Search results for: computational methods

Authors: Hamid Tavakolipour, Mohsen Mokhtarian, Ahmad Kalbasi Ashtari

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In this study, the effect of various drying methods (sun drying, shade drying and industrial drying) on final moisture content, shell splitting degree, shrinkage and color change were studied. Sun drying resulted higher degree of pistachio nuts shell splitting on pistachio nuts relative other drying methods. The ANOVA results showed that the different drying methods did not significantly effects on color change of dried pistachio nut. The results illustrated that pistachio nut dried by industrial drying had the lowest moisture content. After the end of drying process, initially, the experimental drying data were fitted with five famous drying models namely Newton, Page, Silva et al., Peleg and Henderson and Pabis. The results indicated that Peleg and Page models gave better results compared with other models to monitor the moisture ratio’s pistachio nut in industrial drying and open sun (or shade drying) methods, respectively.

Keywords: industrial drying, pistachio, quality properties, traditional drying

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Authors: Mehdi Shahbazian, Alireza Aarabi, Mohsen Hadiyan

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Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.

Keywords: evolutionary algorithm, genetic algorithm, particle swarm optimization, PID controller

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: K. Jagannath, Akhilesh Kotian, S. S. Sharma, Achutha Kini U., P. R. Prabhu

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Boiling process is characterized by the rapid formation of vapour bubbles at the solid–liquid interface (nucleate boiling) with pre-existing vapour or gas pockets. Computational fluid dynamics (CFD) is an important tool to study bubble dynamics. In the present study, CFD simulation has been carried out to determine the bubble detachment diameter and its terminal velocity. Volume of fluid method is used to model the bubble and the surrounding by solving single set of momentum equations and tracking the volume fraction of each of the fluids throughout the domain. In the simulation, bubble is generated by allowing water-vapour to enter a cylinder filled with liquid water through an inlet at the bottom. After the bubble is fully formed, the bubble detaches from the surface and rises up during which the bubble accelerates due to the net balance between buoyancy force and viscous drag. Finally when these forces exactly balance each other, it attains a constant terminal velocity. The bubble detachment diameter and the terminal velocity of the bubble are captured by the monitor function provided in FLUENT. The detachment diameter and the terminal velocity obtained is compared with the established results based on the shape of the bubble. A good agreement is obtained between the results obtained from simulation and the equations in comparison with the established results.

Keywords: bubble growth, computational fluid dynamics, detachment diameter, terminal velocity

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Authors: Masoud Sheidai

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Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species.

Keywords: GWAS analysis, K-Means clustering, LFMM, multidimensional scaling, redundancy analysis

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Authors: Mohammed Shaath

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Aims Technological advancements in teaching and learning have made significant improvements over the past decade and have been incorporated in institutions to aid the learner’s experience. This review aims to assess whether Technology Enhanced Learning (TEL) pedagogy is more effective at improving students’ attitude and knowledge retention in orthodontic training than traditional methods. Methodology The searches comprised Systematic Reviews (SRs) related to the comparison of TEL and traditional teaching methods from the following databases: PubMed, SCOPUS, Medline, and Embase. One researcher performed the screening, data extraction, and analysis and assessed the risk of bias and quality using A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2). Kirkpatrick’s 4-level evaluation model was used to evaluate the educational values. Results A sum of 34 SRs was identified after the removal of duplications and irrelevant SRs; 4 fit the inclusion criteria. On Level 1, students showed positivity to TEL methods, although acknowledging that the harder the platforms to use, the less favourable. Nonetheless, the students still showed high levels of acceptability. Level 2 showed there is no significant overall advantage of increased knowledge when it comes to TEL methods. One SR showed that certain aspects of study within orthodontics deliver a statistical improvement with TEL. Level 3 was the least reported on. Results showed that if left without time restrictions, TEL methods may be advantageous. Level 4 shows that both methods are equally as effective, but TEL has the potential to overtake traditional methods in the future as a form of active, student-centered approach. Conclusion TEL has a high level of acceptability and potential to improve learning in orthodontics. Current reviews have potential to be improved, but the biggest aspect that needs to be addressed is the primary study, which shows a lower level of evidence and heterogeneity in their results. As it stands, the replacement of traditional methods with TEL cannot be fully supported in an evidence-based manner. The potential of TEL methods has been recognized and is already starting to show some evidence of the ability to be more effective in some aspects of learning to cater for a more technology savvy generation.

Keywords: TEL, orthodontic, teaching, traditional

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Authors: Geetanjali Yadav, Arpit Mishra, Parthsarathi Ghosh, Ramkrishna Sen

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The world is facing problems of increasing global CO2 emissions, climate change and fuel crisis. Therefore, several renewable and sustainable energy alternatives should be investigated to replace non-renewable fuels in future. Algae presents itself a versatile feedstock for the production of variety of fuels (biodiesel, bioethanol, bio-hydrogen etc.) and high value compounds for food, fodder, cosmetics and pharmaceuticals. Microalgae are simple microorganisms that require water, light, CO2 and nutrients for growth by the process of photosynthesis and can grow in extreme environments, utilize waste gas (flue gas) and waste waters. Mixing, however, is a crucial parameter within the culture system for the uniform distribution of light, nutrients and gaseous exchange in addition to preventing settling/sedimentation, creation of dark zones etc. The overarching goal of the present study is to improve photobioreactor (PBR) design for enhancing dissolution of CO2 from ambient air (0.039%, v/v), pure CO2 and coal-fired flue gas (10 ± 2%) into microalgal PBRs. Computational fluid dynamics (CFD), a state-of-the-art technique has been used to solve partial differential equations with turbulence closure which represents the dynamics of fluid in a photobioreactor. In this paper, the hydrodynamic performance of the PBR has been characterized and compared with that of the conventional bubble column PBR using CFD. Parameters such as flow rate (Q), mean velocity (u), mean turbulent kinetic energy (TKE) were characterized for each experiment that was tested across different aeration schemes. The results showed that the modified PBR design had superior liquid circulation properties and gas-liquid transfer that resulted in creation of uniform environment inside PBR as compared to conventional bubble column PBR. The CFD technique has shown to be promising to successfully design and paves path for a future research in order to develop PBRs which can be commercially available for scale-up microalgal production.

Keywords: computational fluid dynamics, microalgae, bubble column photbioreactor, flue gas, simulation

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Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Nurul Bin Ibrahim

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Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

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Authors: Jinwooung Kim, Jae-Hwan Jung, Seong-Jun Kim, Sung-Ah Kim

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Because the building envelope has a significant impact on the operation and maintenance stage of the building, designing the facade considering the performance can improve the performance of the building and lower the maintenance cost of the building. In general, however, optimizing two or more performance factors confronts the limits of time and computational tools. The optimization phase typically repeats infinitely until a series of processes that generate alternatives and analyze the generated alternatives achieve the desired performance. In particular, as complex geometry or precision increases, computational resources and time are prohibitive to find the required performance, so an optimization methodology is needed to deal with this. Instead of directly analyzing all the alternatives in the optimization process, applying experimental techniques (heuristic method) learned through experimentation and experience can reduce resource waste. This study proposes and verifies a method to optimize the double envelope of a building composed of a perforated panel using machine learning to the design geometry and quantitative performance. The proposed method is to achieve the required performance with fewer resources by supplementing the existing method which cannot calculate the complex shape of the perforated panel.

Keywords: building envelope, machine learning, perforated metal, multi-factor optimization, façade

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Authors: Tanu Khanuja, Harikrishnan N. Unni

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Head is the most vulnerable part of human body and may cause severe life threatening injuries. As the in vivo brain response cannot be recorded during injury, computational investigation of the head model could be really helpful to understand the injury mechanism. Majority of the physical damage to living tissues are caused by relative motion within the tissue due to tensile and shearing structural failures. The present Finite Element study focuses on investigating intracranial pressure and stress/strain distributions resulting from impact loads on various sites of human head. This is performed by the development of the 3D model of a human head with major segments like cerebrum, cerebellum, brain stem, CSF (cerebrospinal fluid), and skull from patient specific MRI (magnetic resonance imaging). The semi-automatic segmentation of head is performed using AMIRA software to extract finer grooves of the brain. To maintain the accuracy high number of mesh elements are required followed by high computational time. Therefore, the mesh optimization has also been performed using tetrahedral elements. In addition, model validation with experimental literature is performed as well. Hard tissues like skull is modeled as elastic whereas soft tissues like brain is modeled with viscoelastic prony series material model. This paper intends to obtain insights into the severity of brain injury by analyzing impacts on frontal, top, back, and temporal sites of the head. Yield stress (based on von Mises stress criterion for tissues) and intracranial pressure distribution due to impact on different sites (frontal, parietal, etc.) are compared and the extent of damage to cerebral tissues is discussed in detail. This paper finds that how the back impact is more injurious to overall head than the other. The present work would be helpful to understand the injury mechanism of traumatic brain injury more effectively.

Keywords: dynamic impact analysis, finite element analysis, intracranial pressure, MRI, traumatic brain injury, von Misses stress

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

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Authors: Prasansha Kumari

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This research intends to identify and analyze the new trends of usage the traditional farming methods to modern paddy cultivation. Information gathered through conducting interviews with total of 200 farmers in selected paddy cultivation areas in Kurunegalla district. As well as this research utilized by case study and observation in Ulpotha Traditional Village, Galgamuwa of Sri Lanka. Secondary data collected from books, articles, relevant websites and other relevant documents. Collected data analyzed by descriptive research methodology. Outcomes are there is growing interest in usage the traditional farming methods to the small consumption level paddy lands that have emerged during the last few decades as well as the research revealed that traditional farming method has identified the ecofriendly farming practices to restrict long term side effects inherited from the modern methods. The study finds out the demand of traditional rice varieties has been growing among the community as health and nutrition purpose.

Keywords: traditional farming, organic, inorganic, paddy cultivation

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: beam structures, layerwise, optimization, variable stiffness

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Authors: Adel Hanna, Tahar Ayadat, Mohammad Etezad, Cyrille Cros

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A number of theoretical methods have been developed over the years to calculate the amount settlement of the soil reinforced with group of stone columns. The results deduced from these methods sometimes show large disagreement with the experimental observations. The reason of this divergence might be due to the fact that many of the previous methods assumed the deform shape of the columns which is different with the actual case. A new method to calculate settlement of the ground reinforced with group of stone columns is presented in this paper which overcomes the restrictions made by previous theories. This method is based on results deduced from numerical modeling. Results obtained from the model are validated.

Keywords: stone columns, group, soft soil, settlement, prediction

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Regita P. Permata, Ulfa S. Nuraini

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Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies.

Keywords: Euclidean, heuristics, simulation, TSP

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Authors: Johanna Aromaa

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This paper presents a development project aimed at enhancing student-teachers' awareness of the role of the body in teaching and learning. In this project, theory and practice are brought into dialogue through workshops of body work that utilize art-based and somatic methods. They are carried out in a special course for educating teachers in a Finnish University. Expected results from the project include: 1) the participants become aware of the multiple roles that the body has in educational encounters, and with it, develop a more holistic approach to teaching and learning, 2) the participants gain access to and learn to form bodily knowledge, 3) a working model on enhancing student-teachers' awareness of the role of bodily knowledge in teacher’s work is developed. Innovative methods as well as a radical rethinking of the nature of teaching and learning are needed if we are to appreciate knowing body in the future schools.

Keywords: bodily knowledge, the body, somatic methods, teacher education

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Authors: Amandeep Singh Dhaliwal

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Networking solutions, particularly wireless local area networks have revolutionized the technological advancement. Wireless Local Area Networks (WLANs) have gained a lot of popularity as they provide location-independent network access between computing devices. There are a number of access methods used in Wireless Networks among which DCF and PCF are the fundamental access methods. This paper emphasizes on the impact of DCF and PCF access mechanisms on the performance of the IEEE 802.11a, 802.11b and 802.11g standards. On the basis of various parameters viz. throughput, delay, load etc performance is evaluated between these three standards using above mentioned access mechanisms. Analysis revealed a superior throughput performance with low delays for 802.11g standard as compared to 802.11 a/b standard using both DCF and PCF access methods.

Keywords: DCF, IEEE, PCF, WLAN

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Authors: Shaila Rana, Wasim Alhamdani

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Effective cybersecurity training is of the utmost importance, given the plethora of attacks that continue to increase in complexity and ubiquity. VR cybersecurity training remains a starkly understudied discipline. Studies that evaluated the effectiveness of VR cybersecurity training over traditional methods are required. An engaging and interactive platform can support knowledge retention of the training material. Consequently, an effective form of cybersecurity training is required to support a culture of cybersecurity awareness. Measurements of effectiveness varied throughout the studies, with surveys and observations being the two most utilized forms of evaluating effectiveness. Further research is needed to evaluate the effectiveness of VR cybersecurity training and traditional training. Additionally, research for evaluating if VR cybersecurity training is more effective than traditional methods is vital. This paper proposes a methodology to compare the two cybersecurity training methods and their effectiveness. The proposed framework includes developing both VR and traditional cybersecurity training methods and delivering them to at least 100 users. A quiz along with a survey will be administered and statistically analyzed to determine if there is a difference in knowledge retention and user satisfaction. The aim of this paper is to bring attention to the need to study VR cybersecurity training and its effectiveness compared to traditional training methods. This paper hopes to contribute to the cybersecurity training field by providing an effective way to train users for security awareness. If VR training is deemed more effective, this could create a new direction for cybersecurity training practices.

Keywords: virtual reality cybersecurity training, VR cybersecurity training, traditional cybersecurity training

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Authors: Helen Nelly Naiga, Christopher Garimoi Orach

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Introduction: Long-acting reversible contraceptive (LARC) methods are highly effective. However, LARC use in Uganda is low (13%). We assessed the factors associated with the use of long-acting reversible contraceptives among women of reproductive age (15-49 yrs) in Jinja District. Methods: We conducted a facility-based cross-sectional study. A total of 314 women aged 15–49 years attending public health facilities (1 hospital and 3 health center IV) in Jinja district, were randomly selected. A total of 6 key informants and 6 in-depth interviews were conducted. Logistic regression analysis was conducted using Stata version 14. Qualitative data were analysed using thematic analysis. Results: The study found that 40.45% of the respondents had ever used LARC. The commonest LARC method used was implanting (38.22%). The factors significantly associated with use of LARC were employment (AOR =2.91; 95% CI (1.05-8.08), access to LARC methods (AOR =4.48; 95% CI (1.24-16.21), husband support (AOR =4.90; 95% CI (1.56-15.41), and experience of no side effects (AOR =3.48; 95% CI (1.00-12.19). Conclusion and recommendations: The study showed that 4 in 10 women of reproductive age in Jinja District were using LARC. The factors associated with LARC use were employment, husband support, access to LARC methods, and the lack of side effects. There is a need to strengthen client education, improve accessibility to LARC methods at all levels of health centers, improve male partner’s decision-making in LARC use and manage the side effects effectively.

Keywords: family planning, implants, intrauterine device, long-acting reversible contraceptives (LARC)

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Authors: Vladimira Hornackova, Jana Kottasova, Zuzana Vanova, Anna Jungrova

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Present study deals with drama education in preschool education. The research made in this field brings a qualitative comparative survey with the aim to find out the use of methods and techniques of drama education in preschool education at university or secondary school graduate preschool teachers. The research uses a content analysis and an unstandardized questionnaire for preschool teachers and obtained data are processed with the help of descriptive methods and correlations. The results allow a comparison of aspects applied through drama in preschool education. The research brings impulses for education improvement in kindergartens and inspiration for university study programs of drama education in the professional training of preschool teachers.

Keywords: drama education, preschool education, preschool teacher, research

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Authors: Kawser Ahmed, K. Bousson, Milca F. Coelho

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4D flight trajectory optimization is one of the key ingredients to improve flight efficiency and to enhance the air traffic capacity in the current air traffic management (ATM). The present paper explores the iterative dynamic programming (IDP) as a potential numerical optimization method for 4D flight trajectory optimization. IDP is an iterative version of the Dynamic programming (DP) method. Due to the numerical framework, DP is very suitable to deal with nonlinear discrete dynamic systems. The 4D waypoint representation of the flight trajectory is similar to the discretization by a grid system; thus DP is a natural method to deal with the 4D flight trajectory optimization. However, the computational time and space complexity demanded by the DP is enormous due to the immense number of grid points required to find the optimum, which prevents the use of the DP in many practical high dimension problems. On the other hand, the IDP has shown potentials to deal successfully with high dimension optimal control problems even with a few numbers of grid points at each stage, which reduces the computational effort over the traditional DP approach. Although the IDP has been applied successfully in chemical engineering problems, IDP is yet to be validated in 4D flight trajectory optimization problems. In this paper, the IDP has been successfully used to generate minimum length 4D optimal trajectory avoiding any obstacle in its path, such as a no-fly zone or residential areas when flying in low altitude to reduce noise pollution.

Keywords: 4D waypoint navigation, iterative dynamic programming, obstacle avoidance, trajectory optimization

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Authors: Yumeng He

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The purpose of this paper is to establish the “extenics” of children music education, the “extenics” thought and methods are introduced into the children music education field. Discussions are made from the perspective of children music education on how to generate new music cognitive from music cognitive, how to generate new music education from music education and how to generate music learning from music learning. The research methods including the extensibility of music art, extensibility of music education, extensibility of music capability and extensibility of music learning. Results of this study indicate that the thought and research methods of children’s extended music education not only have developed the “extenics” concept and ideological methods, meanwhile, the brand-new thought and innovative research perspective have been employed in discussing the children music education. As indicated in research, the children’s extended music education has extended the horizon of children music education, and has endowed the children music education field with a new thought and research method.

Keywords: comprehensive evaluations, extension thought, extension cognition music education, extensibility

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Yanan Zhu

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Based on the previous work about the influence mechanism of the mobility difference of phenyl and thienyl substituent semiconductors, we have made further exploration towards to design high-performance organic thin-film transistors. The substituent groups effect plays a significant role in materials properties and device performance as well. For the theoretical study, simulation of materials property and crystal packing can supply scientific guidance for materials synthesis in experiments. This time, we have taken the computational methods to design a new material substituent with furan groups, which are the potential to be used in organic thin-film transistors and organic single-crystal transistors. The reorganization energy has been calculated and much lower than 2,6-diphenyl anthracene (DPAnt), which performs large mobility as more than 30 cm²V⁻¹s⁻¹. Moreover, the other important parameter, charge transfer integral is larger than DPAnt, which suggested the furan substituent material may get a much better charge transport data. On the whole, the mechanism investigation based on phenyl and thienyl assisted in designing novel materials with furan substituent, which is predicted to be an outperformed organic field-effect transistors.

Keywords: theoretical calculation, mechanism, mobility, organic transistors

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Wolfram Jahn, Jose Manuel Munita

Abstract:

While computational fluid dynamics (CFD) simulations of fire scenarios are commonly used in the design of buildings, less attention has been given to the use of CFD simulations as an operational tool for the fire services. The reason of this lack of attention lies mainly in the fact that CFD simulations typically take large periods of time to complete, and their results would thus not be available in time to be of use during an emergency. Firefighters often face uncertain conditions when entering a building to attack a fire. They would greatly benefit from a technology based on predictive fire simulations, able to assist their decision-making process. The principal constraint to faster CFD simulations is the fine grid necessary to solve accurately the physical processes that govern a fire. This paper explores the possibility of overcoming this constraint and using coarse grid CFD simulations for fire scenarios, and proposes a methodology to use the simulation results in a meaningful way that can be used by the fire fighters during an emergency. Data from real scale compartment fire tests were used to compare CFD fire models with different grid arrangements, and empirical correlations were obtained to interpolate data points into the grids. The results show that the strongly predominant effect of the heat release rate of the fire on the fluid dynamics allows for the use of coarse grids with relatively low overall impact of simulation results. Simulations with an acceptable level of accuracy could be run in real time, thus making them useful as a forecasting tool for emergency response purposes.

Keywords: CFD, fire simulations, emergency response, forecast

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Authors: Hee-Jae Suh, Kyung-Su Kim, Min-Ji Kim, Yeon-Seong Jeong, Ok-Hwan Lee, Jae-Wook Shin, Hyang-Sook Chun, Chan Lee

Abstract:

Canthaxanthin is keto-carotenoid produced from beta-carotene and it has been approved to be used in many countries as a food coloring agent. Canthaxanthin has been analyzed using High Performance Liquid Chromatography (HPLC) system with various ways of pretreatment methods. Four official methods for verification of canthaxanthin at FSA (UK), AOAC (US), EFSA (EU) and MHLW (Japan) were compared to improve its analytical and the pretreatment method. The Linearity, the limit of detection (LOD), the limit of quantification (LOQ), the accuracy, the precision and the recovery ratio were determined from each method with modification in pretreatment method. All HPLC methods exhibited correlation coefficients of calibration curves for canthaxanthin as 0.9999. The analysis methods from FSA, AOAC, and MLHW showed the LOD of 0.395 ppm, 0.105 ppm, and 0.084 ppm, and the LOQ of 1.196 ppm, 0.318 ppm, 0.254 ppm, respectively. Among tested methods, HPLC method of MHLW with modification in pretreatments was finally selected for the analysis of canthaxanthin in lab, because it exhibited the resolution factor of 4.0 and the selectivity of 1.30. This analysis method showed a correlation coefficients value of 0.9999 and the lowest LOD and LOQ. Furthermore, the precision ratio was lower than 1 and the accuracy was almost 100%. The method presented the recovery ratio of 90-110% with modification in pretreatment method. The cross-validation of coefficient variation was 5 or less among tested three institutions in Korea.

Keywords: analytic method, canthaxanthin, food colorants, pretreatment method

Procedia PDF Downloads 683