adjusted incident rate in the models. The dynamics of the models yield disease free and endemic equilibrium states. The effective reproduction numbers of the models are computed in order to measure the relative impact for the individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models, and we found that stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, endemic state, mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

Procedia PDF Downloads 244

Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

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Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

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Authors: Yoko Yamada, Chizumi Yamada

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Narrative psychology is based on the dialogical relationship between self and other. The dialogue can consist of divided, competitive, or opposite communication between self and other. We constructed models of coexistent dialogue in which self and other were positioned side by side and communicated sympathetically. We propose new mediation models for narrative relationships. The mediation models are based on triadic relationships that incorporate a medium or a mediator along with self and other. We constructed three types of mediation model. In the first type, called the “Joint Attention Model”, self and other are positioned side by side and share attention with the medium. In the second type, the “Triangle Model”, an agent mediates between self and other. In the third type, the “Caring Model”, a caregiver stands beside the communication between self and other. We apply the three models to the illness narratives of medical professionals and patients. As these groups have different views and experiences of disease or illness, triadic mediation facilitates the ability to see things from the other person’s perspective and to bridge differences in people’s experiences and feelings. These models would be useful for medical education in various situations, such as in considering the relationships between senior and junior doctors and between old and young patients.

Keywords: illness narrative, mediation, psychology, model, medical education

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Authors: A. A. A. Aboalnour, Ahmed M. Amasaib, Mohammed-Almujtaba A. Mohammed-Farah, Abdelhakam, A. Noreldien

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This paper presents a design and study of Parabolic Trough Solar Collector (PTC). Mathematical models were used in this work to find the direct and reflected solar radiation from the air layer on the surface of the earth per hour based on the total daily solar radiation on a horizontal surface. Also mathematical models had been used to calculate the radiation of the tilted surfaces. Most of the ingredients used in this project as previews data required on several solar energy applications, thermal simulation, and solar power systems. In addition, mathematical models had been used to study the flow of the fluid inside the tube (receiver), and study the effect of direct and reflected solar radiation on the pressure, temperature, speed, kinetic energy and forces of fluid inside the tube. Finally, the mathematical models had been used to study the (PTC) performances and estimate its thermal efficiency.

Keywords: CFD, experimental, mathematical models, parabolic trough, radiation

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Julio Macedo

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Several methodologies are now available to conceive the improvements of a process so that it becomes competitive as for example total quality, process reengineering, six sigma, define measure analysis improvement control method. These improvements are of different nature and can be external to the process represented by an optimization model or a discrete simulation model. In addition, the process stakeholders are several and have different desired performances for the process. Hence, the methodologies above do not have a tool to aid in the conception of the required improvements. In order to fill this void we suggest the use of intelligent reference models. A reference model is a set of qualitative differential equations and an objective function that minimizes the gap between the current and the desired performance indexes of the process. The reference models are intelligent so when they receive the current state of the problematic process and the desired performance indexes they generate the required improvements for the problematic process. The reference models are fuzzy cognitive maps added with an objective function and trained using the improvements implemented by the high performance firms. Experiments done in a set of students show the reference models allow them to conceive more improvements than students that do not use these models.

Keywords: continuous improvement, fuzzy cognitive maps, process competitiveness, qualitative simulation, system dynamics

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