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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Mishra, Debasish Pattipati, Krishna R. Bollas, George M. Data Science and Information Systems Krea University 5655 Central Expressway Sri City517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs06269 United States Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs06269 United States

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices-such as Bhattacharyya distance, Fourier transform, and Cosine similarity-to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators. © 2025 The Authors. Published by Elsevier B.V.

关键词： Clustering Machine Learning Precision machining Similarity Tool condition monitoring Unsupervised

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Single-shot quantitative phase gradient microscopy using physics-based untrained neural network phase retrieval 11

Single-shot quantitative phase gradient microscopy using phy...

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Quantitative Phase Imaging XI 2025

作者： Hur, Sun Woong Sengupta, Sourya Kwon, Minsung Manoharaan, Revathi Anastasio, Mark Bhargava, Rohit Department of Bioengineering University of Illinois Urbana-ChampaignIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignIL United States Department of Computer Science Carle-Illinois College of Medicine University of Illinois Urbana-ChampaignIL United States Department of Chemical and Biomolecular Engineering Mechanical Science and Engineering and Chemistry Cancer Center at Illinois University of Illinois Urbana-ChampaignIL United States CZ Biohub Chicago LLC IL United States

ISBN: (纸本)9781510684065

Single-shot quantitative phase gradient microscopy (ss-QPGM) enables quantitative phase imaging (QPI) based on differential interference contrast (DIC) microscopy. In ss-QPGM, the analyzer is replaced by a quarter-wave plate and a polarization camera, allowing for phase gradient extraction from a single intensity measurement. The quantitative phase image is reconstructed via numerical integration, but direct integration along a single axis introduces artifacts and phase errors due to missing frequency response along the axis of orthogonal to the shear axis and noise-induced streak lines. In this work, we propose a phase retrieval method for ss-QPGM using an untrained neural network (UNN), which integrates physical priors without requiring paired ground truth and input datasets, overcoming the limitations of traditional regularization methods such as Tikhonov regularization and the alternating direction method of multipliers (ADMM) with total variation (TV) regularization. We demonstrate that the UNN approach not only offers superior phase reconstruction but also alleviates artifacts induced by missing frequency response along the axis and noise-induced streak lines, as shown by phase images of a calibrated target and biological samples. © 2025 SPIE.

关键词： Image reconstruction

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings

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Procedia computer science 2025年 253卷 1412-1421页

作者： Debasish Mishra Krishna R. Pattipati George M. Bollas Data Science and Information Systems Krea University 5655 Central Expressway Sri City 517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs 06269 USA Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs 06269 USA

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices–such as Bhattacharyya distance, Fourier transform, and Cosine similarity–to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators.

关键词： Tool condition monitoring Precision machining Similarity Machine Learning Unsupervised Clustering

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Porous silicon on paper sensors: Factors affecting biosensing performance 17

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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EasyDIVER + : An Advanced Tool for Analyzing High Throughput Sequencing Data from In Vitro Evolution of Nucleic Acids or Amino Acids

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Journal of molecular evolution 2025年第2期93卷 2025 Apr 5页

作者： Celia Blanco Allison Tee Pramesh Sharma Matilda S Newton Kun-Hwa Lee Samuel E Erickson Burckhard Seelig Irene A Chen Blue Marble Space Institute of Science 600 1st Avenue 1st Floor Seattle WA USA. celia.blanco@***. Department of Chemical and Biomolecular Engineering University of California Los Angeles CA USA. celia.blanco@***. Blue Marble Space Institute of Science 600 1st Avenue 1st Floor Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA. College of Engineering University of California Davis CA USA. Department of Biochemistry Molecular Biology and Biophysics University of Minnesota Minneapolis MN USA. BioTechnology Institute University of Minnesota St. Paul MN USA. Royal Society of New Zealand Wellington New Zealand. Department of Chemical and Biomolecular Engineering University of California Los Angeles CA USA.

In vitro evolution is a powerful technique for identifying functional nucleic acids and peptides, but the analysis of the resulting high-throughput sequencing data poses significant challenges, particularly in peptide selections. Existing bioinformatics tools often lack the specificity needed for this task, leaving researchers to navigate complex datasets with inadequate resources. To address these challenges, we present EasyDIVER + , an enhanced pipeline building on the foundation of the original EasyDIVER tool, which was designed for pre-processing sequencing data. EasyDIVER + not only processes raw, paired-end, demultiplexed Illumina read files but also introduces advanced analytical capabilities, including the calculation of enrichment values for each unique sequence across consecutive selection rounds. Furthermore, EasyDIVER + offers a highly flexible and customizable visualization platform, enabling detailed graphical representations of sequence metrics. These new features mark a significant advance in bioinformatics for peptide and protein data, providing researchers with intuitive tools for comprehensive data analysis and interpretation.

关键词： Bioinformatics High-throughput sequencing In vitro evolution SELEX mRNA display

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Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis

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computers in Biology and Medicine 2025年 186卷 109681-109681页

作者： Torok, Rebeka Meszaros, Brigitta Gombas, Veronika Vathy-Fogarassy, Agnes Szabo, Miklos Csanky, Eszter Jarvas, Gabor Guttman, Andras Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem Hungary Horváth Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprem Hungary Department of Pulmonology Borsod Academic County Hospital Miskolc Hungary

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy. © 2025 The Authors

关键词： Lung cancer

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Foundation Models for Atomistic Simulation of Chemistry and Materials

arXiv

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arXiv 2025年

作者： Yuan, Eric C.-Y. Liu, Yunsheng Chen, Junmin Zhong, Peichen Raja, Sanjeev Kreiman, Tobias Vargas, Santiago Xu, Wenbin Head-Gordon, Martin Yang, Chao Blau, Samuel M. Cheng, Bingqing Krishnapriyan, Aditi Head-Gordon, Teresa Kenneth S. Pitzer Theory Center Department of Chemistry United States Department of Chemical and Biomolecular Engineering United States Electrical Engineering and Computer Science United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA94720 United States Chemical Sciences Division United States Applied Mathematics and Computational Research Division United States Energy Technologies Area United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale. © 2025, CC BY-SA.

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Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

arXiv

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arXiv 2025年

作者： Maus, Natalie Kim, Kyurae Zeng, Yimeng Jones, Haydn Thomas Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of T black-box objective functions, f1, . . . fT , simultaneously. Traditional approaches often seek a single Pareto-optimal set that balances trade-offs among all objectives. In this work, we introduce a novel problem setting that departs from this paradigm: finding a smaller set of K solutions, where K 1, . . . fT , there is at least one good solution. A motivating example for this problem setting occurs in drug design. For example, we may have T pathogens and aim to identify a set of K © 2025, CC BY-SA.

关键词： Pathogens

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Large Scale Multi-Task Bayesian Optimization with Large Language Models

arXiv

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arXiv 2025年

作者： Zeng, Yimeng Maus, Natalie Jones, Haydn Thomas Tao, Jeffrey Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Marcus, Ryan Bastani, Osbert Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel transfer exist, the performance improvement is marginal when scaling to more than a moderate number of tasks. We introduce a novel approach leveraging large language models (LLMs) to learn from, and improve upon, previous optimization trajectories, scaling to approximately 2000 distinct tasks. Specifically, we propose an iterative framework in which an LLM is fine-tuned using the high quality solutions produced by BayesOpt to generate improved initializations that accelerate convergence for future optimization tasks based on previous search trajectories. We evaluate our method on two distinct domains: database query optimization and antimicrobial peptide design. Results demonstrate that our approach creates a positive feedback loop, where the LLM’s generated initializations gradually improve, leading to better optimization performance. As this feedback loop continues, we find that the LLM is eventually able to generate solutions to new tasks in just a few shots that are better than the solutions produced by "from scratch" by Bayesian optimization while simultaneously requiring significantly fewer oracle calls. © 2025, CC BY-SA.

关键词： Structured Query Language

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