Search results for: structured and structured training program

Authors: Dipanwita Chakravortty

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Officially, the Israel Defence Forces (IDF) follows a policy of 'gender equality and partnership' which institutionalises norms regarding equal duty towards the nation. It reiterates the equality in unbiased opportunities and resources for Jewish men and women to participate in the military as equal citizens. At the same time, as a military institution, the IDF supports gender biases and crystallises the same through various interactions among women soldiers, male soldiers and the institution. These biases are expressed through various stages and processes in the military institution like biased training, discriminatory postings of women soldiers, lack of combat training and acceptance of sexual harassment. The gender-military debates in Israel is largely devoted to female emancipation and converting the militarised women’s experiences into mainstream debates. This critical scholarship, largely female-based and located in Israel, has been consistently critical of the structural policies of the IDF that have led to continued discriminatory practices against women soldiers. This has compelled the military to increase its intake of women soldiers and make its structural policies more gender-friendly. Nonetheless, the continued thriving of gender discrimination in the IDF resulted in scholars looking deep into the failure of these policies in bringing about a change. This article looks into two research objectives, firstly to analyse existing gender relations in the IDF which impact the practices and prejudices in the institution and secondly to look beyond the structural discrimination as part of the gender debates in the IDF. The proposed research uses the structural-functional model as a framework to study the discourses and norms emerging out of the interaction between gender and military as two distinct social institutions. Changing gender-military debates will be discussed in great detail to understanding the in-depth relation between the Israeli society and the military due to the conscription model. The main arguments of the paper deal with the functional aspect of the military service rather than the structural component of the institution. Traditional stereotypes of military institutions along with cultural notions of a female body restrict the complete integration of women soldiers despite favourable legislations and policies. These result in functional discriminations like uneven promotion, sexual violence, restructuring gender identities and creating militarised bodies. The existing prejudices encourage younger women recruits to choose from within the accepted pink-collared jobs in the military rather than ‘breaking the barriers.’ Some women recruits do try to explore new avenues and make a mark for themselves. Most of them face stiff discrimination but they accept it as part of military life. The cyclical logic behind structural norms leading to functional discrimination which then emphasises traditional stereotypes and hampers change in the institutional norms compels the IDF to continue to strive towards gender equality within the institution without practical realisation.

Keywords: women soldiers, Israel Defence Forces, gender-military debates, security studies

Procedia PDF Downloads 157

Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

Procedia PDF Downloads 283

Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

Procedia PDF Downloads 140

Authors: M. E. Abou-Hashem El Dib, M. K. Swailem, M. M. Metwally, A. I. El Awady

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This paper presents the effect of stiffeners on the behavior of slender steel I-beams. Nonlinear three dimensional finite element models are developed to represent the stiffened steel I-beams. The well established finite element (ANSYS 13.0) program is used to simulate the geometric and material nonlinear nature of the problem. Verification is achieved by comparing the obtained numerical results with the results of previous published experimental work. The parameters considered in the analysis are the horizontal stiffener's position and the horizontal stiffener's dimensions as well as the number of vertical stiffeners. The studied dimensions of the horizontal stiffeners include the stiffener width, the stiffener thickness and the stiffener length. The results of the achieved numerical parametric study for slender steel I-beams show the significant effect of stiffeners on the beam behavior and its failure load.

Keywords: beams, local buckling, slender, stiffener, thin walled section

Procedia PDF Downloads 272

Authors: Dragos Ardeleanu, Silviu-Valentin Petre

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Written to satisfy the needs of Western powers, classical geopolitics bore the stint of Eurocentrism. Both Mackinder’s heartland and Nicholas Spykman’s rimland were intellectual creations set for the purpose of the Anglophone nations dealing with Eurasia. However, while today’s world is moving towards multipolarity, other emerging regional actors are following their own interests using a different geospatial map. Such is the case of Iran which has developed an engagement pattern in Africa, directed mostly towards costal states, in order to break the rimland grip of Arab states and also the international pressure established against Tehran’s nascent nuclear program. Capitalizing on literature review and analysing statements from key public figures, our paper argues that Iranian African geopolitics displays a dual message: on the one hand, it uses tiers-mondiste rhetoric to garner the support of different coastal African states and, on the other hand, it employs Shiism to gain a foothold in strategic parts of the black continent.

Keywords: African geopolitics, Iran, Shiism, tiers-mondisme

Procedia PDF Downloads 199

Authors: C. Patrascioiu, M. Jamali

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The paper deals with the simulation of the crude distillation process using the Unisim Design simulator. The necessity of simulating this process is argued both by considerations related to the design of the crude distillation column, but also by considerations related to the design of advanced control systems. In order to use the Unisim Design simulator to simulate the crude distillation process, the identification of the simulators used in Romania and an analysis of the PRO/II, HYSYS, and Aspen HYSYS simulators were carried out. Analysis of the simulators for the crude distillation process has allowed the authors to elaborate the conclusions of the success of the crude modelling. A first aspect developed by the authors is the implementation of specific problems of petroleum liquid-vapors equilibrium using Unisim Design simulator. The second major element of the article is the development of the methodology and the elaboration of the simulation program for the crude distillation process, using Unisim Design resources. The obtained results validate the proposed methodology and will allow dynamic simulation of the process.  

Keywords: crude oil, distillation, simulation, Unisim Design, simulators

Procedia PDF Downloads 237

Authors: A. Arabzadeh, H. R. Kazemi Nia Korrani

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Composite steel plate shear wall is a lateral loading resistance system, which is used especially in tall buildings. This wall is made of a thin steel plate with reinforced a concrete cover, which is attached to one or both sides of the steel plate. This system is similar to stiffened steel plate shear wall, in which reinforced concrete replaces the steel stiffeners. Composite shear wall have in-plane and out-plane significant strength. Also, they have appropriate ductility. The present numerical investigations were focused on the effects of opening on wall mode shapes. In addition, frequencies of composite shear wall with and without opening are compared. For analyzing composite shear wall, a new program will be developed using of finite element theory and the effects of shape, size and position openings on the behavior of composite shear wall will be studied. Results indicated that the existence of opening decreases wall frequency.

Keywords: composite shear wall, opening, finite element method, modal analysis

Procedia PDF Downloads 522

Authors: Ignacio Sanchez Cohen, Aurelio Pedroza Sandoval, Ricardo Trejo Calzada

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Water harvesting systems (WHS) has become the unique alternative that farmers in dry areas accounts for surviving dry periods. Nevertheless, technicians, agronomists, and users, in general, have to cope with the difficulty of finding suitable technology for optimal design of WHS. In this paper, we describe a user-friendly computer program that uses readily available information for the design of multiple WHS depending upon the water final use (agriculture, household, conservation, etc). The application (APP) itself contains several links to help the user complete the input requirements. It is not a prerequisite to have any computer skills for the use of the APP. Outputs of the APP are the dimensions of the WHS named terraces, micro-catchments, cisterns, and small household cisterns for roof water catchment. The APP also provides guidance on crops for backyard agriculture. We believe that this tool may guide users to better optimize WHS for multiple purposes and to widen the possibility of copping with dry spells in arid lands.

Keywords: rainfall-catchment, models, computer aid, arid lands

Procedia PDF Downloads 166

Authors: Dora Martins, Susana Silva, Cândida Silva

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Human Resources Management (HRM) has been recognized by academics and practitioners as an important element in organizations. Therefore, this paper explores the best practices of HRM and seeks to understand the level of participation in the development of these practices by human resources managers in the hospitality industry and compare it with other industries. Thus, the study compared the HRM practices of companies in the hospitality sector with HRM practices of companies in other sectors, and identifies the main differences between their HRM practices. The results show that the most frequent HRM practices in all companies, independently of its sector of activity, are hiring and training. When comparing hospitality sector with other sectors of activity, some differences were noticed, namely in the adoption of the practices of communication and information sharing, and of recruitment and selection. According to these results, the paper discusses the major theoretical and practical implications. Suggestions for future research are also presented.

Keywords: exploratory study, human resources management practices, human resources manager, hospitality companies, Portuguese companies

Procedia PDF Downloads 471

Authors: Jui-Ching Chou

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Numerical simulation is a popular method used to evaluate the effects of soil liquefaction on a structure or the effectiveness of a mitigation plan. Many constitutive models (UBCSAND model, PM4 model, SANISAND model, etc.) were presented to model the liquefaction phenomenon. In general, inputs of a constitutive model need to be calibrated against the soil cyclic resistance before being applied to the numerical simulation model. Then, simulation results can be compared with results from simplified liquefaction potential assessing methods. In this article, inputs of the UBCSAND model, a simple elastic-plastic stress-strain model, are calibrated against several popular generic liquefaction triggering curves of simplified liquefaction potential assessing methods via FLAC program. Calibrated inputs can provide engineers to perform a preliminary evaluation of an existing structure or a new design project.

Keywords: calibration, liquefaction, numerical simulation, UBCSAND Model

Procedia PDF Downloads 154

Authors: Halima Zaman

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This paper identifies real-life challenges faced by non-English-speaking learners. The author focuses on challenges both inside and outside the classroom. A qualitative approach has been applied to conduct the study with two different groups of ESOL (English to Speakers of Other Languages) learners. The author pays attention to the reasons behind the difficulties in controlling the learners’ focus within the classroom. Learners’ lifestyles, motivations, and previous educational backgrounds have been considered while determining the challenges they face within the classroom. Some existing challenges of teaching English to adults have been discussed in this paper; however, the primary focus is to observe those two groups of learners to identify their challenges. In this paper, the author has applied the academic knowledge of her Master of Arts in English Language teaching program to support and strengthen the observation of this case study. The paper ends with a number of recommendations that can be beneficial for newcomers to ESOL teaching and a scope of further exploratory research.

Keywords: ESOL, challenges, classroom, motivation, adult learners, teaching

Procedia PDF Downloads 72

Authors: Robert Vanderburg, Nicholas Gibson

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Objectives: There has been an increased focus in education students’ mathematics skills in the last two years. Universities have, specifically, had problems teaching students struggling with mathematics. This paper focuses on the ability of a digital platform to significantly improve mathematics skills for struggling students. Methods: 32 students who demonstrated low scores on a mathematics test were selected to take part in a one-month tutorial program using a digital mathematics portal. Students were provided feedback for questions posted on the portal and a fortnightly tutorial session. Results: A pre-test post-test design was analyzed using a one-way analysis of variance (ANOVA). The analysis suggested that students improved skills in algebra, geometry, statistics, probability, ratios, fractions, and probability. Conclusion: Distance university students can improve their mathematics skills using a digital platform.

Keywords: digital education, distance education, higher education, mathematics education

Procedia PDF Downloads 179

Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

Procedia PDF Downloads 466

Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

Procedia PDF Downloads 139

Authors: Mohamed M. Abo Elazm, Ali I. Shehata, Mohamed M. Khairat Dawood

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A computational fluid dynamics (CFD) program FLUENT has been used to predict the fluid flow and heat transfer distribution within concentric heat exchangers. The effect of inlet inclination angle has been investigated with Reynolds number range (3000 – 4000) and Pr=0.71. The heat exchanger is fabricated from copper concentric inner tube with a length of 750 mm. The effects of hot to cold inlet flow rate ratio (MH/MC), Reynolds's number and of inlet inclination angle of 30°, 45°, 60° and 90° are considered. The results showed that the numerical prediction shows a good agreement with experimental measurement. The results present an efficient design of concentric tube heat exchanger to enhance the heat transfer by increasing the swirling effect.

Keywords: heat transfer, swirling effect, CFD, inclination angle, concentric tube heat exchange

Procedia PDF Downloads 309

Authors: P. Krachangthong, N. Limsumalee, L. Sawatdipon, A. Sasipongpreecha, S. Pisailert, J. Thongta, N. Hongkarnjanakul, C. Thipyopas

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The design and development of new UAV are detailed in this paper. The mission requirement is setup for enhancement of flight endurance of a fixed wing UAV. The goal is to achieve flight endurance more than 60 minutes. UAV must be able launched by hand and can be equipped with the Sony A6000 camera. The design of sizing and aerodynamic analysis is conducted. The XFLR5 program and wind tunnel test are used for determination and comparison of aerodynamic characteristics. Lift, drag and pitching moment characteristics are evaluated. Then Kreno-V UAV is designed and proved its better efficiency compared to the Heron UAV who is currently used for mapping mission of Geo-Informatics and Space Technology Development Agency (Public Organization), Thailand. The endurance is improved by 19%. Finally, Kreno-V UAV with a wing span of 2meters, the aspect ratio of 7, and V-tail shape is constructed and successfully test.

Keywords: UAV design, fixed-wing UAV, wind tunnel test, long endurance

Procedia PDF Downloads 376

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 67

Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

Procedia PDF Downloads 91

Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

Procedia PDF Downloads 351

Authors: Pedro B. Antunes, Paulo J. Ramísio

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Road runoff is a linear source of diffuse pollution that can cause significant environmental impacts. During rainfall events, pollutants from both stationary and mobile sources, which have accumulated on the road surface, are dragged through the superficial runoff. Road runoff in coastal zones may present high levels of salinity and chlorides due to the proximity of the sea and transported marine aerosols. Appearing to be correlated to this process, organic matter concentration may also be significant. This study assesses this phenomenon with the purpose of identifying the relationships between monitored water quality parameters and intrinsic site variables. To achieve this objective, an extensive monitoring program was conducted on a Portuguese coastal highway. The study included thirty rainfall events, in different weather, traffic and salt deposition conditions in a three years period. The evaluations of various water quality parameters were carried out in over 200 samples. In addition, the meteorological, hydrological and traffic parameters were continuously measured. The salt deposition rates (SDR) were determined by means of a wet candle device, which is an innovative feature of the monitoring program. The SDR, variable throughout the year, appears to show a high correlation with wind speed and direction, but mostly with wave propagation, so that it is lower in the summer, in spite of the favorable wind direction in the case study. The distance to the sea, topography, ground obstacles and the platform altitude seems to be also relevant. It was confirmed the high salinity in the runoff, increasing the concentration of the water quality parameters analyzed, with significant amounts of seawater features. In order to estimate the correlations and patterns of different water quality parameters and variables related to weather, road section and salt deposition, the study included exploratory data analysis using different techniques (e.g. Pearson correlation coefficients, Cluster Analysis and Principal Component Analysis), confirming some specific features of the investigated road runoff. Significant correlations among pollutants were observed. Organic matter was highlighted as very dependent of salinity. Indeed, data analysis showed that some important water quality parameters could be divided into two major clusters based on their correlations to salinity (including organic matter associated parameters) and total suspended solids (including some heavy metals). Furthermore, the concentrations of the most relevant pollutants seemed to be very dependent on some meteorological variables, particularly the duration of the antecedent dry period prior to each rainfall event and the average wind speed. Based on the results of a monitoring case study, in a coastal zone, it was proven that SDR, associated with the hydrological characteristics of road runoff, can contribute for a better knowledge of the runoff characteristics, and help to estimate the specific nature of the runoff and related water quality parameters.

Keywords: coastal zones, monitoring, road runoff pollution, salt deposition

Procedia PDF Downloads 230

Authors: Laura Naka

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The invention of computer writing programs has changed the way of teaching second language writing. This artificial intelligence engine can provide students with feedback on their essays, on their grammatical and spelling errors, convenient writing and editing tools to facilitate student’s writing process. However, it is not yet proved if this technology is helping students to improve their writing skills. There are several programs that are of great assistance for students concerning their writing skills. New technology provides students with different software programs which enable them to be more creative, to express their opinions and ideas in words, pictures and sounds, but at the end main and most correct feedback should be given by their teachers. No matter how new technology affects in writing skills, always comes from their teachers. This research will try to present some of the advantages and disadvantages that new technology has in writing process for students. The research takes place in the University of Gjakova ‘’Fehmi Agani’’ Faculty of Education-Preschool Program. The research aims to provide random sample response by using questionnaires and observation.

Keywords: English language learning, technology, academic writing, teaching L2.

Procedia PDF Downloads 559

Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

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The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

Procedia PDF Downloads 426

Authors: A. Chenafa, S. Boulenouar, M. Zitouni, M. Boukouria

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The sterilization of medical devices constitutes for all the medical professions, an inescapable obligation. It has for objective to prevent the infectious risk, both for the patient and for the medical team. The Dental surgeon as every healthcare professional has to master perfectly this subject and to train his staff to the various techniques of sterilization. It is the only way to assure the patients all the security for which they are entitled to wait when they undergo a dental care. It’s for it, that we undertook to lead an investigation aiming at estimating the sterilization practice at the dental surgeon of Sidi bel Abbes. The survey result showed a youth marked with the profession with a majority use of autoclave with cycle B and an almost total absence of the sterilization controls (test of Bowie and Dick). However, the majority of the dentists control and validate their sterilizers. Finally, our survey allowed us to describe some practices which must be improved regarding control, regarding qualification and regarding staff training. And suggestions were made in this sense.

Keywords: dental surgeon, medical devices, sterilization, survey

Procedia PDF Downloads 393

Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

Procedia PDF Downloads 141

Authors: Nitin Joseph

Abstract:

Objectives: This study was done to assess emotional intelligence levels and to find out its association with socio demographic variables and perceived stress among medical students. Material and Methods: This study was done among first and second year medical students. Data was collected using a self-administered questionnaire. Results: Emotional intelligence scores was found to significantly increase with age of the participants (F=2.377, P < 0.05). Perceived stress was found to be significantly more among first year (t=1.997, P=0.05). Perceived stress was found to significantly decrease with increasing emotional intelligence scores (r = – 0.226, P < 0.001). Conclusion: First year students were found to be more vulnerable to stress than their seniors probably due to lesser emotional intelligence. As both these parameters are related, ample measures to improve emotional intelligence needs to be supported in the training curriculum of beginners so as to make them more stress free during early student life.

Keywords: emotional intelligence, medical students, perceived stress, socio demographic variables

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Authors: Sanjaya Humagain, Rajendra Koju

Abstract:

Background: Rheumatic heart disease (RHD) is the most common cardiovascular disease in children and young adults. Though declined and almost non-existent in developed nations, RHD is still one of the leading cause for premature death and disability in developing countries. Prevalence of RHD is high in both rural as well as urban area of Nepal. Present study is designed to look at the pattern of valvular involvement and demographic features in RHD. Methods: 326 patients indicated for inj. Benzathine penicillin were selected and echocardiograph performed to see the pattern of vavular involvement. Data analysis was done using SPSS 17. Result: The most common type of lesion was mixed type with mitral valve involvement. MR was the most common isolated lesion. MS was more commonly seen in females whereas AS was more common in males. Secondary prophylaxis was more common than primary prophylaxis. Conclusion: RHD still being a major problem and a preventable disease so extensive screening program is required to identify them early and prevent the complication.

Keywords: acute rheumatic fever, RHD, MS, MR, AS, AR, Inj benzathine penicillin

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Authors: Patricia Agboro

Abstract:

The dominant strands of pedagogic ideas are often western in origin/orientation. This is the case because of the hegemony of the western world in global academia. For this reason, peculiarities and considerations of context are often swept to the margins as educational thinkers emphasize patently Eurocentric and one-size-fits-all solutions to the problems of effective teaching. This paper takes as a starting point the notion that pedagogy must be context specific and pragmatic in its application. It is from this perspective that it focuses on the challenges of teaching philosophy to students in the Nigerian tertiary institutions. Philosophy students in Nigeria usually come across philosophy for the first time at the tertiary level. This raises the problem of inadequate exposure. Beyond this, a substantial number of candidates are admitted into the philosophy program based on the Nigerian version of ‘affirmative action’ which is known as the quota system. This paper addresses the problems highlighted above and hosts of other issues as well as provides recommendations that can improve effectiveness of teaching philosophy at the university level.

Keywords: justice, quota system, pedagogy, federal character

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

Abstract:

In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Darusalam, Jorish Hulstijn, Marijn Janssen

Abstract:

Media has revealed a variety of corruption cases in the regional and local governments all over the world. Many governments pursued many anti-corruption reforms and have created a system of checks and balances. Three types of corruption are faced by citizens; administrative corruption, collusion and extortion. Accountability is one of the benchmarks for building transparent government. The public sector is required to report the results of the programs that have been implemented so that the citizen can judge whether the institution has been working such as economical, efficient and effective. Open Data is offering solutions for the implementation of good governance in organizations who want to be more transparent. In addition, Open Data can create transparency and accountability to the community. The objective of this paper is to build a framework of open data for accountability to combating corruption. This paper will investigate the relationship between open data, and accountability as part of anti-corruption initiatives. This research will investigate the impact of open data implementation on public organization.

Keywords: open data, accountability, anti-corruption, framework

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Authors: Drissi Mokhtaria, Chouaih Abdelkader, Fodil Hamzaoui

Abstract:

Our work is about a comparison of experimental and theoretical results of the electron charge density distribution and the electrostatic potential around the M-Nitrophenol Molecule (m-NPH) kwon for its interesting physical characteristics. The molecular experimental results have been obtained from a high-resolution X-ray diffraction study. Theoretical investigations were performed under the Gaussian program using the Density Functional Theory at B3LYP level of theory at 6-31G*. The multipolar model of Hansen and Coppens was used for the experimental electron charge density distribution around the molecule, while we used the DFT methods for the theoretical calculations. The electron charge density obtained in both methods allowed us to find out the different molecular properties such us the electrostatic potential and the dipole moment which were finally subject to a comparison leading to an outcome of a good matching results obtained in both methods.

Keywords: electron charge density, m-nitrophenol, nonlinear optical compound, electrostatic potential, optimized geometric

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