A Mixed Flow Analysis of Sewer Pipes with Different Shapes Using a Non-Oscillatory Two-Component Pressure Approach (TPA)

This paper aimed to justify the performance of a non-oscillatory TPA-based model proposed by the authors for capturing transient mix flow in sewer systems consisting of a variety of pipe shapes. The model utilizes a first-order Godunov Finite volume numerical scheme in which a Harten–Lax–van Leer (HLL) Riemann solver was used for calculating the fluxes at the cells’ boundaries. The spurious numerical solution associated with the transient mix flow analysis is suppressed by enhancing the numerical viscosity of the scheme when the pipe pressurization is imminent. Due to the lack of experimental data for systems with pipe shapes other than circular and rectangular, a hypothetical pipe system for which analytical solutions exist was employed to verify the model performance. The results reveal that for all pipe shapes considered, the model provides oscillation-free solutions even at a high acoustic speed of 1400 m/s. It is also observed that the numerical results are in perfect agreement with the analytical solution. The obtained results conclude that the proposed model can be utilized to capture transient responses of sewer systems with any pipe shape.

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An Analytical Transient Tire Model

Tire Science and Technology ◽

10.2346/1.2135231 ◽

2001 ◽

Vol 29 (2) ◽

pp. 108-132 ◽

Cited By ~ 1

Author(s):

A. Ghazi Zadeh ◽

A. Fahim

Keyword(s):

Transient Behavior ◽

Stability Control ◽

Vehicle Stability ◽

Tire Model ◽

Steering Angle ◽

First Order ◽

Vehicle Stability Control ◽

Proposed Model ◽

Depth Study ◽

And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

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Leaching Kinetics of Atrazine and Inorganic Chemicals in Tilled and Orchard Soils

International Agrophysics ◽

10.2478/intag-2014-0012 ◽

2014 ◽

Vol 28 (2) ◽

pp. 231-237 ◽

Cited By ~ 2

Author(s):

Lech W. Szajdak ◽

Jerzy Lipiec ◽

Anna Siczek ◽

Artur Nosalewicz ◽

Urszula Majewska

Keyword(s):

Reaction Rate ◽

Inorganic Compounds ◽

Model Performance ◽

Order Reaction ◽

First Order Reaction ◽

Order Kinetic ◽

Time Data ◽

First Order ◽

Concentration Changes ◽

Reaction Constants

Abstract The aim of this study was to verify first-order kinetic reaction rate model performance in predicting of leaching of atrazine and inorganic compounds (K+1, Fe+3, Mg+2, Mn+2, NH4 +, NO3 - and PO4 -3) from tilled and orchard silty loam soils. This model provided an excellent fit to the experimental concentration changes of the compounds vs. time data during leaching. Calculated values of the first-order reaction rate constants for the changes of all chemicals were from 3.8 to 19.0 times higher in orchard than in tilled soil. Higher first-order reaction constants for orchard than tilled soil correspond with both higher total porosity and contribution of biological pores in the former. The first order reaction constants for the leaching of chemical compounds enables prediction of the actual compound concentration and the interactions between compound and soil as affected by management system. The study demonstrates the effectiveness of simultaneous chemical and physical analyses as a tool for the understanding of leaching in variously managed soils.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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Superconformal boundaries in 4 − ϵ dimensions

Journal of High Energy Physics ◽

10.1007/jhep04(2021)167 ◽

2021 ◽

Vol 2021 (4) ◽

Author(s):

Aleix Gimenez-Grau ◽

Pedro Liendo ◽

Philine van Vliet

Keyword(s):

Three Dimensional ◽

Spacetime Dimension ◽

Perfect Agreement ◽

Bootstrap Analysis ◽

First Order ◽

Free Theory ◽

First Order Correction ◽

Free Parameters ◽

Superconformal Theories ◽

Zumino Model

Abstract Boundaries in three-dimensional $$ \mathcal{N} $$ N = 2 superconformal theories may preserve one half of the original bulk supersymmetry. There are two possibilities which are characterized by the chirality of the leftover supercharges. Depending on the choice, the remaining 2d boundary algebra exhibits $$ \mathcal{N} $$ N = (0, 2) or $$ \mathcal{N} $$ N = (1) supersymmetry. In this work we focus on correlation functions of chiral fields for both types of supersymmetric boundaries. We study a host of correlators using superspace techniques and calculate superconformal blocks for two- and three-point functions. For $$ \mathcal{N} $$ N = (1) supersymmetry, some of our results can be analytically continued in the spacetime dimension while keeping the codimension fixed. This opens the door for a bootstrap analysis of the ϵ-expansion in supersymmetric BCFTs. Armed with our analytically-continued superblocks, we prove that in the free theory limit two-point functions of chiral (and antichiral) fields are unique. The first order correction, which already describes interactions, is universal up to two free parameters. As a check of our analysis, we study the Wess-Zumino model with a super-symmetric boundary using Feynman diagrams, and find perfect agreement between the perturbative and bootstrap results.

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Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

The Neuroradiology Journal ◽

10.1177/1971400921998979 ◽

2021 ◽

pp. 197140092199897

Author(s):

Sarv Priya ◽

Caitlin Ward ◽

Thomas Locke ◽

Neetu Soni ◽

Ravishankar Pillenahalli Maheshwarappa ◽

...

Keyword(s):

Machine Learning ◽

Central Nervous System ◽

Nervous System ◽

Diagnostic Performance ◽

Model Performance ◽

Area Under The Curve ◽

Central Nervous System Lymphoma ◽

First Order ◽

Single Slice ◽

Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

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A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

ACM Transactions on Internet Technology ◽

10.1145/3424221 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1-22

Author(s):

Abhinav Kumar ◽

Sanjay Kumar Singh ◽

K Lakshmanan ◽

Sonal Saxena ◽

Sameer Shrivastava

Keyword(s):

Differential Privacy ◽

Model Performance ◽

Healthcare Services ◽

Cloud Services ◽

Efficient Computation ◽

Sensitive Data ◽

Classification Framework ◽

Proposed Model ◽

Diagnostic Framework ◽

Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

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The development of a Riemann solver for the steady supersonic Euler equations

The Aeronautical Journal ◽

10.1017/s0001924000026890 ◽

1994 ◽

Vol 98 (979) ◽

pp. 325-339 ◽

Cited By ~ 16

Author(s):

E. F. Toro ◽

A. Chakraborty

Keyword(s):

Euler Equations ◽

Weighted Average ◽

Godunov Method ◽

Riemann Solver ◽

Flux Method ◽

First Order ◽

Enhanced Resolution ◽

Average Flux ◽

Exact Riemann Solver ◽

Order Weighted Average

Abstract An improved version (HLLC) of the Harten, Lax, van Leer Riemann solver (HLL) for the steady supersonic Euler equations is presented. Unlike the HLL, the HLLC version admits the presence of the slip line in the structure of the solution. This leads to enhanced resolution of computed slip lines by Godunov type methods. We assess the HLLC solver in the context of the first order Godunov method and the second order weighted average flux method (WAF). It is shown that the improvement embodied in the HLLC solver over the HLL solver is virtually equivalent to incorporating the exact Riemann solver.

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A phenomenological model for thermally-induced hysteresis in polycrystalline shape memory alloys with internal loops

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x211048228 ◽

2021 ◽

pp. 1045389X2110482

Author(s):

Yuxiang Han ◽

Haoyuan Du ◽

Linxiang Wang ◽

Roderick Melnik

Keyword(s):

Shape Memory Alloys ◽

Single Crystal ◽

Shape Memory ◽

Phenomenological Model ◽

Landau Theory ◽

Thermally Induced ◽

First Order ◽

Proposed Model ◽

Crystal Model ◽

Hysteretic Response

In the current study, a 1-D phenomenological model is constructed to capture the temperature-induced hysteretic response in polycrystalline shape memory alloys (SMAs). The martensitic and austenitic transformations are regarded as the first-order transitions. A differential single-crystal model is formulated on the basis of Landau theory. It is assumed that the transformation temperatures follow the normal distribution among the grains due to the anisotropic stress field developed in the material. The polycrystalline hysteretic response is expressed as the integration of single-crystal responses. Besides, the prediction strategy for incomplete transitions is presented, and the first-order reversal curves are obtained via density reassignment. The proposed model is numerically implemented for validation. Comparisons between the modeling results and the experimental ones demonstrate the capability of the proposed model in addressing the hysteresis in thermally-induced phase transformations.

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Non-Markovian model for the study of pitting corrosion in a water pipe system

International Journal of Modern Physics C ◽

10.1142/s0129183115501193 ◽

2015 ◽

Vol 26 (10) ◽

pp. 1550119 ◽

Cited By ~ 1

Author(s):

A. C. P. Rosa ◽

P. Vaveliuk ◽

M. A. Moret

Keyword(s):

Nuclear Power ◽

Power Plants ◽

Extreme Values ◽

Tail Probability ◽

Markovian Model ◽

Nonextensive Statistical Mechanics ◽

Long Tail ◽

Pipe System ◽

Proposed Model ◽

State Regime

The main studies on pitting consist in proposing Markovian stochastic models, based on the statistics of extreme values and focused on growing the depth of wells, especially the deepest one. We show that a non-Markovian model, described by a nonlinear Fokker–Planck (nFP) equation, properly depicts the time evolution of a distribution of depth values of pits that were experimentally obtained. The solution of this equation in a steady-state regime is a q-Gaussian distribution, i.e. a long-tail probability distribution that is the main characteristic of a nonextensive statistical mechanics. The proposed model, that is applied to data from four inspections conducted on a section of a line of regular water service in power water reactor (PWR) nuclear power plants, is in agreement with experimental results.

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Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742184 ◽

2020 ◽

Vol 7 (4) ◽

pp. 745

Author(s):

Rizka Indah Armianti ◽

Achmad Fanany Onnilita Gaffar ◽

Arief Bramanto Wicaksono Putra

Keyword(s):

Feature Extraction ◽

Video Data ◽

Clustering Method ◽

Data Frame ◽

First Order ◽

Feature Extraction And Selection ◽

Different Shapes ◽

Frame Set ◽

Selection Of

Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap frame. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap frame-nya. Frame yang memiliki pola terbaik diantara frame lainnya disebut frame dominan. Penelitian ini bertujuan untuk menyeleksi frame dominan dari rangkaian frame dengan menerapkan metode K-means clustering untuk memperoleh centroid dominan (centroid dengan nilai tertinggi) yang digunakan sebagai dasar seleksi frame dominan. Dalam menyeleksi frame dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu Mean. Data hasil ekstraksi ciri berjumlah 93 data frame yang selanjutnya dikelompokkan menjadi 3 cluster menggunakan metode K-Means. Dari hasil clustering, centroid dominan terletak pada cluster 3 dengan nilai centroid 0.0177 dan terdiri dari 41 data frame. Selanjutnya diukur jarak kedekatan seluruh data cluster 3 terhadap centroid, data yang memiliki jarak terdekat dengan centroid itulah frame dominan. Hasil seleksi frame dominan ditunjukkan pada jarak antar centroid dengan anggota cluster, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai 0.0010 yang dimiliki oleh frame ke-59, ke-36 dan ke-35. AbstractThe object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.

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