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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis 43

Finding customer behavior insights for content creation in m...

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43rd International Conference on Information Systems: Digitization for the Next Generation, ICIS 2022

作者： Chotisarn, Noptanit Siriphongwatana, Phanuphong Thanisuwiphat, Phattranit Gulyanon, Sarun Nadee, Winai Department of Management Information Systems Thammasat Business School Thammasat University Bangkok Thailand Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (纸本)9781713893615

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future. © 2022 International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation". All Rights Reserved.

关键词： Sales

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Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis 22

Finding customer behavior insights for content creation in m...

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22nd International Conference on Electronic Business, ICEB 2022

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future. © 2022 International Consortium for Electronic Business. All rights reserved.

关键词： Sales

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Implementation Challenge of Artificial Intelligence in Human Resource Management for Employee Performance Monitoring Indonesia Companies Perspective 11

Implementation Challenge of Artificial Intelligence in Human...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Wirawan, Edi Yusuf Gusan Putra, Merios Nugroho, Budi Program of Management Brawijaya University Jakarta Campus Indonesia Program of Information Systen Diponegoro University Semarang Indonesia Program of Information System Stmik Tazkia Bogor Indonesia Res. Center for Data and Information Science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350389654

The main problem solved is to Improve Employee Performance Monitoring by Integrating Artificial Intelligence (AI) in Human Resource Management for Indonesian Companies. The paper suggests using AI technologies to analyze and process employee performance data. The overarching goal of this new approach is to identify patterns and red flags at the individual and team levels that can be used to inform stronger performance management strategies such as guided coaching, pointed feedback, or calibrated evaluations. This study is written as a schematic literature review, using sourced research articles from Indonesia and other countries to obtain the effects of AI on employee performance. This exercise showed how AI works in performance patterns and assists HR strategies. The findings AI has an impact on recognizing patterns in empirical data of how employees perform, helps to tailor training/development programs, and improves talent management and succession planning. Those results signify that AI has the potential to be a game-changer for HR practices in Indonesia. AI in HRM using employee performance monitoring seems an encouraging alternative for Indonesia. Through data-driven decision-making and automated HR routine tasks with many challenges and should prepare well to implement. © 2024 IEEE.

关键词： Resource allocation

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ALMACAL XIV: X-Shooter Spectroscopy, Infrared Properties and Radio SEDs of Calibrators

arXiv

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arXiv 2025年

作者： Weng, Simon Sadler, Elaine M. Kerrison, Emily Bollo, Victoria Péroux, Céline Zwaan, Martin Mahony, Elizabeth K. Allison, James R. Chen, Jianhang Szakacs, Roland Yoon, Hyein Sydney Institute for Astronomy School of Physics University of Sydney A28 NSW2006 Australia Australia ATNF CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia UMR 7326 Marseille13388 France European Southern Observatory Karl-Schwarzschildstrasse 2 Garching bei MünchenD-85748 Germany First Light Fusion Ltd. Unit 9/10 Oxford Pioneer Park Mead Road Yarnton KidlingtonOX5 1QU United Kingdom Giessenbachstrasse 1 GarchingD–85748 Germany Institute for Data Innovation in Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

The ALMACAL−22 survey includes over 2700 hours of observations of ALMA phase and amplitude calibrators, spanning frequencies from 84 to 950 GHz across bands 3 to 10. In total, 687 out of the 1,047 calibrators have redshifts confirmed with spectroscopy and we find an additional 50 featureless blazars. The redshift distribution of the ALMACAL-22 sample peaks at z ≈ 1 and spans a wide range, from the nuclei of nearby galaxies at z 0.01 to quasars at z = 3.742. 70 new VLT/X-Shooter spectra of these sources covering UV to NIR wavelengths are also presented, which will be used in future stacking experiments to search for cold gas in the circumgalactic medium. Infrared magnitudes from WISE indicate that the majority of the sources are consistent with being quasars or blazars. After fitting the radio spectral energy distributions of the calibrators, we find that most ALMA calibrators exhibit peaked spectra or are re-triggered which is surprising given the large number of blazars in the sample. The peak frequencies span three orders of magnitude from 100 MHz to 170 GHz, corresponding to linear sizes ranging from sub-pc to > 10 kpc. In the future, when combined with high-resolution radio imaging, these results will offer valuable constraints on the molecular gas content of the CGM, as well as the ages and duty cycles of AGN jets. The ever-growing ALMACAL data set will remain an indispensable resource for studying the various aspects of galaxy formation and evolution. © 2025, CC BY.

关键词： Moon

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Integrating knowledge tracing and item response theory: A tale of two frameworks 22

Integrating knowledge tracing and item response theory: A ta...

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Workshop of the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

作者： Khajah, Mohammad M. Huang, Yun González-Brenes, José P. Mozer, Michael C. Brusilovsky, Peter Department of Computer Science Institute of Cognitive Science University of Colorado BoulderCO80309-0430 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Digital Data Analytics and Adaptive Learning Pearson Research and Innovation Network PhiladelphiaPA19103 United States

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing.

关键词： Students

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Phylogeographic and genetic network assessment of COVID-19 mitigation protocols on SARS-CoV-2 transmission in university campus residences

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eBioMedicine 2025年 116卷 105729-105729页

作者： Wertheim, Joel O. Vasylyeva, Tetyana I. Wood, Robert J. Cantrell, Kalen Contreras, Soraya Piña Feldheim, Aryeh Goyal, Ravi Havens, Jennifer L. Knight, Rob Laurent, Louise C. Moshiri, Niema Neuhard, Robert Sathe, Shashank Satterlund, Alysson Scioscia, Angela Song, Angela Y. Aigner, Stefan Andersen, Kristian G. Baer, Nathan A. Betty, Maryann Birmingham, Amanda Castro-Martinez, Anelizze Cheung, Willi De Hoff, Peter Fisch, Kathleen M. King, Alison J. Gangavarapu, Karthik Hakim, Abbas Henson, Benjamin Jepsen, Kristen Mac, Christina H. Ngo, Toan T. Nguyen, Kelly N. Ostrander, Tyler R. Perkins, Sarah Plascencia, Ashley Rivera, Andrea Rivera, Ariana Salido, Rodolfo A. Saucedo, Kieran C. Schwab, Madison Steedman, Allison L. Veder, Anthony Weiss, Alana Yeo, Gene W. Zeller, Mark Schooley, Robert T. Anderson, Cheryl M. Martin, Natasha K. Department of Medicine University of California San Diego La Jolla CA United States Department of Population Health and Disease Prevention University of California Irvine Irvine CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Pediatrics University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA United States Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA United States University of California San Diego La Jolla CA United States

Background: Congregate living provides an ideal setting for SARS-CoV-2 transmission in which many outbreaks and superspreading events occurred. To avoid large outbreaks, universities turned to remote operations during the initial COVID-19 pandemic waves in 2020 and 2021. In late-2021, the University of California San Diego (UC San Diego) facilitated the return of students to campus with comprehensive testing, vaccination, masking, wastewater surveillance, and isolation policies. Methods: We performed molecular epidemiological and phylogeographic analysis of 4418 SARS-CoV-2 genomes sampled from UC San Diego students during the Omicron waves between December 2021 and September 2022, representing 58% of students with confirmed SARS-CoV-2 infection. We overlaid these analyses across on-campus residential information to assess the spread and persistence of SARS-CoV-2 within university residences. Findings: Within campus residences, SARS-CoV-2 transmission was frequent among students residing in the same room or suite. However, a quarter of pairs of suitemates with concurrent infections had distantly related viruses, suggesting separate sources of infection during periods of high incidence in the surrounding community. Students with concurrent infections residing in the same building were not at substantial increased probability of being members of the same transmission cluster. Genetic network and phylogeographic inference indicated that only between 3.1 and 12.4% of infections among students could be associated with transmission within buildings outside of individual suites. The only super-spreading event we detected was related to a large event outside campus residences. Interpretation: We found little evidence for sustained SARS-CoV-2 transmission within individual buildings, aside from students who resided in the same suite. Even in the face of heightened community transmission during the 2021–2022 Omicron waves, congregate living did not result in a heightened risk

关键词： Mitigation Phylogeography SARS-CoV-2 Transmission cluster University

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine 3

Maintaining the Quality and Aroma of Coffee with Fuzzy Logic...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Samodro, Bayu Mahesworo, Bharuno Suparyanto, Teddy Surya Atmaja, Don Bosco Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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