Search results for: index structural equation model

Authors: Ridzuan Ramli, Norhafzan Junadi, Mohammad D.H. Beg, Rosli M. Yunus

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In this study, microcrystalline cellulose (MCC) was extracted from oil palm empty fruit bunch (EFB) cellulose which was earlier isolated from oil palm EFB fibre. In order to isolate the cellulose, the chlorination method was carried out. Then, the MCC was prepared by simultaneous ultrasonic and alkali treatment from the isolated α-cellulose. Based on mass balance calculation, the yields for MCC obtained from EFB was 44%. For fiber characterization, it is observed that the chemical composition of the hemicellulose and lignin for all samples decreased while composition for cellulose increased. The structural property of the MCC was studied by X-ray diffraction (XRD) method and the result shows that the MCC produced is a cellulose-I polymorph, with 73% crystallinity.

Keywords: oil palm empty fruit bunch, microcrystalline cellulose, ultrasonic, alkali treatment, x-ray diffraction

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Authors: Axel Thallemer, Aleksandar Kostadinov, Abel Fam, Alex Teo

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For centuries, the forming processes in nature served as a source of inspiration for both architects and designers. It seems as most human artifacts are based on ideas which stem from the observation of the biological world and its principles of growth. As a fact, in the cultural history of Homo faber, materials have been mostly used in their solid state: From hand axe to computer mouse, the principle of employing matter has not changed ever since the first creation. In the scope of history only recently and by the help of additive-generative fabrication processes through Computer Aided Design (CAD), designers were enabled to deconstruct solid artifacts into an outer skin and an internal lattice structure. The intention behind this approach is to create a new topology which reduces resources and integrates functions into an additively manufactured component. However, looking at the currently employed lattice structures, it is very clear that those lattice structure geometries have not been thoroughly designed, but rather taken out of basic-geometry libraries which are usually provided by the CAD. In the here presented study, a group of 20 industrial design students created new and unique lattice structures using natural paragons as their models. The selected natural models comprise both the animate and inanimate world, with examples ranging from the spiraling of narwhal tusks, off-shooting of mangrove roots, minimal surfaces of soap bubbles, up to the rhythmical arrangement of molecular geometry, like in the case of SiOC (Carbon-Rich Silicon Oxicarbide). This ideation process leads to a design of a geometric cell, which served as a basic module for the lattice structure, whereby the cell was created in visual analogy to its respective natural model. The spatial lattices were fabricated additively in mostly [X]3 by [Y]3 by [Z]3 units’ volumes using selective powder bed melting in polyamide with (z-axis) 50 mm and 100 µm resolution and subdued to mechanical testing of their elastic zone in a biomedical laboratory. The results demonstrate that additively manufactured lattice structures can acquire different properties when they are designed in analogy to natural models. Several of the lattices displayed the ability to store and return kinetic energy, while others revealed a structural failure which can be exploited for purposes where a controlled collapse of a structure is required. This discovery allows for various new applications of functional lattice structures within industrially created objects.

Keywords: bio-inspired, biomimetic, lattice structures, additive manufacturing

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Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

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Authors: Margarita Zavala, Julio Rolón, Gabriel Chavira, José González, Jorge Orozco, Roberto Pichardo

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Adolescence is a neuralgic stage in the formation of every human being, generally at this stage is when the middle school level is studied. In 2006 in Mexico incorporated “mentoring" space to assist students in their integration and participation in life. In public middle schools, is sometimes difficult to be aware of situations that affect students because of the number of them and traditional records management. Whit this they lose the opportunity to provide timely support as a preventive way. In order to provide this support, it is required to know the students by detecting the relevant information that has greater impact on their learning process. This research is looking to check if it is possible to identify student’s relevant information to detect when it is at risk, and then to propose a model to manage in a proper way such information.

Keywords: adolescence, mentoring, middle school students, mentoring system support

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Authors: Rym Zouari, Sami Ben Amar, Abdelwaheb Dogui

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This paper presents an experimental characterization of the anisotropic mechanical behavior of 4 textile woven fabrics with different weaves (Twill 3, Plain, Twill4 and Satin 4) by off-axis tensile testing. These tests are applied according seven directions oriented by 15° increment with respect to the warp direction. Fixed and articulated jaws are used. Analysis of experimental results is done through global (Effort/Elongation curves) and local scales. Global anisotropy was studied from the Effort/Elongation curves: shape, breaking load (Frup), tensile elongation (EMT), tensile energy (WT) and linearity index (LT). Local anisotropy was studied from the measurement of strain tensor components in the central area of the specimen as a function of testing orientation and effort: longitudinal strain ɛL, transverse strain ɛT and shearing ɛLT. The effect of used jaws is also analyzed.

Keywords: anisotropy, off-axis tensile test, strain fields, textile woven fabric

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Authors: Chul Park, Youngseok Kim, Sangsik Choi

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This study developed an electric jig for underwater structure inspection in order to solve the problem of the application of side scan sonar to underwater inspection, and analyzed correlations of empirical data in order to enhance sonar data resolution. For the application of tow-typed sonar to underwater structure inspection, an electric jig was developed. In fact, it was difficult to inspect a cross-section at the time of inspection with tow-typed equipment. With the development of the electric jig for underwater structure inspection, it was possible to shorten an inspection time over 20%, compared to conventional tow-typed side scan sonar, and to inspect a proper cross-section through accurate angle control. The indoor test conducted to enhance sonar data resolution proved that a water depth, the distance from an underwater structure, and a filming angle influenced a resolution and data quality. Based on the data accumulated through field experience, multiple regression analysis was conducted on correlations between three variables. As a result, the relational equation of sonar operation according to a water depth was drawn.

Keywords: underwater structure, SONAR, safety inspection, resolution

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Authors: Saimir Heta, Ilma Robo, Nevila Alliu, Tea Meta

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Background: Plasma levels of inflammatory markers are the expression of the infectious wastes of existing periodontitis, as well as of existing inflammation everywhere in the body. Materials and Methods: The study consists of the clinical part of the measurement of inflammatory markers of 23 patients diagnosed with chronic periodontitis and the recording of parental periodontal parameters of patient periodontal status: hemorrhage index and probe values, before and 7-10 days after non-surgical periodontal treatment. Results: The level of fibrinogen drops according to the categorization of disease progression, active and passive, with the biggest % (18%-30%) at the fluctuation 10-20 mg/d. Fluctuations in fibrinogen level according to the age of patients in the range 0-10 mg/dL under 40 years and over 40 years was 13%-26%, in the range 10-20 mg/dL was 26%-22%, in the 20-40 mg/dL was 9%-4%. Conclusions: Non-surgical periodontal treatment significantly reduces the level of non-inflammatory markers in the blood. Oral health significantly reduces the potential source for periodontal bacteria, with the potential of promoting thromboembolism, through interaction between thrombocytes.

Keywords: chronic periodontitis, atherosclerosis, risk factor, inflammatory markers

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Authors: Abderrahmen Bouguerra, Kamel Kara, Djamel Saigaa, Samir Zeghlache, Keltoum Loukal

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In this paper, a Fault-Tolerant control of 2 DOF Helicopter (TRMS System) Based on Fuzzy Gain-Adaptive PID is presented. In particular, the introduction part of the paper presents a Fault-Tolerant Control (FTC), the first part of this paper presents a description of the mathematical model of TRMS, an adaptive PID controller is proposed for fault-tolerant control of a TRMS helicopter system in the presence of actuator faults, A fuzzy inference scheme is used to tune in real-time the controller gains, The proposed adaptive PID controller is compared with the conventional PID. The obtained results show the effectiveness of the proposed method.

Keywords: fuzzy control, gain-adaptive PID, helicopter model, PID control, TRMS system

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Vitalijs Lusis, Andrejs Krasnikovs

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Fiber reinforced concrete is important material for load bearing structural elements. Usually fibers are homogeneously distributed in a concrete body having arbitrary spatial orientations. At the same time, in many situations, fiber concrete with oriented fibers is more optimal. Is obvious, that is possible to create constructions with oriented short fibers in them, in different ways. Present research is devoted to one of such approaches- fiber reinforced concrete prisms having dimensions 100 mm×100 mm×400 mm with layers of non-homogeneously distributed fibers inside them were fabricated. Simultaneously prisms with homogeneously dispersed fibers were produced for reference as well. Prisms were tested under four point bending conditions. During the tests vertical deflection at the center of every prism and crack opening were measured (using linear displacements transducers in real timescale). Prediction results were discussed.

Keywords: fiber reinforced concrete, 4-point bending, steel fiber, construction engineering

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Authors: Abdulwahab S. Shmailan

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The links between the halalan tayyiban dimensions and their impact on the propensity to purchase halal cosmetics in Muslim culture are investigated in this study. The information was gathered by a self-administered questionnaire survey of 207 Saudi Muslim customers using purposive sampling. The suggested model was tested using Pearson correlation coefficients and an ANOVA test. Significant and positive connections were found between halalan tayyiban dimensions, attitudes, and purchasing intent. There were also substantial changes in the study parameters depending on the respondent's work title. This is one of the first empirical tests of the halalan tayyiban, attitudes, and intention to purchase model among Saudi Muslim customers. The study offers helpful recommendations for cosmetics sector marketers as well as strategy formulation.

Keywords: cosmetics, halal cosmetics, halalan tayyiban, halal certificate, customers attitude, intention to purchase

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Authors: P. Kacejko, M. Wendeker

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The fuel consumption of city buses depends on a number of factors that characterize the technical properties of the bus and driver, as well as traffic conditions. This parameter related to greenhouse gas emissions is regulated by law in many countries. This applies to both fuel consumption and exhaust emissions. Simulation studies are a way to reduce the costs of optimization studies. The paper describes simulation research of fuel consumption city bus driving. Parameters of the developed model are based on experimental results obtained on chassis dynamometer test stand and road tests. The object of the study was a city bus equipped with a compression-ignition engine. The verified model was applied to simulate the behavior of a bus during the CUEDC Australian Driving Cycle. The results of the calculations showed a direct influence of driving dynamics on fuel consumption.

Keywords: Australian Driving Cycle, city bus, diesel engine, fuel consumption

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Authors: Azizah Ismail, Zainab Khalifah, Abbas Mardani

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Sustainable community-based tourism through homestay programme is a growing niche market that has impacted destinations in many countries including Malaysia. With demand predicted to continue increasing, the importance of the homestay product will grow in the tourism industry. This research examines the sustainability criteria for homestay programme in Malaysia covering economic, socio-cultural and environmental dimensions. This research applied a two-stage methodology for data analysis. Specifically, the researcher implements a hybrid method which combines two multi-criteria decision making approaches. In the first stage of the methodology, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is applied. Then, Analytical Network Process (ANP) is employed for the achievement of the objective of the current research. After factors identification and problem formulation, DEMATEL is used to detect complex relationships and to build a Network Relation Map (NRM). Then ANP is used to prioritize and find the weights of the criteria and sub-criteria of the decision model. The research verifies the framework of multi-criteria for sustainable community-based tourism from the perspective of stakeholders. The result also provides a different perspective on the importance of sustainable criteria from the view of multi-stakeholders. Practically, this research gives the framework model and helps stakeholders to improve and innovate the homestay programme and also promote community-based tourism.

Keywords: community-based tourism, homestay programme, sustainable tourism criteria, sustainable tourism development

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Authors: Haitham A. Hussein, Rozi Abdullah, Issa Saket, Md. Azlin

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The gravitational effect has been extensively applied to separate oil from water in water and wastewater treatment systems. The maximum oil globules removal efficiency is improved by obtaining the best flow uniformity in separator tanks. This study used 2D computational fluid dynamics (CFD) to investigate the effect of different inlet baffle positions inside the separator tank. Laboratory experiment has been conducted, and the measured velocity fields which were by Nortek Acoustic Doppler Velocimeter (ADV) are used to verify the CFD model. Computational investigation results indicated that the construction of an inlet baffle in a suitable location provides the minimum recirculation zone volume, creates the best flow uniformity, and dissipates kinetic energy in the oil and water separator tank. Useful formulas were predicted to design the oil and water separator tanks geometry based on an experimental model.

Keywords: oil/water separator tanks, inlet baffles, CFD, VOF

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Authors: Zahra Hamedani, Ebrahim Solgi, Henry Skates, Gillian Isoardi

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Daylighting plays a pivotal role in promoting productivity and user satisfaction in office spaces. There is an ongoing trend in designing office buildings with a high proportion of glazing which relatively increases the risk of high visual discomfort. Providing a more realistic lighting analysis can be of high value at the early stages of building design when necessary changes can be made at a very low cost. This holistic approach can be achieved by incorporating subjective evaluation and user behaviour in computer simulation and provide a comprehensive lighting analysis. In this research, a detailed computer simulation model has been made using Radiance and Daysim. Afterwards, this model was validated by measurements and user feedback. The case study building is the school of science at Griffith University, Gold Coast, Queensland, which features highly glazed office spaces. In this paper, the visual comfort predicted by the model is compared with a preliminary survey of the building users to evaluate how user behaviour such as desk position, orientation selection, and user movement caused by daylight changes and other visual variations can inform perceptions of visual comfort. This work supports preliminary design analysis of visual comfort incorporating the effects of gaze shift patterns and views with the goal of designing effective layout for office spaces.

Keywords: lighting simulation, office buildings, user behaviour, validation, visual comfort

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Authors: Azadeh Farzaneh, Mohammad Reza Abdi, A. Quaranta, Matteo Dalla Palma, Seyedshahram Mortazavi

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We report on the synthesis of cesium-iodide nanoparticles using sol-gel technique. The structural properties of CsI nanoparticles were characterized by X-ray diffraction and Scanning Electron Microscope (SEM) Also, optical properties were followed by optical absorption and UV–vis fluorescence. Intense photoluminescence is also observed, with some spectral tuning possible with ripening time getting a range of emission photon wavelength approximately from 366 to 350 nm. The size effect on CsI luminescence leads to an increase in scintillation light yield, a redshift of the emission bands of the on_center and off_center self_trapped excitons (STEs) and an increase in the contribution of the off_center STEs to the net intrinsic emission yield. The energy transfer from the matrix to CsI nanoparticles is a key characteristic for scintillation detectors. So the scintillation spectra to alpha particles of sample were monitored.

Keywords: nanoparticles, luminescence, sol gel, scintillator

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Authors: Pooja Booluck

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A U-shaped learning curve entails a three-step process: a good performance followed by a bad performance followed by a good performance again. U-shaped curves have been observed not only in language acquisition but also in various fields such as temperature face recognition object permanence to name a few. Building on previous studies of the curve child language acquisition and Second Language Acquisition this empirical study seeks to investigate the relevance of the U-shaped learning model to the acquisition of the difference between cest and il est in the English Learners of French context. The present study was developed to assess whether older learners of French in the ELF context follow the same acquisition pattern. The empirical study was conducted on 15 English learners of French which lasted six weeks. Compositions and questionnaires were collected from each subject at three time intervals (after one week after three weeks after six weeks) after which students work were graded as being either correct or incorrect. The data indicates that there is evidence of a U-shaped learning curve in the acquisition of cest and il est and students did follow the same acquisition pattern as children in regards to rote-learned terms and subject clitics. This paper also discusses the need to introduce modules on U-shaped learning curve in teaching curriculum as many teachers are unaware of the trajectory learners undertake while acquiring core components in grammar. In addition this study also addresses the need to conduct more research on the acquisition of rote-learned terms and subject clitics in SLA.

Keywords: child language acquisition, rote-learning, subject clitics, u-shaped learning model

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Authors: Badr Alsulami, Ahmed S. Elamary

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This paper summarizes and presents main results of an in-depth numerical analysis dealing with the shear buckling resistance of aluminum plate girders. The studies conducted have permitted the development of a simple design expression to determine the critical shear buckling stress in aluminum web panels. This expression takes into account the effects of reduction of strength in aluminum alloys due to the welding process. Ultimate shear resistance (USR) of plate girders can be obtained theoretically using Cardiff theory or Hӧglund’s theory. USR of aluminum alloy plate girders predicted theoretically using BS8118 appear inconsistent when compared with test data. Theoretical predictions based on Hӧglund’s theory, are more realistic. Cardiff theory proposed to predict the USR of steel plate girders only. Welded aluminum alloy plate girders studied experimentally by others; the USR resulted from tests are reviewed. Comparison between the test results with the values obtained from Hӧglund’s theory, BS8118 design method, and Cardiff theory performed theoretically. Finally, a new equation based on Cardiff tension-field theory proposed to predict theoretically the USR of aluminum plate girders.

Keywords: shear resistance, aluminum, Cardiff theory, Hӧglund's theory, plate girder

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Authors: X. Li, J. Sundquist, C. Sjöstedt, M. Winkleby, K. S. Kendler, K. Sundquist

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Aims: This study examines the association between the incidence of drug abuse (DA) and linking (communal) social capital, a theoretical concept describing the amount of trust between individuals and societal institutions. Methods: We present results from an 8-year population-based cohort study that followed all residents in Sweden, aged 15-44, from 2003 through 2010, for a total of 1,700,896 men and 1,642,798 women. Social capital was conceptualized as the proportion of people in a geographically defined neighborhood who voted in local government elections. Multilevel logistic regression was used to estimate odds ratios (ORs) and between-neighborhood variance. Results: We found robust associations between linking social capital (scored as a three level variable) and DA in men and women. For men, the OR for DA in the crude model was 2.11 [95% confidence interval (CI) 2.02-2.21] for those living in areas with the lowest vs. highest level of social capital. After accounting for neighborhood-level deprivation, the OR fell to 1.59 (1.51-1-68), indicating that neighborhood deprivation lies in the pathway between linking social capital and DA. The ORs remained significant after accounting for age, sex, family income, marital status, country of birth, education level, and region of residence, and after further accounting for comorbidities and family history of comorbidities and family history of DA. For women, the OR decreased from 2.15 (2.03-2.27) in the crude model to 1.31 (1.22-1.40) in the final model, adjusted for multiple neighborhood-level and individual-level variables. Conclusions: Our study suggests that low linking social capital may have important independent effects on DA.

Keywords: drug abuse, social linking capital, environment, family

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Aaditya U. Jhamb

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Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

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Authors: Ayşe Yurdakul, Eckehard Schnieder

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Because of the growing multidisciplinarity and multilinguality, communication problems in different technical fields grows more and more. Therefore, each technical field has its own specific language, terminology which is characterised by the different definition of terms. In addition to definition problems, there are also relation problems between terms. Among these problems of relation, there are the synonymy, antonymy, hypernymy/hyponymy, ambiguity, risk of confusion, and translation problems etc. Thus, the terminology management system iglos of the Institute for Traffic Safety and Automation Engineering of the Technische Universität Braunschweig has the target to solve these problems by a methodological standardisation of term definitions with the aid of the iglos sign model and iglos relation types. The focus of this paper should be on solving definition and relation problems between terms in English navigation terminology.

Keywords: iglos, iglos sign model, methodological resolutions, navigation terminology, common language, technical language, positioning, definition problems, relation problems

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Authors: Zahra Yazdani Paraii, Zohreh Yazdani Paraei

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Many urban designers and planners have been involved in the design of environmentally friendly or nature adaptable urban development models due to increase in urban populations in the recent century, limitation on natural resources, climate change, and lack of enough water and food. Ecological city is one of the latest models proposed to accomplish the latter goal. In this work, the existing establishing indicators of the ecological city are used regarding energy, water, land use and transportation issues. The model is used to compare the function of traditional settlements of Iran. The result of investigation shows that the specifications and functions of the traditional settlements of Iran fit well into the ecological city model. It is found that the inhabitants of the old cities and villages in Iran had founded ecological cities based on their knowledge of the environment and its natural opportunities and limitations.

Keywords: ecological city, traditional city, urban design, environment

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Authors: Abirami Baleswaran, Christel Couderc, Loubnah Belahcen, Jean Dayde, Hélène Tormo, Gwénaëlle Jard

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Cheeses are often reported to be spoiled by Pseudomonas spp., responsible for defects in appearance, texture, taste, and smell, leading to their non-marketing and even their destruction. Despite preventive actions, problems linked to Pseudomonas spp. are difficult to control by the lack of knowledge and control of these contaminants during the cheese manufacturing. Lactic goat cheese producers are not spared by this problem and are looking for solutions to decrease the number of spoiled cheeses. To explore different hypotheses, experiments are needed. However, cheese-making experiments at the pilot scale are expensive and time consuming. Thus, there is a real need to develop a miniature cheeses model system under controlled conditions. In a previous study, several miniature cheese models corresponding to different type of commercial cheeses have been developed for different purposes. The models were, for example, used to study the influence of milk, starters cultures, pathogen inhibiting additives, enzymatic reactions, microflora, freezing process on cheese. Nevertheless, no miniature model was described on the lactic goat cheese. The aim of this work was to develop a miniature cheese model system under controlled laboratory conditions which resembles commercial lactic goat cheese to study Pseudomonas spp. spoilage during the manufacturing and ripening process. First, a protocol for the preparation of miniature cheeses (3.5 times smaller than a commercial one) was designed based on the cheese factorymanufacturing process. The process was adapted from “Rocamadour” technology and involves maturation of pasteurized milk, coagulation, removal of whey by centrifugation, moulding, and ripening in a little scale cellar. Microbiological (total bacterial count, yeast, molds) and physicochemical (pH, saltinmoisture, moisture in fat-free)analyses were performed on four key stages of the process (before salting, after salting, 1st day of ripening, and end of ripening). Factory and miniature cheeses volatilomewere also obtained after full scan Sift-MS cheese analysis. Then, Pseudomonas spp. strains isolated from contaminated cheeses were selected on their origin, their ability to produce pigments, and their enzymatic activities (proteolytic, lecithinasic, and lipolytic). Factory and miniature curds were inoculated by spotting selected strains on the cheese surface. The expression of cheese spoilage was evaluated by counting the level of Pseudomonas spp. during the ripening and by visual observation and under UVlamp. The physicochemical and microbiological compositions of miniature cheeses permitted to assess that miniature process resembles factory process. As expected, differences involatilomes were observed, probably due to the fact that miniature cheeses are made usingpasteurized milk to better control the microbiological conditions and also because the little format of cheese induced probably a difference during the ripening even if the humidity and temperature in the cellar were quite similar. The spoilage expression of Pseudomonas spp. was observed in miniature and factory cheeses. It confirms that the proposed model is suitable for the preparation of miniature cheese specimens in the spoilage study of Pseudomonas spp. in lactic cheeses. This kind of model could be deployed for other applications and other type of cheese.

Keywords: cheese, miniature, model, pseudomonas spp, spoilage

Procedia PDF Downloads 124

Authors: V. Keim, J. Spachtholz, J. Hammer

Abstract:

The importance of fibre reinforced plastics continually increases due to the excellent mechanical properties, low material and manufacturing costs combined with significant weight reduction. Today, components are usually designed and calculated numerically by using finite element methods (FEM) to avoid expensive laboratory tests. These programs are based on material models including material specific deformation characteristics. In this research project, material models for short glass fibre reinforced plastics are presented to simulate the visco-elasto-plastic deformation behaviour. Prior to modelling specimens of the material EMS Grivory HTV-5H1, consisting of a Polyphthalamide matrix reinforced by 50wt.-% of short glass fibres, are characterized experimentally in terms of the highly time dependent deformation behaviour of the matrix material. To minimize the experimental effort, the cyclic deformation behaviour under tensile and compressive loading (R = −1) is characterized by isothermal complex low cycle fatigue (CLCF) tests. Combining cycles under two strain amplitudes and strain rates within three orders of magnitude and relaxation intervals into one experiment the visco-elastic deformation is characterized. To identify visco-plastic deformation monotonous tensile tests either displacement controlled or strain controlled (CERT) are compared. All relevant modelling parameters for this complex superposition of simultaneously varying mechanical loadings are quantified by these experiments. Subsequently, two different material models are compared with respect to their accuracy describing the visco-elasto-plastic deformation behaviour. First, based on Chaboche an extended 12 parameter model (EVP-KV2) is used to model cyclic visco-elasto-plasticity at two time scales. The parameters of the model including a total separation of elastic and plastic deformation are obtained by computational optimization using an evolutionary algorithm based on a fitness function called genetic algorithm. Second, the 12 parameter visco-elasto-plastic material model by Launay is used. In detail, the model contains a different type of a flow function based on the definition of the visco-plastic deformation as a part of the overall deformation. The accuracy of the models is verified by corresponding experimental LCF testing.

Keywords: complex low cycle fatigue, material modelling, short glass fibre reinforced polyphthalamides, visco-elasto-plastic deformation

Procedia PDF Downloads 205

Authors: E. H. N. Gashti, J. Razzaghi, K. Kujala

Abstract:

In general, codes and regulations consider seismic loads only for completed structures of the bridges while, evaluation of incomplete structure of bridges, especially those constructed by free cantilever method, under these loads is also of great importance. Hence, this research tried to study the behavior of incomplete structure of common bridge type (box girder bridge), in construction phase under vertical seismic loads. Subsequently, the paper provided suitable guidelines and solutions to withstand this destructive phenomena. Research results proved that use of preventive methods can significantly reduce the stresses resulted from vertical seismic loads in box cross sections to an acceptable range recommended by design codes.

Keywords: box girder bridges, prestress loads, free cantilever method, seismic loads, construction phase

Procedia PDF Downloads 329

Authors: M. Peña Aguilar Juan, Valencia Luis, Pastrana Alberto, Nava Estefany, A. Martinez, M. Vivanco, A. Castañeda

Abstract:

This paper seeks to compare the innovation of Mexico from an economic and human perspective, with the seven most innovative countries according to the Global Innovation Index 2013, done by the World Intellectual Property Organization (WIPO). The above analysis suggests nine dimensions: Expenditure on R & D, intellectual property, appropriate environment to conduct business, economic stability, and triple helix for R & D, ICT Infrastructure, education, human resources and quality of life. Each dimension is represented by an indicator which is later used to construct a radial graph that compares the innovative capacity of the countries analysed. As a result, it is proposed a new indicator of innovation called The Area of Innovation. Observations are made from the results, and finally as a conclusion, those items or dimensions in which Mexico suffers lag in innovation are identify.

Keywords: dimension, measure, innovation level, economy, radar chart

Procedia PDF Downloads 453

Authors: Abdulrahman Abdulrahman

Abstract:

Critical depth meters, such as abroad crested weir, Venture Flume and combined control flume are standard devices for measuring flow in open channels. The discharge relation for these devices cannot be solved directly, but it needs iteration process to account for the approach velocity head. In this paper, analytical solution was developed to calculate the discharge in a combined critical depth-meter namely, a hump combined with lateral contraction in rectangular channel with subcritical approach flow including energy losses. Also analytical formulae were derived for approach velocity head coefficient for different types of critical depth meters. The solution was derived by solving a standard cubic equation considering energy loss on the base of trigonometric identity. The advantage of this technique is to avoid iteration process adopted in measuring flow by these devices. Numerical examples are chosen for demonstration of the proposed solution.

Keywords: broad crested weir, combined control meter, control structures, critical flow, discharge measurement, flow control, hydraulic engineering, hydraulic structures, open channel flow

Procedia PDF Downloads 258