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Web-based rule-based expert system to screen postpartum depression

IOP Conference Series: Materials Science and Engineering 2021年第1期1108卷

作者： D Mufidati M N Widyawati S Suryono Postgraduate Program in Applied Midwifery Science Poltekkes Kemenkes Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

The high prevalence of postpartum depression significantly increases maternal morbidity. Lack of diagnosis for postpartum depression results in mothers missing early treatment necessary. The traditional screening method using questionnaire presents its own disadvantage in diagnosis. Therefore, development of postpartum screening method that focuses on ease in implementation is very much needed to improve postpartum maternal health. This paper proposes a screening information system for postpartum depression to help its proper treatment. This research employed the rule-based method to diagnose postpartum depression and the expert system to input symptoms data that will result in diagnoses. Respondents input symptoms data to be diagnosed by the system. The system then automatically generate output of suggested treatment based on expert input associated with the diagnoses the system is working on. This research employed the Randomized Control Trial method with Control Group Pre and Post Tests Design. Samples of this research were 52 postpartum mothers consisting of 26 respondents in the system group, and the other 26 in the manual group. Results show that 42.31% (n=11) of mothers suffer from postpartum depression. There is a significant difference in EPDS score prior to and after the use of the information system (P=0.000). The system also successfully generated very high TAM score, meaning that it is capable of postpartum screening along with ease of use and some other advantages. Therefore, screening for postpartum depression using information system is very effective and ensures proper and timely treatment.

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Tunable eigenvector-based centralities for multiplex and temporal networks

arXiv

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arXiv 2019年

作者： Taylor, Dane Porter, Mason A. Mucha, Peter J. Department of Mathematics University at Buffalo State University of New York BuffaloNY14260 United States Department of Mathematics University of California Los AngelesCA90095 United States Carolina Center for Interdisciplinary Applied Mathematics Department of Mathematics Department of Applied Physical Sciences University of North Carolina Chapel HillNC27599 United States

Characterizing the importances (i.e., centralities) of nodes in social, biological, and technological networks is a core topic in both network science and data science. We present a linear-algebraic framework that generalizes eigenvector-based centralities, including PageRank and hub/authority scores, to provide a common framework for two popular classes of multilayer networks: multiplex networks (which have layers that encode different types of relationships) and temporal networks (in which the relationships change over time). Our approach involves the study of joint, marginal, and conditional "supracentralities" that one can calculate from the dominant eigenvector of a supracentrality matrix [Taylor et al., 2017], which couples centrality matrices that are associated with individual network layers. We extend this prior work (which was restricted to temporal networks with layers that are coupled by adjacent-in-time coupling) by allowing the layers to be coupled through a (possibly asymmetric) interlayer-adjacency matrix A∼, where the entry Att'≥ 0 encodes the coupling between layers t and t'. Our framework provides a unifying foundation for centrality analysis of multiplex and temporal networks;it also illustrates a complicated dependency of the supracentralities on the topology and weights of interlayer coupling. By scaling A∼by an interlayer-coupling strength ω ≥ 0 and developing a singular perturbation theory for the limits of weak (ω → 0+) and strong coupling (ω → ∞), we also reveal an interesting dependence of supracentralities on the dominant left and right eigenvectors of A∼. We provide additional theoretical and practical insights by applying our framework to two empirical data sets: a multiplex network of airline transportation in Europe and a temporal network that encodes the graduation and hiring of mathematical scientists at United States universities. Copyright © 2019, The Authors. All rights reserved.

关键词： Data integration

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Environmental modelling of CO concentration using AI-based approach supported with filters feature extraction: A direct and inverse chemometrics-based simulation

Sustainable Chemistry for the Environment

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Sustainable Chemistry for the Environment 2023年 2卷

作者： Usman, A.G. Usanase, Natacha Abba, S.I. Ozsahin, Ilker Uzun, Berna Yassin, Mohamed A. Rahman, Syed Masiur Ozsahin, Dilber Uzun Operational Research Centre in Healthcare Near East University Mersin 10 Turkey Department of Analytical Chemistry Faculty of Pharmacy Near East University Mersin 10 Turkey Interdisciplinary Research Centre for Membranes and Water Security King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Department of Biomedical Engineering Faculty of Engineering Near East University Mersin 10 Turkey Department of Statistics Carlos III Madrid University Getafe Madrid Spain Department of Mathematics Faculty of Arts and Sciences Near East University Mersin 10 Turkey Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah Sharjah United Arab Emirates Applied Research Center for Environment and Marine Studies King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia Research Institute for Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates

This study explored the first direct and inverse modelling of Carbon monoxide (CO) concentration by applying three different computational approaches namely, Least Square-boost (L-Boost), Hammerstein Weiner (HW), and Multi-variate regression (MVR) models for modelling CO using Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂) and Ozone (O₃). Two filters feature extraction methods were used in the input-combinations selection, which was classified into the direct modelling approach (C1) and inverse modelling approach (C2). Four different statistical metrics, including Nash-Sutcliffe efficiency, Person correlation coefficient, mean absolute error and mean absolute percentage error was used in evaluating the performance of the models. Based on the performance skills of the models, it is indicated that the direct modelling approach showed superior performance to the inverse approach. Furthermore, the non-linear models (HW and L-Boost) depict higher performance skills than the linear MVR model. Overall, the L-Boost-C1 models showed higher performance based on statistical metrics. © 2023 The Authors

关键词： Artificial intelligence CO Environmental modelling Filters feature extraction Pollutant

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A Robust Light-Curve Diagnostic for Electron-Capture Supernovae and Low-Mass Fe-Core-Collapse Supernovae

arXiv

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arXiv 2024年

作者： Sato, Masato Tominaga, Nozomu Blinnikov, Sergei I. Potashov, Marat Sh. Moriya, Takashi J. Hiramatsu, Daichi Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Tokyo Mitaka181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Department of Physics Faculty of Science and Engineering Konan University 8-9-1 Okamoto Hyogo Kobe658-8501 Japan NRC Kurchatov Institute Moscow123182 Russia Moscow127055 Russia Keldysh Institute of Applied Mathematics RAS 4 Miusskaya Square Moscow125047 Russia School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed ∼ 40 years ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe exploding with energies in circumstellar media (CSM) estimated with theoretically or observationally plausible methods. The plateaus of the ECSNe are shorter, brighter, and bluer than those of the FeCCSNe. To investigate the robustness of their intrinsic differences, we adopted various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. Furthermore, we propose a distance-independent method to identify ECSNe: (g − r)tPT/2 PT − 0.4, i.e., blue g − r at the middle of the plateau (g − r)tPT/2, where tPT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques. © 2024, CC BY.

关键词： Supernovae

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CellBiAge: Improved single-cell age classification using data binarization

引用

Cell Reports 2023年第12期42卷 113500页

作者： Yu, Doudou Li, Manlin Linghu, Guanjie Hu, Yihuan Hajdarovic, Kaitlyn H. Wang, An Singh, Ritambhara Webb, Ashley E. Molecular Biology Cell Biology and Biochemistry Graduate Program Brown University Providence 02912 RI United States Data Science Institute Brown University Providence 02912 RI United States Neuroscience Graduate Program Brown University Providence 02912 RI United States Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore 21218 MD United States Department of Computer Science Brown University Providence 02912 RI United States Center for Computational Molecular Biology Brown University Providence 02912 RI United States Department of Molecular Biology Cell Biology and Biochemistry Brown University Providence 02912 RI United States Center on the Biology of Aging Brown University Providence 02912 RI United States Carney Institute for Brain Science Brown University Providence 02912 RI United States Center for Translational Neuroscience Brown University Providence 02912 RI United States

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods. © 2023 The Authors

关键词： aging brain CP: Cell biology hypothalamus machine learning single-cell RNA-seq

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DDEL-06. CONVECTION-ENHANCED DELIVERY OF SEVEN DIFFERENT MOLECULES SHOW LARGE DIFFERENCES IN CONVECTIVE PROPERTIES BETWEEN MOLECULES. INFORMED DECISION MAKING FOR OPTIMIZED CATHETER PLACEMENT AND SUBSEQUENT INFUSION STRATEGIES

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii122–viii122页

作者： Halle, Bo Woolley, Max Johnson, Dave Williams, Julia Kidd, Charlotte Andersen, Mikkel Schou Poulsen, Frantz Rom Gammelsrød, Vigga Sand Nielsen, Aaraby Yoheswaran Amirrashedi, Mahsa Willem Straathof, Natan Johannes Ravn, Katharina Tang, Qing Pedersen, Christian Glarbo Jensen, Andrea Tue Ingemann Thisgaard, Helge Department of neurosurgery Odense University Hospital Odense Denmark Clinical Institute and BRIDGE (Brain Research InterDisciplinary Guided Excellence) University of Southern Denmark Odense Denmark Renishaw Neuro Solutions Ltd Gloucestershire United Kingdom Department of Clinical Research University of Southern Denmark Odense Denmark Department of Nuclear Medicine Odense University Hospital Odense Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark The Hevesy Laboratory Department of Health Technology Technical University of Denmark Kongens Lyngby Denmark

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A trace theorem for sobolev spaces on the sierpinski gasket

arXiv

引用

arXiv 2019年

作者： CAO, SHIPING LI, SHUANGPING STRICHARTZ, ROBERT S. TALWAI, PREM Department of Mathematics Cornell University Ithaca14853 United States Program in Applied and Computational Mathematics Princeton University NJ08544-1000 United States

We give a discrete characterization of the trace of a class of Sobolev spaces on the Sierpinski gasket to the bottom line. This includes the L2 domain of the Laplacian as a special case. In addition, for Sobolev spaces of low orders, including the domain of the Dirichlet form, the trace spaces are Besov spaces on the *** Codes 28A80 Copyright © 2019, The Authors. All rights reserved.

关键词： Sobolev spaces

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

arXiv

引用

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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A self-sustaining process theory for uniform momentum zones and internal shear layers in high Reynolds number shear flows

arXiv

引用

arXiv 2020年

作者： Montemuro, Brandon White, Christopher M. Klewicki, Joseph C. Chini, Gregory P. Program in Integrated Applied Mathematics University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of Melbourne MelbourneVIC3010 Australia

Many exact coherent states (ECS) arising in wall-bounded shear flows have an asymptotic structure at extreme Reynolds number Re in which the effective Reynolds number governing the streak and roll dynamics is O(1). Consequently, these viscous ECS are not suitable candidates for quasi-coherent structures away from the wall that necessarily are inviscid in the mean. Specifically, viscous ECS cannot account for the singular nature of the inertial domain, where the flow self-organizes into uniform momentum zones (UMZs) separated by internal shear layers and the instantaneous streamwise velocity develops a staircase-like profile. In this investigation, a large-Re asymptotic analysis is performed to explore the potential for a three-dimensional, short streamwise- and spanwise-wavelength instability of the embedded shear layers to sustain a spatially-distributed array of much larger-scale, effectively inviscid streamwise roll motions. In contrast to other self-sustaining process theories, the rolls are sufficiently strong to differentially homogenize the background shear flow, thereby providing a mechanistic explanation for the formation and maintenance of UMZs and interlaced shear layers that respects the leading-order balance structure of the mean dynamics. Copyright © 2020, The Authors. All rights reserved.

关键词： Shear flow

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Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM)

arXiv

引用

arXiv 2021年

作者： Rakita, Yevgeny Hart, James L. Das, Partha Pratim Foley, Daniel L. Nicolopoulos, Stavros Shahrezaei, Sina Mathaudhu, Suveen Nigel Taheri, Mitra L. Billinge, Simon J.L. Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Materials Science and Engineering Johns Hopkins University BaltimoreMD21218 United States NanoMEGAS SPRL Rue Émile Claus 49 Bte 9 BrusselsB1050 Belgium Materials Science and Engineering Program University of California Riverside RiversideCA29521 United States Department of Mechanical Engineering University of California Riverside RiversideCA92521 United States Condensed Matter Physics and Materials Science Department Brookhaven National Laboratory UptonNY11973 United States

Here we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr65Cu17.5Ni10Al7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modelled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods. © 2021, CC BY.

关键词： Electrons

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