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Logistic Regression Model of Relationship between Breast Cancer Pathology Diagnosis with Metastasis

Journal of Physics: Conference Series 2021年第1期1752卷

作者： M N Bustan B Poerwanto Statistics Study Program Universitas Negeri Makassar Makassar Department of Informatics Engineering Universitas Cokroaminoto Palopo

Management of breast cancer requires a diagnosis of breast cancer, including an accurate pathology diagnosis to direct the provision of appropriate treatment. Part of inseparable diagnosis of breast cancer itself is determining the presence of metastasis. This study aims to determine the relationship model of pathological diagnosis with the possibility of metastasis to other organs of the breast. Pathology diagnosis results include tumor position, topography, behavior, grade, and tumor status. This study used secondary data from the Hospital Information System (SIRS) of the hospital in Makassar, Indonesia. Data obtained from the results of pathology and clinical examinations of breast tumor as many as 204 patients were hospitalized during the year of 2018. Binary regression analysis modeling was performed to see the relationship between types of pathological diagnosis (laterality, topography, behavior, grade, tumor status) of breast cancer with the possibility of metastases to other organs. The results of the analysis found a significant relationship between the position and status of the tumor with the presence of metastasis. The results of the metastatic binary logistic regression model was L(Y) = 1.330 – 0.670lat1 – 0.779lat2 – 0.724status Chi-square analysis between metastasis with surgery and chemotherapy found the significant relationships between metastasis with surgery (X2 = 15.951; p value = 0.001) and metastasis with chemotherapy (X2 = 20,796; p value = 0.001). The results of this analysis indicate that the position and status of the tumor have a relationship with the likelihood of metastasis. In addition, this metastasis further determines the management of treatment whether surgery or chemotherapy or both.

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Exploring the Relationship between Saturated Soil Hydraulic Conductivity and Co-Driven Factors Using Artificial Neural Networks at Watershed Scale

SSRN

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SSRN 2024年

作者： Soares, Mauricio Fornalski Nunes, Romulo Félix Siqueira, Tirzah Moreira Pedrollo, Olavo Correa Reichardt, Klaus Barros, Willian Silva She, Dongli Timm, Luís Carlos Water Resources Post-Graduate Program Center of Technological Development Federal University of Pelotas Campus Porto Rua Gomes Carneiro n. 01 Rio Grande do Sul Pelotas96010-610 Brazil Center of Engineering Federal University of Pelotas Rio Grande do Sul Pelotas Brazil Soil and Water Management and Conservation Post-Graduate Program Faculty of Agronomy Federal University of Pelotas Rio Grande do Sul Pelotas Brazil Hydraulic Researchs Institute Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil Soil Physics Laboratory CENA Department of Crop Production ESALQ University of São Paulo São Paulo Piracicaba Brazil Department of Statistics Federal University of Pelotas Campus Universitário s/n Rio Grande do Sul Capão do Leão96010-900 Brazil College of Agricultural Sciences and Engineering Hohai University Nanjing210098 China Department of Rural Engineering Faculty of Agronomy Federal University of Pelotas Campus Universitário s/n Ri o Grande do Sul CEP Capão do Leão96010-900 Brazil

Saturated hydraulic conductivity (KSat) is a key soil attribute that influences hydrological processes at multiple scales. It is well known that KSat is difficult to measure in the field. Field KSat measurement requires time and expensive equipment, skilled labor, and due to the variability of KSat, many samples are required. Artificial neural networks (ANNs) are powerful non-linear mathematical models that can estimate KSat from different soil and environmental input variables as well as using dimensionless variables generated using database resizing techniques via principal component analysis (PCA). Our main goal is to construct artificial neural network models for predicting KSat using soil and different land uses as input variables at a watershed scale. The study area is the Eller Creek watershed of about 0.7 km2 in southern Brazil, in which we established an experimental grid of 178 sampling points. Data of soil bulk density, total porosity, microporosity, macroporosity, soil organic carbon content, sand, silt and clay, and KSat were determined at each point in the 0-20 cm layer. A land-use map was derived from satellite images and used to identify the land use around each soil sampling point. PCA was applied to the original dataset and two selected PCs were included as input variables for ANN modeling. Our results suggest that PC inclusion as input parameters, collectively or individually, combined with the original data, increased the performance of the analyzed ANN models, when compared to similar models using only the original data and without PCs. The use of ANN models showed that the variation of KSat in the ECW watershed is not only associated with soil hydrological and environmental covariates, but also to PCs which are orthogonal linear recombinations that encapsulate the main sources of variation of the original datasets. © 2024, The Authors. All rights reserved.

关键词： Soils

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The comparison of optimum heuristic and deterministic scheduling rules for job shop scheduling in the manufacture 8

The comparison of optimum heuristic and deterministic schedu...

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8th Annual International Conference 2018 on Science and engineering, AIC-SE 2018

作者： Fradinata, E. Kesuma, Z.M. Industrial Engineering Department Syiah Kuala University Banda Aceh Indonesia Statistics Study Program Faculty of Mathematics and Natural Science Syiah Kuala University Banda Aceh Indonesia

There are some types of scheduling problems in the production line of manufacturing. One of the most multiple usages of the operational machine for doing some jobs is job shop scheduling. The implementation of the job shop problem in this study uses the input of random order of eight job numbers. The raw materials for the job numbers to do the process and the five type machines are considered. The machines work in different functions to do the jobs for some materials. The materials have processed the work in process (WIP) before they are sent to the warehouse. The proposed of this study is to find the optimal condition of job shop scheduling heuristic and deterministic rules to minimize the makespan, tardiness with the heuristic and some deterministic rules in the Lekin software. The heuristic methods are General SBRoutine and the DASH (shifting bottleneck). The deterministic use Critical Ratio (CR) Rule, First come First Serve Rule (FCFS), Longest Processing Time (LPT), Shortest Processing Time (SPT), Weighted Shortest Processing Time (WSPT) and Machine Sequence (MS). The result shows that the minimum makespan is FCFS method with 47 minutes, the tardiness is 27, and for the machine no3. It produces the effective percentage of The contras deviation performance among the jobs of DASH, and compare to the FCFS is as follow, DASH, LPT, MS, SPT, WSPT are increasing 4.26%;31,91%;8.51%;4.26% and 12.77% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Job shop scheduling

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Oyster oscillator: a novel mega-stable nonlinear chaotic system

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The European Physical Journal Special Topics 2021年第11期231卷 2143-2151页

作者： Ramakrishnan, Balamurali Ahmadi, Atefeh Nazarimehr, Fahimeh Natiq, Hayder Jafari, Sajad Hussain, Iqtadar Centre for Nonlinear Systems Chennai Institute of technology Chennai India Department of Biomedical Engineering Amirkabir University of Technology Tehran Iran Information Technology Collage Imam Ja’afar Al-Sadiq University Baghdad Iraq Health Technology Research Institute Amirkabir University of Technology Tehran Iran Mathematics Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University Doha Qatar

Designing novel mega-stable chaotic oscillators has been a hot topic of research lately. In the current paper, a two-dimensional mega-stable oscillator is presented. The oscillator has a vast amount of coexisting limit cycles that spread on a surface. The forced version of this system is a novel nonlinear oscillator with an immense number of coexisting limit cycles and chaotic attractors. The dynamical features of this oscillator are examined by the assistance of equilibria study, the plot of the basin of attraction, bifurcation scheme, Lyapunov Exponents (LEs) perspective, and Kaplan-Yorke dimension.

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Differential gene expression in trabecular bone osteocytes is related to the local strain and strain gradient

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Scientific reports 2025年第1期15卷 18501页

作者： Meghana Machireddy Sarah Nano Lucas Debiase Jonathan Lewis Sara Cole Jun Li Glen L Niebur University of Notre Dame Bioengineering Graduate Program Notre Dame IN USA. Department of Aerospace and Mechanical Engineering Notre Dame IN USA. Notre Dame Integrated Imaging Facility Notre Dame IN USA. Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN USA. University of Notre Dame Bioengineering Graduate Program Notre Dame IN USA. gniebur@nd.edu. Department of Aerospace and Mechanical Engineering Notre Dame IN USA. gniebur@nd.edu.

Osteocytes regulate the response of osteoclasts and osteoblasts to mechanical loading through signaling molecules, the levels of which are controlled by post-translational modification or degradation and by differential gene transcription and translation. The magnitude and mode of bone tissue deformation that elicits a transcriptional response in individual osteocytes in situ has been difficult to quantify. We measured SOST, Wnt11, TNF, and FRZB gene expression in osteocytes within loaded and unloaded control porcine trabecular bone explants using RNAScope® and compared the local tissue level strain and strain gradient-which we used as an indicator of potential poroelastic fluid flow-in the tissue surrounding osteocytes with high vs. low gene expression. The measured expression of all four genes differed between loaded and unloaded explants, on average, with the mean SOST expression level decreasing by 45%. In the loaded explants, gene expression was altered from baseline in about 30% of the osteocytes, and they were surrounded by tissue with higher strain and strain gradient than the 20 to 25% of osteocytes that remained near baseline expression. Both deviatoric strain and hydrostatic strain gradient were sensitive and specific predictors of the mechanobiological response for individual genes as well as combinations. SOST expression was highly related to elevated strain gradient, providing evidence that osteocytes respond to fluid flow in the lacunar-canalicular system.

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A computational information criterion for particle-tracking with sparse or noisy data

arXiv

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arXiv 2021年

作者： Tran, Nhat Thanh Benson, David A. Schmidt, Michael J. Pankavich, Stephen D. Department of Mathematics University of California Irvine IrvineCA92697 United States Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. We address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and mass-transfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical domain, or the error distribution of the data is non-IID Gaussian. Keywords: Computational Information Criterion, Lagrangian Modeling, Particle Methods, Diffusion-reaction Equation, Non-Gaussian Error Distribution Copyright © 2021, The Authors. All rights reserved.

关键词： Advection

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Hybrid Particle Swarm and Conjugate Gradient Optimization in Neural Network for Prediction of Suspended Particulate Matter 4

Hybrid Particle Swarm and Conjugate Gradient Optimization in...

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4th International Conference on Energy, Environment, Epidemiology and Information System, ICENIS 2019

作者： Warsito, Budi Prahutama, Alan Yasin, Hasbi Sumiyati, Sri Department of Statistics Faculty of Sciences and Mathematics Diponegoro University Semarang Indonesia Master Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Environmental Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

The scope of this research is the use of artificial neural network models and meta-heuristic optimization of Particle Swarm Optimization (PSO) for the prediction of ambient air pollution parameter data at air quality monitoring stations in the city of Semarang, Central Java. The observed parameter is an indicator of ambient air quality, Suspended Particulate Matter (SPM). Based on air quality parameter data in previous times which is a time series data, modeling is done using Neural Networks (NN). Estimation of weights from NN is done using a hybrid method between meta-heuristic and gradient optimization. The meta-heuristic optimization method used is Particle Swarm Optimization (PSO) while the gradient based method is the Conjugate Gradient. Optimization with PSO is done first, then proceed with optimization using the Conjugate Gradient. Four scenarios of iteration selection at the PSO stage are 10, 25, 50 and 100. At the Conjugate Gradient, stage iteration is carried out up to 1000 epohs. The predicted results were compared with the PSOs and Conjugate Gradient respectively. The results show that the hybrid method provides better predictions. The number of iterations needed at the PSO stage is not too much so it is efficient in combining the two methods. © The Authors, published by EDP Sciences, 2019.

关键词： Forecasting

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Magnetized and quadratic convection based thermal transport in ternary radiative bio-nanofluid via intelligent neural networks: Two hidden layers mechanism

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Results in Physics 2024年 65卷

作者： Ayub, Assad Darvesh, Adil Shah, Syed Zahir Hussain Junjua, Moin-ud-Din Santos, Elizaldo Domingues dos Ahmad, Shabbir Hendy, Ahmed S. Ragab, Adham E. Department of Mathematics & Statistics Hazara University Manshera Pakistan School of Mathematical Sciences Zhejiang Normal University Zhejiang Jinhua 321004 China Programa de Pós-Graduação em Modelagem Computacional/LMCE Escola de Engenharia Universidade Federal do Rio Grande – FURG RS Rio Grande 96203900 Brazil FURG Escola de Engenharia RS Rio Grande Brazil Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan 60000 Pakistan Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg 620002 Russian Federation Department of Industrial Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Mathematics Ghazi University Dera Ghazi Khan 32200 Pakistan Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 96201-900 Rio Grande Brazil Department of Mechanics and Mathematics Western Caspian University Baku 1001 Azerbaijan

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al₂O₃, and TiO₂. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter. © 2024

关键词： Blood fluid Intelligent neural network Magnetized environment Ternary nanoparticles Thermal scrutiny

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Prediction of carbon nanostructure mechanical properties and role of defects using machine learning

arXiv

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arXiv 2021年

作者： Zhao, Qi Winetrout, Jordan J. Xu, Yanxun Wang, Yusu Heinz, Hendrik Halıcıoglu Data Science Institute University of California San Diego San diegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States

Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 100 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties. © 2021, CC BY.

关键词： Defects

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Two-dimensional Bose-Hubbard model for helium on graphene

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Physical Review B 2021年第23期103卷 235414-235414页

作者： Jiangyong Yu Ethan Lauricella Mohamed Elsayed Kenneth Shepherd, Jr. Nathan S. Nichols Todd Lombardi Sang Wook Kim Carlos Wexler Juan M. Vanegas Taras Lakoba Valeri N. Kotov Adrian Del Maestro Department of Physics University of Vermont Burlington Vermont 05405 USA Materials Science Program University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Department of Mathematics & Statistics University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

An exciting development in the field of correlated systems is the possibility of realizing two-dimensional (2D) phases of quantum matter. For a system of bosons, an example of strong correlations manifesting themselves in a 2D environment is provided by helium adsorbed on graphene. We construct the effective Bose-Hubbard model for this system which involves hard-core bosons (U≈∞), repulsive nearest-neighbor (V>0) and small attractive (V′<0) next-nearest-neighbor interactions. The mapping onto the Bose-Hubbard model is accomplished by a variety of many-body techniques which take into account the strong He-He correlations on the scale of the graphene lattice spacing. Unlike the case of dilute ultracold atoms where interactions are effectively pointlike, the detailed microscopic form of the short-range electrostatic and long-range dispersion interactions in the helium-graphene system is crucial for the emergent Bose-Hubbard description. The result places the ground state of the first layer of He4 adsorbed on graphene deep in the commensurate solid phase with 1/3 of the sites on the dual triangular lattice occupied. Because the parameters of the effective Bose-Hubbard model are very sensitive to the exact lattice structure, this opens up an avenue to tune quantum phase transitions in this solid-state system.

关键词： Adsorption Quantum phase transitions Graphene Helium-4 superfluids Bose-Hubbard model Density functional theory Extended Hubbard model Quantum Monte Carlo Wannier function methods

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