Search results for: academic speed and accuracy

Authors: Muhammad Alif Bin Razali, Cheng-Gang Xie, Wai Lam Loh

Abstract:

A venturi device is commonly used as an integral part of a multiphase flowmeter (MPFM) in real-time oil-gas production monitoring. For an accurate determination of individual phase fraction and flowrate, a gas-liquid flow ideally needs to be well mixed in the venturi measurement section. Partial flow mixing is achieved by installing a venturi vertically downstream of the blind-tee pipework that ‘homogenizes’ the incoming horizontal gas-liquid flow. In order to study in-depth the flow-mixing effect of the blind-tee, gas-liquid flows are captured at blind-tee and venturi sections by using a high-speed video camera and a purpose-built transparent test rig, over a wide range of superficial liquid velocities (0.3 to 2.4m/s) and gas volume fractions (10 to 95%). Electrical capacitance sensors are built to measure the instantaneous holdup (of oil-gas flows) at the venturi inlet and throat. Flow regimes and flow (a)symmetry are investigated based on analyzing the statistical features of capacitance sensors’ holdup time-series data and of the high-speed video time-stacked images. The perceived homogenization effect of the blind-tee on the incoming intermittent horizontal flow regimes is found to be relatively small across the tested flow conditions. A horizontal (blind-tee) to vertical (venturi) flow-pattern transition map is proposed based on gas and liquid mass fluxes (weighted by the Baker parameters).

Keywords: blind-tee, flow visualization, gas-liquid two-phase flow, MPFM

Procedia PDF Downloads 132

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

Abstract:

A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

Procedia PDF Downloads 391

Authors: Jinshou Shi, Xiaozhou Zhou, Yingwei Zhou, Tuoyang Zhou, Ning Li, Chi Zhang, Zhanshuo Zhang, Ziang Chen

Abstract:

In order to select the optimal interaction method for common computer click tasks, the click experiment test adopts the ISO 9241-9 task paradigm, using four common operations: mouse, trackball, touch, and eye control under visual feedback, auditory feedback, and no feedback. Through data analysis of various parameters of movement time, throughput, and accuracy, it is found that the movement time of touch-control is the shortest, the operation accuracy and throughput are higher than others, and the overall operation performance is the best. In addition, the motion time of the click operation with auditory feedback is significantly lower than the other two feedback methods in each operation mode experiment. In terms of the size of the click target, it is found that when the target is too small (less than 14px), the click performance of all aspects is reduced, so it is proposed that the design of the interface button should not be less than 28px. In this article, we discussed in detail the advantages and disadvantages of the operation and feedback methods, and the results of the discussion of the click operation can be applied to the design of the buttons in the interactive interface.

Keywords: cursor control performance, feedback, human computer interaction, throughput

Procedia PDF Downloads 200

Authors: Margarita Dufresne

Abstract:

This study devoted to development TPS for small space re-usable launchers. We have used SIRIUS design for S1 prototype. Multiphysics coupling for hypersonic reactive flow and thermos-structural analysis with and without ablation is provided by -CCM+ and COMSOL Multiphysics and FASTRAN and ACE+. Flow around hypersonic flight vehicles is the interaction of multiple shocks and the interaction of shocks with boundary layers. These interactions can have a very strong impact on the aeroheating experienced by the flight vehicle. A real gas implies the existence of a gas in equilibrium, non-equilibrium. Mach number ranged from 5 to 10 for first stage flight.The goals of this effort are to provide validation of the iterative coupling of hypersonic physics models in STAR-CCM+ and FASTRAN with COMSOL Multiphysics and ACE+. COMSOL Multiphysics and ACE+ are used for thermal structure analysis to simulate Conjugate Heat Transfer, with Conduction, Free Convection and Radiation to simulate Heat Flux from hypersonic flow. The reactive simulations involve an air chemical model of five species: N, N2, NO, O and O2. Seventeen chemical reactions, involving dissociation and recombination probabilities calculation include in the Dunn/Kang mechanism. Forward reaction rate coefficients based on a modified Arrhenius equation are computed for each reaction. The algorithms employed to solve the reactive equations used the second-order numerical scheme is obtained by a “MUSCL” (Monotone Upstream-cantered Schemes for Conservation Laws) extrapolation process in the structured case. Coupled inviscid flux: AUSM+ flux-vector splitting The MUSCL third-order scheme in STAR-CCM+ provides third-order spatial accuracy, except in the vicinity of strong shocks, where, due to limiting, the spatial accuracy is reduced to second-order and provides improved (i.e., reduced) dissipation compared to the second-order discretization scheme. initial unstructured mesh is refined made using this initial pressure gradient technique for the shock/shock interaction test case. The suggested by NASA turbulence models are the K-Omega SST with a1 = 0.355 and QCR (quadratic) as the constitutive option. Specified k and omega explicitly in initial conditions and in regions – k = 1E-6 *Uinf^2 and omega = 5*Uinf/ (mean aerodynamic chord or characteristic length). We put into practice modelling tips for hypersonic flow as automatic coupled solver, adaptative mesh refinement to capture and refine shock front, using advancing Layer Mesher and larger prism layer thickness to capture shock front on blunt surfaces. The temperature range from 300K to 30 000 K and pressure between 1e-4 and 100 atm. FASTRAN and ACE+ are coupled to provide high-fidelity solution for hot hypersonic reactive flow and Conjugate Heat Transfer. The results of both approaches meet the CIRCA wind tunnel results.

Keywords: hypersonic, first stage, high speed compressible flow, shock wave, aerodynamic heating, conugate heat transfer, conduction, free convection, radiation, fastran, ace+, comsol multiphysics, star-ccm+, thermal protection system (tps), space launcher, wind tunnel

Procedia PDF Downloads 76

Authors: Muteeb Ulhaq, Rizwan Latif, Sayed Adnan Qasim, Imran Shafi

Abstract:

Diesel engines are most efficient and reliable in terms of efficiency, reliability and adaptability. Most of the research and development up till now have been directed towards High-Speed Diesel Engine, for Commercial use. In these engines objective is to optimize maximum acceleration by reducing exhaust emission to meet international standards. In high torque low-speed engines the requirement is altogether different. These types of Engines are mostly used in Maritime Industry, Agriculture industry, Static Engines Compressors Engines etc. Unfortunately due to lack of research and development, these engines have low efficiency and high soot emissions and one of the most effective way to overcome these issues is by efficient combustion in an engine cylinder, the fuel spray atomization process plays a vital role in defining mixture formation, fuel consumption, combustion efficiency and soot emissions. Therefore, a comprehensive understanding of the fuel spray characteristics and atomization process is of a great importance. In this research, we will examine the effects of primary breakup modeling on the spray characteristics under diesel engine conditions. KH-ACT model is applied to cater the effect of aerodynamics in an engine cylinder and also cavitations and turbulence generated inside the injector. It is a modified form of most commonly used KH model, which considers only the aerodynamically induced breakup based on the Kelvin–Helmholtz instability. Our model is extensively evaluated by performing 3-D time-dependent simulations on Open FOAM, which is an open source flow solver. Spray characteristics like Spray Penetration, Liquid length, Spray cone angle and Souter mean diameter (SMD) were validated by comparing the results of Open Foam and Matlab. Including the effects of cavitation and turbulence enhances primary breakup, leading to smaller droplet sizes, decrease in liquid penetration, and increase in the radial dispersion of spray. All these properties favor early evaporation of fuel which enhances Engine efficiency.

Keywords: Kelvin–Helmholtz instability, open foam, primary breakup, souter mean diameter, turbulence

Procedia PDF Downloads 217

Authors: Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, Fahad Taha Al-Dhief, Mohammad Kamrul Hasan

Abstract:

Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification.

Keywords: breast cancer, machine learning, online sequential extreme learning machine, artificial intelligence

Procedia PDF Downloads 118

Authors: T. Heiman, D. Olenik-Shemesh

Abstract:

Over the past decade, with the rapid growth of electronic communication, the internet and, in particular, social networking has become an inseparable part of people's daily lives. Along with its benefits, a new type of online aggression has emerged, defined as cyber bullying, a form of interpersonal aggressive behavior that takes place through electronic means. Cyber-bullying is characterized by repetitive behavior over time of maladaptive authority and power usage using computers and cell phones via sending insulting messages and hurtful pictures. Preliminary findings suggest that the prevalence of involvement in cyber-bullying among higher education students varies between 10 and 35%. As to date, universities are facing an uphill effort in trying to restrain online misbehavior. As no studies examined the relationships between cyber-bullying involvement with personal aspects, and its impacts on academic achievement and work functioning, this present study examined the nature of cyber-bullying involvement among 1,052 undergraduate students (mean age = 27.25, S.D = 4.81; 66.2% female), coping with, as well as the effects of social support, perceived self-efficacy, well-being, and body-perception, in relation to cyber-victimization. We assume that students in higher education are a vulnerable population and at high risk of being cyber-victims. We hypothesize that social support might serve as a protective factor and will moderate the relationships between the socio-emotional variables and the occurrence of cyber- victimization. The findings of this study will present the relationships between cyber-victimization and the social-emotional aspects, which constitute risk and protective factors. After receiving approval from the Ethics Committee of the University, a Google Drive questionnaire was sent to a random sample of students, studying in the various University study centers. Students' participation was voluntary, and they completed the five questionnaires anonymously: Cyber-bullying, perceived self-efficacy, subjective well-being, social support and body perception. Results revealed that 11.6% of the students reported being cyber-victims during last year. Examining the emotional and behavioral reactions to cyber-victimization revealed that female emotional and behavioral reactions were significantly greater than the male reactions (p < .001). Moreover, females reported on a significant higher social support compared to men; male reported significantly on a lower social capability than female; and men's body perception was significantly more positive than women's scores. No gender differences were observed for subjective well-being scale. Significant positive correlations were found between cyber-victimization and fewer friends, lower grades, and work ineffectiveness (r = 0.37- .40, p < 0 .001). The results of the Hierarchical regression indicated significantly that cyber-victimization can be predicted by lower social support, lower body perception, and gender (female), that explained 5.6% of the variance (R2 = 0.056, F(5,1047) = 12.47, p < 0.001). The findings deepen our understanding of the students' involvement in cyber-bullying, and present the relationships of the social-emotional and academic aspects on cyber-victim students. In view of our findings, higher education policy could help facilitate coping with cyber-bullying incidents, and student support units could develop intervention programs aimed at reducing cyber-bullying and its impacts.

Keywords: academic and personal factors, cyber-victimization, social support, higher education

Procedia PDF Downloads 293

Authors: Shriya Shukla, Lachin Fernando

Abstract:

Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

Procedia PDF Downloads 136

Authors: Nguyen-Thanh Son

Abstract:

Flood is among the most frequent and costliest natural hazards. The flood disasters especially affect the poor people in rural areas, who are heavily dependent on agriculture and have lower incomes. Cambodia is identified as one of the most climate-vulnerable countries in the world, ranked 13th out of 181 countries most affected by the impacts of climate change. Flood monitoring is thus a strategic priority at national and regional levels because policymakers need reliable spatial and temporal information on flood-prone areas to form successful monitoring programs to reduce possible impacts on the country’s economy and people’s likelihood. This study aims to develop methods for flood mapping and assessment from MODIS data in Cambodia. We processed the data for the period from 2000 to 2017, following three main steps: (1) data pre-processing to construct smooth time-series vegetation and water surface indices, (2) delineation of flood-prone areas, and (3) accuracy assessment. The results of flood mapping were verified with the ground reference data, indicating the overall accuracy of 88.7% and a Kappa coefficient of 0.77, respectively. These results were reaffirmed by close agreement between the flood-mapping area and ground reference data, with the correlation coefficient of determination (R²) of 0.94. The seasonally flooded areas observed for 2010, 2015, and 2016 were remarkably smaller than other years, mainly attributed to the El Niño weather phenomenon exacerbated by impacts of climate change. Eventually, although several sources potentially lowered the mapping accuracy of flood-prone areas, including image cloud contamination, mixed-pixel issues, and low-resolution bias between the mapping results and ground reference data, our methods indicated the satisfactory results for delineating spatiotemporal evolutions of floods. The results in the form of quantitative information on spatiotemporal flood distributions could be beneficial to policymakers in evaluating their management strategies for mitigating the negative effects of floods on agriculture and people’s likelihood in the country.

Keywords: MODIS, flood, mapping, Cambodia

Procedia PDF Downloads 132

Authors: Zhenming Wang, Jun Zhu, Yuchen Yang, Ning Zhao

Abstract:

In the present paper, an alternative framework is proposed to construct a class of finite difference multi-resolution nested weighted essentially non-oscillatory (WENO) schemes with an increasingly higher order of accuracy for solving inviscid Euler equations. These WENO schemes firstly obtain a set of reconstruction polynomials by a hierarchy of nested central spatial stencils, and then recursively achieve a higher order approximation through the lower-order precision WENO schemes. The linear weights of such WENO schemes can be set as any positive numbers with a requirement that their sum equals one and they will not pollute the optimal order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near discontinuities. Numerical results obtained indicate that these alternative finite-difference multi-resolution nested WENO schemes with different accuracies are very robust with low dissipation and use as few reconstruction stencils as possible while maintaining the same efficiency, achieving the high-resolution property without any equivalent multi-resolution representation. Besides, its finite volume form is easier to implement in unstructured grids.

Keywords: finite-difference, WENO schemes, high order, inviscid Euler equations, multi-resolution

Procedia PDF Downloads 151

Authors: Chaca Nugraha Zaid, Hikmat Ronaldo, Emerald Falah Brayoga, Azizah Eddy Setiawati, Soviandini Dwiki Kartika Putri, Novita Wijayanti

Abstract:

In the tofu stick production, the manual cutting process takes a half of working day or 4 hours for 21 kg of tofu. This issue has hampered the small and medium enterprises (SMEs) to increase the capacity of production to fulfill the market demand. In order to address the issue, the cutting process should be automized to create fast, efficient, and effective tools. This innovation to tackle this problem is an automatic cutter tool that is able to move continuously to cut the tofu into stick size. The tool uses the 78,5-watt electric motor and automatic sensors to drive the cutting tool automatically, resulting faster process time with more uniform size compared to the manual cutter. The component of this tool, i.e., cutting knife and the driver, electric motor, limit switch sensors, riley, Arduino nano, and power supply. The cutting speed cutting speed of this tool is 101,25 mm/s producing 64 tofu sticks. Benefits that can be obtained from the use of automatic tofu stick cutter, i.e. (1) Faster process (2) More uniform cutting result; (3) The quality of the tofu stick is maintained due to minimal contact with humans so that contamination can be suppressed; (4) The cutting knife can be modified to the desired size of the owner.

Keywords: automatic, cutter, small and medium enterprise, tofu stick

Procedia PDF Downloads 173

Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

Abstract:

The utilization of Large Eddy Simulation (LES) has been extensive in turbulence research. LES concentrates on resolving the significant grid-scale motions while representing smaller scales through subfilter-scale (SFS) models. The deconvolution model, among the available SFS models, has proven successful in LES of engineering and geophysical flows. Nevertheless, the thorough investigation of how sub-filter scale dynamics and filter anisotropy affect SFS modeling accuracy remains lacking. The outcomes of LES are significantly influenced by filter selection and grid anisotropy, factors that have not been adequately addressed in earlier studies. This study examines two crucial aspects of LES: Firstly, the accuracy of direct deconvolution models (DDM) is evaluated concerning sub-filter scale (SFS) dynamics across varying filter-to-grid ratios (FGR) in isotropic turbulence. Various invertible filters are employed, including Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The importance of FGR becomes evident as it plays a critical role in controlling errors for precise SFS stress prediction. When FGR is set to 1, the DDM models struggle to faithfully reconstruct SFS stress due to inadequate resolution of SFS dynamics. Notably, prediction accuracy improves when FGR is set to 2, leading to accurate reconstruction of SFS stress, except for cases involving Helmholtz I and II filters. Remarkably high precision, nearly 100%, is achieved at an FGR of 4 for all DDM models. Furthermore, the study extends to filter anisotropy and its impact on SFS dynamics and LES accuracy. By utilizing the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with anisotropic filters, aspect ratios (AR) ranging from 1 to 16 are examined in LES filters. The results emphasize the DDM’s proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. Notably high correlation coefficients exceeding 90% are observed in the a priori study for the DDM’s reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as filter anisotropy increases. In the a posteriori analysis, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, including velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strainrate tensors, and SFS stress. It is evident that as filter anisotropy intensifies, the results of DSM and DMM deteriorate, while the DDM consistently delivers satisfactory outcomes across all filter-anisotropy scenarios. These findings underscore the potential of the DDM framework as a valuable tool for advancing the development of sophisticated SFS models for LES in turbulence research.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

Procedia PDF Downloads 79

Authors: Derya Duran, Christine Jacknick

Abstract:

Despite a growing body of research on classroom, especially language classroom interactions, much more is yet to be discovered on how interaction is organized in higher education settings. This study investigates how the discourse marker 'I mean' in teacher questioning turns functions as a resource to promote student participation as well as to enhance collective understanding in whole-class discussions. This paper takes a conversation analytic perspective, drawing on 30-hour video recordings of classroom interaction in an English as a medium of instruction university in Turkey. Two content classrooms (i.e., Guidance) were observed during an academic term. The course was offered to 4th year students (n=78) in the Faculty of Education; students were majoring in different subjects (i.e., Early Childhood Education, Foreign Language Education, Mathematics Education). Results of the study demonstrate the multi-functionality of discourse marker 'I mean' in teacher questioning turns. In the context of English as a medium of instruction classrooms where possible sources of confusion may occur, we found that 'I mean' is primarily used to indicate upcoming adjustments. More specifically, it is employed for a variety of interactional purposes such as elaboration, clarification, specification, reformulation, and reference to the instructional activity. The study sheds light on the multiplicity of functions of the discourse marker in academic interactions and it uncovers how certain linguistic resources serve functions to the organization of repair such as the maintenance of understanding in classroom interaction. In doing so, it also shows the ways in which participation is routinely enacted in shared interactional events through linguistic resources.

Keywords: conversation analysis, discourse marker, English as a medium of instruction, repair

Procedia PDF Downloads 164

Authors: Peshawa Jalal Mohammed

Abstract:

Following the recent political conflict in the Middle East, the number of refugees and internally displaced people increased in the last decades. The flood of displaced people became a big issue for the host communities in the neighbouring countries and Europe. The need for research about the education and integration of the refugees became urgent. After the appearance of the Islamic State and displacing millions of Syrian people, the Kurdistan Region of Iraq became a safe shelter for hundreds of thousands of Syrians and international organisations helping the refugees. This study focuses on the factors of parental involvement among the host community and refugee parents and its role in the academic success of children. The setting is the three provinces of Iraqi Kurdistan (Erbil, Sulaimani, and Dohuk), including the refugee camps in the three provinces. Based on the purpose of the study, the study was designed as a descriptive survey study with a mixed approach, qualitative (open-ended), and quantitative (questionnaire) questions and both forms of data were integrated and analysed. The current study participants were 8th and 9th graders at the basic school level, studying at public schools and their parents. The sampling design was the selection of local schools and schools in the refugee camps in the region's three provinces. The number of participants for each of the two groups was 250 students and 250 parents. The results showed that parents' socioeconomic status, gender, and place of residency have significant roles in students' parental involvement and academic success of their students. The results also show the characteristics of parental inspiration to their children's future and their expectations from education.

Keywords: refugee, education, parental involvement, socioeconomic

Procedia PDF Downloads 197

Authors: Seyyed Javad Ziaolhagh

Abstract:

Background: The purpose of present study was to investigate, the effects of anabolic steroid Boldenone (BOL) with eight weeks of resistance training on structural changes in rat liver. Method: 21 Male adult Wistar rats, 12 weeks old and 228/53±7/94 g initial body weight were randomly assigned to three groups: group1: Control+ Placebo (C), group2: training+ Placebo (T), group3: Boldenone intramuscular injections 5mg/kg (B). The endurance training protocol consisted three exercise sessions weekly started by a 30-minute run with the speed of 12 m/min and lasted by 60min run with the speed of 30 m/min in 8 weeks. At the end of the experiment, for light microscopic study Slides were prepared. Results: Sections stained of rat's livers showed no any cell degeneration and cytoplasmic lipid vacuoles in all groups, but few samples were seen. Indeed, congested blood sinusoids, cell infiltration and degeneration were seen in the Boldenone-treated group. Hepatotoxic effects were severe in group treatment received 5 mg/kg and directly depended on the doses. Indeed, training group was no any hepatocyte degeneration, inflammation and congestion. Conclusion: The present results showed that BOL has a marked adverse effect on the liver tissue, even with low– dose and endurance training. As a result, athletes should aware of Boldenone dosage consumption.

Keywords: anabolic androgenic steroids, Boldenone, blood congestion, cellular inflammation, cellular degeneration, lipid vocuolations, endurance training

Procedia PDF Downloads 432

Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

Abstract:

The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

Procedia PDF Downloads 105

Authors: Endalew Fufa Kufi

Abstract:

The research investigated the role of stakeholders such as students, teachers and administrators in the practices of good governance in higher education by looking into the special contributions of top-officials, teachers and students in ensuring workable ties and productive interchanges in Adama Science and Technology University. Attention was given to participation, fairness and exemplariness as key indicators of good governance. The target university was chosen for its familiarity for the researcher to get dependable data, access to respondent and management of the processing of data. Descriptive survey design was used for the purpose of describing concerned roles the stakeholders in the university governance in order to reflect on the nature of participation of the practices. Centres of the research were administration where supportive groups such as central administrators and underlying service-givers had parts and academia where teachers and students were target. Generally, 60 teachers, 40 students and 15 administrative officers were referents. Data were collected in the form of self-report through open-ended questionnaires. The findings indicated that, while vertical interchanges in terms of academic and administrative routines were had normal flow on top-down basis, planned practices of stakeholders in decision-making and reasonably communicating roles and changes in decisions with top-officials were not efficiently practiced. Moreover, the practices of good modelling were not witnessed to have existed to the fullest extent. Rather, existence of a very wide gap between the academic and administrative staffs was witnessed as was reflected the case between teachers and students. The implication was such that for shortage in participative atmosphere and weaning of fairness in governance, routine practices have been there as the vicious circles of governance.

Keywords: governance, participative, stakeholders, transformative, role-assumption

Procedia PDF Downloads 402

Authors: Vasantha Gouri Prathapagiri

Abstract:

Innovations in ICT sector facilitate qualitative life style for citizens across the globe. Countries that facilitate usage of new techniques in ICT, i.e., big data analytics find it easier to fulfil the needs of their citizens. Big data is characterised by its volume, variety, and speed. Analytics involves its processing in a cost effective way in order to draw conclusion for their useful application. Big data also involves into the field of machine learning, artificial intelligence all leading to accuracy in data presentation useful for public policy making. Hence using data analytics in public policy making is a proper way to march towards all round development of any country. The data driven insights can help the government to take important strategic decisions with regard to socio-economic development of her country. Developed nations like UK and USA are already far ahead on the path of digitization with the support of Big Data analytics. India is a huge country and is currently on the path of massive digitization being realised through Digital India Mission. Internet connection per household is on the rise every year. This transforms into a massive data set that has the potential to improvise the public services delivery system into an effective service mechanism for Indian citizens. In fact, when compared to developed nations, this capacity is being underutilized in India. This is particularly true for administrative system in rural areas. The present paper focuses on the need for big data analytics adaptation in Indian rural administration and its contribution towards development of the country on a faster pace. Results of the research focussed on the need for increasing awareness and serious capacity building of the government personnel working for rural development with regard to big data analytics and its utility for development of the country. Multiple public policies are framed and implemented for rural development yet the results are not as effective as they should be. Big data has a major role to play in this context as can assist in improving both policy making and implementation aiming at all round development of the country.

Keywords: Digital India Mission, public service delivery system, public policy, Indian administration

Procedia PDF Downloads 163

Authors: Ziv Kassner, Gal Ribak

Abstract:

Members of the Odonata order (dragonflies and damselflies) stand out for their maneuverability and superb flight control, which allow them to catch flying prey in the air. These outstanding aerial abilities were fine-tuned during millions of years of an evolutionary arms race between Odonata and their prey, providing an attractive research model for studying the relationship between sensory input – and aerodynamic output in a flying insect. The ability to catch a maneuvering target in air is interesting not just for insect behavioral ecology and neuroethology but also for designing small and efficient robotic air vehicles. While the aerial prey interception of dragonflies (suborder: Anisoptera) have been studied before, little is known about how damselflies (suborder: Zygoptera) intercept prey. Here, high-speed cameras (filming at 1000 frames per second) were used to explore how damselflies catch unpredictable targets that move through air. Blue-tailed damselflies - Ischnura elegans (family: Coenagrionidae) were introduced to a flight arena and filmed while landing on moving targets that were oscillated harmonically. The insects succeeded in capturing targets that were moved with an amplitude of 6 cm and frequencies of 0-2.5 Hz (fastest mean target speed of 0.3 m s⁻¹) and targets that were moved in 1 Hz (an average speed of 0.3 m s⁻¹) but with an amplitude of 15 cm. To land on stationary or slow targets, damselflies either flew directly to the target, or flew sideways, up to a point in which the target was fixed in the center of the field of view, followed by direct flight path towards the target. As the target moved in increased frequency, damselflies demonstrated an ability to track the targets while flying sideways and minimizing the changes of their body direction on the yaw axis. This was likely an attempt to keep the targets at the center of the visual field while minimizing rotational optic flow of the surrounding visual panorama. Stabilizing rotational optic flow helps in estimation of the velocity and distance of the target. These results illustrate how dynamic visual information is used by damselflies to guide them towards a maneuvering target, enabling the superb aerial hunting abilities of these insects. They also exemplifies the plasticity of the damselfly flight apparatus which enables flight in any direction, irrespective of the direction of the body.

Keywords: bio-mechanics, insect flight, target fixation, tracking and interception

Procedia PDF Downloads 161

Authors: Suraj Mehrotra

Abstract:

The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

Procedia PDF Downloads 101

Authors: S. Jurado, E. Pazmino

Abstract:

Determination of the medial axis of a porous media sample is a non-trivial problem of interest for several disciplines, e.g., hydrology, fluid dynamics, contaminant transport, filtration, oil extraction, etc. However, the computational tools available for researchers are limited and restricted. The primary aim of this work was to develop a series of algorithms to extract porosity, medial axis structure, and pore-throat size distributions from porous media domains. A complementary objective was to provide the algorithms as free computational software available to the academic community comprising researchers and students interested in 3D data processing. The burn algorithm was tested on porous media data obtained from High-Resolution X-Ray Microtomography (HRXMT) and idealized computer-generated domains. The real data and idealized domains were discretized in voxels domains of 550³ elements and binarized to denote solid and void regions to determine porosity. Subsequently, the algorithm identifies the layer of void voxels next to the solid boundaries. An iterative process removes or 'burns' void voxels in sequence of layer by layer until all the void space is characterized. Multiples strategies were tested to optimize the execution time and use of computer memory, i.e., segmentation of the overall domain in subdomains, vectorization of operations, and extraction of single burn layer data during the iterative process. The medial axis determination was conducted identifying regions where burnt layers collide. The final medial axis structure was refined to avoid concave-grain effects and utilized to determine the pore throat size distribution. A graphic user interface software was developed to encompass all these algorithms, including the generation of idealized porous media domains. The software allows input of HRXMT data to calculate porosity, medial axis, and pore-throat size distribution and provide output in tabular and graphical formats. Preliminary tests of the software developed during this study achieved medial axis, pore-throat size distribution and porosity determination of 100³, 320³ and 550³ voxel porous media domains in 2, 22, and 45 minutes, respectively in a personal computer (Intel i7 processor, 16Gb RAM). These results indicate that the software is a practical and accessible tool in postprocessing HRXMT data for the academic community.

Keywords: medial axis, pore-throat distribution, porosity, porous media

Procedia PDF Downloads 120

Authors: Mohammad R. Aghakashkooli, Milutin G. Jovanovic

Abstract:

The brushless doubly-fed reluctance generator (BDFRG) is an emerging, medium-speed alternative to a conventional wound rotor slip-ring doubly-fed induction generator (DFIG) in wind energy conversion systems (WECS). It can provide competitive overall performance and similar low failure rates of a typically 30% rated back-to-back power electronics converter in 2:1 speed ranges but with the following important reliability and cost advantages over DFIG: the maintenance-free operation afforded by its brushless structure, 50% synchronous speed with the same number of rotor poles (allowing the use of a more compact, and more efficient two-stage gearbox instead of a vulnerable three-stage one), and superior grid integration properties including simpler protection for the low voltage ride through compliance of the fractional converter due to the comparatively higher leakage inductances and lower fault currents. Vector controlled pulse-width-modulated converters generally feature a much lower total harmonic distortion relative to hysteresis counterparts with variable switching rates and as such have been a predominant choice for BDFRG (and DFIG) wind turbines. Eliminating a shaft position sensor, which is often required for control implementation in this case, would be desirable to address the associated reliability issues. This fact has largely motivated the recent growing research of sensorless methods and developments of various rotor position and/or speed estimation techniques for this purpose. The main limitation of all the observer-based control approaches for grid-connected wind power applications of the BDFRG reported in the open literature is the requirement for pre-commissioning procedures and prior knowledge of the machine inductances, which are usually difficult to accurately identify by off-line testing. A model reference adaptive system (MRAS) based sensor-less vector control scheme to be presented will overcome this shortcoming. The true machine parameter independence of the proposed field-oriented algorithm, offering robust, inherently decoupled real and reactive power control of the grid-connected winding, is achieved by on-line estimation of the inductance ratio, the underlying rotor angular velocity and position MRAS observer being reliant upon. Such an observer configuration will be more practical to implement and clearly preferable to the existing machine parameter dependent solutions, and especially bearing in mind that with very little modifications it can be adapted for commercial DFIGs with immediately obvious further industrial benefits and prospects of this work. The excellent encoder-less controller performance with maximum power point tracking in the base speed region will be demonstrated by realistic simulation studies using large-scale BDFRG design data and verified by experimental results on a small laboratory prototype of the WECS emulation facility.

Keywords: brushless doubly fed reluctance generator, model reference adaptive system, sensorless vector control, wind energy conversion

Procedia PDF Downloads 64

Authors: Vijay Reena Durai, Krithica Srinivasan

Abstract:

Introduction: The two fundamental skills involved in the growth and wellbeing of any child can be categorized into visual motor and perceptual skills. Visual stress is a disorder which is characterized by visual discomfort, blurred vision, misspelling words, skipping lines, letters bunching together. There is a need to understand the deficits in perceptual skills among children with visual stress. Aim: To estimate the association between visual stress and visual perceptual skills Objective: To compare visual perceptual skills of children with and without visual stress Methodology: Children between 8 to 15 years of age participated in this cross-sectional study. All children with monocular visual acuity better than or equal to 6/6 were included. Visual perceptual skills were measured using test for visual perceptual skills (TVPS) tool. Reading speed was measured with the chosen colored overlay using Wilkins reading chart and pattern glare score was estimated using a 3cpd gratings. Visual stress was defined as change in reading speed of greater than or equal to 10% and a pattern glare score of greater than or equal to 4. Results: 252 children participated in this study and the male: female ratio of 3:2. Majority of the children preferred Magenta (28%) and Yellow (25%) colored overlay for reading. There was a significant difference between the two groups (MD=1.24±0.6) (p<0.04, 95% CI 0.01-2.43) only in the sequential memory skills. The prevalence of visual stress in this group was found to be 31% (n=78). Binary logistic regression showed that odds ratio of having poor visual perceptual skills was OR: 2.85 (95% CI 1.08-7.49) among children with visual stress. Conclusion: Children with visual stress are found to have three times poorer visual perceptual skills than children without visual stress.

Keywords: visual stress, visual perceptual skills, colored overlay, pattern glare

Procedia PDF Downloads 390

Authors: A. Khalina, K. Shaharuddin, M. S. Wahab, M. P. Saiman, H. A. Aisyah

Abstract:

This paper investigates the effect of alkali treatment and mechanical properties of kenaf (Hibiscus cannabinus) fibre for the development of yarn. Two different fibre sources are used for the yarn production. Kenaf fibres were treated with sodium hydroxide (NaOH) in the concentration of 3, 6, 9, and 12% prior to fibre opening process and tested for their tensile strength and Young’s modulus. Then, the selected fibres were introduced to fibre opener at three different opening processing parameters; namely, speed of roller feeder, small drum, and big drum. The diameter size, surface morphology, and fibre durability towards machine of the fibres were characterized. The results show that concentrations of NaOH used have greater effects on fibre mechanical properties. From this study, the tensile and modulus properties of the treated fibres for both types have improved significantly as compared to untreated fibres, especially at the optimum level of 6% NaOH. It is also interesting to highlight that 6% NaOH is the optimum concentration for the alkaline treatment. The untreated and treated fibres at 6% NaOH were then introduced to fibre opener, and it was found that the treated fibre produced higher fibre diameter with better surface morphology compared to the untreated fibre. Higher speed parameter during opening was found to produce higher yield of opened-kenaf fibres.

Keywords: alkaline treatment, kenaf fibre, tensile strength, yarn production

Procedia PDF Downloads 254

Authors: Ashok Pandey, Rajan Adhikari

Abstract:

Introduction: Menstruation is part of the female reproductive cycle that starts when girls become sexually mature at the time of puberty. It is a phenomenon unique to the females. During a menstrual period, a woman bleeds from her uterus via the vagina. For decades, in many countries, academic school ‘type,’ private or public, as a predictor of or factor in future academic success has been researched and debated. MATERIAL AND METHODS: The comparative study was carried out with adolescent girls studying in both public and private schools of Kathmandu valley. A total of 100 girls participated in the survey, and out of them 21 participated in the FGD and 5 in the in- depth interview. Quantitative data from the survey was analyzed using SPSS 16.0 software. Informed verbal consent with the respective head of school and the respondents were taken before data collection. Results:The age of the respondents ranges from 11 to 18 years, with mean age of menarche being 12.37 years in both school adolescent girls. 70 percent of the public school adolescent girls and 72 percent of the private school adolescent girls are feeling upset and tension during menarche. There is a statistically significant difference on take rest during the period and good hygienic practice during menstruation of public/private school, at α=0. 05 level of significance. There is a statistically significant difference on overall score of practice during menstruation between public and private adolescent girls. Conclusion: Private schools children are more knowledgeable and maintain hygiene as compere to public school even though, it can be said that among the adolescent school girls both in public and private school, menstrual knowledge and perceptions are poor and practices often not optimal for proper hygiene. Often ignored issues of privacy affect the hygienic practices and daily lives.

Keywords: Comparison, Menstruation, Private school, Public School

Procedia PDF Downloads 449

Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

Abstract:

Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

Procedia PDF Downloads 211

Authors: Mayank Mundhra, Chester Rebeiro

Abstract:

Cryptocurrencies are rapidly finding wide application in areas such as Real Time Gross Settlements and Payments Systems. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General’s Problem with its Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called Unique Node List (UNL) that represents the network for the server, and will not collectively defraud it. The server believes that the network has come to a consensus when members of the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security, last mile connectivity and ease of use. We implement guidelines and automated systems for building and maintaining UNLs for resilience, robustness, improved security, and efficient information propagation. We enhance the system so as to ensure that each server receives information from across the whole network rather than just from the UNL members. We also introduce the paradigm of UNL overlap as a function of information propagation and the trust a server assigns to its own UNL. Our design not only reduces vulnerabilities such as eclipse attacks, but also makes it easier to identify malicious behaviour and entities attempting to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe ≥ 4.97x and 98.22x in speedup and success rate for information propagation respectively, and ≥ 3.16x and 51.70x in speedup and success rate in consensus.

Keywords: Ripple, Kelips, unique node list, consensus, information propagation

Procedia PDF Downloads 153

Authors: Waleed Mandour

Abstract:

This study presents multidimensional scrutiny of Benson et al.’s seven-category taxonomy of lexical collocations used by Egyptian medical authors and their peers of native-English speakers. It investigates 212 medical papers, all published during a span of 6 years (from 2013 to 2018). The comparison is held to the medical research articles submitted by native speakers of English (25,238 articles in total with over 103 million words) as derived from the Directory of Open Access Journals (a 2.7 billion-word corpus). The non-native speakers compiled corpus was properly annotated and marked-up manually by the researcher according to the standards of Weisser. In terms of statistical comparisons, though, deployed were the conventional frequency-based analysis besides the relevant criteria, such as association measures (AMs) in which LogDice is deployed as per the recommendation of Kilgariff et al. when comparing large corpora. Despite the terminological convergence in the subject corpora, comparison results confirm the previous literature of which the non-native speakers’ compositions reveal limited ranges of lexical collocations in terms of their distribution. However, there is a ubiquitous tendency of overusing the NS-high-frequency multi-words in all lexical categories investigated. Furthermore, Egyptian authors, conversely to their English-speaking peers, tend to embrace more collocations denoting quantitative rather than qualitative analyses in their produced papers. This empirical work, per se, contributes to the English for Academic Purposes (EAP) and English as a Lingua Franca in Academic settings (ELFA). In addition, there are pedagogical implications that would promote a better quality of medical research papers published in Egyptian universities.

Keywords: corpus linguistics, EAP, ELFA, lexical collocations, medical discourse

Procedia PDF Downloads 134

Authors: Milica Lilic, Marina Rosales Martínez

Abstract:

Academic research cannot be oblivious to social problems and needs, so projects that have the capacity for transformation and impact should have the opportunity to go beyond the University circles and bring benefit to society. Apart from patents and R&D research contracts, this opportunity can be achieved through entrepreneurship as one of the most direct tools to turn knowledge into a tangible product. Thus, as an example of good practices, it is intended to analyze the case of an institutional entrepreneurship program carried out at the University of Seville, aimed at researchers interested in assessing the business opportunity of their research and expanding their knowledge on procedures for the commercialization of technologies used at academic projects. The program is based on three pillars: training, teamwork sessions and networking. The training includes aspects such as product-client fit, technical-scientific and economic-financial feasibility of a spin-off, institutional organization and decision making, public and private fundraising, and making the spin-off visible in the business world (social networks, key contacts, corporate image and ethical principles). On the other hand, the teamwork sessions are guided by a mentor and aimed at identifying research results with potential, clarifying financial needs and procedures to obtain the necessary resources for the consolidation of the spin-off. This part of the program is considered to be crucial in order for the participants to convert their academic findings into a business model. Finally, the networking part is oriented to workshops about the digital transformation of a project, the accurate communication of the product or service a spin-off offers to society and the development of transferable skills necessary for managing a business. This blended program results in the final stage where each team, through an elevator pitch format, presents their research turned into a business model to an experienced jury. The awarded teams get a starting capital for their enterprise and enjoy the opportunity of formally consolidating their spin-off company at the University. Studying the results of the program, it has been shown that many researchers have basic or no knowledge of entrepreneurship skills and different ways to turn their research results into a business model with a direct impact on society. Therefore, the described program has been used as an example to highlight the importance of knowledge transfer at the University and the role that this institution should have in providing the tools to promote entrepreneurship within it. Keeping in mind that the University is defined by three main activities (teaching, research and knowledge transfer), it is safe to conclude that the latter, and the entrepreneurship as an expression of it, is crucial in order for the other two to comply with their purpose.

Keywords: good practice, knowledge transfer, a spin-off company, university

Procedia PDF Downloads 151

Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

Abstract:

The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

Procedia PDF Downloads 141