Search results for: color models
6816 Removal of Basic Yellow 28 Dye from Aqueous Solutions Using Plastic Wastes
Authors: Nadjib Dahdouh, Samira Amokrane, Elhadj Mekatel, Djamel Nibou
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The removal of Basic Yellow 28 (BY28) from aqueous solutions by plastic wastes PMMA was investigated. The characteristics of plastic wastes PMMA were determined by SEM, FTIR and chemical composition analysis. The effects of solution pH, initial Basic Yellow 28 (BY28) concentration C, solid/liquid ratio R, and temperature T were studied in batch experiments. The Freundlich and the Langmuir models have been applied to the adsorption process, and it was found that the equilibrium followed well Langmuir adsorption isotherm. A comparison of kinetic models applied to the adsorption of BY28 on the PMMA was evaluated for the pseudo-first-order and the pseudo-second-order kinetic models. It was found that used models were correlated with the experimental data. Intraparticle diffusion model was also used in these experiments. The thermodynamic parameters namely the enthalpy ∆H°, entropy ∆S° and free energy ∆G° of adsorption of BY28 on PMMA were determined. From the obtained results, the negative values of Gibbs free energy ∆G° indicated the spontaneity of the adsorption of BY28 by PMMA. The negative values of ∆H° revealed the exothermic nature of the process and the negative values of ∆S° suggest the stability of BY28 on the surface of SW PMMA.Keywords: removal, Waste PMMA, BY28 dye, equilibrium, kinetic study, thermodynamic study
Procedia PDF Downloads 1456815 Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters
Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu
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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters
Procedia PDF Downloads 3036814 Photoelastic Analysis and Finite Elements Analysis of a Stress Field Developed in a Double Edge Notched Specimen
Authors: A. Bilek, M. Beldi, T. Cherfi, S. Djebali, S. Larbi
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Finite elements analysis and photoelasticity are used to determine the stress field developed in a double edge notched specimen loaded in tension. The specimen is cut in a birefringent plate. Experimental isochromatic fringes are obtained with circularly polarized light on the analyzer of a regular polariscope. The fringes represent the loci of points of equal maximum shear stress. In order to obtain the stress values corresponding to the fringe orders recorded in the notched specimen, particularly in the neighborhood of the notches, a calibrating disc made of the same material is loaded in compression along its diameter in order to determine the photoelastic fringe value. This fringe value is also used in the finite elements solution in order to obtain the simulated photoelastic fringes, the isochromatics as well as the isoclinics. A color scale is used by the software to represent the simulated fringes on the whole model. The stress concentration factor can be readily obtained at the notches. Good agreements are obtained between the experimental and the simulated fringe patterns and between the graphs of the shear stress particularly in the neighborhood of the notches. The purpose in this paper is to show that one can obtain rapidly and accurately, by the finite element analysis, the isochromatic and the isoclinic fringe patterns in a stressed model as the experimental procedure can be time consuming. Stress fields can therefore be analyzed in three dimensional models as long as the meshing and the limit conditions are properly set in the program.Keywords: isochromatic fringe, isoclinic fringe, photoelasticity, stress concentration factor
Procedia PDF Downloads 2276813 Statistical Assessment of Models for Determination of Soil–Water Characteristic Curves of Sand Soils
Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha
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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering
Procedia PDF Downloads 3316812 Data Poisoning Attacks on Federated Learning and Preventive Measures
Authors: Beulah Rani Inbanathan
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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.Keywords: data poisoning, federated learning, Internet of Things, edge computing
Procedia PDF Downloads 826811 Lean Impact Analysis Assessment Models: Development of a Lean Measurement Structural Model
Authors: Catherine Maware, Olufemi Adetunji
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The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.Keywords: impact measurement model, lean bundles, lean manufacturing, organizational performance
Procedia PDF Downloads 4776810 Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models
Authors: Panudet Saengseedam, Nanthachai Kantanantha
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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series
Procedia PDF Downloads 3886809 Multi-Layer Perceptron and Radial Basis Function Neural Network Models for Classification of Diabetic Retinopathy Disease Using Video-Oculography Signals
Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer
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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)
Procedia PDF Downloads 2516808 Oryzanol Recovery from Rice Bran Oil: Adsorption Equilibrium Models Through Kinetics Data Approachments
Authors: A.D. Susanti, W. B. Sediawan, S.K. Wirawan, Budhijanto, Ritmaleni
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Oryzanol content in rice bran oil (RBO) naturally has high antioxidant activity. Its reviewed has several health properties and high interested in pharmacy, cosmetics, and nutrition’s. Because of the low concentration of oryzanol in crude RBO (0.9-2.9%) then its need to be further processed for practical usage, such as via adsorption process. In this study, investigation and adjustment of adsorption equilibrium models were conducted through the kinetic data approachments. Mathematical modeling on kinetics of batch adsorption of oryzanol separation from RBO has been set-up and then applied for equilibrium results. The size of adsorbent particles used in this case are usually relatively small then the concentration in the adsorbent is assumed to be not different. Hence, the adsorption rate is controlled by the rate of oryzanol mass transfer from the bulk fluid of RBO to the surface of silica gel. In this approachments, the rate of mass transfer is assumed to be proportional to the concentration deviation from the equilibrium state. The equilibrium models applied were Langmuir, coefficient distribution, and Freundlich with the values of the parameters obtained from equilibrium results. It turned out that the models set-up can quantitatively describe the experimental kinetics data and the adjustment of the values of equilibrium isotherm parameters significantly improves the accuracy of the model. And then the value of mass transfer coefficient per unit adsorbent mass (kca) is obtained by curve fitting.Keywords: adsorption equilibrium, adsorption kinetics, oryzanol, rice bran oil
Procedia PDF Downloads 3166807 Vibration of a Beam on an Elastic Foundation Using the Variational Iteration Method
Authors: Desmond Adair, Kairat Ismailov, Martin Jaeger
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Modelling of Timoshenko beams on elastic foundations has been widely used in the analysis of buildings, geotechnical problems, and, railway and aerospace structures. For the elastic foundation, the most widely used models are one-parameter mechanical models or two-parameter models to include continuity and cohesion of typical foundations, with the two-parameter usually considered the better of the two. Knowledge of free vibration characteristics of beams on an elastic foundation is considered necessary for optimal design solutions in many engineering applications, and in this work, the efficient and accurate variational iteration method is developed and used to calculate natural frequencies of a Timoshenko beam on a two-parameter foundation. The variational iteration method is a technique capable of dealing with some linear and non-linear problems in an easy and efficient way. The calculations are compared with those using a finite-element method and other analytical solutions, and it is shown that the results are accurate and are obtained efficiently. It is found that the effect of the presence of the two-parameter foundation is to increase the beam’s natural frequencies and this is thought to be because of the shear-layer stiffness, which has an effect on the elastic stiffness. By setting the two-parameter model’s stiffness parameter to zero, it is possible to obtain a one-parameter foundation model, and so, comparison between the two foundation models is also made.Keywords: Timoshenko beam, variational iteration method, two-parameter elastic foundation model
Procedia PDF Downloads 1876806 Positive Bias and Length Bias in Deep Neural Networks for Premises Selection
Authors: Jiaqi Huang, Yuheng Wang
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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning
Procedia PDF Downloads 1766805 Modified Clusterwise Regression for Pavement Management
Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella
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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.Keywords: clusterwise regression, pavement management system, performance model, optimization
Procedia PDF Downloads 2446804 Using the Bootstrap for Problems Statistics
Authors: Brahim Boukabcha, Amar Rebbouh
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The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models
Procedia PDF Downloads 3766803 Low Sulfur Diesel-Like Fuel From Quick Remediation Process of Waste Oil Sludge
Authors: Isam A. H. Al Zubaidy
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A quick process may be needed to get the benefit the big generated quantity of waste oil sludge (WOS). The process includes the mixing process of WOS with commercial diesel fuel. Different ratios of WOS to diesel fuel were prepared ranging 1:1 to 20:1 by mass. The mixture was continuously mixing for 10 minutes using bench type overhead stirrer and followed by filtration process to separate the soil waste from filtrate oil product. The quantity and the physical properties of the oil filtrate were measured. It was found that the addition of up to 15% WOS to diesel fuel was accepted without dramatic changes to the properties of diesel fuel. The amount of waste oil sludge was decreased by about 60% by mass. This means that about 60 % of the mass of sludge was recovered as light fuel oil. The physical properties of the resulting fuel from 10% sludge mixing ratio showed that the specific gravity, ash content, carbon residue, asphaltene content, viscosity, diesel index, cetane number, and calorific value were affected slightly. The color was changed to light black color. The sulfur content was increased also. This requires other processes to reduce the sulfur content of the resulting light fuel. A new desulfurization process was achieved using adsorption techniques with activated biomaterial to reduce the sulfur content to acceptable limits. Adsorption process by ZnCl₂ activated date palm kernel powder was effective for improvement of the physical properties of diesel like fuel. The final sulfur content was increased to 0.185 wt%. This diesel like fuel can be used in all tractors, buses, tracks inside and outside the refineries. The solid remaining seems to be smooth and can be mixed with asphalt mixture for asphalting the roads or can be used with other materials as an asphalt coating material for constructed buildings. Through this process, valuable fuel has been recovered, and the amount of waste material had decreased.Keywords: oil sludge, diesel fuel, blending process, filtration process
Procedia PDF Downloads 1116802 Framework for Developing Change Team to Maximize Change Initiative Success
Authors: Mohammad Z. Ansari, Lisa Brodie, Marilyn Goh
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Change facilitators are individuals who utilize change philosophy to make a positive change to organizations. The application of change facilitators can be seen in various change models; Lewin, Lippitt, etc. The facilitators within numerous change models are considered as internal/external consultants. Whilst most of the scholarly paper considers change facilitation as a consensus attempt to improve organization, there is a lack of a framework that develops both the organization and the change facilitator creating a self-sustaining change environment. This research paper introduces the development of the framework for change Leaders, Planners, and Executers (LPE), aiming at various organizational levels (Process, Departmental, and Organisational). The LPE framework is derived by exploring interrelated characteristics between facilitator(s) and the organization through qualitative research for understanding change management techniques and facilitator(s) behavioral aspect from existing Change Management models and Organisation behavior works of literature. The introduced framework assists in highlighting and identify the most appropriate change team to successfully deliver the change initiative within any organization (s).Keywords: change initiative, LPE framework, change facilitator(s), sustainable change
Procedia PDF Downloads 1886801 3D Building Model Utilizing Airborne LiDAR Dataset and Terrestrial Photographic Images
Authors: J. Jasmee, I. Roslina, A. Mohammed Yaziz & A.H Juazer Rizal
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The need of an effective building information collection method is vital to support a diversity of land development activities. At present, advances in remote sensing such as airborne LiDAR (Light Detection and Ranging) is an established technology for building information collection, location, and elevation of the reflecting laser points towards the construction of 3D building models. In this study, LiDAR datasets and terrestrial photographic images of buildings towards the construction of 3D building models is explored. It is found that, the quantitative accuracy of the constructed 3D building model, namely in the horizontal and vertical components were ± 0.31m (RMSEx,y) and ± 0.145m (RMSEz) respectively. The accuracies were computed based on sixty nine (69) horizontal and twenty (20) vertical surveyed points. As for the qualitative assessment, it is shown that the appearance of the 3D building model is adequate to support the requirements of LOD3 presentation based on the OGC (Open Geospatial Consortium) standard CityGML.Keywords: LiDAR datasets, DSM, DTM, 3D building models
Procedia PDF Downloads 3146800 Improving University Operations with Data Mining: Predicting Student Performance
Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević
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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.Keywords: data mining, knowledge discovery in databases, prediction models, student success
Procedia PDF Downloads 4036799 Investigating the Effectiveness of Multilingual NLP Models for Sentiment Analysis
Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar
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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.Keywords: NLP, multilingual, sentiment analysis, texts
Procedia PDF Downloads 896798 Drying Kinects of Soybean Seeds
Authors: Amanda Rithieli Pereira Dos Santos, Rute Quelvia De Faria, Álvaro De Oliveira Cardoso, Anderson Rodrigo Da Silva, Érica Leão Fernandes Araújo
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The study of the kinetics of drying has great importance for the mathematical modeling, allowing to know about the processes of transference of heat and mass between the products and to adjust dryers managing new technologies for these processes. The present work had the objective of studying the kinetics of drying of soybean seeds and adjusting different statistical models to the experimental data varying cultivar and temperature. Soybean seeds were pre-dried in a natural environment in order to reduce and homogenize the water content to the level of 14% (b.s.). Then, drying was carried out in a forced air circulation oven at controlled temperatures of 38, 43, 48, 53 and 58 ± 1 ° C, using two soybean cultivars, BRS 8780 and Sambaíba, until reaching a hygroscopic equilibrium. The experimental design was completely randomized in factorial 5 x 2 (temperature x cultivar) with 3 replicates. To the experimental data were adjusted eleven statistical models used to explain the drying process of agricultural products. Regression analysis was performed using the least squares Gauss-Newton algorithm to estimate the parameters. The degree of adjustment was evaluated from the analysis of the coefficient of determination (R²), the adjusted coefficient of determination (R² Aj.) And the standard error (S.E). The models that best represent the drying kinetics of soybean seeds are those of Midilli and Logarítmico.Keywords: curve of drying seeds, Glycine max L., moisture ratio, statistical models
Procedia PDF Downloads 6186797 Antibacterial and Antioxidant Capacity of Fabric Treated with Purple-Fleshed Sweet Potato Extract
Authors: Kyung Hwa Hong, Eunmi Koh
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Wool and cotton fabrics are pretreated by a tannic acid aqueous solution to increase their dyeability and then dyed by Purple-Fleshed Sweet Potato (PSP) extract. The dyed fabrics are then investigated by various analysis techniques. The results revealed that wool and cotton fabrics can be dyed bluish red through the pretreatment and dyeing process. Both wool and cotton fabrics only pretreated with tannic acid display decreased L* value but no significant changes in a* and b* values as the concentration of tannic acid increases. And, as expected, the pretreated fabrics are even darker and show a richer purple color after the dyeing process with the PSP extract. With regard to the colorfastness of wool and cotton fabrics dyed by PSP extract in cleaning circumstances, such as dry-cleaning (for wool) and washing (for cotton), the wool and cotton fabrics had a 4.0 and 4.0 grade of colorfastness to dry-cleaning and washing, respectively. However, they both exhibited significantly inferior colorfastness to light (grade of 1.5). Thus, it was found that there is still a need for improvement with regard to color fastness, particularly against light. On the other hand, the wool and cotton fabrics also showed antibacterial and antioxidant characteristics. In addition, both the wool and cotton fabrics showed potential antibacterial ability (>99%) against Staphylococcus aureus; however, they showed somewhat insufficient antibacterial ability (60.8% for wool and 94.8% for cotton) against Klebsiella pneumoniae. Also, their antioxidant abilities increased up to ca. 90% with an increase in the tannic acid concentration (up to 0.5%). However, after the dyeing process, the antibacterial and antioxidant ability tended to decrease. This is assumed to have occurred because functional moieties such as phenolic acids were detached from the pretreated fabrics into the hot water (the dyeing solution) during the dyeing process. Therefore, further study would be necessary to derive the optimum treatment and dyeing conditions so as to maximize the coloring effect and functionalities of the fabrics.Keywords: antibacterial activity, antioxidant activity, purple-fleshed sweet potato, fabrics
Procedia PDF Downloads 2846796 Infilling Strategies for Surrogate Model Based Multi-disciplinary Analysis and Applications to Velocity Prediction Programs
Authors: Malo Pocheau-Lesteven, Olivier Le Maître
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Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program
Procedia PDF Downloads 1536795 Effect of Different Level of Pomegranate Molasses on Performance, Egg Quality Trait, Serological and Hematological Parameters in Older Laying Hens
Authors: Ismail Bayram, Aamir Iqbal, E. Eren Gultepe, Cangir Uyarlar, Umit Ozcınar, I. Sadi Cetingul
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The current study was planned with the objective to explore the potential of pomegranate molasses (PM) on performance, egg quality and blood parameters in older laying hens. A total of 240 Babcock white laying hens (52 weeks old) were divided into 5 groups (n=48) with 8 subgroups having 6 hens in each. Pomegranate molasses was added in the drinking water to experimental groups with 0 %, 0.1%, 0.25 %, 0.5%, and 1%, respectively during one month. In our results, egg weight values were remained the same in all pomegranate molasses supplemented groups except 1% group over control. However, feed consumption, egg production, feed conversion ratio (FCR), egg mass, egg yolk cholesterol, body weights, and water consumption remained unaffected (P > 0.05). During mid-study (15 Days) analyses, egg quality parameters such as Haugh unit, eggshell thickness, albumin index, yolk index, and egg yolk color were remained non-significant (P > 0.05) while after final (30 Days) egg analyses, only egg yolk color had positively (P < 0.05) increased in 0.5% group. Moreover, Haugh unit, eggshell thickness, and albumin index were not significantly (P > 0.05) affected by the supplementation of pomegranate molasses. Regarding serological parameters, pomegranate molasses did not show any positive effect on cholesterol, total protein, LDL, HDL, GGT, AST, ALT, and glucose level. Similarly, pomegranate molasses also showed non-significant (P > 0.05) results on different blood parameters such as HCT, RBC, MCV, MCH, MCHC, PLT, RDWC, MPV except hemoglobin level. Only hemoglobin level was increased in all experimental groups over control showing that pomegranate molasses can be used as an enhancer in animals with low hemoglobin level.Keywords: pomegranate molasses, laying hen, egg yield, blood parameters
Procedia PDF Downloads 1596794 Legal Considerations in Fashion Modeling: Protecting Models' Rights and Ensuring Ethical Practices
Authors: Fatemeh Noori
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The fashion industry is a dynamic and ever-evolving realm that continuously shapes societal perceptions of beauty and style. Within this industry, fashion modeling plays a crucial role, acting as the visual representation of brands and designers. However, behind the glamorous façade lies a complex web of legal considerations that govern the rights, responsibilities, and ethical practices within the field. This paper aims to explore the legal landscape surrounding fashion modeling, shedding light on key issues such as contract law, intellectual property, labor rights, and the increasing importance of ethical considerations in the industry. Fashion modeling involves the collaboration of various stakeholders, including models, designers, agencies, and photographers. To ensure a fair and transparent working environment, it is imperative to establish a comprehensive legal framework that addresses the rights and obligations of each party involved. One of the primary legal considerations in fashion modeling is the contractual relationship between models and agencies. Contracts define the terms of engagement, including payment, working conditions, and the scope of services. This section will delve into the essential elements of modeling contracts, the negotiation process, and the importance of clarity to avoid disputes. Models are not just individuals showcasing clothing; they are integral to the creation and dissemination of artistic and commercial content. Intellectual property rights, including image rights and the use of a model's likeness, are critical aspects of the legal landscape. This section will explore the protection of models' image rights, the use of their likeness in advertising, and the potential for unauthorized use. Models, like any other professionals, are entitled to fair and ethical treatment. This section will address issues such as working conditions, hours, and the responsibility of agencies and designers to prioritize the well-being of models. Additionally, it will explore the global movement toward inclusivity, diversity, and the promotion of positive body image within the industry. The fashion industry has faced scrutiny for perpetuating harmful standards of beauty and fostering a culture of exploitation. This section will discuss the ethical responsibilities of all stakeholders, including the promotion of diversity, the prevention of exploitation, and the role of models as influencers for positive change. In conclusion, the legal considerations in fashion modeling are multifaceted, requiring a comprehensive approach to protect the rights of models and ensure ethical practices within the industry. By understanding and addressing these legal aspects, the fashion industry can create a more transparent, fair, and inclusive environment for all stakeholders involved in the art of modeling.Keywords: fashion modeling contracts, image rights in modeling, labor rights for models, ethical practices in fashion, diversity and inclusivity in modeling
Procedia PDF Downloads 606793 A Stepwise Approach to Automate the Search for Optimal Parameters in Seasonal ARIMA Models
Authors: Manisha Mukherjee, Diptarka Saha
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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function
Procedia PDF Downloads 1576792 MCDM Spectrum Handover Models for Cognitive Wireless Networks
Authors: Cesar Hernández, Diego Giral, Fernando Santa
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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work proposes a benchmarking of performance of the three spectrum handoff models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handoff models was validated with captured data of spectral occupancy in experiments realized at the GSM frequency band (824 MHz-849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparative show that VIKOR Algorithm provides 15.8% performance improvement compared to a SAW Algorithm and, 12.1% better than the MEW Algorithm.Keywords: cognitive radio, decision making, MEW, SAW, spectrum handoff, VIKOR
Procedia PDF Downloads 4286791 Nonlinear Estimation Model for Rail Track Deterioration
Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami
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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.Keywords: ANFIS, MGT, prediction modeling, rail track degradation
Procedia PDF Downloads 3246790 Parametric Study for Obtaining the Structural Response of Segmental Tunnels in Soft Soil by Using No-Linear Numerical Models
Authors: Arturo Galván, Jatziri Y. Moreno-Martínez, Israel Enrique Herrera Díaz, José Ramón Gasca Tirado
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In recent years, one of the methods most used for the construction of tunnels in soft soil is the shield-driven tunneling. The advantage of this construction technique is that it allows excavating the tunnel while at the same time a primary lining is placed, which consists of precast segments. There are joints between segments, also called longitudinal joints, and joints between rings (called as circumferential joints). This is the reason because of this type of constructions cannot be considered as a continuous structure. The effect of these joints influences in the rigidity of the segmental lining and therefore in its structural response. A parametric study was performed to take into account the effect of different parameters in the structural response of typical segmental tunnels built in soft soil by using non-linear numerical models based on Finite Element Method by means of the software package ANSYS v. 11.0. In the first part of this study, two types of numerical models were performed. In the first one, the segments were modeled by using beam elements based on Timoshenko beam theory whilst the segment joints were modeled by using inelastic rotational springs considering the constitutive moment-rotation relation proposed by Gladwell. In this way, the mechanical behavior of longitudinal joints was simulated. On the other hand for simulating the mechanical behavior of circumferential joints elastic springs were considered. As well as, the stability given by the soil was modeled by means of elastic-linear springs. In the second type of models, the segments were modeled by means of three-dimensional solid elements and the joints with contact elements. In these models, the zone of the joints is modeled as a discontinuous (increasing the computational effort) therefore a discrete model is obtained. With these contact elements the mechanical behavior of joints is simulated considering that when the joint is closed, there is transmission of compressive and shear stresses but not of tensile stresses and when the joint is opened, there is no transmission of stresses. This type of models can detect changes in the geometry because of the relative movement of the elements that form the joints. A comparison between the numerical results with two types of models was carried out. In this way, the hypothesis considered in the simplified models were validated. In addition, the numerical models were calibrated with (Lab-based) experimental results obtained from the literature of a typical tunnel built in Europe. In the second part of this work, a parametric study was performed by using the simplified models due to less used computational effort compared to complex models. In the parametric study, the effect of material properties, the geometry of the tunnel, the arrangement of the longitudinal joints and the coupling of the rings were studied. Finally, it was concluded that the mechanical behavior of segment and ring joints and the arrangement of the segment joints affect the global behavior of the lining. As well as, the effect of the coupling between rings modifies the structural capacity of the lining.Keywords: numerical models, parametric study, segmental tunnels, structural response
Procedia PDF Downloads 2246789 Bridging the Gap between M and E, and KM: Towards the Integration of Evidence-Based Information and Policy Decision-Making
Authors: Xueqing Ivy Chen, Christo De Coning
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It is clear from practice that a gap exists between Result-Based Monitoring and Evaluation (RBME) as a discipline, and Knowledge Management (KM) on the other hand. Whereas various government departments have institutionalised these functions, KM and M&E has functioned in isolation from each other in a practical sense in the public sector. It’s therefore necessary to explore the relationship between KM and M&E and the necessity for integration, so that a convergence of these disciplines can be established. An integration of KM and M&E will lead to integration and improvement of evidence-based information and policy decision-making. M&E and KM process models are available but the complementarity between specific process steps of these process models are not exploited. A need exists to clarify the relationships between these functions in order to ensure evidence based information and policy decision-making. This paper will depart from the well-known policy process models, such as the generic model and consider recent on the interface between policy, M&E and KM.Keywords: result-based monitoring and evaluation, RBME, knowledge management, KM, evident based decision making, public policy, information systems, institutional arrangement
Procedia PDF Downloads 1466788 Prediction of Mechanical Strength of Multiscale Hybrid Reinforced Cementitious Composite
Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi
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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction
Procedia PDF Downloads 1576787 Assisting Dating of Greek Papyri Images with Deep Learning
Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou
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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.Keywords: image classification, papyri images, dating
Procedia PDF Downloads 74