Search results for: conditional variance coefficient
1794 Development of a One-Window Services Model for Accessing Cancer Immunotherapies
Authors: Rizwan Arshad, Alessio Panza, Nimra Inayat, Syeda Mariam Batool Kazmi, Shawana Azmat
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The rapidly expanding use of immunotherapy for a wide range of cancers from late to early stages has, predictably, been accompanied by evidence of inequities in access to these highly effective but costly treatments. In this survey-based case study, we aimed to develop a One-window services model (OWSM) based on Anderson’s behavioral model to enhance competence in accessing cancer medications, particularly immunotherapies, through the analysis of 20 patient surveys conducted in the Armed forces bone marrow transplant center of the district, Rawalpindi from November to December 2022. The purposive sampling technique was used. Cronbach’s alpha coefficient was found to be 0.71. It was analyzed using SPSS version 26 with descriptive analysis, and results showed that the majority of the cancer patients were non-competent to access their prescribed cancer immunotherapy because of individual-level, socioeconomic, and organizational barriers.Keywords: cancer immunotherapy, one-window services model, accessibility, competence
Procedia PDF Downloads 761793 Effects of Moringa oleifera Leaf Powder on the Feed Intake and Average Weight of Pullets
Authors: Cajethan U. Ugwuoke, Hyginus O. Omeje, Emmanuel C. Osinem
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The study was carried out to determine the effects of Moringa oleifera leaf powder additive on the feed intake and average weight of pullets. A completely Randomized Design (CRD) was adopted for the study. On the procedure of the experiment, 240 chicks were randomly selected from 252 Isa Brown day-old chicks. The chicks were equally randomly allotted to 12 pens with 20 chicks each. The pens were randomly assigned to four different treatment groups with three replicates each. T1 was fed with control feed while T2, T3, and T4 were fed with 2.5%, 5% and 7.5% Moringa oleifera leaf powder fortified feed respectively. The chicks were fed with uniform feed up to week four. From week five, experimental feeds were given to the pullet up to 20 weeks of age. The birds were placed on the same treatment conditions except different experimental feeds given to different groups. Data on the feed intake were collected daily while the average weight of the pullets was collected weekly using weighing scale. Data collected were analyzed using mean, bar charts and Analysis of Variance. The layers fed with control feed consumed the highest amount of feed in most of the weeks under study. The average weights of all the treatment groups were equal from week 1 to week 4. Little variation in average weight started in week 5 with T2 topping the groups. However, there was no statistically significant difference (p>0.05) in the feed intake and average weight of layers fed with different inclusion rates of Moringa oleifera leaf powder in feeds.Keywords: average weight, feed intake, Moringa oleifera, pullets
Procedia PDF Downloads 1921792 The Effects and Interactions of Synthesis Parameters on Properties of Mg Substituted Hydroxyapatite
Authors: S. Sharma, U. Batra, S. Kapoor, A. Dua
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In this study, the effects and interactions of reaction time and capping agent assistance during sol-gel synthesis of magnesium substituted hydroxyapatite nanopowder (MgHA) on hydroxyapatite (HA) to β-tricalcium phosphate (β-TCP) ratio, Ca/P ratio and mean crystallite size was examined experimentally as well as through statistical analysis. MgHA nanopowders were synthesized by sol-gel technique at room temperature using aqueous solution of calcium nitrate tetrahydrate, magnesium nitrate hexahydrate and potassium dihydrogen phosphate as starting materials. The reaction time for sol-gel synthesis was varied between 15 to 60 minutes. Two process routes were followed with and without addition of triethanolamine (TEA) in the solutions. The elemental compositions of as-synthesized powders were determined using X-ray fluorescence (XRF) spectroscopy. The functional groups present in the as-synthesized MgHA nanopowders were established through Fourier Transform Infrared Spectroscopy (FTIR). The amounts of phases present, Ca/P ratio and mean crystallite sizes of MgHA nanopowders were determined using X-ray diffraction (XRD). The HA content in biphasic mixture of HA and β-TCP and Ca/P ratio in as-synthesized MgHA nanopowders increased effectively with reaction time of sols (p < 0.0001, two way Anova), however, these were independent of TEA addition (p > 0.15, two way Anova). The MgHA nanopowders synthesized with TEA assistance exhibited 14 nm lower crystallite size (p < 0.018, 2 sample t-test) compared to the powder synthesized without TEA assistance.Keywords: capping agent, hydroxyapatite, regression analysis, sol-gel, 2- sample t-test, two-way analysis of variance (ANOVA)
Procedia PDF Downloads 3721791 Mindfulness among Educators in General and Special Education at Independent Schools in Qatar and Its Effects on Their Academic Performance and Self-Efficacy
Authors: Mohamed S. Osman, Mohamed R. Nosair
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The study aims to determine the effects of mindfulness on self-efficacy and professional success among educators of general and special education at Qatar Independent. The study sample will consist of 100 educators from the males and females divided to (50) educators of general education and (50) educators of Special Education in primary, and high schools. They will response to mindfulness scale and the scale of self-efficacy. In addition, use reports of the assessment by the Department of Education for their performance and assessments of their supervisors. The study will examine the effect of some variables such as differences between educators from general and special education, as well as the differences between males and females and years of experience. The study will use a statistic descriptive approach and Correlative analysis such as; means and the Pearson correlation coefficient. The study may predicts differences between educators in all variables study.Keywords: mindfulness, educators, general education, special education, academic performance, self-efficacy
Procedia PDF Downloads 3551790 Effect of Manganese Doping Percentage on Optical Band Gap and Conductivity of Copper Sulphide Nano-Films Prepared by Electrodeposition Method
Authors: P. C. Okafor, A. J. Ekpunobi
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Mn doped copper sulphide (CuS:Mn) nano-films were deposited on indiums coated tin oxide (ITO) glass substrates using electrodeposition method. Electrodeposition was carried out using bath of PH = 3 at room temperature. Other depositions parameters such as deposition time (DT) are kept constant while Mn doping was varied from 3% to 23%. Absorption spectra of CuS:Mn films was obtained by using JENWAY 6405 UV-VIS -spectrophotometer. Optical band gap (E_g ), optical conductivity (σo) and electrical conductivity (σe) of CuS:Mn films were determined using absorption spectra and appropriate formula. The effect of Mn doping % on these properties were investigated. Results show that film thickness (t) for the 13.27 nm to 18.49 nm; absorption coefficient (α) from 0.90 x 1011 to 1.50 x 1011 optical band gap from 2.29eV to 2.35 eV; optical conductivity from 1.70 x 1013 and electrical conductivity from 160 millions to 154 millions. Possible applications of such films for solar cells fabrication and optoelectronic devices applications were also discussed.Keywords: copper sulphide (CuS), Manganese (Mn) doping, electrodeposition, optical band gap, optical conductivity, electrical conductivity
Procedia PDF Downloads 7231789 A Stable Method for Determination of the Number of Independent Components
Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor
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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock
Procedia PDF Downloads 1001788 [Keynote Speech]: Feature Selection and Predictive Modeling of Housing Data Using Random Forest
Authors: Bharatendra Rai
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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error
Procedia PDF Downloads 3241787 Antiinflammatory and Antinociceptive of Hydro Alcoholic Tanacetum balsamita L. Extract
Authors: S. Nasri, G. H. Amin, A. Azimi
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The use of herbs to treat disease is accompanied with the history of human life. This research is aimed to study the anti-inflammatory and antinociceptive effects of hydroalcoholic extract of aerial parts of "Tanacetum balsamita balsamita". In the experimental studies 144 male mice are used. In the inflammatory test, animals were divided into six groups: Control, positive control (receiving Dexamethason at dose of 15mg/kg), and four experimental groups receiving Tanacetum balsamita balsamita hydroalcoholic extract at doses of 25, 50, 100 and 200mg/kg. Xylene was used to induce inflammation. Formalin was used to study the nociceptive effects. Animals were divided into six groups: control group, positive control group (receiving morphine) and four experimental groups receiving Tanacetum balsamita balsamita (Tb.) hydroalcoholic extract at doses of 25, 50, 100 and 200mg/kg. I.p. injection of drugs or normal saline was performed 30 minutes before test. The data were analyzed by using one way Variance analysis and Tukey post-test. Aerial parts of Tanacetum balsamita balsamita hydroalcoholic extract decreased significantly inflammatory at dose of 200mg/kg (P<0/001) and caused a significant decrease and alleviated the nociception in both first and second phases at doses of 200mg/kg (p<0/001) and 100mg/kg (P<0/05). Tanacetum balsamita balsamita extract has the anti-inflammatory and anti-nociceptive effects which seems to be related with flavonoids especially Quercetin.Keywords: inflammation, nociception, hydroalcoholic extract, aerial parts of Tanacetum balsamita balsamita L.
Procedia PDF Downloads 1991786 Contextual and Personal Factors as Predictor of Academic Resilience among Female Undergraduates in Boko Haram Neighbourhood in North-Eastern Nigeria
Authors: Ndidi Ofole
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Ongoing Boko Haram crisis and instability in North-Eastern Nigeria has placed additional stress on academic resilience of female undergraduates who are already challenged by gender discrimination in educational opportunities. Students without resilience lack stress hardiness to cope with academic challenges. There is a limited study on academic resilience targeting this disadvantaged population in Nigeria. Consequently, survey research design was employed to investigate the contextual and personal factors that could predict academic resilience among female undergraduates in Boko Haram Neighbourhood in North-Eastern, Nigeria. Five hundred and thirty female students with age range of 18 to 24 years ( = 19.2; SD=6.9) were randomly drawn from 3 Universities in North-Eastern Nigeria. They responded to five instruments, namely; Academic Resilience scale (r=0.72); Social Support questionnaire (r=0. 64); Social Connectedness questionnaire (r=0.75); Self-Efficacy scale (r=0. 68) and Emotional Regulation questionnaire (r=78). Results showed that there was significant positive relationship between the four independent variables and academic resilience. The variables jointly contributed 5.9% variance in the prediction of academic resilience. In terms of magnitude, social support was most potent while self-efficacy was the least. It concluded that the factors considered in this study are academic resilience facilitators. The outcomes of the study have both theoretical and practical implications.Keywords: academic resilience, emotional regulation, school connectedness, self-efficacy , social support
Procedia PDF Downloads 2101785 Comparative Study of Medical and Fine Art Students on the Level of Perceived Stress and Coping Skills
Authors: Bushra Mussawar, Saleha Younus
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Students often view their academic life demanding and stressful. However, apart from academics, stress springs from various other sources namely, finance, family, health, friends etc. The present study aims to assess the level of perceived stress in medical and fine arts students, and to determine the coping strategies used by the students to mitigate stress. The sample of the study consisted of 178 medical and fine arts students. The sample was selected through purposive sampling. Pearson correlation coefficient and T-test were used to analyze data. Results of the study revealed that there exists a positive relationship between perceived stress and coping strategies. Additionally, the two groups showed marked differences in terms of stress perception and coping styles. The level of perceived stress was found to be high in medical students nonetheless, they employed more positive coping strategies than fine arts students who scored high on negative coping strategies which are deleterious to the overall wellbeing.Keywords: perceived stress, coping strategies, medical, fine arts students
Procedia PDF Downloads 3091784 Thin-Layer Drying Characteristics and Modelling of Instant Coffee Solution
Authors: Apolinar Picado, Ronald Solís, Rafael Gamero
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The thin-layer drying characteristics of instant coffee solution were investigated in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (80, 100 and 120 °C) and an air velocity of 1.2 m/s. Drying experimental data obtained are fitted to six (6) thin-layer drying models using the non-linear least squares regression analysis. The acceptability of the thin-layer drying model has been based on a value of the correlation coefficient that should be close to one, and low values for root mean square error (RMSE) and chi-square (x²). According to this evaluation, the most suitable model for describing drying process of thin-layer instant coffee solution is the Page model. Further, the effective moisture diffusivity and the activation energy were computed employing the drying experimental data. The effective moisture diffusivity values varied from 1.6133 × 10⁻⁹ to 1.6224 × 10⁻⁹ m²/s over the temperature range studied and the activation energy was estimated to be 162.62 J/mol.Keywords: activation energy, diffusivity, instant coffee, thin-layer models
Procedia PDF Downloads 2621783 The Practices and Challenges of Secondary School Cluster Supervisors in Implementing School Improvement Program in Saesie Tsaeda Emba Woreda, Eastern Zone of Tigray Region
Authors: Haftom Teshale Gebre
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According to the ministry of education’s school improvement program blueprint document (2007), the timely and basic aim of the program is to improve students’ academic achievement through creating conducive teaching and learning environments and with the active involvement of parents in the teaching and learning process. The general objective of the research is to examine the practices of cluster school supervisors in implementing school improvement programs and the major factors affecting the study area. The study used both primary and secondary sources, and the sample size was 93. Twelve people are chosen from each of the two clusters (Edaga Hamus and Adi-kelebes). And cluster ferewyni are Tekli suwaat, Edaga robue, and Kiros Alemayo. In the analysis stage, several interrelated pieces of information were summarized and arranged to make the analysis easily manageable by using statistics and data (STATA). Study findings revealed that the major four domains impacted by school improvement programs through their mean, standard deviation, and variance were 2.688172, 1.052724, and 1.108228, respectively. And also, the researcher can conclude that the major factors of the school improvement program and mostly cluster supervisors were inadequate attention given to supervision service and no experience in the practice of supervision in the study area.Keywords: cluster, eastern Tigray, Saesie Tsaeda Emba, SPI
Procedia PDF Downloads 341782 Theoretical Investigation of the Structural, Electronic, Optical and Elastic Properties of the Perovskite ScRhO₃
Authors: L. Foudia, K. Haddadi, M. Reffas
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First principles study of structural, elastic, electronic and optical properties of the monoclinic perovskite type ScRhO₃ has been reported using the pseudo-potential plane wave method within the local density approximation. The calculated lattice parameters, including the lattice constants and angle β are in excellent agreement with the available experimental data, which proving the reliability of the chosen theoretical approach. Pressure dependence up to 20 GPa of the single crystal and polycrystalline elastic constants has been investigated in details using the strain-stress approach. The mechanical stability, ductility, average elastic wave velocity, Debye temperature and elastic anisotropy were also assessed. Electronic band structure and density of states (DOS) demonstrated its semiconducting nature showing a direct band gap of 1.38 eV. Furthermore, several optical properties, such as absorption coefficient, reflectivity, refractive index, dielectric function, optical conductivity and electron energy loss function have been calculated for radiation up to 40 eV.Keywords: ab-initio, perovskite, DFT, band gap.
Procedia PDF Downloads 751781 Prediction of the Transmittance of Various Bended Angles Lightpipe by Using Neural Network under Different Sky Clearness Condition
Authors: Li Zhang, Yuehong Su
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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.Keywords: neural network, bended lightpipe, transmittance, Photopia
Procedia PDF Downloads 1531780 Numerical Study of Heat Transfer Nanofluid TiO₂ through a Solar Flat Plate Collector
Authors: A. Maouassi, A. Beghidja, S. Daoud, N. Zeraibi
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This paper illustrates a practical application of nanoparticles (TiO₂) as working fluid to stimulate solar flat plate collector efficiency with heat transfer modification properties. A numerical study of nanofluids laminar forced convection, permanent and stationary, is conducted in a solar flat plate collector. The effectiveness of these nanofluids are compared to conventional working fluid (water), wherein the dynamic and thermal properties are evaluated for four volume concentrations of nanoparticles (1%, 3%, 5% and 10%), and this done for Reynolds number from 25 to 800. Results from the application of those nonfluids are obtained versus pressure drop coefficient and Nusselt number are discussed later in this paper. Finally, we concluded that the heat transfer increases with increasing both nanoparticles concentration and Reynolds number.Keywords: CFD, forced convection, nanofluid, solar flat plate collector efficiency, TiO₂ nanoparticles
Procedia PDF Downloads 1601779 Comparative Evaluation of Different Extenders and Sperm Protectors to Keep the Spermatozoa Viable for More than 24 Hours
Authors: A. M. Raseona, D. M. Barry, T. L. Nedambale
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Preservation of semen is an important process to ensure that semen quality is sufficient for assisted reproductive technology. This study evaluated the effectiveness of different extenders to preserve Nguni bull semen stored at controlled room temperature 24 °C for three days, as an alternative to frozen-thawed semen straws used for artificial insemination. Semen samples were collected from two Nguni bulls using an electro-ejaculator and transported to the laboratory for evaluation. Pooled semen was aliquot into three extenders Triladyl, Ham’s F10 and M199 at a dilution ratio of 1:4 then stored at controlled room temperature 24 °C. Sperm motility was analysed after 0, 24, 48 and 72 hours. Morphology and viability were analysed after 72 hours. The study was replicated four times and data was analysed by analysis of variance (ANOVA). Triladyl showed higher viability percentage and consistent total motility for three days. Ham’s F10 showed higher progressive motility compared to the other extenders. There was no significant difference in viability between Ham’s F10 and M199. No significant difference was also observed in total abnormality between the two Nguni bulls. In conclusion, Nguni semen can be preserved in Triladyl or Ham’s F10 and M199 culture media stored at 24 °C and stay alive for three days. Triladyl proved to be the best extender showing high viability and consistency in total motility as compared to Ham’s F10 and M199.Keywords: bull semen, artificial insemination, Triladyl, Ham’s F10, M199, viability
Procedia PDF Downloads 5001778 A Convolution Neural Network PM-10 Prediction System Based on a Dense Measurement Sensor Network in Poland
Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski
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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks
Procedia PDF Downloads 1501777 Passenger Flow Characteristics of Seoul Metropolitan Subway Network
Authors: Kang Won Lee, Jung Won Lee
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Characterizing the network flow is of fundamental importance to understand the complex dynamics of networks. And passenger flow characteristics of the subway network are very relevant for an effective transportation management in urban cities. In this study, passenger flow of Seoul metropolitan subway network is investigated and characterized through statistical analysis. Traditional betweenness centrality measure considers only topological structure of the network and ignores the transportation factors. This paper proposes a weighted betweenness centrality measure that incorporates monthly passenger flow volume. We apply the proposed measure on the Seoul metropolitan subway network involving 493 stations and 16 lines. Several interesting insights about the network are derived from the new measures. Using Kolmogorov-Smirnov test, we also find out that monthly passenger flow between any two stations follows a power-law distribution and other traffic characteristics such as congestion level and throughflow traffic follow exponential distribution.Keywords: betweenness centrality, correlation coefficient, power-law distribution, Korea traffic DB
Procedia PDF Downloads 2921776 The Plasma Additional Heating Systems by Electron Cyclotron Waves
Authors: Ghoutia Naima Sabri, Tayeb Benouaz
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The interaction between wave and electron cyclotron movement when the electron passes through a layer of resonance at a fixed frequency results an Electron Cyclotron (EC) absorption in Tokamak plasma and dependent magnetic field. This technique is the principle of additional heating (ECRH) and the generation of non-inductive current drive (ECCD) in modern fusion devices. In this paper we are interested by the problem of EC absorption which used a microscopic description of kinetic theory treatment versus the propagation which used the cold plasma description. The power absorbed depends on the optical depth which in turn depends on coefficient of absorption and the order of the excited harmonic for O-mode or X-mode. There is another possibility of heating by dissipation of Alfven waves, based on resonance of cold plasma waves, the shear Alfven wave (SW) and the compressional Alfven wave (FW). Once the (FW) power is coupled to (SW), it stays on the magnetic surface and dissipates there, which cause the heating of bulk plasmas.Keywords: electron cyclotron, heating, plasma, tokamak
Procedia PDF Downloads 5161775 Comparison of Different Methods to Produce Fuzzy Tolerance Relations for Rainfall Data Classification in the Region of Central Greece
Authors: N. Samarinas, C. Evangelides, C. Vrekos
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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.Keywords: classification, fuzzy logic, tolerance relations, rainfall data
Procedia PDF Downloads 3151774 Safe and Efficient Deep Reinforcement Learning Control Model: A Hydroponics Case Study
Authors: Almutasim Billa A. Alanazi, Hal S. Tharp
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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.Keywords: control system, hydroponics, machine learning, reinforcement learning
Procedia PDF Downloads 1861773 Self-Efficacy as a Predictor of Well-Being in University Students
Authors: Enes Ergün, Sedat Geli̇bolu
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The purpose of this study is to determine the relationship between self-efficacy and subjective well-being among university students. We are aiming to determine whether self efficacy of university students predicts their subjective well-being and if there is a statistically significant difference among boys and girls in this context. Sample of this study consists of 245 university students from Çanakkale, ages ranging between 17 and 24. 72% (n=171) of the participants were girls and 28% (n=69) boys. Three different scales were utilized as data collection tools that Life Satisfaction Scale, General Self-Efficacy Scale, and Positive Negative Experiences Scale. Pearson correlation coefficient, independent sample t test and simple linear regression were used for data analyses. Results showed that well-being is significantly correlated with self-efficacy and self-efficacy is a statistically significant predictor of well-being too. In terms of gender differences, there is no significant difference between self-efficacy scores of boys and girls which shows the same case with well being scores, as well. Fostering university students' academic, social and emotional self-efficacy will increase their well-being which is very important for young adults especially their freshman years.Keywords: positive psychology, self-efficacy, subjective well being, university students
Procedia PDF Downloads 2831772 Ab Initio Study of Structural, Elastic, Electronic and Thermal Properties of Full Heusler
Authors: M. Khalfa, H. Khachai, F. Chiker, K. Bougherara, R. Khenata, G. Murtaza, M. Harmel
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A theoretical study of structural, elastic, electronic and thermodynamic properties of Fe2VX, (with X = Al and Ga), were studied by means of the full-relativistic version of the full-potential augmented plane wave plus local orbitals method. For exchange and correlation potential we used both generalized-gradient approximation (GGA) and local-density approximation (LDA). Our calculated ground state properties like as lattice constants, bulk modulus and elastic constants appear more accurate when we employed the GGA rather than the LDA approximation, and these results agree very well with the available experimental and theoretical data. Further, prediction of the thermal effects on some macroscopic properties of Fe2VAl and Fe2VGa are given in this paper using the quasi-harmonic Debye model in which the lattice vibrations are taken into account. We have obtained successfully the variations of the primitive cell volume, volume expansion coefficient, heat capacities and Debye temperature with pressure and temperature in the ranges of 0–40 GPa and 0–1500 K.Keywords: full Heusler, FP-LAPW, electronic properties, thermal properties
Procedia PDF Downloads 4941771 Classroom Management Practices of Hotel, Restaurant, and Institution Management Instructors
Authors: Diana Ruth Caga-Anan
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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices
Procedia PDF Downloads 1721770 ANOVA-Based Feature Selection and Machine Learning System for IoT Anomaly Detection
Authors: Muhammad Ali
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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection
Procedia PDF Downloads 1261769 Relationship of Internal Communication Channels Effecting to Job Satisfaction of Company Employees: in Rayong Province
Authors: Nititorn Ounpipat
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The objective of this study was to find the relationship between internal communication and job satisfaction, and to find out the best communication channel to contact employees for a quality working within the operation or organizational rules. The sample size of 100% who were working as a shop floor level employee in the company. The study used the quantitative research method by distributing a structured questionnaire to collect data from 150 employees as the sample size. Inferential statistics and forward multiple regression analysis were used to analyze the results of this research. The result shows that communication channel correlated with job satisfaction. Each channel has a correlation with the satisfaction of working with the Department Board Information and All Employee / Weekly Meeting Relevance high. Since there is a correlation coefficient equal. 851 and. 840, respectively. Company Board Information, Memo, Letter, Leader, Supervisor, Friends and Email Relevance moderate as well.Keywords: internal communication channels, job satisfaction, personal feedback, Rayong province
Procedia PDF Downloads 2221768 Effect of Palm Oil Mill Effluent on Microbial Composition in Soil Samples in Isiala Mbano Lga
Authors: Eze Catherine Chinwe, J. D. Njoku
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Background: Palm oil mill effluent is the voluminous liquid waste that comes from the sterilization and clarification sections of the oil palm milling process. The raw effluent contains 90-95% water and includes residual oil, soil particles, and suspended solids. Palm oil mill effluent is a highly polluting material and much research has been dedicated to means of alleviating its threat to the environment. Objectives: 1. To compare Physico-chemical and microbiological analysis of soil samples from POME and non-POME sites. 2. To make recommendations on how best to handle POME in the study area. Methods: Quadrant approach was adopted for sampling POME (A) and Non POME (B) locations. Qualities were determined using standard analytical procedures. Conclusions: Results of the analysis were obtained in the following range; pH (3.940 –7.435), dissolved oxygen (DO) (1.582–6.234mg/l), biological oxygen demand (BOD) (50–5463mg/l etc. For the various locations, the population of total heterotrophic bacteria (THB) ranged from 1.36x106–2.42x106 cfu/ml, the total heterotrophic fungi (THF) ranged from 1.22–3.05 x 104 cfu/ml. The frequency of occurrence revealed the microbial isolates Pseudomonas sp., Bacillus sp., Staphylococcus, as the most frequently occurring isolates. Analysis of variance showed that there were significant differences (P<0.05) in microbial populations among locations. The discharge of industrial effluents into the soil in Nigeria invariably results in the presence of high concentrations of pollutant in the soil environment.Keywords: effluents, mirobial composition, soil samples, isiala mbano
Procedia PDF Downloads 3141767 The Optimization Process of Aortic Heart Valve Stent Geometry
Authors: Arkadiusz Mezyk, Wojciech Klein, Mariusz Pawlak, Jacek Gnilka
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The aortic heart valve stents should fulfill many criterions. These criteria have a strong impact on the geometrical shape of the stent. Usually, the final construction of stent is a result of many year experience and knowledge. Depending on patents claims, different stent shapes are produced by different companies. This causes difficulties for biomechanics engineers narrowing the domain of feasible solutions. The paper present optimization method for stent geometry defining by a specific analytical equation based on various mathematical functions. This formula was implemented as APDL script language in ANSYS finite element environment. For the purpose of simulation tests, a few parameters were separated from developed equation. The application of the genetic algorithms allows finding the best solution due to selected objective function. Obtained solution takes into account parameters such as radial force, compression ratio and coefficient of expansion on the transverse axial.Keywords: aortic stent, optimization process, geometry, finite element method
Procedia PDF Downloads 2841766 Modeling Sediment Yield Using the SWAT Model: A Case Study of Upper Ankara River Basin, Turkey
Authors: Umit Duru
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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.Keywords: calibration, GIS, sediment yield, SWAT, validation
Procedia PDF Downloads 2831765 Quantifying Multivariate Spatiotemporal Dynamics of Malaria Risk Using Graph-Based Optimization in Southern Ethiopia
Authors: Yonas Shuke Kitawa
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Background: Although malaria incidence has substantially fallen sharply over the past few years, the rate of decline varies by district, time, and malaria type. Despite this turn-down, malaria remains a major public health threat in various districts of Ethiopia. Consequently, the present study is aimed at developing a predictive model that helps to identify the spatio-temporal variation in malaria risk by multiple plasmodium species. Methods: We propose a multivariate spatio-temporal Bayesian model to obtain a more coherent picture of the temporally varying spatial variation in disease risk. The spatial autocorrelation in such a data set is typically modeled by a set of random effects that assign a conditional autoregressive prior distribution. However, the autocorrelation considered in such cases depends on a binary neighborhood matrix specified through the border-sharing rule. Over here, we propose a graph-based optimization algorithm for estimating the neighborhood matrix that merely represents the spatial correlation by exploring the areal units as the vertices of a graph and the neighbor relations as the series of edges. Furthermore, we used aggregated malaria count in southern Ethiopia from August 2013 to May 2019. Results: We recognized that precipitation, temperature, and humidity are positively associated with the malaria threat in the area. On the other hand, enhanced vegetation index, nighttime light (NTL), and distance from coastal areas are negatively associated. Moreover, nonlinear relationships were observed between malaria incidence and precipitation, temperature, and NTL. Additionally, lagged effects of temperature and humidity have a significant effect on malaria risk by either species. More elevated risk of P. falciparum was observed following the rainy season, and unstable transmission of P. vivax was observed in the area. Finally, P. vivax risks are less sensitive to environmental factors than those of P. falciparum. Conclusion: The improved inference was gained by employing the proposed approach in comparison to the commonly used border-sharing rule. Additionally, different covariates are identified, including delayed effects, and elevated risks of either of the cases were observed in districts found in the central and western regions. As malaria transmission operates in a spatially continuous manner, a spatially continuous model should be employed when it is computationally feasible.Keywords: disease mapping, MSTCAR, graph-based optimization algorithm, P. falciparum, P. vivax, waiting matrix
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