Search results for: numerical weather prediction

Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Edip Kemal, Sheshov Vlatko, Bojadjieva Julijana, Bogdanovic ALeksandra, Gjorgjeska Irena

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The wave propagation phenomenon in porous domains is of great importance in the field of geotechnical earthquake engineering. In these kinds of problems, the elastic waves propagate from the interior to the exterior domain and require special treatment at the computational level since apart from displacement in the solid-state there is a p-wave that takes place in the pore water phase. In this paper, a study on the implementation of multiphase finite elements is presented. The proposed algorithm is implemented in the ANSYS finite element software and tested on one-dimensional wave propagation considering both pore pressure wave propagation and displacement fields. In the simulation of porous media such as soils, the behavior is governed largely by the interaction of the solid skeleton with water and/or air in the pores. Therefore, coupled problems of fluid flow and deformation of the solid skeleton are considered in a detailed way.

Keywords: wave propagation, multiphase model, numerical methods, finite element method

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Authors: Teng Zhang, Xuelin Zhang, Shouxiang Lu, Changhai Li

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The existence of animal groups and collective migration are common in nature, and collective behavior is attracting more and more attention of researchers. Previous results have shown that architectural design had an important effect on the process of crowd evacuation. In this paper, collective behavior of mice passing through a middle-exit or corner-exit under panic was investigated. Selfish behavior and herd behavior were easily observed in our video, which caused the congregation with high density near the exit. Triangle structure of congregation formed near the middle-exit while arch structure formed near the corner-exit. It is noteworthy that the exit located at the middle of the wall was more effective for evacuation than at the corner. Meanwhile, the escape sequence of mouse passing through the exit was investigated, and the result showed that the priority depends largely on its location in the congregation. With the level of stimulus increasing, these phenomena still exist. The frequency distributions of time intervals and the burst sizes were also analyzed in this study to explore the secret of collective behavior of mice. These results could provide evidence for the hypothesis or prediction about human behavior in crowd evacuation. However, it is not clear whether the simulated results from different species can correspond to reality or not. Broader comparison among different species about this topic will be eager to be conducted to deepen our understanding of collective behavior in nature.

Keywords: collective behavior, mice, evacuation, exit location

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Manju Pandey, Nilay Khare, S. C. Shrivastava

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This paper is the final in a series, which has defined two new classes of aggregation operators for triangular and trapezoidal fuzzy numbers based on the geometrical characteristics of their fuzzy membership functions. In the present paper, a new aggregation operator for trapezoidal fuzzy numbers has been defined. The new operator is based on the geometric mean of the membership lines to the left and right of the maximum possibility interval. The operator is defined and the analytical relationships have been derived. Computation of the aggregate is demonstrated with a numerical example. Corresponding arithmetic and geometric aggregates as well as results from the recent work of the authors on TrFN aggregates have also been computed.

Keywords: LR fuzzy number, interval fuzzy number, triangular fuzzy number, trapezoidal fuzzy number, apex angle, left apex angle, right apex angle, aggregation operator, arithmetic and geometric mean

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Sujoy Das, M. M. Ghosh

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The thermal conductivity of a fluid can be significantly enhanced by dispersing nano-sized particles in it, and the resultant fluid is termed as "nanofluid". A theoretical model for estimating the thermal conductivity of a nanofluid has been proposed here. It is based on the mechanism that evenly dispersed nanoparticles within a nanofluid undergo Brownian motion in course of which the nanoparticles repeatedly collide with the heat source. During each collision a rapid heat transfer occurs owing to the solid-solid contact. Molecular dynamics (MD) simulation of the collision of nanoparticles with the heat source has shown that there is a pulse-like pick up of heat by the nanoparticles within 20-100 ps, the extent of which depends not only on thermal conductivity of the nanoparticles, but also on the elastic and other physical properties of the nanoparticle. After the collision the nanoparticles undergo Brownian motion in the base fluid and release the excess heat to the surrounding base fluid within 2-10 ms. The Brownian motion and associated temperature variation of the nanoparticles have been modeled by stochastic analysis. Repeated occurrence of these events by the suspended nanoparticles significantly contributes to the characteristic thermal conductivity of the nanofluids, which has been estimated by the present model for a ethylene glycol based nanofluid containing Cu-nanoparticles of size ranging from 8 to 20 nm, with Gaussian size distribution. The prediction of the present model has shown a reasonable agreement with the experimental data available in literature.

Keywords: brownian dynamics, molecular dynamics, nanofluid, thermal conductivity

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Authors: Roshanak Khodabakhsh Jolfaei, Javad Akbari Torkestani

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As a cooperative environment for problem-solving, it is necessary that grids develop efficient job scheduling patterns with regard to their goals, domains and structure. Since the Grid environments facilitate distributed calculations, job scheduling appears in the form of a critical problem for the management of Grid sources that influences severely on the efficiency for the whole Grid environment. Due to the existence of some specifications such as sources dynamicity and conditions of the network in Grid, some algorithm should be presented to be adjustable and scalable with increasing the network growth. For this purpose, in this paper a job scheduling algorithm has been presented on the basis of learning automata in computational Grid which the performance of its results were compared with FPSO algorithm (Fuzzy Particle Swarm Optimization algorithm) and GJS algorithm (Grid Job Scheduling algorithm). The obtained numerical results indicated the superiority of suggested algorithm in comparison with FPSO and GJS. In addition, the obtained results classified FPSO and GJS in the second and third position respectively after the mentioned algorithm.

Keywords: computational grid, job scheduling, learning automata, dynamic scheduling

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Yongjun Kim

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Since the agreement on the historicity of historical Buddha in eastern India, the beginning, heyday and decline of Buddhism in Early India have been discussed in urbanization, commercialism and state formation context, in short, Weberian socio-politico frame. Recent Scholarship, notably in archaeology and anthropology, has proposed ‘re-materialization of Buddhism in Early India’ based on what Buddhist had actually done rather than what they should do according to canonical teachings or philosophies. But its historical narrations still remain with a domain of socio-politico meta-narrative which tends to unjustifiably dismiss the naturally existing heterogeneity and often chaotic dynamic of diverse agencies, landscape perceptions, localized traditions, etc. An author will argue the multiplicity of theoretical standpoints for the reconstruction on the Buddhism in Early India. For this, at first, the diverse agencies, localized traditions, landscape patterns of Buddhist communities and monasteries in Trans-Himalayan regions; focusing Zanskar Valley and Spiti Valley in India will be illustrated based on an author’s field work. And then an author will discuss this anthropological landscape analysis is better appropriated with textual and archaeological evidences on the tension between urban monastic and forest Buddhism, the phenomena of sacred landscape, cemetery, garden, natural cave along with socio-economic landscape, the demographic heterogeneity in Early India. Finally, it will be attempted to compare between anthropological landscape of present Trans-Himalayan and archaeological one of ancient Western India. The study of Buddhism in Early India has hardly been discussed through multivalent theoretical archaeology and anthropology of religion, thus traditional and recent scholarship have produced historical meta-narrative though heterogeneous among them. The multidisciplinary approaches of textual critics, archaeology and anthropology will surely help to deconstruct the grand and all-encompassing historical description on Buddhism in Early India and then to reconstruct the localized, behavioral and multivalent narratives. This paper expects to highlight the importance of lesser-studied Buddhist archaeological sites and the dynamic views on religious landscape in Early India with a help of critical anthropology of religion.

Keywords: analogy by living traditions, Buddhism in Early India, landscape analysis, meta-narrative

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Authors: Hyu Sang Jo, Gyo Woo Lee

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In this study, the evaluation of thermal stability of the micrometer-sized silica particle reinforced epoxy composite was carried out through the measurement of thermal expansion coefficient and Young’s modulus of the specimens. For all the specimens in this study from the baseline to those containing 50 wt% silica filler, the thermal expansion coefficients and the Young’s moduli were gradually decreased down to 20% and increased up to 41%, respectively. The experimental results were compared with filler-volume-based simple empirical relations. The experimental results of thermal expansion coefficients correspond with those of Thomas’s model which is modified from the rule of mixture. However, the measured result for Young’s modulus tends to be increased slightly. The differences in increments of the moduli between experimental and numerical model data are quite large.

Keywords: thermal stability, silica-reinforced, epoxy composite, coefficient of thermal expansion, empirical model

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Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

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In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

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Authors: Natina Yaduma, Allan Williams, Sangwon Park, Andrew Lockwood

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This paper analyses flexible working, and the employment of migrants, as determinants of productivity in hotels. Controlling for the institutional environment, by focussing on a single firm, it analyses data on actual hours worked and outputs, on a weekly basis, over an 8 year period. The unusually disaggregated data allows the paper to examine not only inter-establishment, but also intra-establishment (departmental) variations in productivity, and to compare financial versus physical measures. The findings emphasise the complexity of productivity findings, sometimes contrasting evidence for establishments versus departments, and the positive but scale and measure-specific contributions of both the employment of migrants and flexible working, especially the utilisation of zero hours contracts.

Keywords: labour productivity, physical productivity, financial productivity, numerical flexibility, functional flexibility, migrant employment, cero-contract employment

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: Chaimongkol Limpianchob

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This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: aromatic coconut, supply chain management, production planning, mixed-integer linear programming

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Authors: Anwar Ul Haque, Waqar Asrar, Erwin Sulaeman, Jaffar S. M. Ali

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Pressure distribution data of the standard airfoils is usually used for the calibration purposes in subsonic wind tunnels. Results of such experiments are quite old and obtained by using the model in the spanwise direction. In this manuscript, pressure distribution over NACA 4412 airfoil model was presented by placing the 3D model in the lateral direction. The model is made of metal with pressure ports distributed longitudinally as well as in the lateral direction. The pressure model was attached to the floor of the tunnel with the help of the base plate to give the specified angle of attack to the model. Before the start of the experiments, the pressure tubes of the respective ports of the 128 ports pressure scanner are checked for leakage, and the losses due to the length of the pipes were also incorporated in the results for the specified pressure range. Growth rate maps of the boundary layer thickness were also plotted. It was found that with the increase in the velocity, the dynamic pressure distribution was also increased for the alpha seep. Plots of pressure distribution so obtained were overlapped with those obtained by using XFLR software, a low fidelity tool. It was found that at moderate and high angles of attack, the distribution of the pressure coefficients obtained from the experiments is high when compared with the XFLR ® results obtained along with the span of the wing. This under-prediction by XFLR ® is more obvious on the windward than on the leeward side.

Keywords: subsonic flow, boundary layer, wind tunnel, pressure testing

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Authors: E. Vlachopapadopoulou, E. Dikaiakou, E. Anagnostou, I. Panagiotopoulos, E. Kaloumenou, M. Kafetzi, A. Fotinou, S. Michalacos

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Aim: To investigate the relation and impact of Body Mass Index (BMI), Waist Circumference (WC) and Body Fat Weight (BFW) on insulin resistance (MATSUDA INDEX < 2.5) in children and adolescents. Methods: Data from 95 overweight and obese children (47 boys and 48 girls) with mean age 10.7 ± 2.2 years were analyzed. ROC analysis was used to investigate the predictive ability of BMI, WC and BFW for insulin resistance and find the optimal cut-offs. The overall performance of the ROC analysis was quantified by computing area under the curve (AUC). Results: ROC curve analysis indicated that the optimal-cut off of WC for the prediction of insulin resistance was 97 cm with sensitivity equal to 75% and specificity equal to 73.1%. AUC was 0.78 (95% CI: 0.63-0.92, p=0.001). The sensitivity and specificity of obesity for the discrimination of participants with insulin resistance from those without insulin resistance were equal to 58.3% and 75%, respectively (AUC=0.67). BFW had a borderline predictive ability for insulin resistance (AUC=0.58, 95% CI: 0.43-0.74, p=0.101). The predictive ability of WC was equivalent with the correspondence predictive ability of BMI (p=0.891). Obese subjects had 4.2 times greater odds for having insulin resistance (95% CI: 1.71-10.30, p < 0.001), while subjects with WC more than 97 had 8.1 times greater odds for having insulin resistance (95% CI: 2.14-30.86, p=0.002). Conclusion: BMI and WC are important clinical factors that have significant clinical relation with insulin resistance in children and adolescents. The cut off of 97 cm for WC can identify children with greater likelihood for insulin resistance.

Keywords: body fat weight, body mass index, insulin resistance, obese children, waist circumference

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Authors: Milad Zoghi, M. Zahangir Kabir

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Performance of armchair graphene nanoribbon (AGNR) resonant tunneling diodes (RTD) alter if they go under strain. This may happen due to either using stretchable substrates or real working conditions such as heat generation. Therefore, it is informative to understand how mechanical deformations such as uniaxial strain can impact the performance of AGNR RTDs. In this paper, two platforms of AGNR RTD consist of width-modified AGNR RTD and electric-field modified AGNR RTD are subjected to both compressive and tensile uniaxial strain ranging from -2% to +2%. It is found that characteristics of AGNR RTD markedly change under both compressive and tensile strain. In particular, peak to valley ratio (PVR) can be totally disappeared upon strong enough strain deformation. Numerical tight binding (TB) coupled with Non-Equilibrium Green's Function (NEGF) is derived for this study to calculate corresponding Hamiltonian matrices and transport properties.

Keywords: armchair graphene nanoribbon, resonant tunneling diode, uniaxial strain, peak to valley ratio

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Authors: Bhavesh Dadhich, Fenil Bamnoliya, Akshita Swaminathan

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This project aims to design a system to generate energy from flowing wind due to the motion of a vehicle on the road or from the flow of wind in compact areas to utilize the wasteful energy into a useful one. It is envisaged through a design and aerodynamic performance improvement of a Savonius vertical axis wind turbine rotor and used in an integrated system with a Triboelectric Nanogenerator (TENG) that can generate a good amount of electrical energy. Aerodynamic calculations are performed numerically using Computational Fluid Dynamics software, and TENG's performance is evaluated analytically. The Turbine's coefficient of power is validated with published results for an inlet velocity of 7 m/s with a Tip Speed Ratio of 0.75 and found to reasonably agree with that of experiment results. The baseline design is modified with a new blade arc angle and rotor position angle based on the recommended parameter ranges suggested by previous researchers. Simulations have been performed for different T.S.R. values ranging from 0.25 to 1.5 with an interval of 0.25 with two applicable free stream velocities of 5 m/s and 7m/s. Finally, the newly designed VAWT CFD performance results are used as input for the analytical performance prediction of the triboelectric nanogenerator. The results show that this approach could be feasible and useful for small power source applications.

Keywords: savonius turbine, power, overlap ratio, tip speed ratio, TENG

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Authors: Jinu Phonamontham

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Coronavirus, also referred to as COVID-19, is a virus caused by the SARS-Cov-2 virus. The pandemic has caused over 10 million cases and 500,000 deaths worldwide through the end of June 2020. In a previous study, HLA-DQA1*01:02 allele was associated with COVID-19 disease (p-value = 0.0121). Furthermore, there was a statistical significance between HLA- DQB1*06:02 and COVID-19 in the Italian population by Bonferroni’s correction (p-value = 0.0016). Nevertheless, there is no data describing the distribution of HLA alleles as a valid marker for prediction of COVID-19 in the Thai population. We want to investigate the prevalence of HLA-DQA1*01:02 and HLA-DQB1*06:02 alleles that are associated with severe COVID-19 in the Thai population. In this study, we recruited 200 healthy Thai individuals. Genomic DNA samples were isolated from EDTA blood using Genomic DNA Mini Kit. HLA genotyping was conducted using the Lifecodes HLA SSO typing kits (Immucor, West Avenue, Stamford, USA). The frequency of HLA-DQA1 alleles in Thai population, consisting of HLA-DQA1*01:01 (27.75%), HLA-DQA1*01:02 (24.50%), HLA-DQA1*03:03 (13.00%), HLA-DQA1*06:01 (10.25%) and HLA-DQA1*02:01 (6.75%). Furthermore, the distributions of HLA-DQB1 alleles were HLA-DQB1*05:02 (21.50%), HLA-DQB1*03:01 (15.75%), HLA-DQB1*05:01 (14.50%), HLA-DQB1*03:03 (11.00%) and HLA-DQB1*02:02 (8.25%). Particularly, HLA- DQA1*01:02 (29.00%) allele was the highest frequency in the NorthEast group, but there was not significant difference when compared with the other regions in Thais (p-value = 0.4202). HLA-DQB1*06:02 allele was similarly distributed in Thai population and there was no significant difference between Thais and China (3.8%) and South Korea (6.4%) and Japan (8.2%) with p-value > 0.05. Whereas, South Africa (15.7%) has a significance with Thais by p-value of 0.0013. This study supports the specific genotyping of the HLA-DQA1*01:02 and HLA-DQB1*06:02 alleles to screen severe COVID-19 in Thai and many populations.

Keywords: HLA-DQA1*01:02, HLA-DQB1*06:02, Asian, Thai population

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Authors: Saba Riaz, Syed A. Hussain

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This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators.

Keywords: study variable, auxiliary variable, finite population variance, bias, asymptotic variance, percent relative efficiency

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Authors: Arturs Macanovskis, Vitalijs Lusis, Andrejs Krasnikovs

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Work has three stages. In the first stage was examined pull-out process for steel fiber was embedded into a concrete by one end and was pulled out of concrete under the angle to pulling out force direction. Angle was varied. Length of steel fiber was 26 mm, diameter 0.5 mm. On the obtained force- displacement diagrams were observed jumps. For such mechanical behavior explanation, fiber channel in concrete surface microscopical experimental investigation, using microscope KEYENCE VHX2000, was performed. Surface of fiber channel in concrete matrix after pull-out test (fiber angle to pulling out force direction 70°). At the second stage were obtained diagrams for load- crack opening displacement for breaking homogeneously reinforced and layered fiber concrete prisms (with dimensions 10x10x40 cm) subjected to 4-point bending. After testing was analyzed main crack. On the main crack’s both surfaces were recognized all pulled out fibers their locations, angles to crack surface and lengths of pull-out fibers parts. At the third stage elaborated prediction model for the fiber-concrete beam, failure under bending, using the following data: a) diagrams for fibers pulling out at different angles; b) experimental data about steel-straight fibers locations in the main crack.

Keywords: fiberconcrete, pull-out, fiber channel, layered fiberconcrete

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Authors: Heqian Zhao, Huaizhong Shi, Zhongwei Huang, Zhengliang Chen, Ziang Gu, Fei Gao

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Hydraulics mechanics is significantly important in the drilling process of oil or gas exploration, especially for the drill bit. The fluid flows through the nozzles on the bit and generates a water jet to remove the cutting at the bottom hole. In this paper, a simplified bottom hole model is established. The Particle Image Velocimetric (PIV) is used to capture the flow field of the single nozzle. Due to the limitation of the bottom and wellbore, the potential core is shorter than that of the free water jet. The velocity magnitude rapidly attenuates when fluid close to the bottom is lower than about 5 mm. Besides, a vortex zone appears near the middle of the bottom beside the water jet zone. A modified exponential function can be used to fit the centerline velocity well. On the one hand, the results of this paper can provide verification for the numerical simulation of the bottom hole flow field. On the other hand, it also can provide an experimental basis for the hydraulic design of the drill bit.

Keywords: oil and gas, hydraulic mechanic of drilling, PIV, bottom hole

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Authors: Seyed Abolhasan Naeini, Mohammad Hossein Zade

Abstract:

This article is an attempt to present a numerically study of the behaviour of an eccentrically loaded circular footing resting on sand to determine ‎its ultimate bearing capacity. A surface circular footing of diameter 12 cm (D) was used as ‎shallow foundation. For this purpose, three dimensional models consist of foundation, and medium sandy soil was modelled by ABAQUS software. Bearing capacity of footing was evaluated and the ‎effects of the load eccentricity on bearing capacity, its settlement, and modulus of subgrade reaction were studied. Three different values of load eccentricity with equal space from inside the core on the core boundary and outside the core boundary, which were respectively e=0.75, 1.5, and 2.25 cm, were considered. The results show that by increasing the load eccentricity, the ultimate load and the ‎modulus of subgrade reaction decreased.

Keywords: circular foundation, sand, eccentric loading, modulus of subgrade reaction

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Authors: Jiajia Pan, Hung Tao Shen

Abstract:

Freeze and thaw occurs seasonally in river banks in northern countries. Little is known on how the riverbank soil temperature responds to air temperature changes and how freeze and thaw develops in a river bank seasonally. This study presents a two-dimensional heat conduction model for numerical investigations of seasonal freeze and thaw processes in an idealized river bank. The model uses the finite difference method and it is convenient for applications. The model is validated with an analytical solution and a field case with soil temperature distributions. It is then applied to the idealized river bank in terms of partially and fully saturated conditions with or without ice cover influence. Simulated results illustrate the response processes of the river bank to seasonal air temperature variations. It promotes the understanding of freeze and thaw processes in river banks and prepares for further investigation of frost and thaw impacts on riverbank stability.

Keywords: freeze and thaw, riverbanks, 2D model, heat conduction

Procedia PDF Downloads 133

Authors: Ippei Torii, Kaoruko Ohtani, Takahito Niwa, Naohiro Ishii

Abstract:

This paper shows developing an objectivity index using the measurement of subtle eyeball movement to diagnose autism. The developmentally disabled assessment varies, and the diagnosis depends on the subjective judgment of professionals. Therefore, a supplementary inspection method that will enable anyone to obtain the same quantitative judgment is needed. The diagnosis are made based on a comparison of the time of gazing an object in the conventional autistic study, but the results do not match. First, we divided the pupil into four parts from the center using measurements of subtle eyeball movement and comparing the number of pixels in the overlapping parts based on an afterimage. Then we developed the objective evaluation indicator to judge non-autistic and autistic people more clearly than conventional methods by analyzing the differences of subtle eyeball movements between the right and left eyes. Even when a person gazes at one point and his/her eyeballs always stay fixed at that point, their eyes perform subtle fixating movements (ie. tremors, drifting, microsaccades) to keep the retinal image clear. Particularly, the microsaccades link with nerves and reflect the mechanism that process the sight in a brain. We converted the differences between these movements into numbers. The process of the conversion is as followed: 1) Select the pixel indicating the subject's pupil from images of captured frames. 2) Set up a reference image, known as an afterimage, from the pixel indicating the subject's pupil. 3) Divide the pupil of the subject into four from the center in the acquired frame image. 4) Select the pixel in each divided part and count the number of the pixels of the overlapping part with the present pixel based on the afterimage. 5) Process the images with precision in 24 - 30fps from a camera and convert the amount of change in the pixels of the subtle movements of the right and left eyeballs in to numbers. The difference in the area of the amount of change occurs by measuring the difference between the afterimage in consecutive frames and the present frame. We set the amount of change to the quantity of the subtle eyeball movements. This method made it possible to detect a change of the eyeball vibration in numerical value. By comparing the numerical value between the right and left eyes, we found that there is a difference in how much they move. We compared the difference in these movements between non-autistc and autistic people and analyzed the result. Our research subjects consists of 8 children and 10 adults with autism, and 6 children and 18 adults with no disability. We measured the values through pasuit movements and fixations. We converted the difference in subtle movements between the right and left eyes into a graph and define it in multidimensional measure. Then we set the identification border with density function of the distribution, cumulative frequency function, and ROC curve. With this, we established an objective index to determine autism, normal, false positive, and false negative.

Keywords: subtle eyeball movement, autism, microsaccade, pursuit eye movements, ROC curve

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Authors: Alam Ali, Ashok Kumar Pathak

Abstract:

Path analysis is a statistical technique used to evaluate the strength of the direct and indirect effects of variables. One or more structural regression equations are used to estimate a series of parameters in order to find the better fit of data. Sometimes, exogenous variables do not show a significant strength of their direct and indirect effect when the assumption of classical regression (ordinary least squares (OLS)) are violated by the nature of the data. The main motive of this article is to investigate the efficacy of the copula-based regression approach over the classical regression approach and calculate the direct and indirect effects of variables when data violates the OLS assumption and variables are linked through an elliptical copula. We perform this study using a well-organized numerical scheme. Finally, a real data application is also presented to demonstrate the performance of the superiority of the copula approach.

Keywords: path analysis, copula-based regression models, direct and indirect effects, k-fold cross validation technique

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Authors: Birome Holo Ba, Christian Prins, Caroline Prodhon

Abstract:

Optimization is an important aspect of logistics management. It can reduce significantly logistics costs and also be a good tool for decision support. In this paper, we address a planning problem specific to biomass supply chain. We propose a new mixed integer linear programming (MILP) model dealing with different feed stock production operations such as harvesting, packing, storage, pre-processing and transportation, with the objective of minimizing the total logistic cost of the system on a regional basis. It determines the optimal number of harvesting machine, the fleet size of trucks for transportation and the amount of each type of biomass harvested, stored and pre-processed in each period to satisfy demands of refineries in each period. We illustrate the effectiveness of the proposal model with a numerical example, a case study in Aube (France department), which gives preliminary and interesting, results on a small test case.

Keywords: biomass logistics, supply chain, modelling, optimization, bioenergy, biofuels

Procedia PDF Downloads 520