Search results for: drying models

Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

Procedia PDF Downloads 248

Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

Procedia PDF Downloads 297

Authors: P. M. Ikonić, T. A. Tasić, L. S. Petrović, S. B. Škaljac, M. R. Jokanović, V. M. Tomović, B. V. Šojić, N. R. Džinić, A. M. Torbica, B. B. Ikonić

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The aim of the study was to determine how different ripening processes (traditional vs. industrial) influenced the proteolysis in traditional Serbian dry-fermented sausage Petrovská klobása. The obtained results indicated more intensive pH decline (0.7 units after 9 days) in industrially ripened products (I), what had a positive impact on drying process and proteolytic changes in these samples. Thus, moisture content in I sausages was lower at each sampling time, amounting 24.7% at the end of production period (90 days). Likewise, the process of proteolysis was more pronounced in I samples, resulting in higher contents of non-protein nitrogen (NPN) and free amino acids nitrogen (FAAN), as well as in faster and more intensive degradation of myosin (≈220 kDa), actin (≈45 kDa) and other polypeptides during processing. Consequently, the appearance and accumulation of several protein fragments were registered.

Keywords: dry-fermented sausage, Petrovská klobása, proteolysis, ripening process

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Sara El Mansouria Beghdadi

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Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

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Authors: Ricardo Merlo

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Attitude is a hypothetical construct that can be significantly measured to know the favorable or unfavorable predisposition that students have towards the teaching of sciences such as Physics. Although the state-of-the-art attitude test used in Physics teaching indicated different design and validation models in different groups of students, the analysis of the weight given to each dimension that supported the attitude was scarcely evaluated. Then, in this work, a methodology of attitude questionnaire construction process was proposed that allowed the teacher to design and validate the measurement instrument for different subjects of Physics at the university level developed in the classroom according to the weight considered to the affective, knowledge, and behavioural dimensions. Finally, questionnaire models were tested for the case of incoming university students, achieving significant results in the improvement of Physics teaching.

Keywords: attitude, physics teaching, motivation, academic performance

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: Vinayak Malaghan, Digvijay Pawar

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Speed is the independent parameter which plays a vital role in the highway design. Design consistency of the highways is checked based on the variation in the operating speed. Often the design consistency fails to meet the driver’s expectation which results in the difference between operating and design speed. Literature reviews have shown that significant crashes take place in horizontal curves due to lack of design consistency. The paper focuses on continuous speed profile study on tangent to curve transition for both day and night daytime. Data is collected using GPS device which gives continuous speed profile and other parameters such as acceleration, deceleration were analyzed along with Tangent to Curve Transition. In this present study, models were developed to predict operating speed on tangents and horizontal curves as well as model indicating the speed reduction from tangent to curve based on continuous speed profile data. It is observed from the study that vehicle tends to decelerate from approach tangent to between beginning of the curve and midpoint of the curve and then accelerates from curve to tangent transition. The models generated were compared for both day and night and can be used in the road safety improvement by evaluating the geometric design consistency.

Keywords: operating speed, design consistency, continuous speed profile data, day and night time

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Authors: Daniel Zarzo Montes, Manuel Zarzo Castelló

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Proteins play a fundamental role in biology, but their structure is complex, and it is a challenge for teachers to conceptually explain the differences between their primary, secondary, tertiary, and quaternary structures. On the other hand, there are currently many computer programs to visualize the 3D structure of proteins, but they require advanced training and knowledge. Moreover, it becomes difficult to visualize the sequence of amino acids in these models, and how the protein conformation is reached. Given this drawback, a simple and instructive procedure is proposed in order to teach the protein structure to undergraduate and graduate students. For this purpose, insulin has been chosen because it is a protein that consists of 51 amino acids, a relatively small number. The methodology has consisted of the use of plastic atom models, which are frequently used in organic chemistry and biochemistry to explain the chirality of biomolecules. For didactic purposes, when the aim is to teach the biochemical foundations of proteins, a manipulative system seems convenient, starting from the chemical structure of amino acids. It has the advantage that the bonds between amino acids can be conveniently rotated, following the pattern marked by the 3D models. First, the 51 amino acids were modeled, and then they were linked according to the sequence of this protein. Next, the three disulfide bonds that characterize the stability of insulin have been established, and then the alpha-helix structure has been formed. In order to reach the tertiary 3D conformation of this protein, different interactive models available on the Internet have been visualized. In conclusion, the proposed methodology seems very suitable for biology and biochemistry students because they can learn the fundamentals of protein modeling by means of a manipulative procedure as a basis for understanding the functionality of proteins. This methodology would be conveniently useful for a biology or biochemistry laboratory practice, either at the pre-graduate or university level.

Keywords: protein structure, 3D model, insulin, biomolecule

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Authors: H. Al-Hajeri

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The computation of the effectiveness of turbulence enhancement surface features, such as ribs as means of promoting mixing and hence heat transfer, has attracted the continued attention of the engineering community. In this study, the simulation of a three-dimensional cooling passage is carried out employing a number of turbulence models including Durbin model. The cooling passage consists of a square section duct whose upper and lower surfaces feature staggered cuboid ribs. The main objective of this paper is to provide comparisons of the performance of the v2-f model against other established turbulence models as implemented in the commercial CFD code Ansys Fluent. The present study demonstrates that the v2-f model can successfully capture the isothermal air flow phenomena in flow over obstacles.

Keywords: CFD, cooling passage, Durbin model, turbulence model

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Authors: J. Y. Heo, H. G. Sung

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Numerical simulations have been performed for assessment of compressibility correction of two-equation turbulence models suitable for large scale separation flows perturbed by pintle strokes. In order to take into account pintle movement, a sliding mesh method was applied. The chamber pressure, mass flow rate, and thrust have been analyzed, and the response lag and sensitivity at the chamber and nozzle were estimated for a movable pintle. The nozzle performance for pintle reciprocating as its insertion and extraction processes, were analyzed to better understand the dynamic performance of the pintle nozzle.

Keywords: pintle, sliding mesh, turbulent model, compressibility correction

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

Procedia PDF Downloads 136

Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Ali Haghiri

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This research has been to investigate the cause of the stress corrosion cracking on dryer equipment (2DR-1745 and DR-1402) in Shahid Tondguyan Petrochemical Company (STPC). These dryers are as a drying powder Terphetalic acid in CTA2 and CTA1 unit. After passing through RVF equipment, wet cake moisture content of about 14% and temperature of 90C changed into a dry cake with a moisture content of less than 0.1% and the final temperature of about 140C and sent out Final Silo (FS-1820). After the declaration of the operation department concerning the observation of acid leakage under the primary insulation was decided that at the first opportunity, this issue must be investigated. So, after the shutdown of a unit at the date 2012/10/20 (2DR-1745) and 2021/11/24 (DR-1402) and after washing the dryer wall, insulation around the wall opened and it was found to crack and leakage from some points.

Keywords: stress corrosion cracking, residual stress, austenitic stainless steel, Br- ion

Procedia PDF Downloads 161

Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Thee Chowwanonthapunya, Junhua Dong, Wei Ke

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The atmospheres in many cities along the coastal lines in the world have been rapidly changed to coastal-industrial atmosphere. Hence, it is vital to investigate the corrosion behavior of steel exposed to this kind of environment. In this present study, Electrochemical Impedance Spectrography (EIS) and film thickness measurements were applied to monitor the corrosion behavior of weathering steel covered with a thin layer of the electrolyte in a wet-dry cyclic condition, simulating a coastal-industrial environment at 25 oC and 60 % RH. The results indicate that in all cycles, the corrosion rate increases during the drying process due to an increase in anion concentration and an acceleration of oxygen diffusion enhanced by the effect of the thinning out of the electrolyte. During the wet-dry cyclic corrosion test, the long-term corrosion behavior of this steel depends on the periods of exposure. Corrosion process is first accelerated and then decelerated. The decelerating corrosion process is contributed to the formation of the protective rust, favored by the wet-dry cycle and the acid regeneration process during the rusting process.

Keywords: atmospheric corrosion, EIS, low alloy, rust

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Authors: Victoria Goodall, Paul Fatti, Norman Owen-Smith

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Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data.

Keywords: hidden Markov Models, irregular observations, animal movement modelling, nocturnal predator

Procedia PDF Downloads 244

Authors: F. Al-Sheikh, C. Moralejo, M. Pritzker, W. A. Anderson, A. Elkamel

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Ammonia in mining wastewater is a significant problem, and treatment can be especially difficult in cold climates where biological treatment is not feasible. An adsorption process is one of the alternative processes that can be used to reduce ammonia concentrations to acceptable limits, and therefore a LEWATIT resin strongly acidic H+ form ion exchange resin and a Bowie Chabazite Na form AZLB-Na zeolite were tested to assess their effectiveness. For these adsorption tests, two packed bed columns (a mini-column constructed from a 32-cm long x 1-cm diameter piece of glass tubing, and a 60-cm long x 2.5-cm diameter Ace Glass chromatography column) were used containing varying quantities of the adsorbents. A mining wastewater with ammonia concentrations of 22.7 mg/L was fed through the columns at controlled flowrates. In the experimental work, maximum capacities of the LEWATIT ion exchange resin were 0.438, 0.448, and 1.472 mg/g for 3, 6, and 9 g respectively in a mini column and 1.739 mg/g for 141.5 g in a larger Ace column while the capacities for the AZLB-Na zeolite were 0.424, and 0.784 mg/g for 3, and 6 g respectively in the mini column and 1.1636 mg/g for 38.5 g in the Ace column. In the theoretical work, Thomas, Adams-Bohart, and Yoon-Nelson models were constructed to describe a breakthrough curve of the adsorption process and find the constants of the above-mentioned models. In the regeneration tests, 5% hydrochloric acid, HCl (v/v) and 10% sodium hydroxide, NaOH (w/v) were used to regenerate the LEWATIT resin and AZLB-Na zeolite with 44 and 63.8% recovery, respectively. In conclusion, continuous flow adsorption using a LEWATIT ion exchange resin and an AZLB-Na zeolite is efficient when using a co-flow technique for removal of the ammonia from wastewater. Thomas, Adams-Bohart, and Yoon-Nelson models satisfactorily fit the data with R² closer to 1 in all cases.

Keywords: AZLB-Na zeolite, continuous adsorption, Lewatit resin, models, regeneration

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Authors: Angelika-Ioanna Gialleli, Gousi Mantha, Maria Kanellaki, Bekatorou Argyro, Athanasios Koutinas

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In this study, a new brewing technology with dry raw materials is proposed with potential application in home brewing. Bio catalysts were prepared by immobilization of the psychrotolerant yeast strain Saccharomyces cerevisiae AXAZ-1 on tubular cellulose. Both the word and the biocatalysts were freeze-dried without any cryoprotectants and used for low temperature brewing. The combination of immobilization and freeze-drying techniques was applied successfully, giving a potential for supplying breweries with preserved and ready-to-use immobilized cells. The effect of wort sugar concentration (7°, 8.5°, 10°Be), temperature (2, 5, 7° C) and carrier concentration (5, 10, 20 g/L) on fermentation kinetics and final product quality (volatiles, colour, polyphenols, bitterness) was assessed. The same procedure was repeated with free cells for comparison of the results. The results for immobilized cells were better compared to free cells regarding fermentation kinetics and organoleptic characteristics.

Keywords: brewing, tubular cellulose, low temperature, biocatalyst

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Authors: Marcel Rojahn, Edzard Weber, Norbert Gronau

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Terminology is a critical instrument for each researcher. Different terminologies for the same research object may arise in different research communities. By this inconsistency, many synergistic effects get lost. Theories and models will be more understandable and reusable if a common terminology is applied. This paper examines the terminological (in) consistency for the research field of job-shop scheduling through a literature review. There is an enormous variety in the choice of terms and mathematical notation for the same concept. The comparability, reusability, and combinability of scheduling methods are unnecessarily hampered by the arbitrary use of homonyms and synonyms. The acceptance in the community of used variables and notation forms is shown by means of a compliance quotient. This is proven by the evaluation of 240 scientific publications on planning methods.

Keywords: job-shop scheduling, terminology, notation, standardization

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Authors: Jian Gu, Sea-Hoon Lee, Jun-Seop Kim

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SiC particulate-reinforced SiC ceramic composites (SiCp/SiC) were successfully fabricated using polymer impregnation and pyrolysis (PIP) method. The effects of green density, infiltrated method, pyrolytic temperature, and heating rate on the densification behavior of the composites were investigated. SiCp/SiC particulate reinforced composites with high relative density up to 88.06% were fabricated after 4 PIP cycles using SiC pellets with high green density. The pellets were prepared by drying 62-70 vol.% aqueous SiC slurries, and the maximum relative density of the pellets was 75.5%. The hardness of the as-fabricated SiCp/SiCs was 21.05 GPa after 4 PIP cycles, which value increased to 23.99 GPa after a heat treatment at 2000℃. Excellent mechanical properties, thermal stability, and short processing time render the SiCp/SiC composite as a challenging candidate for the high-temperature application.

Keywords: high green density, mechanical property, polymer impregnation and pyrolysis, structural application

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Authors: Ali Osman Güney, Bahattin Kanber

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In this work, the effect of material type, diameter, orientation and closeness of fibers on the general performance of reinforced vulcanized rubbers are investigated using finite element method with experimental verification. Various fiber materials such as hemp, nylon, polyester are used for different fiber diameters, orientations and closeness. 3D finite element models are developed by considering bonded contact elements between fiber and rubber sheet interfaces. The fibers are assumed as linear elastic, while vulcanized rubber is considered as hyper-elastic. After an experimental verification of finite element results, the developed models are analyzed under prescribed displacement that causes tension. The normal stresses in fibers and shear stresses between fibers and rubber sheet are investigated in all models. Large deformation of reinforced rubber sheet also represented with various fiber conditions under incremental loading. A general assessment is achieved about best fiber properties of reinforced rubber sheets for tension-load conditions.

Keywords: reinforced vulcanized rubbers, fiber properties, out of plane loading, finite element method

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Authors: Behzad Babaei, B. Gangadhara Prusty

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The objective of this study is to assess the hypotheses that a restored tooth with a class II occlusal-distal (OD) cavity can be strengthened by designing an optimized cavity geometry, as well as selecting the composite restoration with optimized elastic moduli when there is a sharp de-bonded edge at the interface of the tooth and restoration. Methods: A scanned human maxillary molar tooth was segmented into dentine and enamel parts. The dentine and enamel profiles were extracted and imported into a finite element (FE) software. The enamel rod orientations were estimated virtually. Fifteen models for the restored tooth with different cavity occlusal depths (1.5, 2, and 2.5 mm) and internal cavity angles were generated. By using a semi-circular stone part, a 400 N load was applied to two contact points of the restored tooth model. The junctions between the enamel, dentine, and restoration were considered perfectly bonded. All parts in the model were considered homogeneous, isotropic, and elastic. The quadrilateral and triangular elements were employed in the models. A mesh convergence analysis was conducted to verify that the element numbers did not influence the simulation results. According to the criteria of a 5% error in the stress, we found that a total element number of over 14,000 elements resulted in the convergence of the stress. A Python script was employed to automatically assign 2-22 GPa moduli (with increments of 4 GPa) for the composite restorations, 18.6 GPa to the dentine, and two different elastic moduli to the enamel (72 GPa in the enamel rods’ direction and 63 GPa in perpendicular one). The linear, homogeneous, and elastic material models were considered for the dentine, enamel, and composite restorations. 108 FEA simulations were successively conducted. Results: The internal cavity angles (α) significantly altered the peak maximum principal stress at the interface of the enamel and restoration. The strongest structures against the contact loads were observed in the models with α = 100° and 105. Even when the enamel rods’ directional mechanical properties were disregarded, interestingly, the models with α = 100° and 105° exhibited the highest resistance against the mechanical loads. Regarding the effect of occlusal cavity depth, the models with 1.5 mm depth showed higher resistance to contact loads than the model with thicker cavities (2.0 and 2.5 mm). Moreover, the composite moduli in the range of 10-18 GPa alleviated the stress levels in the enamel. Significance: For the class II OD cavity models in this study, the optimal geometries, composite properties, and occlusal cavity depths were determined. Designing the cavities with α ≥100 ̊ was significantly effective in minimizing peak stress levels. The composite restoration with optimized properties reduced the stress concentrations on critical points of the models. Additionally, when more enamel was preserved, the sturdier enamel-restoration interface against the mechanical loads was observed.

Keywords: dental composite restoration, cavity geometry, finite element approach, maximum principal stress

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Authors: Samai'la Abdullahi

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A graph is a pair of two set and so that a graph is a pictorial representation of a system using two basic element nodes and edges. A node is represented by a circle (either hallo shade) and edge is represented by a line segment connecting two nodes together. In this paper, we present a circuit network in the concept of graph theory application and also circuit models of graph are represented in logical connection method were we formulate matrix method of adjacency and incidence of matrix and application of truth table.

Keywords: euler circuit and path, graph representation of circuit networks, representation of graph models, representation of circuit network using logical truth table

Procedia PDF Downloads 561

Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

Procedia PDF Downloads 572

Authors: Mohsen Saleh Asheghabadi, Alireza Jafari Jebeli

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Increasing population growth requires more sustainable development of energy. This non-contaminated energy has an inexhaustible energy source. One of the vital parameters in such structures is the choice of foundation type. Suction caissons are now used extensively worldwide for offshore wind turbine. Considering the presence of a number of offshore wind farms in earthquake areas, the study of the seismic behavior of suction caisson is necessary for better design. In this paper, the results obtained from three suction caisson models with different diameter (D) and skirt length (L) in saturated sand were compared with centrifuge test results. All models are analyzed using 3D finite element (FE) method taking account of elasto-plastic Mohr–Coulomb constitutive model for soil which is available in the ABAQUS library. The earthquake load applied to the base of models with a maximum acceleration of 0.65g. The results showed that numerical method is in relative good agreement with centrifuge results. The settlement and rotation of foundation decrease by increasing the skirt length and foundation diameter. The sand soil outside the caisson is prone to liquefaction due to its low confinement.

Keywords: liquefaction, suction caisson foundation, offshore wind turbine, numerical analysis, seismic behavior

Procedia PDF Downloads 119

Authors: Sereen Itani

Abstract:

As the world aspires for technological innovation, Innovative Robotics Technology-Supported Pedagogic Models in STEAM Education (Science, Technology, Engineering, Arts, and Mathematics) are critical in our global education system to build and enhance the next generation 21st century skills. Thus, diverse international schools endeavor in attempts to construct an integrated robotics and technology enhanced curriculum based on interdisciplinary subjects. Accordingly, it is vital that the globe remains resilient in STEAM fields by equipping the future learners and educators with Innovative Technology Experiences through robotics to support such fields. A variety of advanced teaching methods is employed to learn about Robotics Technology-integrated pedagogic models. Therefore, it is only when STEAM and innovations in Robotic Technology becomes integrated with real-world applications that transformational learning can occur. Robotics STEAM education implementation faces major challenges globally. Moreover, STEAM skills and concepts are communicated in separation from the real world. Instilling the passion for robotics and STEAM subjects and educators’ preparation could lead to the students’ majoring in such fields by acquiring enough knowledge to make vital contributions to the global STEAM industries. Thus, this necessitates the establishment of Pedagogic models such as Innovative Robotics Technologies to enhance STEAM education and develop students’ 21st-century skills. Moreover, an ICT innovative supported robotics classroom will help educators empower and assess students academically. Globally, the Robotics Design System and platforms are developing in schools and university labs creating a suitable environment for the robotics cross-discipline STEAM learning. Accordingly, the research aims at raising awareness about the importance of robotics design systems and methodologies of effective employment of robotics innovative technology-supported pedagogic models to enhance and develop (STEAM) education globally and enhance the next generation 21st century skills.

Keywords: education, robotics, STEAM (Science, Technology, Engineering, Arts and Mathematics Education), challenges

Procedia PDF Downloads 384

Authors: Shengyong Zhang, Mike Mikulich

Abstract:

Vibration is a crucial limiting consideration in the analysis and design of airplane wing structures to avoid disastrous failures due to the propagation of existing cracks in the material. In this paper, we build CAD models of aircraft wings to capture the design intent with configurations. Subsequent FEA vibration analysis is performed to study the natural vibration properties and impulsive responses of the resulting user-defined wing models. This study reveals the variations of the wing’s vibration characteristics with respect to changes in its structural configurations. Integrating CAD modelling and FEA vibration analysis enables designers to improve wing architectures for implementing design requirements in the preliminary design stage.

Keywords: aircraft wing, CAD modelling, FEA, vibration analysis

Procedia PDF Downloads 165