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SSRN 2024年

作者： Henkes, Alexander Herrmann, Leon Wessels, Henning Kollmannsberger, Stefan Computational Mechanics Group ETH Zürich Switzerland Chair of Computational Modeling and Simulation TU Munich Germany Division Data-Driven Modeling of Mechanical Systems TU Braunschweig Germany

Additive manufacturing technologies have seen significant economic growth over the past years. Although additive manufacturing processes have matured in many areas, difficulties with regard to printing accuracy persist. Possible defects are, e.g., the generation of unwanted internal pores or a lack of fusion between layers. In general, defects result in a deviation between as-planned and as-built geometries, all of which can be difficult to detect in an automatized fashion. Previous work has shown that image-based simulation can assist in quality monitoring of produced parts and may complement experimental testing. Yet, both experimental testing and simulation-based approaches are involved and not yet directly applicable to all manufactured parts in a series production. The paper at hand suggests a remedy to this problem by using a generative adversarial network. Generative adversarial networks have shown to be able to emulate as-built geometries of engineering relevance. Moreover, they can realistically reproduce the distributions of such deviations. To this end, we present how this feature can be harvested to employ generative adversarial networks for outlier detection. To this end, we use the discriminator as a classifier on as-built parts to judge whether an as-built structure is acceptable or defective. The viability of the approach is demonstrated on basic artificial structures with spherical voids as well as additively manufactured lattice structures whose geometry is acquired after production via computed tomography. The methodology is not only applicable for automated property monitoring but potentially also for reliability estimates of neural network based property predictors. © 2024, The Authors. All rights reserved.

关键词： 3D printing

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Can we promote neural regeneration through microbiota-targeted strategies? Introducing the new concept of neurobiotics

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Neural Regeneration Research 2022年第9期17卷 1965-1966页

作者： Celia Herrera-Rincon Julia Murciano-Brea Stefano Geuna Department of Biodiversity Ecology&Evolutionand ModelingData Analysis&Computational Tools for Biology Research GroupBiomathematics UnitComplutense University of MadridMadridSpain Department of Clinical and Biological Sciences and Cavalieri Ottolenghi Neuroscience InstituteUniversity of TurinTurinItaly

The human body is populated by a large number of microbial colonies,with an estimated 10-100 trillion *** total genome size of human microbial colonies by far overwhelms the size of the host’s *** heterogenous group of microbial colonies(primarily bacteria,but also archaea,eukaryotes and viruses)is referred to with the term microbiota,and although most of them populate the gut,microbes are also detectable in many other organs of the body,especially in the distal tracts of the genitourinary system and the *** the last years,an increasing amount of evidence has been accumulated on how the microbiota exerts a significant influence on the development and physiology of the human body.

关键词： neural organs size

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Arrival time for the fastest among N switching stochastic particles

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The European Physical Journal B 2022年第7期95卷 1-25页

作者： Toste, S. Holcman, D. Group of Data Modeling Computational Biology IBENS Ecole Normale Superieure-PSL Paris France

The first arrivals among N Brownian particles is ubiquitous in the life sciences, as it often triggers cellular processes from the molecular level. We study here the case where stochastic particles, which represent proteins or molecules can switch between two states inside the non-negative real line. The switching process is modeled as a two-state Markov chain and particles can only escape in state 1. We estimate the fastest arrival time by solving asymptotically the Fokker–Planck equations for three different initial distributions: Dirac-delta, uniformly distributed and long-tail decay. The derived formulas reveal that the fastest particle avoids switching when the switching rates are much smaller than the diffusion time scale, but switches twice when the diffusion in state 2 is much faster than in state 1. The present results are compared to stochastic simulations revealing the range of validity of the derived formulas.

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Feature Subset Weighting for Distance-based Supervised Learning through Choquet Integration

arXiv

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arXiv 2025年

作者： Theerens, Adnan Saeys, Yvan Cornelis, Chris Department of Mathematics Computer Science and Statistics Ghent University Ghent Belgium Data Mining and Modeling for Biomedicine Group VIB Inflammation Research Center Belgium

This paper introduces feature subset weighting using monotone measures for distance-based supervised learning. The Choquet integral is used to define a distance metric that incorporates these weights. This integration enables the proposed distances to effectively capture non-linear relationships and account for interactions both between conditional and decision attributes and among conditional attributes themselves, resulting in a more flexible distance measure. In particular, we show how this approach ensures that the distances remain unaffected by the addition of duplicate and strongly correlated features. Another key point of this approach is that it makes feature subset weighting computationally feasible, since only m feature subset weights should be calculated each time instead of calculating all feature subset weights (2m), where m is the number of attributes. Next, we also examine how the use of the Choquet integral for measuring similarity leads to a non-equivalent definition of distance. The relationship between distance and similarity is further explored through dual measures. Additionally, symmetric Choquet distances and similarities are proposed, preserving the classical symmetry between similarity and distance. Finally, we introduce a concrete feature subset weighting distance, evaluate its performance in a k-nearest neighbors (KNN) classification setting, and compare it against Mahalanobis distances and weighted distance methods. © 2025, CC BY-NC-ND.

关键词： Choquet integral

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Additive Manufacturing of Ceramics opens the door to Components for Biomanufacturing Applications with high Functionality 6

Additive Manufacturing of Ceramics opens the door to Compone...

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6th CIRP Conference on BioManufacturing, BioM 2024

作者： Scheithauer, Uwe Holtzhausen, Stefan Wiemer, Hajo Fraunhofer Institute for Ceramic Technologies and Systems Group Additive and Hybrid Manufacturing Winterbergstr. 28 Dresden01277 Germany TUD Dresden University of Technology Institute of Machine Elements and Machine Design Virtual Product Development Group Advanced Geometric Modeling And Analysis Dresden01062 Germany TUD Dresden University of Technology Institute of Mechatronic Engineering Machine Tools Development and Adaptive Controls Department of Machine Data Processing Dresden01062 Germany

Significant advancements in production technology, materials, component design, and data science foster innovation across industries, including biomanufacturing. Ceramic materials, adapted additive manufacturing processes, and novel construction methods hold immense innovation potential due to ceramics' exceptional thermal, chemical, and mechanical properties. Additive manufacturing enables the creation of intricate ceramic components with unprecedented functionality. Hybridizing manufacturing technologies and materials facilitates direct integration of additional features like sensors and actuators. Despite their potential, ceramic materials remain underutilized. This article aims to spark a discourse on leveraging high-performance ceramic components in biomanufacturing, leveraging superior material properties, innovative design methods, additive manufacturing, process data analysis, and AI. © 2024 The Authors.

关键词： Ceramic materials

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Adisutjipto Institute of Aerospace Technology Virtual Tour with Artificial Intelligence (AI) Agent 1

Adisutjipto Institute of Aerospace Technology Virtual Tour w...

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1st International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics, ICAAEEI 2024

作者： Ayuningtyas, Astika Wahyudi Sumari, Arwin Datumaya Aryanto, Salam Nuryatno, Edi Triono Mulyani, Sri Kusumaningrum, Anggraini Adisutjipto Institute of Aerospace Technology Aerospace Intelligent Systems and Data Engineering Research Group Faculty of Industrial Technology Yogyakarta Indonesia Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Modeling and Simulation Research Group Faculty of Industrial Technology Yogyakarta Indonesia The University of Western Australia Uwa Centre for Medical Research Perth Australia Adisutjipto Institute of Aerospace Technology Faculty of Aerospace Engineering Technology Yogyakarta Indonesia

ISBN: (纸本)9798331507930

This study aims to develop an interactive virtual tour based on Android with a first-person shooter approach to introduce the Adisutjipto Institute of Aerospace Technology (ITD Adisutjipto) virtually. This virtual tour utilizes an Artificial Intelligence (AI) agent as an intelligent guide who can answer questions and provide relevant information related to facilities and locations inside the ITD Adisutjipto building. The test results show that this virtual tour is effective and feasible to use, with a user satisfaction rate reaching 87.72%. This study contributes to the field of educational virtual technology by offering a more immersive and interactive experience. Users can easily explore the ITD Adisutjipto building virtually and obtain the information they need efficiently. This research is expected to be an innovative solution to introduce the ITD Adisutjipto to wider community, as well as support learning and research activities in the aerospace technology field. © 2024 IEEE.

关键词： Virtual environments

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Voltage laws in nanodomains revealed by asymptotics and simulations of electro-diffusion equations

arXiv

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arXiv 2024年

作者： Paquin-Lefebvre, Frédéric Moreno, Alejandro Barea Holcman, David Group of Data Modeling and Computational Biology IBENS Ecole Normale Supérieure Paris75005 France Churchill College University of Cambridge CB30DS United Kingdom

Characterizing the local voltage distribution within nanophysiological domains, driven by ionic currents through membrane channels, is crucial for studying cellular activity in modern biophysics, yet it presents significant experimental and theoretical challenges. Theoretically, the complexity arises from the difficulty of solving electro-diffusion equations in three-dimensional domains. Currently, there are no methods available for obtaining asymptotic computations or approximated solutions of nonlinear equations, and numerically, it is challenging to explore solutions across both small and large spatial scales. In this work, we develop a method to solve the Poisson-Nernst-Planck equations with ionic currents entering and exiting through two narrow, circular window channels located on the boundary. The inflow through the first window is composed of a single cation, while the outflow maintains a constant ionic density satisfying local electro-neutrality conditions. Employing regular expansions and Green’s function representations, we derive the ionic profiles and voltage drops in both small and large charge regimes. We explore how local surface curvature and window channels size influence voltage dynamics and validate our theoretical predictions through numerical simulations, assessing the accuracy of our asymptotic computations. These novel relationships between current, voltage, concentrations and geometry can enhance the characterization of physiological behaviors of *** Codes 35J05, 35J08, 35J25 Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

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Investigation on the Effect of Household Income and Demographic Factors on Healthcare Cost in Perak 7th

Investigation on the Effect of Household Income and Demograp...

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7th International Conference on the Applications of Science and Mathematics, SCIEMATHIC 2021

作者： Pillay, Khuneswari Gopal Yee, Wong Pui Data Analytics Sciences and Modeling Focus Group Universiti Tun Hussein Onn Malaysia Johor Pagoh84600 Malaysia Department of Mathematics and Statistics Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Pagoh84600 Malaysia

ISBN: (纸本)9789811689024

This paper focusing on the factor that effect the household income and demographic factors on healthcare cost in Perak as the increasing healthcare cost had brought many concerns to the community. Factors affecting the choice of hospital and healthcare is the main issue raise in this study. In the pilot study, 50 respondents were used to test for reliability using Cronbach’s alpha. Result shows a reliability value of 0.785. Then, convenience sampling was used to collect response from 307 respondents who visited private and public hospitals in Perak. The data collected was used to determine the healthcare awareness of people in Perak by using the Chi-square test and cross-tabulation analysis. Meanwhile, LASSO and LASSO logistic regression were used to identify the factors affecting the choice of hospital. The better model in identifying factors affecting the choice of hospital was computed based on the accuracy, Brier score and AUC. Next, LASSO regression was applied to explore the effect of household income and demographic factors on healthcare cost in Perak. Based on the findings, people in Perak had high awareness of health insurance and personal healthcare. LASSO logistic regression was found to be the best model in identifying factors affecting the choice of hospital with a higher prediction accuracy, 0.7917 and lower Brier score, 0.3123. Variables affecting the choice of hospital and healthcare cost included education level, employment status, household income, ownership of household and having/not having an insurance policy. These findings could guide authority to improve the healthcare system for the benefits of people in Perak. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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The FGVH and GDEX Stock Price modeling Using ARIMA and Holt’s Linear Trend Methods 7th

The FGVH and GDEX Stock Price Modeling Using ARIMA and Holt...

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7th International Conference on the Applications of Science and Mathematics, SCIEMATHIC 2021

作者： Asrah, Norhaidah Mohd Sua’aif, Rabia’tul Adawiyah Wahab, Nur Hazlina Abdul Data Analytics Sciences and Modeling Focus Group Universiti Tun Hussein Onn Malaysia Pagoh Johor84600 Malaysia Department of Mathematics and Statistics Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Pagoh Johor84600 Malaysia

ISBN: (纸本)9789811689024

Nowadays, stock market represents a company performance and an essential asset to promote an investment to the company. It is crucial to any company for the reflection on the growth and fall of the company. It helps the company to attract more investors when the stock is growing and can contribute to the growth of an economical state of a country. The analysis of the stock market price would be essential to know the fluctuation of the stock market prices. When the company's capital is increase, it will be adequate for the growth of the company. Thus, it will attract more investors to rise the economic trade, growth, and prosperity of the company. To boost the confidence of investors to invest, it is important to study the stock price market and find the suitable model so that the direction of the stock price can be determine. Hence, this study focuses on two different type of industries stock market namely Felda Global Ventures Holdings (FGVH) and GD Express Carrier Bhd (GDEX). The plantation industry is represented by the FGVH stock market, while the services industry is represented by the GDEX. The modeling of both stock price is studied using the Autoregressive Model Integrated Moving Average Model (ARIMA) and the model of exponential smoothing, Holt’s linear trend. The results revealed that the best model for FGVH stock market is ARIMA (0, 1, 2) and for GDEX stock market is the Holt’s linear trend with smoothing constant. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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Exploring the Racial Bias in Pain Detection with a Computer Vision Model 44

Exploring the Racial Bias in Pain Detection with a Computer ...

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44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Fabi, Sarah Xu, Xiaojing de Sa, Virginia R. Department of Cognitive Science Halicioglu Data Science Institute University of California San Diego United States Neuro-Cognitive Modeling Group University of Tübingen Germany Department of Electrical and Computer Engineering University of California San Diego United States

People detect painful expressions more easily in members of their racial ingroup than outgroup. Here, we wanted to investigate this racial bias with a machine learning model trained to detect activations of different action units of painful facial expressions. We examined whether the model detected higher action unit activation for European than African faces when trained on datasets with mostly White faces. To control for confounding variables, pictures of faces were generated with the FaceGen Modeller. Results revealed that there exist differences in the visual detectability of some facial muscle activations due to skin color or other race-dependent facial features. Despite the bias towards European looking faces in the training data, some activations were more easily detectable in African faces. Thus, neither the perceptual detectability, nor the larger exposure to own-race faces seems to solely explain the racial bias in pain detection. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Chemical activation

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