Search results for: pre-service teacher training program

Authors: Azeez Ayodele Ayodele

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This paper investigated the problem of picture books design and the quality of the pictures in picture books. The research surveyed nursery and primary schools in four major cities in southwest of Nigeria. The instruments including the descriptive survey questionnaire and a structured interview were developed, validated and administered for collection of relevant data. Descriptive statistics was used in analyzing the data. The result of the study revealed that there were poor quality of pictures in picture books and this is due to scarcity of trained graphic designers who understand systems approach to picture books design and production. There is thus a need for more qualified graphic designers, given in-service professional training as well as a refresher course as criteria for upgrading by the stakeholders.

Keywords: pictures, picture books, pre-primary schools, trained graphic designers

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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

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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)

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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

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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

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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

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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

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Authors: Felix E. Gbenoba, Opeyemi Dahunsi

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Self-instructional materials are at the heart of instructional delivery in Open and Distance Learning (ODL). The success of any ODL institution depends on the availability of instructional materials in quality and quantity. An ODL study material is expected to fully play the teacher plays in the face-to-face learning environment. In Nigeria, efforts to deliver ODL learning materials have been peculiarly challenging. Although researchers are unrelenting in hewing out ways to make ODL delivery in Africa generally and Nigeria in particular, meet the learners’ needs and acceptable global practices, the prospects of integrating instructional media into distance learning courses are largely unexplored. In the present study, we critically examine the prospects of integration of instructional media into ODL courses for pedagogic and other benefits it portends for delivery via the distance learning mode. Although efforts to integrate media in ODL have been recorded before now, the reality has not matched the expectation so far in Nigeria. This does not mean that the existing instructional materials have not produced any significant positive results in improving the overall learning (and teaching) experience in its institutions; it implies that increased integration as suggested here will further improve the experience as well as bring up the new challenges. Obstacles and problems of instructional materials and media development that could have affected the open educational resource initiatives are well established. The first aspect of this paper recalls the revolutionary strides that ODL brought to delivery of education in Nigeria particularly. The other aspect is on what instructional media are, their role, prospects and challenges for ODL in Nigeria; these are examined vis a vis the challenges of development, production and distribution of print instructional materials as the major format of instructional delivery at Nigeria’s only single mode ODL institution, NOUN. In the third aspect, we justify the need and benefits of integrating instructional media into the courses and make recommendations.

Keywords: instructional delivery, instructional media, ODL, media integration, Nigeria, self-instructional materials

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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

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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.

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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

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Authors: Sanjaya Humagain, Rajendra Koju

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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: 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

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Authors: Patricia Agboro

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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: Darusalam, Jorish Hulstijn, Marijn Janssen

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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

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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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Authors: Ozden Saygili, Eser Cakti

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The masonry building stock in Istanbul and in other cities of Turkey are exposed to significant earthquake hazard. Determination of the safety of masonry structures against earthquakes is a complex challenge. This study deals with experimental tests and non-linear dynamic analysis of masonry structures modeled through discrete element method. The 1:10 scale model of Mustafa Paşa Mosque was constructed and the data were obtained from the sensors on it during its testing on the shake table. The results were used in the calibration/validation of the numerical model created on the basis of the 1:10 scale model built for shake table testing. 3D distinct element model was developed that represents the linear and nonlinear behavior of the shake table model as closely as possible during experimental tests. Results of numerical analyses with those from the experimental program were compared and discussed.

Keywords: dynamic analysis, non-linear modeling, shake table tests, masonry

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Authors: Pieter Conradie, M. Marina Moller

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Web 2.0 Internet-based technologies have intruded all aspects of human life. Presently, this phenomenon is especially evident in the educational context, with increased disruptive Web 2.0 technology infusions dramatically changing educational practice. The most prominent of these Web 2.0 intrusions can be identified as Massive Open Online Courses (Coursera, EdX), video and photo sharing sites (Youtube, Flickr, Instagram), and Web 2.0 online tools utilize to create Personal Learning Environments (PLEs) (Symbaloo (aggregator), Delicious (social bookmarking), PBWorks (collaboration), Google+ (social networks), Wordspress (blogs), Wikispaces (wiki)). These Web 2.0 technologies have supported the realignment from a teacher-based pedagogy (didactic presentation) to a learner-based pedagogy (problem-based learning, project-based learning, blended learning), allowing greater learner autonomy. No longer is the educator the source of knowledge. Instead the educator has become the facilitator and mediator of the learner, involved in developing learner competencies to support life-long learning (continuous learning) in the 21st century. In this study, the self-regulated learning skills of thirty first-year university learners were explored by utilizing the Online Self-regulated Learning Questionnaire. Implementing an action research method, an intervention was affected towards improving the self-regulation skill set of the participants. Statistical significant results were obtained with increased self-regulated learning proficiency, positively impacting learner performance. Goal setting, time management, environment structuring, help seeking, task (learning) strategies and self-evaluation skills were confirmed as determinants of improved learner success.

Keywords: andragogy, online self-regulated learning questionnaire, self-regulated learning, web 2.0

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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

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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

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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

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Authors: Yadi Xu, Qingping Luo

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Currently, there is a great tendency that more and more villages in China are trying to increase income by development of tourism. 'Beautiful Rural Construction' provides an excellent opportunity for the development of tourism. In this context, development orientation, transportation routes, and tourism service facilities are analyzed under the perspective of existing landscape utilization and regional integration based on the development tourism industry of the Nanliangdu Village in Jingxing Town, Shijiazhuang Province as a research object. In the program, the biggest issue is the contradiction between the ecological development and protection of the river and the development of economy. How to deal with the relationship between protection and development is the key to the design of this case. Furthermore, the streets and courtyard space, existing buildings, public environment, specific landscape of the ancient village with a history of thousands of years have strong regional characteristics. The article is actively exploring for suggestions and countermeasures to promote the development premised on protection and based on a regional view.

Keywords: development, integration, protection, rural tourism

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Authors: Susana Aldaba Yaben, Marga Echauri Ozcoidi, Rosario Osinaga Cenoz

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It was decided to make a diagnosis with students of Berriozar High School between 12 and 15 years (both included) for their lifestyles in relation to eating habits, BMI (Body Mass Index), physical activity, drugs, interpersonal relationships and screen time. The aim of this survey is identifying needs of this population and depending on the results, we could program socio-educational activities. This action is part of the Community Health Promotion Programme and healthy lifestyles in childhood and youth of Berriozar. The eating habits, a lack of physical activity and an excessive screen time are causes of 26,75% of obese or overweight young people. First of all, many of them have got a diet enriched in saturated fats and sugars. Secondly, most of them do not practise physical exercise daily and finally, their screen time are higher than the recommendation (until 2 hours a day).

Keywords: lifestyle, diet, BMI, physical activity, screen time, education, youth

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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

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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

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Authors: Ahmad Almohana

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Technology has led to rapid changes in the world, and most importantly to education, particularly in the 21st century. Technology has enhanced teachers’ potential and has resulted in the provision of greater interaction and choices for learners. In addition, technology is helping to improve individuals’ learning experiences and building their capacity to read, listen, speak, search, analyse, memorise and encode languages, as well as bringing learners together and creating a sense of greater involvement. This paper has been organised in the following way: the first section provides a review of the literature related to the implementation of CALL (computer assisted learning language), and it explains CALL and its phases, as well as attempting to highlight and analyse Warschauer’s article. The second section is an attempt to describe the move from CALL to mobilised systems of edutainment, which challenge existing forms of teaching and learning. It also addresses the role of the teacher and the curriculum content, and how this is affected by the computerisation of learning that is taking place. Finally, an empirical study has been conducted to collect data from teachers in Saudi Arabia using quantitive and qualitative method tools. Connections are made between the area of study and the personal experience of the researcher carrying out the study with a methodological reflection on the challenges faced by the teachers of this same system. The major findings were that it is worth spelling out here that despite the circumstances in which students and lecturers are currently working, the participants revealed themselves to be highly intelligent and articulate individuals who were constrained from revealing this criticality and creativity by the system of learning and teaching operant in most schools.

Keywords: CALL, computer assisted learning language, EFL, English as a foreign language, ELT, English language teaching, ETL, enhanced technology learning, MALL, mobile assisted learning language

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

Abstract:

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

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Authors: L. Miranda

Abstract:

The requirements of the engineering education are nowadays very broad and demand a set of skills which demands not only technical knowledge but also the ability to lead and innovate and personal and interpersonal skills. A framework for the assessment of a potential curricular change is necessary to guide the analysis of the program with respect to the stakeholders and the legislation of the country, in order to develop appropriate learning outcomes. A Conceive-Design-Implement-Operate (CDIO) approach was chosen for an evaluation conducted in a mechanical engineering degree in Brazil. The work consisted in the application of a survey with students and professors and a literature review of the legislation and studies that raised the required competences and skills for the modern engineer. The results show a great potential for a CDIO set of skills in engineering degrees in Brazil and reveal the frequent demands of stakeholders before a curricular change.

Keywords: curriculum change, conceive design implement operate, accreditation, personal and interpersonal skills

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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: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

Abstract:

An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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