Search results for: approximate bayesian computation

Authors: Benson Ade Eniola Afere

Abstract:

This paper introduces a family of fourth-order hybrid beta polynomial kernels developed for statistical analysis. The assessment of these kernels' performance centers on two critical metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Through the utilization of both simulated and real-world datasets, a comprehensive evaluation was conducted, facilitating a thorough comparison with conventional fourth-order polynomial kernels. The evaluation procedure encompassed the computation of AMISE and efficiency values for both the proposed hybrid kernels and the established classical kernels. The consistently observed trend was the superior performance of the hybrid kernels when compared to their classical counterparts. This trend persisted across diverse datasets, underscoring the resilience and efficacy of the hybrid approach. By leveraging these performance metrics and conducting evaluations on both simulated and real-world data, this study furnishes compelling evidence in favour of the superiority of the proposed hybrid beta polynomial kernels. The discernible enhancement in performance, as indicated by lower AMISE values and higher efficiency scores, strongly suggests that the proposed kernels offer heightened suitability for statistical analysis tasks when compared to traditional kernels.

Keywords: AMISE, efficiency, fourth-order Kernels, hybrid Kernels, Kernel density estimation

Procedia PDF Downloads 70

Authors: Andras Dezső, Peter Baumli, George Kaptay

Abstract:

The phosphorous is the important components in the steels, because it makes the changing of the mechanical properties and possibly modifying the structure. The phosphorous can be create the Fe3P compounds, what is segregated in the ferrite grain boundary in the intervals of the nano-, or microscale. This intermetallic compound is decreasing the mechanical properties, for example it makes the blue brittleness which means that the brittle created by the segregated particles at 200 ... 300°C. This work describes the phosphide solubility by the other components effect. We make calculations for the Ni, Mo, Cu, S, V, C, Si, Mn, and the Cr elements by the Thermo-Calc software. We predict the effects by approximate functions. The binary Fe-P system has a solubility line, which has a determinating equation. The result is below: lnwo = -3,439 – 1.903/T where the w0 means the weight percent of the maximum soluted concentration of the phosphorous, and the T is the temperature in Kelvin. The equation show that the P more soluble element when the temperature increasing. The nickel, molybdenum, vanadium, silicon, manganese, and the chromium make dependence to the maximum soluted concentration. These functions are more dependent by the elements concentration, which are lower when we put these elements in our steels. The copper, sulphur and carbon do not make effect to the phosphorous solubility. We predict that all of cases the maximum solubility concentration increases when the temperature more and more high. Between 473K and 673 K, in the phase diagram, these systems contain mostly two or three phase eutectoid, and the singe phase, ferritic intervals. In the eutectoid areas the ferrite, the iron-phosphide, and the metal (III)-phospide are in the equilibrium. In these modelling we predicted that which elements are good for avoid the phosphide segregation or not. These datas are important when we make or choose the steels, where the phosphide segregation stopping our possibilities.

Keywords: phosphorous, steel, segregation, thermo-calc software

Procedia PDF Downloads 625

Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

Abstract:

Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

Procedia PDF Downloads 254

Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

Abstract:

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

Procedia PDF Downloads 177

Authors: Ehsan Gholamalizadeh, Jae Dong Chung

Abstract:

Solar chimney power plant is a feasible solar thermal system which produces electricity from the Sun. The objective of this study is to investigate buoyancy-driven flow and heat transfer through a built pilot solar chimney system called 'Kerman Project'. The system has a chimney with the height and diameter of 60 m and 3 m, respectively, and the average radius of its solar collector is about 20 m, and also its average collector height is about 2 m. A three-dimensional simulation was conducted to analyze the system, using computational fluid dynamics (CFD). In this model, radiative transfer equation was solved using the discrete ordinates (DO) radiation model taking into account a non-gray radiation behavior. In order to modelling solar irradiation from the sun’s rays, the solar ray tracing algorithm was coupled to the computation via a source term in the energy equation. The model was validated with comparing to the experimental data of the Manzanares prototype and also the performance of the built pilot system. Then, based on the numerical simulations, velocity and temperature distributions through the system, the temperature profile of the ground surface and the system performance were presented. The analysis accurately shows the flow and heat transfer characteristics through the pilot system and predicts its performance.

Keywords: buoyancy-driven flow, computational fluid dynamics, heat transfer, renewable energy, solar chimney power plant

Procedia PDF Downloads 262

Authors: Daniel Fundi Murithi

Abstract:

Data from economic, social, clinical, and industrial studies are in some way incomplete or incorrect due to censoring. Such data may have adverse effects if used in the estimation problem. We propose the use of Maximum Likelihood Estimation (MLE) under a progressive type-II censoring scheme to remedy this problem. In particular, maximum likelihood estimates (MLEs) for the location (µ) and scale (λ) parameters of two Parameter Rayleigh distribution are realized under a progressive type-II censoring scheme using the Expectation-Maximization (EM) and the Newton-Raphson (NR) algorithms. These algorithms are used comparatively because they iteratively produce satisfactory results in the estimation problem. The progressively type-II censoring scheme is used because it allows the removal of test units before the termination of the experiment. Approximate asymptotic variances and confidence intervals for the location and scale parameters are derived/constructed. The efficiency of EM and the NR algorithms is compared given root mean squared error (RMSE), bias, and the coverage rate. The simulation study showed that in most sets of simulation cases, the estimates obtained using the Expectation-maximization algorithm had small biases, small variances, narrower/small confidence intervals width, and small root of mean squared error compared to those generated via the Newton-Raphson (NR) algorithm. Further, the analysis of a real-life data set (data from simple experimental trials) showed that the Expectation-Maximization (EM) algorithm performs better compared to Newton-Raphson (NR) algorithm in all simulation cases under the progressive type-II censoring scheme.

Keywords: expectation-maximization algorithm, maximum likelihood estimation, Newton-Raphson method, two-parameter Rayleigh distribution, progressive type-II censoring

Procedia PDF Downloads 163

Authors: Arnab Adhikari, Adrija Majumdar, Gaurav Gupta, Arnab Bisi

Abstract:

Team formation plays an instrumental role in the sports like cricket. Existing literature reveals that most of the works on player selection focus only on the players’ efficiency and ignore the consistency. It motivates us to design an improved player selection method based on both player’s efficiency and consistency. To measure the players’ efficiency measurement, we employ a modified data envelopment analysis (DEA) technique namely ‘super-efficient DEA model’. We design a modified consistency index based on semi variance approach. Here, we introduce a new parameter called ‘fitness index’ for consistency computation to assess a player’s fitness level. Finally, we devise a single performance score using both efficiency score and consistency score with the help of a linear programming model. To test the robustness of our method, we perform a rigorous numerical analysis to determine the all-time best One Day International (ODI) Cricket XI. Next, we conduct extensive comparative studies regarding efficiency scores, consistency scores, selected team between the existing methods and the proposed method and explain the rationale behind the improvement.

Keywords: decision support systems, sports, super-efficient data envelopment analysis, semi variance approach

Procedia PDF Downloads 399

Authors: Amine Ben Slama, Aymen Mouelhi, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Mounir Sayadi, Farhat Fnaiech

Abstract:

Vertigo is a sensation of feeling off balance; the cause of this symptom is very difficult to interpret and needs a complementary exam. Generally, vertigo is caused by an ear problem. Some of the most common causes include: benign paroxysmal positional vertigo (BPPV), Meniere's disease and vestibular neuritis (VN). In clinical practice, different tests of videonystagmographic (VNG) technique are used to detect the presence of vestibular neuritis (VN). The topographical diagnosis of this disease presents a large diversity in its characteristics that confirm a mixture of problems for usual etiological analysis methods. In this study, a vestibular neuritis analysis method is proposed with videonystagmography (VNG) applications using an estimation of pupil movements in the case of an uncontrolled motion to obtain an efficient and reliable diagnosis results. First, an estimation of the pupil displacement vectors using with Hough Transform (HT) is performed to approximate the location of pupil region. Then, temporal and frequency features are computed from the rotation angle variation of the pupil motion. Finally, optimized features are selected using Fisher criterion evaluation for discrimination and classification of the VN disease.Experimental results are analyzed using two categories: normal and pathologic. By classifying the reduced features using the Support Vector Machine (SVM), 94% is achieved as classification accuracy. Compared to recent studies, the proposed expert system is extremely helpful and highly effective to resolve the problem of VNG analysis and provide an accurate diagnostic for medical devices.

Keywords: nystagmus, vestibular neuritis, videonystagmographic system, VNG, Fisher criterion, support vector machine, SVM

Procedia PDF Downloads 136

Authors: Nanjiang Chen

Abstract:

In the backdrop of expanding urban landscapes, accurately assessing spatial openness is critical. Traditional visibility analysis methods grapple with discretization errors and inefficiencies, creating a gap in truly capturing the human experi-ence of space. Addressing these gaps, this paper introduces a distinct continuous visibility algorithm, a leap in measuring urban spaces from a human-centric per-spective. This study presents a methodological breakthrough by applying this algorithm to urban visibility analysis. Unlike conventional approaches, this tech-nique allows for a continuous range of visibility assessment, closely mirroring hu-man visual perception. By eliminating the need for predefined subdivisions in ray casting, it offers a more accurate and efficient tool for urban planners and architects. The proposed algorithm not only reduces computational errors but also demonstrates faster processing capabilities, validated through a case study in Bei-jing's urban setting. Its key distinction lies in its potential to benefit a broad spec-trum of stakeholders, ranging from urban developers to public policymakers, aid-ing in the creation of urban spaces that prioritize visual openness and quality of life. This advancement in urban analysis methods could lead to more inclusive, comfortable, and well-integrated urban environments, enhancing the spatial experience for communities worldwide.

Keywords: visual openness, spatial continuity, ray-tracing algorithms, urban computation

Procedia PDF Downloads 46

Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

Abstract:

Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

Procedia PDF Downloads 205

Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

Abstract:

The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

Procedia PDF Downloads 188

Authors: Ebraheem Al-Jouri, Youssef Abu-Ahmad, Ramasamy Srinivasan

Abstract:

The parasitoid, Diadegma semiclausum (Hymenoptera: Ichneumonidae) is one of the most effective exotic parasitoids of diamondback moth (DBM), Plutella xylostella in the lowland areas of Homs, Syria. Molecular evolution studies are useful tools to shed light on the molecular bases of insect geographical spread and adaptation to new hosts and environment and for designing better control strategies. In this study, molecular evolution analysis was performed based on the 42 nuclear internal transcribed spacer-2 (ITS2) sequences representing the D. semiclausum and eight other Diadegma spp. from Syria and worldwide. Possible recombination events were identified by RDP4 program. Four potential recombinants of the American D. insulare and D. fenestrale (Jeju) were detected. After detecting and removing recombinant sequences, the ratio of non-synonymous (dN) to synonymous (dS) substitutions per site (dN/dS=ɷ) has been used to identify codon positions involved in adaptive processes. Bayesian techniques were applied to detect selective pressures at a codon level by using five different approaches including: fixed effects likelihood (FEL), internal fixed effects likelihood (IFEL), random effects method (REL), mixed effects model of evolution (MEME) and Program analysis of maximum liklehood (PAML). Among the 40 positively selected amino acids (aa) that differed significantly between clades of Diadegma species, three aa under positive selection were only identified in D. semiclausum. Additionally, all D. semiclausum branches tree were highly found under episodic diversifying selection (EDS) at p≤0.05. Our study provide evidence that both recombination and positive selection have contributed to the molecular diversity of Diadegma spp. and highlights the significant contribution of D. semiclausum in adaptive evolution and influence the fitness in the DBM parasitoid.

Keywords: diadegma sp, DBM, ITS2, phylogeny, recombination, dN/dS, evolution, positive selection

Procedia PDF Downloads 416

Authors: Ashish Agarwal, Vaibhav Singh

Abstract:

Harmful emissions from oil and gas processing facilities have long remained a matter of concern for governments and environmentalists throughout the world. This paper analyses Oil and Natural Gas Corporation (ONGC) gas processing plant in Hazira, Gujarat, India. It is the largest gas-processing complex in the country designed to process 41MMSCMD sour natural gas & associated sour condensate. The complex, sprawling over an area of approximate 705 hectares is the mother plant for almost all industries at Hazira and enroute Hazira Bijapur Jagdishpur pipeline. Various sources of pollution from each unit starting from Gas Terminal to Dew Point Depression unit and Caustic Wash unit along the processing chain were examined with the help of different emission data obtained from ONGC. Pollution discharged to the environment was classified into Water, Air, Hazardous Waste and Solid (Non-Hazardous) Waste so as to analyze each one of them efficiently. To protect air environment, Sulphur recovery unit along with automatic ambient air quality monitoring stations, automatic stack monitoring stations among numerous practices were adopted. To protect water environment different effluent treatment plants were used with due emphasis on aquaculture of the nearby area. Hazira plant has obtained the authorization for handling and disposal of five types of hazardous waste. Most of the hazardous waste were sold to authorized recyclers and the rest was given to Gujarat Pollution Control Board authorized vendors. Non-Hazardous waste was also handled with an overall objective of zero negative impact on the environment. The effect of methods adopted is evident from emission data of the plant which was found to be well under Gujarat Pollution Control Board limits.

Keywords: sulphur recovery unit, effluent treatment plant, hazardous waste, sour gas

Procedia PDF Downloads 226

Authors: Tan Zhang, Zhongjian Wang, Jack Xin, Zhiwen Zhang

Abstract:

We aim to efficiently compute the spreading speeds of reaction-diffusion-advection (RDA) fronts in divergence-free random flows under the Kolmogorov-Petrovsky-Piskunov (KPP) nonlinearity. We study a stochastic interacting particle method (IPM) for the reduced principal eigenvalue (Lyapunov exponent) problem of an associated linear advection-diffusion operator with spatially random coefficients. The Fourier representation of the random advection field and the Feynman-Kac (FK) formula of the principal eigenvalue (Lyapunov exponent) form the foundation of our method implemented as a genetic evolution algorithm. The particles undergo advection-diffusion and mutation/selection through a fitness function originated in the FK semigroup. We analyze the convergence of the algorithm based on operator splitting and present numerical results on representative flows such as 2D cellular flow and 3D Arnold-Beltrami-Childress (ABC) flow under random perturbations. The 2D examples serve as a consistency check with semi-Lagrangian computation. The 3D results demonstrate that IPM, being mesh-free and self-adaptive, is simple to implement and efficient for computing front spreading speeds in the advection-dominated regime for high-dimensional random flows on unbounded domains where no truncation is needed.

Keywords: KPP front speeds, random flows, Feynman-Kac semigroups, interacting particle method, convergence analysis

Procedia PDF Downloads 46

Authors: William J. Crowther, Conor Marsh

Abstract:

Object-based flow simulation allows fast computation of arbitrarily complex aerodynamic models made up of simple objects with limited flow interactions. The proposed approach is universally applicable to objects made from arbitrarily scaled ellipsoid primitives at arbitrary aerodynamic attitude and angular rate. The use of a component-based aerodynamic modelling approach increases efficiency by allowing selective inclusion of different physics models at run-time and allows extensibility through the development of new models. Insight into the numerical stability of the model under first order fixed-time step integration schemes is provided by stability analysis of the drag component. The compute cost of model components and functions is evaluated and compared against numerical benchmarks. Model static outputs are verified against theoretical expectations and dynamic behaviour using falling plate data from the literature. The model is applied to a range of case studies to demonstrate the efficacy of its application in extensibility, ease of use, and low computational cost. Dynamically complex multi-body systems can be implemented in a transparent and efficient manner, and we successfully demonstrate large scenes with hundreds of objects interacting with diverse flow fields.

Keywords: aerodynamics, real-time simulation, low-order model, flight dynamics

Procedia PDF Downloads 102

Authors: Chahrazed Zouaoui

Abstract:

In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

Procedia PDF Downloads 375

Authors: Akram Khelaifia, Salah Guettala, Nesreddine Djafar Henni, Rachid Chebili

Abstract:

Recent seismic events have underscored the significant influence of masonry infill walls on the resilience of structures. The irregular positioning of these walls exacerbates their adverse effects, resulting in substantial material and human losses. Research and post-earthquake evaluations emphasize the necessity of considering infill walls in both the design and assessment phases. This study delves into the presence of soft stories in reinforced concrete structures with infill walls. Employing an approximate method relying on pushover analysis results, fiber-section-based macro-modeling is utilized to simulate the behavior of infill walls. The findings shed light on the presence of soft first stories, revealing a notable 240% enhancement in resistance for weak column—strong beam-designed frames due to infill walls. Conversely, the effect is more moderate at 38% for strong column—weak beam-designed frames. Interestingly, the uniform distribution of infill walls throughout the structure's height does not influence soft-story emergence in the same seismic zone, irrespective of column-beam strength. In regions with low seismic intensity, infill walls dissipate energy, resulting in consistent seismic behavior regardless of column configuration. Despite column strength, structures with open-ground stories remain vulnerable to soft first-story emergence, underscoring the crucial role of infill walls in reinforced concrete structural design.

Keywords: masonry infill walls, soft Storey, pushover analysis, fiber section, macro-modeling

Procedia PDF Downloads 67

Authors: Hamed K. Esfahani, Bithin Datta

Abstract:

Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site.

Keywords: geochemical transport simulation, acid mine drainage, surrogate models, ensemble genetic programming, contaminated aquifers, mine sites

Procedia PDF Downloads 277

Authors: Saeid Haghjoo, Ebrahim Reyhani, Fahimeh Kolahdouz

Abstract:

The present study aimed to evaluate the understanding of the students in Tehran universities (Iran) about the numerical representation of the average rate of change based on the Structure of Observed Learning Outcomes (SOLO). In the present descriptive-survey research, the statistical population included undergraduate students (basic sciences and engineering) in the universities of Tehran. The samples were 604 students selected by random multi-stage clustering. The measurement tool was a task whose face and content validity was confirmed by math and mathematics education professors. Using Cronbach's Alpha criterion, the reliability coefficient of the task was obtained 0.95, which verified its reliability. The collected data were analyzed by descriptive statistics and inferential statistics (chi-squared and independent t-tests) under SPSS-24 software. According to the SOLO model in the prestructural, unistructural, and multistructural levels, basic science students had a higher percentage of understanding than that of engineering students, although the outcome was inverse at the relational level. However, there was no significant difference in the average understanding of both groups. The results indicated that students failed to have a proper understanding of the numerical representation of the average rate of change, in addition to missconceptions when using physics formulas in solving the problem. In addition, multiple solutions were derived along with their dominant methods during the qualitative analysis. The current research proposed to focus on the context problems with approximate calculations and numerical representation, using software and connection common relations between math and physics in the teaching process of teachers and professors.

Keywords: average rate of change, context problems, derivative, numerical representation, SOLO taxonomy

Procedia PDF Downloads 92

Authors: Loai AbdAllah, Mahmoud Kaiyal

Abstract:

Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

Procedia PDF Downloads 225

Authors: Juhee Lee, Yongjun Lee

Abstract:

In designing a low-energy-consuming buildings, the heat transfer through a large glass or wall becomes critical. Multiple layers of the window glasses and walls are employed for the high insulation. The gravity driven air flow between window glasses or wall layers is a natural heat convection phenomenon being a key of the heat transfer. For the first step of the natural heat transfer analysis, in this study the development and application of a finite volume method for the numerical computation of viscous incompressible flows is presented. It will become a part of the natural convection analysis with high-order scheme, multi-grid method, and dual-time step in the future. A finite volume method based on a fully-implicit second-order is used to discretize and solve the fluid flow on unstructured grids composed of arbitrary-shaped cells. The integrations of the governing equation are discretised in the finite volume manner using a collocated arrangement of variables. The convergence of the SIMPLE segregated algorithm for the solution of the coupled nonlinear algebraic equations is accelerated by using a sparse matrix solver such as BiCGSTAB. The method used in the present study is verified by applying it to some flows for which either the numerical solution is known or the solution can be obtained using another numerical technique available in the other researches. The accuracy of the method is assessed through the grid refinement.

Keywords: finite volume method, fluid flow, laminar flow, unstructured grid

Procedia PDF Downloads 286

Authors: Pratibha Srivastava, Chithra V. J., Sudhakar S., Nitin K. D.

Abstract:

A closed-loop system comprises of a controller, a response system, and an actuating system. The controller, which is the system under test for us, excites the actuators based on feedback from the sensors in a periodic manner. The sensors should provide the feedback to the System Under Test (SUT) within a deterministic time post excitation of the actuators. Any delay or miss in the generation of response or acquisition of excitation pulses may lead to control loop controller computation errors, which can be catastrophic in certain cases. Such systems categorised as hard real-time systems that need special strategies. The real-time operating systems available in the market may be the best solutions for such kind of simulations, but they pose limitations like the availability of the X Windows system, graphical interfaces, other user tools. In this paper, we present strategies that can be used on a general purpose operating system (Bare Linux Kernel) to achieve a deterministic deadline and hence have the added advantages of a GPOS with real-time features. Techniques shall be discussed how to make the time-critical application run with the highest priority in an uninterrupted manner, reduced network latency for distributed architecture, real-time data acquisition, data storage, and retrieval, user interactions, etc.

Keywords: real time data acquisition, real time kernel preemption, scheduling, network latency

Procedia PDF Downloads 147

Authors: Kreso Ivandic, Filip Dodigovic, Damir Stuhec

Abstract:

The paper deals with the proposed 'Material' and 'Action-resistance factor' design methods in designing the embedded retaining walls. The parametric analysis of evaluating the differences of the output values mutually and compared with classic approach computation was performed. There is a challenge with the criteria for choosing the proposed calculation design methods in Eurocode 7 with respect to current technical regulations and regular engineering practice. The basic criterion for applying a particular design method is to ensure minimum an equal degree of reliability in relation to the current practice. The procedure of combining the relevant partial coefficients according to design methods was carried out. The use of mentioned partial coefficients should result in the same level of safety, regardless of load combinations, material characteristics and problem geometry. This proposed approach of the partial coefficients related to the material and/or action-resistance should aimed at building a bridge between calculations used so far and pure probability analysis. The measure to compare the results was to determine an equivalent safety factor for each analysis. The results show a visible wide span of equivalent values of the classic safety factors.

Keywords: action-resistance factor design, classic approach, embedded retaining wall, Eurocode 7, limit states, material factor design

Procedia PDF Downloads 231

Authors: Ishani D. Goonasekara, Erio Camporesi, Timur Bulgakov, Rungtiwa Phookamsak, Kevin D. Hyde

Abstract:

Phaeosphaeriaceae comprises a large number of species occurring mainly on grasses and cereal crops as endophytes, saprobes and especially pathogens. Parastagonospora is an important genus in Phaeosphaeriaceae that includes pathogens causing leaf and glume blotch on cereal crops. Currently, there are fifteen Parastagonospora species described, including both pathogens and saprobes. In this study, one sexual morph species and an asexual morph species, occurring as saprobes on members of Poaceae are introduced based on morphology and a combined molecular analysis of the LSU, SSU, ITS, and RPB2 gene sequence data. The sexual morph species Parastagonospora elymi was isolated from a Russian sample of Elymus repens, a grass commonly known as couch grass, and important for grazing animals, as a weed and used in traditional Austrian medicine. P. elymi is similar to the sexual morph of P. avenae in having cylindrical asci, bearing 8, overlapping biseriate, fusiform ascospores but can be distinguished by its subglobose to conical shaped, wider ascomata. In addition, no sheath was observed surrounding the ascospores. The asexual morph species was isolated from a specimen from Italy, on Dactylis glomerata, a commonly found grass distributed in temperate regions. It is introduced as Parastagonospora macrouniseptata, a coelomycete, and bears a close resemblance to P. allouniseptata and P. uniseptata in having globose to subglobose, pycnidial conidiomata and hyaline, cylindrical, 1-septate conidia. However, the new species could be distinguished in having much larger conidiomata. In the phylogenetic analysis which consisted of a maximum likelihood and Bayesian analysis P. elymi showed low bootstrap support, but well segregated from other strains within the Parastagonospora clade. P. neoallouniseptata formed a sister clade with P. allouniseptata with high statistical support.

Keywords: dothideomycetes, multi-gene analysis, Poaceae, saprobes, taxonomy

Procedia PDF Downloads 119

Authors: Omar A. Abuobidalla, Mingyuan Chen, Satyaveer S. Chauhan

Abstract:

Railroad transportation of hazardous materials freights is important to the North America economics that supports the national’s supply chain. This paper introduces various extensions of the dynamic hazardous materials trip plan problems. The problem captures most of the operational features of a real-world railroad transportations systems that dynamically initiates a set of blocks and assigns each shipment to a single block path or multiple block paths. The dynamic hazardous materials trip plan policies have distinguishing features that are integrating the blocking plan, and the block activation decisions. We also present a non-linear mixed integer programming formulation for each variant and present managerial insights based on a hypothetical railroad network. The computation results reveal that the dynamic car scheduling policies are not only able to take advantage of the capacity of the network but also capable of diminishing the population, and environment risks by rerouting the active blocks along the least risky train services without sacrificing the cost advantage of the railroad. The empirical results of this research illustrate that the issue of integrating the blocking plan, and the train makeup of the hazardous materials freights must receive closer attentions.

Keywords: dynamic car scheduling, planning and scheduling hazardous materials freights, airborne hazardous materials, gaussian plume model, integrated blocking and routing plans, box model

Procedia PDF Downloads 205

Authors: Guclu Insel, Efe Gumuslu, Gulten Yuksek, Nilay Sayi Ucar, Emine Ubay Cokgor, Tugba Olmez Hanci, Didem Okutman Tas, Fatos Germirli Babuna, Derya Firat Ertem, Okmen Yildirim, Ozge Erturan, Betul Kirci

Abstract:

In order to eliminate water scarcity problems, effective precautions must be taken. Growing competition for water is increasingly forcing facilities to tackle their own water scarcity problems. At this point, application of wastewater reclamation and reuse results in considerable economic advantageous. In this study, an enamel coating facility, which is one of the high water consumed facilities, is evaluated in terms of its wastewater reuse potential. Wastewater reclamation and reuse can be defined as one of the best available techniques for this sector. Hence, process and pollution profiles together with detailed characterization of segregated wastewater sources are appraised in a way to find out the recoverable effluent streams arising from enamel coating operations. Daily, 170 m³ of process water is required and 160 m³ of wastewater is generated. The segregated streams generated by two enamel coating processes are characterized in terms of conventional parameters. Relatively clean segregated wastewater streams (reusable wastewaters) are separately collected and experimental treatability studies are conducted on it. The results reflected that the reusable wastewater fraction has an approximate amount of 110 m³/day that accounts for 68% of the total wastewaters. The need for treatment applicable on reusable wastewaters is determined by considering water quality requirements of various operations and characterization of reusable wastewater streams. Ultra-filtration (UF), Nano-filtration (NF) and Reverse Osmosis (RO) membranes are subsequently applied on reusable effluent fraction. Adequate organic matter removal is not obtained with the mentioned treatment sequence.

Keywords: enamel coating, membrane, reuse, wastewater reclamation

Procedia PDF Downloads 328

Authors: Shazia Anwer

Abstract:

The current TV Talk Shows, especially on domestic politics in Pakistan are following the Aristotelian techniques, including deductive reasoning, three modes of persuasion, and guidelines for communication. The application of “Approximate Truth is also seen when Talk Show presenters create doubts against political personalities or national issues. Mainstream media of Pakistan, being a key carrier of narrative construction for the sake of the primary function of national consensus on regional and extended public diplomacy, is failing the purpose. This paper has highlighted the Aristotelian communication methodology, its purposes and its limitations for a serious discussion, and its connection to the mistrust among the Pakistani population regarding fake or embedded, funded Information. Data has been collected from 3 Pakistani TV Talk Shows and their analysis has been made by applying the Aristotelian communication method to highlight the core issues. Paper has also elaborated that current media education is impaired in providing transparent techniques to train the future journalist for a meaningful, thought-provoking discussion. For this reason, this paper has given an overview of HEC’s (Higher Education Commission) graduate-level Mass Com Syllabus for Pakistani Universities. The idea of ethos, logos, and pathos are the main components of TV Talk Shows and as a result, the educated audience is lacking trust in the mainstream media, which eventually generating feelings of distrust and betrayal in the society because productions look like the genre of Drama instead of facts and analysis thus the line between Current Affairs shows and Infotainment has become blurred. In the last section, practical implication to improve meaningfulness and transparency in the TV Talk shows has been suggested by replacing the Aristotelian communication method with the cognitive semiotic communication approach.

Keywords: Aristotelian techniques of communication, current affairs talk shows, drama, Pakistan

Procedia PDF Downloads 204

Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

Abstract:

In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

Procedia PDF Downloads 125

Authors: Nicolò Vaiana, Giorgio Serino

Abstract:

In this paper, an advanced Nonlinear Exponential Model (NEM), able to simulate the uniaxial dynamic behavior of seismic isolators having a continuously decreasing tangent stiffness with increasing displacement in the relatively large displacements range and a hardening or softening behavior at large displacements, is presented. The mathematical model is validated by comparing the experimental force-displacement hysteresis loops obtained during cyclic tests, conducted on a helical wire rope isolator and a recycled rubber-fiber reinforced bearing, with those predicted analytically. Good agreement between the experimental and simulated results shows that the proposed model can be an effective numerical tool to predict the force-displacement relationship of seismic isolation devices within the large displacements range. Compared to the widely used Bouc-Wen model, unable to simulate the response of seismic isolators at large displacements, the proposed one allows to avoid the numerical solution of a first order nonlinear ordinary differential equation for each time step of a nonlinear time history analysis, thus reducing the computation effort. Furthermore, the proposed model can simulate the smooth transition of the hysteresis loops from small to large displacements by adopting only one set of five parameters determined from the experimental hysteresis loops having the largest amplitude.

Keywords: base isolation, hardening behavior, nonlinear exponential model, seismic isolators, softening behavior

Procedia PDF Downloads 329

Authors: Davit Shahnazaryan, Diogo Gomes, Mher Safaryan

Abstract:

Many symbolic computations and manipulations required in the analysis of partial differential equations (PDE) or systems of PDEs are tedious and error-prone. These computations arise when determining conservation laws, entropies or integral identities, which are essential tools for the study of PDEs. Here, we discuss a new Mathematica package for the symbolic analysis of PDEs that automate multiple tasks, saving time and effort. Methodologies: During the research, we have used concepts of linear algebra and partial differential equations. We have been working on creating algorithms based on theoretical mathematics to find results mentioned below. Major Findings: Our package provides the following functionalities; finding symmetry group of different PDE systems, generation of polynomials invariant with respect to different symmetry groups; simplification of integral quantities by integration by parts and null Lagrangian cleaning, computing general forms of expressions by integration by parts; finding equivalent forms of an integral expression that are simpler or more symmetric form; determining necessary and sufficient conditions on the coefficients for the positivity of a given symbolic expression. Conclusion: Using this package, we can simplify integral identities, find conserved and dissipated quantities of time-dependent PDE or system of PDEs. Some examples in the theory of mean-field games and semiconductor equations are discussed.

Keywords: partial differential equations, symbolic computation, conserved and dissipated quantities, mathematica

Procedia PDF Downloads 163