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arXiv 2025年

作者： Vendrell-Gallart, Oriol Negarandeh, Nima Foumani, Zahra Zanjani Amiri, Mahsa Valdevit, Lorenzo Bostanabad, Ramin Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Materials and Manufacturing Technology Program University of California IrvineCA92697 United States Department of Materials Science and Engineering University of California IrvineCA92697 United States Department of Civil and Environmental Engineering University of California IrvineCA92697 United States

Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs’ interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications. © 2025, CC BY.

关键词： Data fusion

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The technology of rob water treatment becomes clean water by using the combination of electrolysis, filtration, and ozonation methods 10

The technology of rob water treatment becomes clean water by...

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10th International Seminar on New Paradigm and Innovation of Natural sciences and itsApplication, ISNPINSA 2020

作者： Wahyono, Y. Nofanda, T.T. Budiyanto, I. Nugroho, H. Rosyad, M.S. Halim, M.A.R. Riski, I. Amilia, A. Rahmawati, D.F. Nurauliyaa, A.H. Baihaqi, R.A. Syafira, F. Lestari, P. Doctoral Program of Environmental Science School of Postgraduate Studies Diponegoro University Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Mathematics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Chemistry Faculty of Science and Mathematics Diponegoro University Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Indonesia

Coastal residents have difficulty in fulfilling the needs of clean water due to tidal water and tidal water intrusion. Meanwhile, clean water is a crucial component in carrying out activities of daily life. This initiative aims to provide an alternative solution in the form of technology to treat water polluted by saline water into clean water. This initiative used the method of electrolysis, filtration, and ozonation. Electrolysis has functions to remove salt contained and reduce levels of heavy metals while filtration functions to filter out the impurities in the form of solid particles, and ozonation takes a role to kill bacteria. Moreover, the target output of this work is the creation of a prototype by using this technology as a technology that can treat water polluted by saline water into clean water. The parameters assessed from this initiative are physical, chemical, and biological parameters of clean water. The results of the total test of dissolved solids of water before processing were 7130 ppm while it was 2490 ppm after getting treatment. The decreasing total value of dissolved solids is 65.077%, which indicates that the test was successful. The result of an electrical conductivity test of water before processing is 310 mA while after processing is 88.6 mA. The decrease in the current value of 71.419% indicates that the test was successful. The results of the turbidity test of water before processing are 0 FAU while after getting treatment is 0 FAU. The turbidity value remains 0, which indicates the test was successful. The results of the pH test of water before processing were 5.7 while the result after treatment got 6.1. The increase in pH value towards pH 7 (neutral) indicates that the test was successful. The test results of salt and heavy metals (Fe, Cu, Cl, Na, and Pb) of water before processing were 0.073 ppm, 0.07 ppm, 0.17 ppm, 0.04 ppm, and 4.24 ppm while after processing was 0 ppm, 0 ppm, 0.07 ppm, 0.03 ppm, and 1.36 ppm. There was a decrease

关键词： Ozonization

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Harmful Algal Blooms in Cayuga Lake, Ny: From Microbiome Analysis to Edna Monitoring

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SSRN 2023年

作者： Wang, Nan Mark, Noah Launer, Nathaniel Hirtler, Adrianna Faehndrich, Chloe Weston, Claire Cleckner, Lisa LaGorga, Lydia Xia, Lingzi Pyrek, Daniel Penningroth, Stephen M. Richardson, Ruth E. Civil and Environmental Engineering Cornell University IthacaNY14853 United States Community Science Institute IthacaNY14850 United States New York State Department of Health-Environmental Laboratory Approval Program ID11790 United States Finger Lakes Institute Hobart and William Smith Colleges GenevaNY United States Skidmore College Environmental Studies and Sciences Program Saratoga SpringsNY12866 United States Civil and Environmental Engineering Cornell University IthacaNY14853 United States

The global increase in harmful algal blooms (HABs) has become a growing concern over the years, and New York State (NYS) is no exception. The Finger Lakes region in NYS has been identified as a hotspot for HABs, with Cayuga Lake having the highest number of blooms reported. The Cayuga Lake HABs Monitoring program has been tracking cHABs (dominant bloom taxa, chlorophyll A, and microcystin levels) since 2018. Analysis of data from 2018-2020 revealed that the genus Microcystis and Dolichospermum dominated cHABs in Cayuga Lake, with a strong correlation between microcystin and chlorophyll A concentrations when Microcystis was present. However, limited research has been conducted on the microbiome of HABs in this region. In this study, the microbiome of HABs in the Cayuga Lake was surveyed and compared with non-HAB baseline samples. Using 16S rDNA community analysis, common bloom-forming cyanobacteria, including Dolichospermum, Planktothrix, Microcystis, Cylindrospermopsis, Nodularia, and Trichodesmium, were identified, with Microcystis being the dominant taxa in high toxin blooms. Further, this study evaluated the ability of Microcystis mcyA qPCR to detect elevated levels of potential toxigenic Microcystis in water samples using both benchtop and handheld qPCR devices. The results showed good performance of the qPCR assay as a screening for high toxin versus low/no toxin blooms, with low specificity attributed to sub-toxic samples that tested positive. Additionally, the handheld qPCR device holds potential for in-field rapid ( © 2023, The Authors. All rights reserved.

关键词： Lakes

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An open-source, automated, and cost-effective platform for COVID-19 diagnosis and rapid portable genomic surveillance using nanopore sequencing

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Scientific Reports 2023年第1期13卷 1-12页

作者： Ramos-Mandujano, Gerardo Grünberg, Raik Zhang, Yingzi Bi, Chongwei Guzmán-Vega, Francisco J. Shuaib, Muhammad Gorchakov, Rodion V. Xu, Jinna Tehseen, Muhammad Takahashi, Masateru Takahashi, Etsuko Dada, Ashraf Ahmad, Adeel Nazir Hamdan, Samir M. Pain, Arnab Arold, Stefan T. Li, Mo Stem Cell and Regeneration Laboratory Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Structural Biology and Engineering Computational Biology Research Center. Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Pathogen Genomics Laboratory Bioscience Program Biological and Environmental Science and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Health Safety and Environment Department King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Laboratory of DNA Replication and Recombination Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Pathology and Laboratory Medicine King Faisal Specialist Hospital and Research Center Jeddah Saudi Arabia College of Medicine Al Faisal University Riyadh Saudi Arabia KAUST Health King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Bioengineering Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

The COVID-19 pandemic, caused by SARS-CoV-2, has emphasized the necessity for scalable diagnostic workflows using locally produced reagents and basic laboratory equipment with minimal dependence on global supply chains. We introduce an open-source automated platform for high-throughput RNA extraction and pathogen diagnosis, which uses reagents almost entirely produced in-house. This platform integrates our methods for self-manufacturing magnetic nanoparticles and qRT-PCR reagents-both of which have received regulatory approval for clinical use–with an in-house, open-source robotic extraction protocol. It also incorporates our "Nanopore Sequencing of Isothermal Rapid Viral Amplification for Near Real-time Analysis" (NIRVANA) technology, designed for tracking SARS-CoV-2 mutations and variants. The platform exhibits high reproducibility and consistency without cross-contamination, and its limit of detection, sensitivity, and specificity are comparable to commercial assays. Automated NIRVANA effectively identifies circulating SARS-CoV-2 variants. Our in-house, cost-effective reagents, automated diagnostic workflows, and portable genomic surveillance strategies provide a scalable and rapid solution for COVID-19 diagnosis and variant tracking, essential for current and future pandemic responses. © 2023, The Author(s).

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Design, Development and Performance Evaluation of a Miniature Electrostatic Precipitator in an Indoor Environment

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SSRN 2024年

作者： Kumar, Aiswarya Nawale, Prashant Rohidas Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

Different PM capture technologies are available in market as well as on lab scale, they pose several drawbacks. Therefore, a miniature ESP was designed in laboratory and operated at optimal operating conditions to obtain maximised capture of PM. Results from study confirmed that designed ESP was capable of capturing various standard aerosol, major indoor sources and infiltrated PM with a total removal efficiency of 97.87% to 99.97% in PM sizes from 10 nm to 800 nm. Additionally, energy consumed/clean air delivery rate and emission of by-products were found to be comparatively lesser compared to commercial purifiers. © 2024, The Authors. All rights reserved.

关键词： Electrostatic precipitators

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Future Glycemic Events Prediction Model Based On Artificial Neural Network

Future Glycemic Events Prediction Model Based On Artificial ...

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2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Syafrudin, Muhammad Alfian, Ganjar Fitriyani, Norma Latif Hadibarata, Tony Rhee, Jongtae Anshari, Muhammad Sejong University Department of Artificial Intelligence Seoul Korea Republic of Universitas Gadjah Mada Department of Electrical Engineering and Informatics Vocational College Yogyakarta Indonesia Sejong University Department of Data Science Seoul Korea Republic of Curtin University Environmental Engineering Program Miri Malaysia Dongguk University Department of Industrial and Systems Engineering Seoul Korea Republic of Universiti Brunei Darussalam School of Business and Economics Brunei

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Predicting future glycemic events such as hypoglycemia, hyperglycemia, and normal for type 1 diabetes (T1D) remains a significant and challenging issue. In this study, an artificial neural network (ANN)-based model is proposed to predict the future glycemic events of T1D patients. We utilized five T1D patient datasets to build the models and predict future glycemic events with a prediction horizon (PH) of 30 and 60 minutes ahead of time. We applied the data preprocessing method based on the sliding window approach by sliding the blood glucose time-series data from the past 60 minutes (the last 12 data points) as input and using the next 30 and 60 minutes (the next 6 and 12-th data points) as output. All the numeric blood glucose output data are then transformed into a multi-class classification label, such as hypoglycemia, hyperglycemia, and normal. Our proposed model is then used to learn and create the prediction model from the preprocessed blood glucose dataset. Four performance metrics such as accuracy, precision, recall, and f-1 score were utilized to measure the performance of the classification models used in this study, such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). The results showed that our proposed ANN-based model performed better at predicting future glycemic events than other models, with an average accuracy, precision, recall, and f-1 score of 88.649%, 76.661%, 71.731%, 72.609%, and 83.364%, 60.437%, 61.345%, 60.62% for the PH of 30 and 60 minutes, respectively. As a result, knowing this future glycemic event sooner can help patients avoid potentially dangerous conditions and can eventually be used to improve diabetes management. © 2022 IEEE.

关键词： Decision trees

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Concentrate and Degrade Pfoa with a Photo-Regenerable Composite of In-Doped Tnts@Ac

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SSRN 2022年

作者： Juve, Jan-Max Arana Li, Fan Zhu, Yangmo Liu, Wen Ottosen, Lars D.M. Zhao, Dongye Wei, Zongsu Department of Biological and Chemical Engineering Aarhus University Universitetsbyen 36 Aarhus8000 Denmark Environmental Engineering Program Department of Civil & Environmental Engineering Auburn University AuburnAL36849 United States The Key Laboratory of Water and Sediment Sciences Ministry of Education College of Environmental Science and Engineering Peking University Beijing100871 China

"Concentrate-and-degrade" is an effective strategy to promote mass transfer and degradation of pollutants in photocatalytic systems, yet suitable and cost-effective photocatalysts are required to practice the new concept. In this study, we doped a post-transition metal of Indium (In) on a novel composite adsorptive photocatalyst, activated carbon-supported titanate nanotubes (TNTs@AC), to effectively degrade perfluorooctanoic acid (PFOA). In/TNTs@AC exhibited both excellent PFOA adsorption (100% in 30 min) and photodegradation (100% in 4 hours) under normal environmental conditions. The heterojunction structure of the composite facilitated a cooperative adsorption mode of PFOA, i.e., binding of the carboxylic head group of PFOA to the metal oxide and attachment of the hydrophobic tail to AC. The resulting side-on adsorption mode facilitates the electron (e‒) transfer from the carboxylic head to the photogenerated hole (h+), which was the major oxidant verified by scavenger tests. Furthermore, the presence of In enables direct electron transfer and facilitates the subsequent stepwise defluorination. Finally, In/TNTs@AC was amenable to repeated uses in four consecutive adsorption-photodegradation runs. The findings showed that adsorptive photocatalysts can be prepared by hybridization of carbon and photoactive semiconductors and the enabled "concentrate-and-degrade" strategy is promising for removal and degradation of trace levels PFOA from polluted waters. © 2022, The Authors. All rights reserved.

关键词： Heterojunctions

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Critical Review on the Abatement Of Endocrine Disrupting Compounds In Iron-Based Materials Catalyzed Persulfate Systems: Reactive Species, Reaction Mechanisms, and Perspectives

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SSRN 2024年

作者： Sun, Ye Huang, Ying Zheng, Shujie Chen, Haojia Yang, Yan Ji, Hongbing Han, Changseok School of Chemical Engineering and Light Industry School of Environmental Science and Engineering Guangdong University of Technology Guangzhou510006 China School of Environment Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China School of Chemistry and Chemical Engineering Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology Guangxi University Nanning530004 China Synergy Innovation Institute of Guangdong University of Technology Shantou515041 China Department of Environmental Engineering INHA University Incheon22212 Korea Republic of Program in Environmental and Polymer Engineering Graduate School of INHA University Incheon22212 Korea Republic of

Persulfate-based advanced oxidation processes have emerged as promising technologies for mitigating endocrine-disrupting compounds (EDCs) in wastewater treatment. Economically viable Fe-based materials with tailored properties have demonstrated significant efficacy in activating persulfate for the decomposition of EDCs through both radical-routes (•OH, SO4•−, and O2•−) and non-radical routes (1O2, surface activated PS complex, and high-valent Fe species). This review summarized the latest advancements in the persulfate activation mechanisms mediated by Fe-based materials, categorized by Fe valence states and the types of reactive species. Furthermore, it critically assesses the influence of active sites of Fe-based catalysts on persulfate activation pathways and summarizes the selective reaction of reactive species towards EDCs. Despite these advancements, several challenges persist in non-radical route-dominated persulfate activation, particularly regarding the impact of Fe-Fe distance on the reactive species and the catalytic role of Fe-based materials for reactions between inorganic ions and persulfate. This review proposes prospective research directions, provides design strategies, and expands the application of Fe-based materials in PS activation for effective EDC abatement in real situations. © 2024, The Authors. All rights reserved.

关键词： Endocrine disrupters

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Interannual variability and climatic sensitivity of global wildfire activity

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Advances in Climate Change Research 2021年第5期12卷 686-695页

作者： Rongyun TANG Jiafu MAO Mingzhou JIN Anping CHEN Yan YU Xiaoying SHI Yulong ZHANG Forrest M.HOFFMAN Min XU Yaoping WANG Institute for a Secure and Sustainable Environment University of TennesseeKnoxville37996USA Environmental Sciences Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA Department of Biology and Graduate Degree Program in Ecology Colorado State UniversityFort Collins80523USA Atmospheric and Oceanic Sciences Program Princeton UniversityPrinceton08544USA Computational Sciences and Engineering Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources;2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations;3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs;and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.

关键词： Wildfire Burned area Climatic sensitivity E3SM land model Global fire emission database

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Learn How to Design High-Quality Qualitative Educational Research! - A Workshop for Disciplinary STEM Faculty by Disciplinary STEM Faculty 53

Learn How to Design High-Quality Qualitative Educational Res...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Morelock, John R. Jarvie-Eggart, Michelle Chenette, Heather Hooshangi, Sara Chestnutt, Betsy Wilson, Sarah Bolhari, Azadeh Dodson, Kirsten Pavlova, Iglika Reck, Rebecca Transformations Institute University of Georgia Engineering Education AthensGA United States Michigan Technological University Engineering Fundamentals HoughtonMI United States Rose-Hulman Institute of Technology Chemical Engineering Terre HauteIN United States Virginia Tech Computer Science - Northern Virginia Center WashingtonDC United States University of Tennessee Knoxville Engineering Fundamentals KnoxvilleTN United States University of Kentucky Chemical Engineering LexingtonKY United States University of Colorado Boulder Environmental Engineering Program BoulderCO United States Lipscomb University Mechanical Engineering NashvilleTN United States University of North Carolina Greensboro Department of Biology GreensboroNC United States University of Illinois Urbana-Champaign Bioengineering ChampaignIL United States

ISBN: (纸本)9798350336429

The purpose of this workshop-designed for instructional and disciplinary STEM faculty interested in learning about qualitative research-is to (1) introduce participants to high-quality qualitative research design and (2) practice this design process alongside disciplinary STEM faculty to expand their STEM education research abilities and network. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods [1]. This workshop will be conducted by ProQual Institute alumni, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change [2], the workshop leaders will serve as a community of practice to help participants move their educational research ideas forward during and after the workshop. In doing so, we strive to further FIE's mission to create a collaborative, supportive, and inclusive community of educational researchers. © 2023 IEEE.

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