Search results for: innovative teaching-learning techniques

Authors: R. S. Arun Raj

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In today's climate of growing energy needs and increasing environmental issues, alternatives to the use of non-renewable and polluting fossil fuels have to be investigated. One such alternative is the solar energy. The SUN provides every hour as much energy as mankind consumes in one year. This paper clearly explains about the solar panel design and new models and methodologies that can be implemented for better utilization of solar energy. Minimisation of losses in solar panel as heat is my innovative idea revolves around. The pay back calculations by implementation of solar panels is also quoted.

Keywords: on-grid and off-grid systems, pyro-electric effect, pay-back calculations, solar panel

Procedia PDF Downloads 593

Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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Authors: Cheng Sim Quah, Sandra Phek Lin Sim

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This study examined how individuals in their respective teams contributed to innovation performance besides defining the term of innovation in their own respective views. This study also identified factors that motivated University staff to contribute to the innovation products. In addition, it examined whether there is a significant relationship between professional training level and the length of service among university staff towards innovation and to what extent do the two variables contributed towards innovative products. The significance of this study is that it revealed the strengths and weaknesses of the university staff when contributing to innovation performance. Stratified-random sampling was employed to determine the samples representing the population of lecturers in the study, involving 123 lecturers in one of the local universities in Malaysia. The method employed to analyze the data is through categorizing into themes for the open-ended questions besides using descriptive and inferential statistics for the quantitative data. This study revealed that two types of definition for the term “innovation” exist among the university staff, namely, creation of new product or new approach to do things as well as value-added creative way to upgrade or improve existing process and service to be more efficient. This study found that the most prominent factor that propel them towards innovation is to improve the product in order to benefit users, followed by self-satisfaction and recognition. This implies that the staff in the organization viewed the creation of innovative products as a process of growth to fulfill the needs of others and also to realize their personal potential. This study also found that there was only a significant relationship between the professional training level and the length of service of 4-6 years among the university staff. The rest of the groups based on the length of service showed that there was no significant relationship with the professional training level towards innovation. Moreover, results of the study on directional measures depicted that the relationship for the length of service of 4 - 6 years with professional training level among the university staff is quite weak. This implies that good organization management lies on the shoulders of the key leaders who enlighten the path to be followed by the staff.

Keywords: innovation, length of service, performance, professional training level, motivation

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Authors: Rajeshwar Singh

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The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents.

Keywords: conflicting claim on credit of discovery of ridge regression, multicollinearity, principal components and ridge regressions, variance inflation factor

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Authors: M. Jathaveda, Joben Leons, G. Vidya

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Reentry from orbit is a critical phase in the entry trajectory. For a non-propulsive ballistic entry, static and dynamic stability play an important role in the trajectory, especially for the safe deployment of parachutes, typically at subsonic Mach numbers. Static stability of flight vehicles are being estimated through CFD techniques routinely. Advances in CFD software as well as computational facilities have enabled the estimation of the dynamic stability derivatives also through CFD techniques. Longitudinal static and dynamic stability of a typical reentry body for subsonic Mach number of 0.6 is predicted using commercial software CFD++ and presented here. Steady state simulations are carried out for α = 2° on an unstructured grid using SST k-ω model. Transient simulation using forced oscillation method is used to compute pitch damping derivatives.

Keywords: stability, typical reentry body, subsonic, static and dynamic

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Authors: Hajer Rahali, Zied Hajaiej, Noureddine Ellouze

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The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.

Keywords: auditory filter, impulsive noise, MFCC, prosodic features, RASTA filter

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Authors: B. E. Tarazona-Romero, J. G. Ascanio-Villabona, O. Lengerke-Perez, A. D. Rincon-Quintero, C. L. Sandoval-Rodriguez

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Desalination offers solutions for the shortage of water in the world, however, the process of eliminating salts generates a by-product known as brine, generally eliminated in the environment through techniques that mitigate its impact. Brine treatment techniques are vital to developing an environmentally sustainable desalination process. Consequently, this document evaluates three different geometric configurations of solar stills as an alternative for brine treatment to be integrated into a low-scale desalination process. The geometric scenarios to be studied were selected because they have characteristics that adapt to the concept of appropriate technology; low cost, intensive labor and material resources for local manufacturing, modularity, and simplicity in construction. Additionally, the conceptual design of the collectors was carried out, and the ray tracing methodology was applied through the open access software SolTrace and Tonatiuh. The simulation process used 600.00 rays and modified two input parameters; direct normal radiation (DNI) and reflectance. In summary, for the scenarios evaluated, the ladder-type distiller presented higher efficiency values compared to the pyramid-type and single-slope collectors. Finally, the efficiency of the collectors studied was directly related to their geometry, that is, large geometries allow them to receive a greater number of solar rays in various paths, affecting the efficiency of the device.

Keywords: appropriate technology, brine treatment techniques, desalination, monte carlo ray tracing

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Authors: Mounir Adal, Baalal Azeddine, Afifi Moulay Larbi

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Numerical analysis is a branch of mathematics devoted to the development of iterative matrix calculation techniques. We are searching for operations optimization as objective to calculate and solve systems of equations of order n with time and energy saving for computers that are conducted to calculate and analyze big data by solving matrix equations. Furthermore, this scientific discipline is producing results with a margin of error of approximation called rates. Thus, the results obtained from the numerical analysis techniques that are held on computer software such as MATLAB or Simulink offers a preliminary diagnosis of the situation of the environment or space targets. By this we can offer technical procedures needed for engineering or scientific studies exploitable by engineers for water.

Keywords: numerical analysis methods, obstacles solving, engineering, simulation, numerical modeling, iteration, computer, MATLAB, water, underground, velocity

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Authors: Elizabeth M. Annoni, Elena G. Tolkacheva

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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that is a known prognostic marker for stroke, heart failure and death. Reentrant mechanisms of rotor formation, which are stable electrical sources of cardiac excitation, are believed to cause AF. No existing commercial mapping systems have been demonstrated to consistently and accurately predict rotor locations outside of the pulmonary veins in patients with persistent AF. There is a clear need for robust spatio-temporal techniques that can consistently identify rotors using unique characteristics of the electrical recordings at the pivot point that can be applied to clinical intracardiac mapping. Recently, we have developed four new signal analysis approaches – Shannon entropy (SE), Kurtosis (Kt), multi-scale frequency (MSF), and multi-scale entropy (MSE) – to identify the pivot points of rotors. These proposed techniques utilize different cardiac signal characteristics (other than local activation) to uncover the intrinsic complexity of the electrical activity in the rotors, which are not taken into account in current mapping methods. We validated these techniques using high-resolution optical mapping experiments in which direct visualization and identification of rotors in ex-vivo Langendorff-perfused hearts were possible. Episodes of ventricular tachycardia (VT) were induced using burst pacing, and two examples of rotors were used showing 3-sec episodes of a single stationary rotor and figure-8 reentry with one rotor being stationary and one meandering. Movies were captured at a rate of 600 frames per second for 3 sec. with 64x64 pixel resolution. These optical mapping movies were used to evaluate the performance and robustness of SE, Kt, MSF and MSE techniques with respect to the following clinical limitations: different time of recordings, different spatial resolution, and the presence of meandering rotors. To quantitatively compare the results, SE, Kt, MSF and MSE techniques were compared to the “true” rotor(s) identified using the phase map. Accuracy was calculated for each approach as the duration of the time series and spatial resolution were reduced. The time series duration was decreased from its original length of 3 sec, down to 2, 1, and 0.5 sec. The spatial resolution of the original VT episodes was decreased from 64x64 pixels to 32x32, 16x16, and 8x8 pixels by uniformly removing pixels from the optical mapping video.. Our results demonstrate that Kt, MSF and MSE were able to accurately identify the pivot point of the rotor under all three clinical limitations. The MSE approach demonstrated the best overall performance, but Kt was the best in identifying the pivot point of the meandering rotor. Artifacts mildly affect the performance of Kt, MSF and MSE techniques, but had a strong negative impact of the performance of SE. The results of our study motivate further validation of SE, Kt, MSF and MSE techniques using intra-atrial electrograms from paroxysmal and persistent AF patients to see if these approaches can identify pivot points in a clinical setting. More accurate rotor localization could significantly increase the efficacy of catheter ablation to treat AF, resulting in a higher success rate for single procedures.

Keywords: Atrial Fibrillation, Optical Mapping, Signal Processing, Rotors

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Authors: André Smith, Amr Abdel-Dayem

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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Chih-Wen Fan

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Backgrounds/ Research goals : School improvement requires change leadership, which often means discomfort. Principals are the key people that determine the effectiveness of schools. In an era of organization’s pursuit of speed and effectiveness, school administration has to be accountable and innovative. Effective principals work to improve achievement by focusing on the administrative and teaching quality of improvement. However, there is a lack of literature addressing the relevant case studies on school change leadership. This article explores how principals can use change leadership to drive school change. It analyze the driving factors of principal changes in the case school, the beliefs of change leadership, specific methods, and what impact they have. Methods: This study applies the case study research method to the selected primary school located in an urban area for case study, which has achieved excellent performance after reform and innovation. The researchers selected an older primary school located in an urban area that was transformed into a high-performance primary school after changes were enacted by the principal. The selected case was recommended by three supervisors of the Education Department. The case school underwent leadership change by the new principal during his term, and won an award from the Ministry of Education. Total of 8 teachers are interviewed. The data encoding includes interviews and documents. Expected results/ conclusions: The conclusions of the study are, as follows: (1) The influence for Principal Lin's change leadership is from internal and external environmental development and change pressures. (2) The principal's belief in change leadership is to recognize the sense of crisis, and to create a climate of change and demand for change. (3) The principal's specific actions are intended to identify key members, resolve resistance, use innovative thinking, and promote organizational learning. (4) Principal Lin's change leadership can enhance the professional functions of all employees through appropriate authorization. (5) The effectiveness of change leadership lies in teachers' participation in decision-making; the school's reputation has been enhanced through featured courses.

Keywords: change leadership, empowerment, crisis awareness, case study

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Authors: Zine-Eddine Bouras, Abdelouaheb Talai

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During the last two decades, the software evolution community has intensively tackled the software merging issue whose main objective is to merge in a consistent way different versions of software in order to obtain a new version. Well-established approaches, mainly based on the dependence analysis techniques, have been used to bring suitable solutions. These approaches concern the source code or software architectures. However, these solutions are more expensive due to the complexity and size. In this paper, we overcome this problem by operating at a high level of abstraction. The objective of this paper is to investigate the software merging at the level of UML activity diagrams, which is a new interesting issue. Its purpose is to merge activity diagrams instead of source code. The proposed approach, based on dependence analysis techniques, is illustrated through an appropriate case study.

Keywords: activity diagram, activity diagram slicing, dependency analysis, software merging

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Authors: Qing K. Zhu

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Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.

Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise

Procedia PDF Downloads 252

Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Rasha F. Sharaf

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Treatment of dental caries in young children is considered a great challenge for all dentists, especially with uncooperative children. Recently non-invasive techniques have been highlighted as they alleviate the need for local anesthesia and other painful procedures during management of carious teeth and, at the same time, increase the success rate of the treatment done. Silver Diamine Fluoride (SDF) is one of the most effective cariostatic materials that arrest the progression of carious lesions and aid in remineralizing the demineralized tooth structure. Both fluoride and silver ions proved to have an antibacterial action and aid in the precipitation of an insoluble layer that prevents further decay. At the same time, Moringa proved to have an effective antibacterial action against different types of bacteria, therefore, it can be used as a non-invasive technique for the management of caries in children. One of the important theories for the control of caries is by depriving the cariogenic bacteria from nutrients causing their starvation and death, which can be achieved by applying stainless steel crown on primary molars with carious lesions which are not involving the pulp, and this technique is known as Hall technique. The success rate of the Hall technique can be increased by arresting the carious lesion using either SDF or Moringa and gaining the benefit of their antibacterial action. Multiple clinical cases with 1 year follow up will be presented, comparing different treatment options, and using various materials and techniques for non-invasive and non-painful management of carious primary teeth.

Keywords: SDF, hall technique, carious primary teeth, moringa extract

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Authors: Nourhane Mohamed El Haridi, Mostafa El Arabi, Zeyad El Sayad

Abstract:

Biomimicry is defined, by Benyus the natural sciences writer, as imitating or taking inspiration from nature’s forms and processes to solve human problems. Biomimicry is the conscious emulation of life’s genius. As the design community realizes the tremendous impact human constructions have on the world, environmental designers look to new approaches like biomimicry to advance sustainable design. Building leading the declaration made by biomimicry scientists that a full imitation of nature engages form, ecosystem, and process; this paper uses a logic approach to interpret human and environmental wholeness. Designers would benefit from both integrating social theory with environmental thinking and from combining their substantive skills with techniques for getting sustainable biomimic urban design. Integrating biomimicryʹs “Life’s Principles” into a built environment process model will make biomimicry more accessible and thus more widely accepted throughout the industry, and the sustainability of all species will benefit. The Biomimicry Guild hypothesizes the incorporation of these principles, called Lifeʹs Principles, increase the likelihood of sustainability for a respective design, and make it more likely that the design will have a greater impact on sustainability for future generations of all species as mentioned by Benyus in her book. This thesis utilizes Life’s Principles as a foundation for a design process model intended for application on built environment projects at various scales. This paper takes a look at the importance of the integration of biomimicry in urban design to get more sustainable cities and better life, by analyzing the principles of both sustainability and biomimicry, and applying these ideas on futuristic or existing cities to make a biomimic sustainable city more healthier and more conductive to life, and get a better biomimic urban design. A group of experts, architects, biologists, scientists, economists and ecologists should work together to face all the financial and designing difficulties, to have better solutions and good innovative ideas for biomimic sustainable urban design, it is not the only solution, but it is one of the best studies for a better future.

Keywords: biomimicry, built environment, sustainability, urban design

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Authors: Khadija Al Farei, Prakash Kumar, Vikas Rao Naidu

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Games are being developed with different genres for different age groups, for many decades. In many places, educational games are playing a vital role for active classroom environment and better learning among students. Currently, the educational games have assumed an important place in children and teenagers lives. The role of educational games is important for improving the learning capability among the students especially of this generation, who really live among electronic gadgets. Hence, it is now important to make sure that in our educational system, we are updated with all such advancement in technologies. Already much research is going on in this area of edutainment. This research paper will review around ten different research papers to find the relation between the education learning techniques and games. The result of this review provides guidelines for enhanced teaching and learning solutions in education. In-house developed educational games proved to be more effective, compared to the one which is readily available in the market.

Keywords: education, education game, educational technology, edutainment, game genres, gaming in education

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Authors: Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Alireza Norouzi

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Medical image analysis is one of the great effects of computer image processing. There are several processes to analysis the medical images which the segmentation process is one of the challenging and most important step. In this paper the segmentation method proposed in order to segment the dental radiograph images. Thresholding method has been applied to simplify the images and to morphologically open binary image technique performed to eliminate the unnecessary regions on images. Furthermore, horizontal and vertical integral projection techniques used to extract the each individual tooth from radiograph images. Segmentation process has been done by applying the level set method on each extracted images. Nevertheless, the experiments results by 90% accuracy demonstrate that proposed method achieves high accuracy and promising result.

Keywords: integral production, level set method, morphological operation, segmentation

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Authors: Priya Tomar, Pallavi Mittal

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As industrialization is inevitable and progress with rapid acceleration, the need for innovative ways to get rid of waste has increased. Recent advancement in bioresource technology paves novel ideas for recycling of factory waste that has been polluting the agro-industry, soil and water bodies. Paper industries in India are in a considerable number, where molasses and impure alcohol are still being used as raw materials for manufacturing of paper. Paper mills based on nonconventional agro residues are being encouraged due to increased demand of paper and acute shortage of forest-based raw materials. The colouring body present in the wastewater from pulp and paper mill is organic in nature and is comprised of wood extractives, tannin, resins, synthetic dyes, lignin and its degradation products formed by the action of chlorine on lignin which imparts an offensive colour to the water. These mills use different chemical process for paper manufacturing due to which lignified chemicals are released into the environment. Therefore, the chemical oxygen demand (COD) of the emanating stream is quite high. This paper presents some new techniques that were developed for the efficiency of bioremediation on paper industry. A short introduction to paper industry and a variety of presently available methods of bioremediation on paper industry and different strategies are also discussed here. For solving the above problem, two bacterial strains (Pseudomonas aeruginosa and Bacillus subtilis) and their consortia (Pseudomonas aeruginosa and Bacillus subtilis) were utilized for the pulp and paper mill effluent. Pseudomonas aeruginosa and Bacillus subtilis named as T–1, T–2, T–3, T–4, T–5, T–6, for the decolourisation of paper industry effluent. The results indicated that a maximum colour reduction is (60.5%) achieved by Pseudomonas aeruginosa and COD reduction is (88.8%) achieved by Bacillus subtilis, maximum pH changes is (4.23) achieved by Pseudomonas aeruginosa, TSS reduction is (2.09 %) achieved by Bacillus subtilis, and TDS reduction is (0.95 %) achieved by Bacillus subtilis. When the wastewater was supplemented with carbon (glucose) and nitrogen (yeast extract) source and data revealed the efficiency of Bacillus subtilis, having more with glucose than Pseudomonas aeruginosa.

Keywords: bioremediation, paper and pulp mill effluent, treated effluent, lignin

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Authors: Gurjit Kaur, Neena Gupta

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In this paper, we have analyzed and compared the performance of various coding schemes. The basic ID prime sequence codes are unique in only dimension, i.e. time slots, whereas 2D coding techniques are not unique by their time slots but with their wavelengths also. In this research, we have evaluated and compared the performance of 1D and 2D coding techniques constructed using prime sequence coding pattern for Optical Code Division Multiple Access (OCDMA) system on a single platform. Analysis shows that 2D prime code supports lesser number of active users than 1D codes, but they are having large code family and are the most secure codes compared to other codes. The performance of all these codes is analyzed on basis of number of active users supported at a Bit Error Rate (BER) of 10-9.

Keywords: CDMA, OCDMA, BER, OOC, PC, EPC, MPC, 2-D PC/PC, λc, λa

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Authors: Parisa Afshin, Terje Grønning

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Norway, similar to many other countries, has set the focus of its policies in creating new strong and highly innovative sectors in recent years, as the oil and gas sector profitability is declining. Biotechnology sector in Norway has a great potential, especially in marine-biotech and cancer medicine. However, Norway being a periphery faces especial challenges in the path of creating internationally well-known biotech sector and an international knowledge hub. The aim of this article is to analyze the progress of the Norwegian biotechnology industry, its pathway to build up an innovation network and conduct collaborative innovation based on its initial conditions and its own advantage and disadvantages. The findings have important implications not only for politicians and academic in understanding the infrastructure of biotechnology sector in the country, but it has important lessons for other periphery countries or regions aiming in creating strong biotechnology sector and catching up with the strong internationally-recognized regions. Data and methodology: To achieve the main goal of this study, information has been collected via secondary resources such as web pages and annual reports published by the officials and mass media along with interviews were used. The data were collected with the goal to shed light on a brief history and current status of Norway biotechnology sector, as well as geographic distribution of biotech industry, followed by the role of academic and industry collaboration and public policies in Norway biotech. As knowledge is the key input in innovation, knowledge perspective of the system such as knowledge flow in the sector regarding the national and regional innovation system has been studied. Primary results: The internationalization has been an important element in development of periphery regions' innovativeness enabling them to overcome their weakness while putting more weight on the importance of regional policies. Following such findings, suggestions on policy decision and international collaboration, regarding national and regional system of innovation, has been offered as means of promoting strong innovative sector.

Keywords: biotechnology sector, knowledge-based industry, national innovation system, regional innovation system

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Shohreh Ghasemi

Abstract:

Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Tarek Aboueldahab, Hanan Farag

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Parallel Job Shop Scheduling Problem (JSP) is a multi-objective and multi constrains NP- optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution, so we propose a hybrid Artificial Intelligence model (AI) with Discrete Breeding Swarm (DBS) added to traditional Artificial Intelligence to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: parallel job shop scheduling problem, artificial intelligence, discrete breeding swarm, car sequencing and operator allocation, cost minimization

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Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

Abstract:

Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

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Authors: Chamtouri I., Kechida M.

Abstract:

Background: Evaluation has a prominent role in medical education and graduation. This evaluation has usually done in face-to-face, by written or oral questions. Simulation is increasingly taking a part as a method of evaluation. Due to the Covid-19 pandemic, which disrupted face-to-face evaluation, simulation using serious games (SG) is emerging in the field of training and assessment of medical students. The aim of our study is to compare the results of the evaluation of medical students by virtual simulation by online serious games versus clinical case-multiple choice questions (MCQ) and to assess the degree of satisfaction from these two evaluation methods. Methods: Medical students from the same study level were voluntarily participated in this study. Groupe 1 had an evaluation by SG dealing with “diagnosis and management of ST-segment elevationmyocardialinfarction (STEMI)alreadyprepared on the website www.Mediactiv.com. Groupe 2 were evaluated by clinical case-MCQ having thes same topic as SG. Results of the two groups were compared. Satisfaction questionnaire was filled by the two groups. Satisfaction degree was compared between the two groups. Results. In this study, 64 medical students (G1:31 and G2: 33) were enrolled. Obtaining complete notes in the "questioning" and "clinical examination" parts is significantly more important in-group 1 compared to group 2. No significant difference detected between the two groups in terms of “ECG interpretation” and “diagnosis of STEMI” parts. A greater number of students of group 1 obtained the full note compared to group 2 in “the initial treatment part” (54.8% vs. 39.4%; p = 0.04). Thirty learners (96.8%) in-group 1 obtained a total score ≥ 50% versus 69.7% in-group 2 (p = 0.004). The full score of 100% was obtained in three learners in-group1, while no student scored 100% in-group2 (p = 0.027). Medical evaluation using SG was reported as more innovative, fun, and realistic compared to evaluation by clinical case-MCQ. No significant difference detected between the two methods in terms of stress. Conclusion: Simulation by SG can be considered as an innovative and effective method in evaluating medical students with a higher degree of satisfaction.

Keywords: evaluation, serious games, medical students, satisfaction

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Authors: Morinta Rosandini

Abstract:

Bima Tembe is an excellent sample of cultural artifact that many people regard it as: (1) manufactured by a traditional techniques, (2) contained with variety forms and great philosophical motifs, and (3) having valued functions related to women status in the society. This research examined elements of Bima Tembe and their relations and one of the usage of tembe, named Rimpus. The elements include: (1) the traditional techniques of making Bima Tembe, (2) the variety forms (3) and philosophical motifs of Bima Tembe. Rimpu, is a cultural women clothes in Bima, which use Bima Tembe as a main part. From this reseacrh found that the Bima Tembe made by weaving technique using a traditional loom, and has two types of Tembe; Tembe Istana and Tembe Rakyat, with various motif each type. The The usage of Rimpu is as a symbol of the obedience to God and the type of Rimpu indicate the women status in the society.

Keywords: bima, tembe, rimpu, clothes

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Authors: Ali Mohammed Yimer, Ayalew Assen, Youssef Belmabkhout

Abstract:

Environmental remediation is a pressing global concern, necessitating innovative strategies to address the challenges posed by industrial waste and pollution. This study aims to advance environmental remediation by developing cutting-edge functional porous materials from phosphorite residue tailings. Phosphorite mining activities generate vast amounts of waste, which pose significant environmental risks due to their contaminants. The proposed approach involved transforming these phosphorite residue tailings into valuable porous materials through a series of physico-chemical processes including milling, acid-base leaching, designing or templating as well as formation processes. The key components of the tailings were extracted and processed to produce porous arrays with high surface area and porosity. These materials were engineered to possess specific properties suitable for environmental remediation applications, such as enhanced adsorption capacity and selectivity for target contaminants. The synthesized porous materials were thoroughly characterized using advanced analytical techniques (XRD, SEM-EDX, N2 sorption, TGA, FTIR) to assess their structural, morphological, and chemical properties. The performance of the materials in removing various pollutants, including heavy metals and organic compounds, were evaluated through batch adsorption experiments. Additionally, the potential for material regeneration and reusability was investigated to enhance the sustainability of the proposed remediation approach. The outdoors of this research holds significant promise for addressing the environmental challenges associated with phosphorite residue tailings. By valorizing these waste materials into porous materials with exceptional remediation capabilities, this study contributes to the development of sustainable and cost-effective solutions for environmental cleanup. Furthermore, the utilization of phosphorite residue tailings in this manner offers a potential avenue for the remediation of other contaminated sites, thereby fostering a circular economy approach to waste management.

Keywords: functional porous materials, phosphorite residue tailings, adsorption, environmental remediation, sustainable solutions

Procedia PDF Downloads 57