Search results for: model robustness and stability
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 19920

Search results for: model robustness and stability

17220 Forecasting the Future Implications of ChatGPT Usage in Education Based on AI Algorithms

Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

Abstract:

Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

Procedia PDF Downloads 239
17219 Estimation of Chronic Kidney Disease Using Artificial Neural Network

Authors: Ilker Ali Ozkan

Abstract:

In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

Procedia PDF Downloads 449
17218 Biodiversity and Climate Change: Consequences for Norway Spruce Mountain Forests in Slovakia

Authors: Jozef Mindas, Jaroslav Skvarenina, Jana Skvareninova

Abstract:

Study of the effects of climate change on Norway Spruce (Picea abies) forests has mainly focused on the diversity of tree species diversity of tree species as a result of the ability of species to tolerate temperature and moisture changes as well as some effects of disturbance regime changes. The tree species’ diversity changes in spruce forests due to climate change have been analyzed via gap model. Forest gap model is a dynamic model for calculation basic characteristics of individual forest trees. Input ecological data for model calculations have been taken from the permanent research plots located in primeval forests in mountainous regions in Slovakia. The results of regional scenarios of the climatic change for the territory of Slovakia have been used, from which the values are according to the CGCM3.1 (global) model, KNMI and MPI (regional) models. Model results for conditions of the climate change scenarios suggest a shift of the upper forest limit to the region of the present subalpine zone, in supramontane zone. N. spruce representation will decrease at the expense of beech and precious broadleaved species (Acer sp., Sorbus sp., Fraxinus sp.). The most significant tree species diversity changes have been identified for the upper tree line and current belt of dwarf pine (Pinus mugo) occurrence. The results have been also discussed in relation to most important disturbances (wind storms, snow and ice storms) and phenological changes which consequences are little known. Special discussion is focused on biomass production changes in relation to carbon storage diversity in different carbon pools.

Keywords: biodiversity, climate change, Norway spruce forests, gap model

Procedia PDF Downloads 292
17217 A Bi-Objective Model to Optimize the Total Time and Idle Probability for Facility Location Problem Behaving as M/M/1/K Queues

Authors: Amirhossein Chambari

Abstract:

This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

Procedia PDF Downloads 587
17216 A System Dynamics Approach to Exploring Personality Traits in Young Children

Authors: Misagh Faezipour

Abstract:

System dynamics is a systems engineering approach that can help address the complex challenges in different systems. Little is known about how the brain represents people to predict behavior. This work is based on how the brain simulates different personal behavior and responds to them in the case of young children ages one to five. As we know, children’s minds/brains are just as clean as a crystal, and throughout time, in their surroundings, families, and education center, they grow to develop and have different kinds of behavior towards the world and the society they live in. Hence, this work aims to identify how young children respond to various personality behavior and observes their reactions towards them from a system dynamics perspective. We will be exploring the Big Five personality traits in young children. A causal model is developed in support of the system dynamics approach. These models graphically present the factors and factor relationships that contribute to the big five personality traits and provide a better understanding of the entire behavior model. A simulator will be developed that includes a set of causal model factors and factor relationships. The simulator models the behavior of different factors related to personality traits and their impacts and can help make more informed decisions in a risk-free environment.

Keywords: personality traits, systems engineering, system dynamics, causal model, behavior model

Procedia PDF Downloads 100
17215 Pomegranate Peel Based Edible Coating Treatment for Safety and Quality of Chicken Nuggets

Authors: Muhammad Sajid Arshad, Sadaf Bashir

Abstract:

In this study, the effects of pomegranate peel based edible coating were determined on safety and quality of chicken nuggets. Four treatment groups were prepared as control (without coating), coating with sodium alginate (SA) (1.5%), pomegranate peel powder (PPP) (1.5%), and combination of SA and PPP. There was a significant variation observed with respect to coating treatments and storage intervals. The chicken nuggets were subjected to refrigerated storage (40C) and were analyzed at regular intervals of 0, 7, 14 1 and 21 days. The microbiological quality was determined by total aerobic and coliform counts. Total aerobic (5.09±0.05 log CFU/g) and coliforms (3.91±0.06 log CFU/g) counts were higher in uncoated chicken nuggets whereas lower was observed in coated chicken nuggets having combination of SA and PPP. Likewise, antioxidants potential of chicken nuggets was observed by assessing total phenolic contents (TPC) and DPPH activity. Higher TPC (135.66 GAE/100g) and DPPH (64.65%) were found in combination with SA and PPP, whereas minimum TPC (91.38) and DPPH (41.48) was observed in uncoated chicken nuggets. Regarding the stability analysis of chicken nuggets, thiobarbituric acid reactive substances (TBARS) and peroxide value (POV) were determined. Higher TBARS (1.62±0.03 MDA/Kg) and POV (0.92±0.03 meq peroxide/kg) were found in uncoated chicken nuggets. Hunter color values were also observed in both uncoated and coated chicken nuggets. Sensorial attributes were also observed by the trained panelists. The higher sensory score for appearance, color, taste, texture and overall acceptability were observed in control (uncoated) while in coated treatments, it was found within acceptable limits. In nutshell, the combination of SA and PPP enhanced the overall quality, antioxidant potential, and stability of chicken nuggets.

Keywords: chicken nuggets, edible coatings, pomegranate peel powder, sodium alginate

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17214 Zebrafish Larvae Model: A High Throughput Screening Tool to Study Autism

Authors: Shubham Dwivedi, Raghavender Medishetti, Rita Rani, Aarti Sevilimedu, Pushkar Kulkarni, Yogeeswari Perumal

Abstract:

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder of early onset, characterized by impaired sociability, cognitive function and stereotypies. There is a significant urge to develop and establish new animal models with ASD-like characteristics for better understanding of underlying mechanisms. The aim of the present study was to develop a cost and time effective zebrafish model with quantifiable parameters to facilitate mechanistic studies as well as high-throughput screening of new molecules for autism. Zebrafish embryos were treated with valproic acid and a battery of behavioral tests (anxiety, inattentive behavior, irritability and social impairment) was performed on larvae at 7th day post fertilization, followed by study of molecular markers of autism. This model shows a significant behavioural impairment in valproic acid treated larvae in comparison to control which was again supported by alteration in few marker genes and proteins of autism. The model also shows a rescue of behavioural despair with positive control drugs. The model shows robust parameters to study behavior, molecular mechanism and drug screening approach in a single frame. Thus we postulate that our 7 days zebrafish larval model for autism can help in high throughput screening of new molecules on autism.

Keywords: autism, zebrafish, valproic acid, neurodevelopment, behavioral assay

Procedia PDF Downloads 164
17213 High-Resolution Flood Hazard Mapping Using Two-Dimensional Hydrodynamic Model Anuga: Case Study of Jakarta, Indonesia

Authors: Hengki Eko Putra, Dennish Ari Putro, Tri Wahyu Hadi, Edi Riawan, Junnaedhi Dewa Gede, Aditia Rojali, Fariza Dian Prasetyo, Yudhistira Satya Pribadi, Dita Fatria Andarini, Mila Khaerunisa, Raditya Hanung Prakoswa

Abstract:

Catastrophe risk management can only be done if we are able to calculate the exposed risks. Jakarta is an important city economically, socially, and politically and in the same time exposed to severe floods. On the other hand, flood risk calculation is still very limited in the area. This study has calculated the risk of flooding for Jakarta using 2-Dimensional Model ANUGA. 2-Dimensional model ANUGA and 1-Dimensional Model HEC-RAS are used to calculate the risk of flooding from 13 major rivers in Jakarta. ANUGA can simulate physical and dynamical processes between the streamflow against river geometry and land cover to produce a 1-meter resolution inundation map. The value of streamflow as an input for the model obtained from hydrological analysis on rainfall data using hydrologic model HEC-HMS. The probabilistic streamflow derived from probabilistic rainfall using statistical distribution Log-Pearson III, Normal and Gumbel, through compatibility test using Chi Square and Smirnov-Kolmogorov. Flood event on 2007 is used as a comparison to evaluate the accuracy of model output. Property damage estimations were calculated based on flood depth for 1, 5, 10, 25, 50, and 100 years return period against housing value data from the BPS-Statistics Indonesia, Centre for Research and Development of Housing and Settlements, Ministry of Public Work Indonesia. The vulnerability factor was derived from flood insurance claim. Jakarta's flood loss estimation for the return period of 1, 5, 10, 25, 50, and 100 years, respectively are Rp 1.30 t; Rp 16.18 t; Rp 16.85 t; Rp 21.21 t; Rp 24.32 t; and Rp 24.67 t of the total value of building Rp 434.43 t.

Keywords: 2D hydrodynamic model, ANUGA, flood, flood modeling

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17212 A Framework for SQL Learning: Linking Learning Taxonomy, Cognitive Model and Cross Cutting Factors

Authors: Huda Al Shuaily, Karen Renaud

Abstract:

Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: pattern, SQL, learning, model

Procedia PDF Downloads 258
17211 Ecological Systems Theory, the SCERTS Model, and the Autism Spectrum, Node and Nexus

Authors: C. Surmei

Abstract:

Autism Spectrum Disorder (ASD) is a complex developmental disorder that can affect an individual’s (but is not limited to) cognitive development, emotional development, language acquisition and the capability to relate to others. Ecological Systems Theory is a sociocultural theory that focuses on environmental systems with which an individual interacts. The SCERTS Model is an educational approach and multidisciplinary framework that addresses the challenges confronted by individuals on the autism spectrum and other developmental disabilities. To aid the understanding of ASD and educational philosophies for families, educators, and the global community alike, a Comparative Analysis was undertaken to examine key variables (the child, society, education, nurture/care, relationships, communication). The results indicated that the Ecological Systems Theory and the SCERTS Model were comparable in focus, motivation, and application, attaining to a viable and notable relationship between both theories. This paper unpacks two child development philosophies and their relationship to each other.

Keywords: autism spectrum disorder, ecological systems theory, education, SCERTS model

Procedia PDF Downloads 598
17210 Comparison of Two Neural Networks To Model Margarine Age And Predict Shelf-Life Using Matlab

Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

Abstract:

The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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17209 Fluorescence Quenching as an Efficient Tool for Sensing Application: Study on the Fluorescence Quenching of Naphthalimide Dye by Graphene Oxide

Authors: Sanaz Seraj, Shohre Rouhani

Abstract:

Recently, graphene has gained much attention because of its unique optical, mechanical, electrical, and thermal properties. Graphene has been used as a key material in the technological applications in various areas such as sensors, drug delivery, super capacitors, transparent conductor, and solar cell. It has a superior quenching efficiency for various fluorophores. Based on these unique properties, the optical sensors with graphene materials as the energy acceptors have demonstrated great success in recent years. During quenching, the emission of a fluorophore is perturbed by a quencher which can be a substrate or biomolecule, and due to this phenomenon, fluorophore-quencher has been used for selective detection of target molecules. Among fluorescence dyes, 1,8-naphthalimide is well known for its typical intramolecular charge transfer (ICT) and photo-induced charge transfer (PET) fluorophore, strong absorption and emission in the visible region, high photo stability, and large Stokes shift. Derivatives of 1,8-naphthalimides have found applications in some areas, especially fluorescence sensors. Herein, the fluorescence quenching of graphene oxide has been carried out on a naphthalimide dye as a fluorescent probe model. The quenching ability of graphene oxide on naphthalimide dye was studied by UV-VIS and fluorescence spectroscopy. This study showed that graphene is an efficient quencher for fluorescent dyes. Therefore, it can be used as a suitable candidate sensing platform. To the best of our knowledge, studies on the quenching and absorption of naphthalimide dyes by graphene oxide are rare.

Keywords: fluorescence, graphene oxide, naphthalimide dye, quenching

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17208 Predicting Durability of Self Compacting Concrete Using Artificial Neural Network

Authors: R. Boudjelthia

Abstract:

The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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17207 Advanced Phosphorus-Containing Polymer Materials towards Eco-Friendly Flame Retardant Epoxy Thermosets

Authors: Ionela-Daniela Carja, Diana Serbezeanu, Tachita Vlad-Bubulac, Corneliu Hamciuc

Abstract:

Nowadays, epoxy materials are extensively used in ever more areas and under ever more demanding environmental conditions due to their remarkable combination of properties, light weight and ease of processing. However, these materials greatly increase the fire risk due to their flammability and possible release of toxic by-products as a result of their chemical composition which consists mainly from carbon and hydrogen atoms. Therefore, improving the fire retardant behaviour to prevent the loss of life and property is of particular concern among government regulatory bodies, consumers and manufacturers alike. Modification of epoxy resins with organophosphorus compounds, as reactive flame retardants or additives, is the key to achieving non-flammable advanced epoxy materials. Herein, a detailed characterization of fire behaviour for a series of phosphorus-containing epoxy thermosets is reported. A carefully designed phosphorus flame retardant additive was simply blended with a bifunctional bisphenol-A based epoxy resin. Further thermal cross-linking in the presence of various aminic hardeners led to eco-friendly flame retardant epoxy resins. The type of hardener, concentration of flame retardant additive, compatibility between the components of the mixture, char formation and morphology, thermal stability, flame retardant mechanisms were investigated. It was found that even a very low content of phosphorus introduced into the epoxy matrix increased the limiting oxygen index value to about 30%. In addition, the peak of the heat release rate value decreased up to 45% as compared to the one of the neat epoxy system. The main flame retardant mechanism was the condensed-phase one as revealed by SEM and XPS measurements.

Keywords: condensed-phase mechanism, eco-friendly phosphorus flame retardant, epoxy resin, thermal stability

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17206 Multi-Criteria Goal Programming Model for Sustainable Development of India

Authors: Irfan Ali, Srikant Gupta, Aquil Ahmed

Abstract:

Every country needs a sustainable development (SD) for its economic growth by forming suitable policies and initiative programs for the development of different sectors of the country. This paper is comprised of modeling and optimization of different sectors of India that form a multi-criterion model. In this paper, we developed a fractional goal programming (FGP) model that helps in providing the efficient allocation of resources simultaneously by achieving the sustainable goals in gross domestic product (GDP), electricity consumption (EC) and greenhouse gasses (GHG) emission by the year 2030. Also, a weighted model of FGP is presented to obtain varying solution according to the priorities set by the policy maker for achieving future goals of GDP growth, EC, and GHG emission. The presented models provide a useful insight to the decision makers for implementing strategies in a different sector.

Keywords: sustainable and economic development, multi-objective fractional programming, fuzzy goal programming, weighted fuzzy goal programming

Procedia PDF Downloads 226
17205 Energy Analysis of Seasonal Air Conditioning Demand of All Income Classes Using Bottom up Model in Pakistan

Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Tanzeel Rashid, Burhan Ali, Juntakan Taweekun

Abstract:

Currently, the energy crisis is taking serious attention. Globally, industries and building are major share takers of energy. 72% of total global energy is consumed by residential houses, markets, and commercial building. Additionally, in appliances air conditioners are major consumer of electricity; about 60% energy is used for cooling purpose in houses due to HVAC units. Energy demand will aid in determining what changes will be needed whether it is the estimation of the required energy for households or instituting conservation measures. Bottom-up model is one of the most famous methods for forecasting. In current research bottom-up model of air conditioners' energy consumption in all income classes in comparison with seasonal variation and hourly consumption is calculated. By comparison of energy consumption of all income classes by usage of air conditioners, total consumption of actual demand and current availability can be seen.

Keywords: air conditioning, bottom up model, income classes, energy demand

Procedia PDF Downloads 254
17204 Reliable Soup: Reliable-Driven Model Weight Fusion on Ultrasound Imaging Classification

Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

Abstract:

It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

Procedia PDF Downloads 89
17203 Comparative Analysis of the Expansion Rate and Soil Erodibility Factor (K) of Some Gullies in Nnewi and Nnobi, Anambra State Southeastern Nigeria

Authors: Nzereogu Stella Kosi, Igwe Ogbonnaya, Emeh Chukwuebuka Odinaka

Abstract:

A comparative analysis of the expansion rate and soil erodibility of some gullies in Nnewi and Nnobi both of Nanka Formation were studied. The study involved an integration of field observations, geotechnical analysis, slope stability analysis, multivariate statistical analysis, gully expansion rate analysis, and determination of the soil erodibility factor (K) from Revised Universal Soil Loss Equation (RUSLE). Fifteen representative gullies were studied extensively, and results reveal that the geotechnical properties of the soil, topography, vegetation cover, rainfall intensity, and the anthropogenic activities in the study area were major factors propagating and influencing the erodibility of the soils. The specific gravity of the soils ranged from 2.45-2.66 and 2.54-2.78 for Nnewi and Nnobi, respectively. Grain size distribution analysis revealed that the soils are composed of gravel (5.77-17.67%), sand (79.90-91.01%), and fines (2.36-4.05%) for Nnewi and gravel (7.01-13.65%), sand (82.47-88.67%), and fines (3.78-5.02%) for Nnobi. The soils are moderately permeable with values ranging from 2.92 x 10-5 - 6.80 x 10-4 m/sec and 2.35 x 10-6 - 3.84 x 10⁻⁴m/sec for Nnewi and Nnobi respectively. All have low cohesion values ranging from 1–5kPa and 2-5kPa and internal friction angle ranging from 29-38° and 30-34° for Nnewi and Nnobi, respectively, which suggests that the soils have low shear strength and are susceptible to shear failure. Furthermore, the compaction test revealed that the soils were loose and easily erodible with values of maximum dry density (MDD) and optimum moisture content (OMC) ranging from 1.82-2.11g/cm³ and 8.20-17.81% for Nnewi and 1.98-2.13g/cm³ and 6.00-17.80% respectively. The plasticity index (PI) of the fines showed that they are nonplastic to low plastic soils and highly liquefiable with values ranging from 0-10% and 0-9% for Nnewi and Nnobi, respectively. Multivariate statistical analyses were used to establish relationship among the determined parameters. Slope stability analysis gave factor of safety (FoS) values in the range of 0.50-0.76 and 0.82-0.95 for saturated condition and 0.73-0.98 and 0.87-1.04 for unsaturated condition for both Nnewi and Nnobi, respectively indicating that the slopes are generally unstable to critically stable. The erosion expansion rate analysis for a fifteen-year period (2005-2020) revealed an average longitudinal expansion rate of 36.05m/yr, 10.76m/yr, and 183m/yr for Nnewi, Nnobi, and Nanka type gullies, respectively. The soil erodibility factor (K) are 8.57x10⁻² and 1.62x10-4 for Nnewi and Nnobi, respectively, indicating that the soils in Nnewi have higher erodibility potentials than those of Nnobi. From the study, both the Nnewi and Nnobi areas are highly prone to erosion. However, based on the relatively lower fine content of the soil, relatively lower topography, steeper slope angle, and sparsely vegetated terrain in Nnewi, soil erodibility and gully intensity are more profound in Nnewi than Nnobi.

Keywords: soil erodibility, gully expansion, nnewi-nnobi, slope stability, factor of safety

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17202 A Physical Theory of Information vs. a Mathematical Theory of Communication

Authors: Manouchehr Amiri

Abstract:

This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

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17201 Genetic Algorithms Multi-Objective Model for Project Scheduling

Authors: Elsheikh Asser

Abstract:

Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: genetic algorithms, time-cost trade-off, multi-objective model, project scheduling

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17200 An Estimating Parameter of the Mean in Normal Distribution by Maximum Likelihood, Bayes, and Markov Chain Monte Carlo Methods

Authors: Autcha Araveeporn

Abstract:

This paper is to compare the parameter estimation of the mean in normal distribution by Maximum Likelihood (ML), Bayes, and Markov Chain Monte Carlo (MCMC) methods. The ML estimator is estimated by the average of data, the Bayes method is considered from the prior distribution to estimate Bayes estimator, and MCMC estimator is approximated by Gibbs sampling from posterior distribution. These methods are also to estimate a parameter then the hypothesis testing is used to check a robustness of the estimators. Data are simulated from normal distribution with the true parameter of mean 2, and variance 4, 9, and 16 when the sample sizes is set as 10, 20, 30, and 50. From the results, it can be seen that the estimation of MLE, and MCMC are perceivably different from the true parameter when the sample size is 10 and 20 with variance 16. Furthermore, the Bayes estimator is estimated from the prior distribution when mean is 1, and variance is 12 which showed the significant difference in mean with variance 9 at the sample size 10 and 20.

Keywords: Bayes method, Markov chain Monte Carlo method, maximum likelihood method, normal distribution

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17199 An Improved GA to Address Integrated Formulation of Project Scheduling and Material Ordering with Discount Options

Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

Abstract:

Concurrent planning of the resource constraint project scheduling and material ordering problems have received significant attention within the last decades. Hence, the issue has been investigated here with the aim to minimize total project costs. Furthermore, the presented model considers different discount options in order to approach the real world conditions. The incorporated alternatives consist of all-unit and incremental discount strategies. On the other hand, a modified version of the genetic algorithm is applied in order to solve the model for larger sizes, in particular. Finally, the applicability and efficiency of the given model is tested by different numerical instances.

Keywords: genetic algorithm, material ordering, project management, project scheduling

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17198 Application of ANN and Fuzzy Logic Algorithms for Runoff and Sediment Yield Modelling of Kal River, India

Authors: Mahesh Kothari, K. D. Gharde

Abstract:

The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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17197 Groundwater Treatment of Thailand's Mae Moh Lignite Mine

Authors: A. Laksanayothin, W. Ariyawong

Abstract:

Mae Moh Lignite Mine is the largest open-pit mine in Thailand. The mine serves coal to the power plant about 16 million tons per year. This amount of coal can produce electricity accounting for about 10% of Nation’s electric power generation. The mining area of Mae Moh Mine is about 28 km2. At present, the deepest area of the pit is about 280 m from ground level (+40 m. MSL) and in the future the depth of the pit can reach 520 m from ground level (-200 m.MSL). As the size of the pit is quite large, the stability of the pit is seriously important. Furthermore, the preliminary drilling and extended drilling in year 1989-1996 had found high pressure aquifer under the pit. As a result, the pressure of the underground water has to be released in order to control mine pit stability. The study by the consulting experts later found that 3-5 million m3 per year of the underground water is needed to be de-watered for the safety of mining. However, the quality of this discharged water should meet the standard. Therefore, the ground water treatment facility has been implemented, aiming to reduce the amount of naturally contaminated Arsenic (As) in discharged water lower than the standard limit of 10 ppb. The treatment system consists of coagulation and filtration process. The main components include rapid mixing tanks, slow mixing tanks, sedimentation tank, thickener tank and sludge drying bed. The treatment process uses 40% FeCl3 as a coagulant. The FeCl3 will adsorb with As(V), forming floc particles and separating from the water as precipitate. After that, the sludge is dried in the sand bed and then be disposed in the secured land fill. Since 2011, the treatment plant of 12,000 m3/day has been efficiently operated. The average removal efficiency of the process is about 95%.

Keywords: arsenic, coagulant, ferric chloride, groundwater, lignite, coal mine

Procedia PDF Downloads 312
17196 Sliding Mode Control of Variable Speed Wind Energy Conversion Systems

Authors: Zine Souhila Rached, Mazari Benyounes Bouzid, Mohamed Amine, Allaoui Tayeb

Abstract:

Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In other words, having a power coefficient is maximum and therefore the maximum power point tracking. In this case, the MPPT control becomes important.To realize this control, strategy conventional proportional and integral (PI) controller is usually used. However, this strategy cannot achieve better performance. This paper proposes a robust control of a turbine which optimizes its production, that is improve the quality and energy efficiency, namely, a strategy of sliding mode control. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine; the proposed sliding mode control approach has been simulated on three-blade wind turbine. The simulation result under Matlab\Simulink has validated the performance of the proposed MPPT strategy.

Keywords: wind turbine, maximum power point tracking, sliding mode, energy conversion systems

Procedia PDF Downloads 615
17195 Estimating PM2.5 Concentrations Based on Landsat 8 Imagery and Historical Field Data over the Metropolitan Area of Mexico City

Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Francisco Andree Ramirez-Casas, Alondra Orozco-Gomez, Miguel Angel Sanchez-Caro, Carlos Herrera-Ventosa

Abstract:

High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air pollution modeling, Landsat 8, PM2.5, remote sensing

Procedia PDF Downloads 203
17194 Model Reference Adaptive Control and LQR Control for Quadrotor with Parametric Uncertainties

Authors: Alia Abdul Ghaffar, Tom Richardson

Abstract:

A model reference adaptive control and a fixed gain LQR control were implemented in the height controller of a quadrotor that has parametric uncertainties due to the act of picking up an object of unknown dimension and mass. It is shown that an adaptive control, unlike a fixed gain control, is capable of ensuring a stable tracking performance under such condition, although adaptive control suffers from several limitations. The combination of both adaptive and fixed gain control in the controller architecture results in an enhanced tracking performance in the presence of parametric uncertainties.

Keywords: UAV, quadrotor, robotic arm augmentation, model reference adaptive control, LQR control

Procedia PDF Downloads 478
17193 The Conceptual Exploration of Comfort Zone by Using Content Analysis

Authors: Lilla Szabó Hangya, Szilvia Jambori

Abstract:

The comfort zone is less studied area in the field of psychology. One of the most important definitions is that comfort zone is a psychological state in which things feel familiar to a person with low level of anxiety and stress. But the validity of comfort zone does not confirm till now. The aim of our pilot research is to test which psychological factors could determine how young adults behave during their decision process to stay in one’s comfort zone or to leave it. Every person has a number of comfort zones, so we are not able to measure it directly, only those personality traits which predict if someone leaves his comfort zone easier or harder. In our study at first we wanted to clarify the meaning of comfort zone. 110 young adults (male: 37, female: 73; ages from 18 to 70, average age: 26,6) took part in the study. Beside their demographic datas we asked them what does the comfort zone mean for them. The results showed that the meaning of the comfort zone can be grouped in five dimensions: comfort (49,6 %), leaving it-change (8,1%), ambivalent feelings (10,6%), related to other people (10,6%), pursuit of self-realization (16,8%). Our results demonstrated age related characteristics. For young people at the age of 19 the comfort zone is related to other people, because during adolescents peer relationships become more important. Subjects at the age 20-30 answered that the comfort zone means comfort and stability for them. Their life becomes stable for a while, they are studying or working. But at the age of 25, when they finish university, most of them answered comfort zone means a changing process for them. On the other hand for subjects at the age of 27 the means of the comfort zone is pursuit of self-realization. After that period at the age of 31 when they have families and stable job the stability will also dominant. We saw that the comfort zone has much more meaning besides a pleasant psychological trait. Further we would like to determine which psychological factors relate to comfort zone, and what kind of personality traits could predict leaving or staying in one’s comfort zone. We want to observe the relationship between comfort zone and subjective well-being, life satisfaction self-efficacy or self-esteem.

Keywords: comfort zone, development, personality trait, young adults

Procedia PDF Downloads 347
17192 Clinical Effectiveness of Bulk-fill Resin Composite: A Review

Authors: Taraneh Estedlal

Abstract:

The objective of this study was to review in-vivo and in-vitro studies to compare the effectiveness of bulk-fill and conventional resin composites with regard to marginal adaptation, polymerization shrinkage, and other mechanical properties.PubMed and Scopus databases was investigated for in-vitro studies and randomized clinical trials comparing incidence of fractures, color stability, marginal adaptation, pain and discomfort, recurrent caries, occlusion, pulpal reaction, and proper proximal contacts of restorations made with conventional and bulk resins. The failure rate of conventional and flowable bulk-fill resin composites was not significantly different to sculptable bulk-fill resin composites. The objective of this study was to review in-vivo and in-vitro studies to compare the effectiveness of bulk-fill and conventional resin composites with regard to marginal adaptation, polymerization shrinkage, and other mechanical properties. PubMed and Scopus databases was investigated for in-vitro studies and randomized clinical trials comparing one of the pearlier mentioned properties between bulk-fill and control composites. Despite differences in physical and in-vitro properties, failure rate of conventional and flowable bulk-fill resin composites was not significantly different to sculptable bulk-fill resin composites.

Keywords: polymerization shrinkage, color stability, marginal adaptation, recurrent caries, occlusion, pulpal reaction

Procedia PDF Downloads 148
17191 Revised Technology Acceptance Model Framework for M-Commerce Adoption

Authors: Manish Gupta

Abstract:

Following the E-Commerce era, M-Commerce is the next big phase in the technology involvement and advancement. This paper intends to explore how Indian consumers are influenced to adopt the M-commerce. In this paper, the revised Technology Acceptance Model (TAM) has been presented on the basis of the most dominant factors that affect the adoption of M-Commerce in Indian scenario. Furthermore, an analytical questionnaire approach was carried out to collect data from Indian consumers. These collected data were further used for the validation of the presented model. Findings indicate that customization, convenience, instant connectivity, compatibility, security, download speed in M-Commerce affect the adoption behavior. Furthermore, the findings suggest that perceived usefulness and attitude towards M-Commerce are positively influenced by number of M-Commerce drivers (i.e. download speed, compatibility, convenience, security, customization, connectivity, and input mechanism).

Keywords: M-Commerce, perceived usefulness, technology acceptance model, perceived ease of use

Procedia PDF Downloads 319