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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Large Pre-Trained Models and Few-Shot FineTuning for Virtual Metrology: A Framework for Uncertainty-Driven Adaptive Process Control in Semiconductor Manufacturing

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IEEE Transactions on Automation science and engineering 2025年 22卷 15351-15370页

作者： Lin, Chin-Yi Tseng, Tzu-Liang Emon, Solayman Hossain Tsai, Tsung-Han University of Texas at EL Paso Department of Industrial Manufacturing and Systems Engineering Texas El PasoTX79968 United States Computational Science Program University of Texas at EL Paso Texas El PasoTX79968 United States National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan

High-precision wafer metrology poses significant cost and throughput challenges in modern semiconductor manufacturing, where frequent process changes and recipe variations demand highly adaptive and scalable solutions. In this paper, we present a Generative-FewShot-Active Virtual Metrology (GFA-VM) framework that unifies large-scale generative modeling, few-shot fine-tuning, and uncertainty-driven active sampling into a single, data-centric system. A foundational generative model, built on a hybrid architecture of Transformer networks and Variational Autoencoders (VAEs), learns diverse sensor characteristics in an offline stage without relying on extensive labeled data. During online inference, the model produces both wafer quality predictions and predictive uncertainties;samples exceeding a dynamic uncertainty threshold are selected for physical measurement and few-shot model recalibration. This selective sampling both reduces measurement costs and adapts rapidly to new process conditions (e.g., novel recipes or equipment upgrades), requiring only a handful of freshly labeled wafers. The paper further addresses the long-term stability of the system through a self-updating mechanism that adjusts the uncertainty threshold when distributional shifts occur. Empirical evaluations confirm that our GFA-VM approach achieves state-of-the-art accuracy while significantly reducing metrology overhead compared to conventional virtual metrology methods. Additionally, rigorous theoretical analyses—including proofs of convergence and label cost bounds—demonstrate the reliability of using a generative foundation plus meta-learning technique. By fostering on-demand adaptation within a closedloop framework, GFA-VM offers a comprehensive, scalable strategy for next-generation semiconductor process control. © 2004-2012 IEEE.

关键词： Semiconductor device measurement Metrology Semiconductor device modeling Semiconductor process modeling Uncertainty Adaptation models Accuracy Data models Predictive models Manufacturing

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Drivers of the Sustainability Performance of Induction Stove Conversion program in Indonesia

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International Journal of Sustainable Energy Planning and Management 2025年 43卷 5-30页

作者： Damayanti, Retno Wulan Safira, Silvia Dhea Laksono, Pringgo Widyo Setiadi, Haryono Entifar, Nisa Aqilla Ellenahaya Industrial Engineering Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Center of Technology Development and Industrial Collaboration Institute for Research and Community Service Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Informatics and Science Data Study Program Sebelas Maret University Ir.Sutami 36A Surakarta57121 – 57162 Indonesia Smart Green Technology Engineering Department Pukyong National University 45 Yongso-ro Nam-Gu Busan Korea Republic of

In 2022, Indonesia initiated a pilot project to transition from liquid petroleum gas (LPG) stoves to induction stoves, targeting 8.2 million households by 2025. While LPG is considered a clean cooking fuel by the World Health Organization due to its low PM2.5 emissions, this program aims to further reduce CO2 emissions from cooking activities, aligning with Indonesia’s clean energy transition and 2060 Net Zero Emission goals. Throughout the program, doubts and criticisms arose among various sectors of society, particularly concerning the sustainability of this program. This research aims to determine and analyze the driving factors that support the sustainability of the induction stove transition program. Five sustainability aspects, namely, economic, social, environmental, technical, and institutional, were analyzed. A total of 315 Indonesian citizens participated as respondents in this research survey. The Partial Least Squares-Structural Equation Model (PLS–SEM) method was utilized as the analytical approach for the study. The results revealed that nine factors significantly influenced program sustainability, with the strongest impacts from technology costs (path coefficient = 0.859), contextual factors (0.803), electrical infrastructure (0.792), and community perception (0.773). Economic sustainability was driven by technology costs and business opportunities (0.643), while environmental sustainability was strongly influenced by electrical infrastructure. Post-installation support (0.307) and financial management (0.391) significantly impacted technical and institutional sustainability respectively. These empirical findings improve previous research on the sustainability of an energy transition program, which provides insights for researchers and stakeholders in the energy management field, especially in developing countries, for policy determination and decision-making. © 2025, Aalborg University Press. All rights reserved.

关键词： Clean energy

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Honoring Robert E. W. Hancock: A Legacy of Innovation in Microbiology and Infectious Disease Research

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ACS INFECTIOUS DISEASES 2025年第4期11卷 796-798页

作者： de la Fuente-Nunez, Cesar Haldar, Jayanta 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 Philadelphia Pennsylvania 19104 United States of America；Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America；Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America；Penn Institute for Computational Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America Antimicrobial Research Laboratory New Chemistry Unit Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) Jakkur Bengaluru Karnataka-560064 India；School of Advanced Materials Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) Jakkur Bengaluru Karnataka-560064 India

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The association between urinary tract infections and diet: a literature review

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Discover Medicine 2025年第1期2卷 1-17页

作者： Çelik, Haluk Kozan, Esra Caf, Balım Bengisu Çebi, Gizem Koç, Murat Vivosens Inc. San Francisco USA Program of Stem Cell and Tissue Engineering Institute of Graduate Education Istinye University Istanbul Türkiye Department of Health Informatics University of Health Science Hamidiye Institute of Health Sciences Istanbul Türkiye

Urinary tract infections (UTIs) are a significant global health concern, affecting millions worldwide, with particular prevalence among specific vulnerable populations. Women of sexually active age, post-menopausal women, pregnant women, individuals with diabetes, catheterized patients, childs and older indivisuals face heightened risk due to various physiological, hormonal, and anatomical factors. While anatomical abnormalities and behavioral factors contribute to UTI susceptibility, the growing challenge of antibiotic resistance has necessitated exploration of alternative treatment approaches. Non-antibiotic interventions, including probiotics, cranberry products, and natural remedies, are gaining attention for their potential role in UTI prevention and management. Recent studies suggest the efficacy of probiotics and plant extracts in promoting urinary tract health and preventing bacterial colonization. Additionally, research highlights the importance of dietary factors, such as vitamin D and probiotic intake, in reducing UTI susceptibility. This review examines the emerging shift toward non-drug strategies, offering a more comprehensive approach to UTI management while addressing the pressing concern of antibiotic resistance.

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Connectivity of single neurons classifies cell subtypes in mouse brains

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Nature Methods 2025年第4期22卷 861-873页

作者： Liu, Lijuan Yun, Zhixi Manubens-Gil, Linus Chen, Hanbo Xiong, Feng Dong, Hongwei Zeng, Hongkui Hawrylycz, Michael Ascoli, Giorgio A. Peng, Hanchuan New Cornerstone Science Laboratory SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China School of Biological Science & Medical Engineering Southeast University Nanjing China Tencent AI Lab Bellevue WA United States UCLA Brain Research and Artificial Intelligence Nexus Department of Neurobiology David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Allen Institute for Brain Science Seattle WA United States Center for Neural Informatics Bioengineering Department and Neuroscience Program Krasnow Institute for Advanced Study George Mason University Fairfax VA United States Shanghai Academy of Natural Sciences Fudan University Shanghai China

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy–morphology–connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities. © The Author(s) 2025.

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Frog-derived synthetic peptides display anti-infective activity against Gram-negative pathogens

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Trends in Biotechnology 2025年

作者： Ageitos, Lucía Boaro, Andreia Cesaro, Angela Torres, Marcelo D.T. Broset, Esther de la Fuente-Nunez, Cesar 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

Novel antibiotics are urgently needed since bacteria are becoming increasingly resistant to existing antimicrobial drugs. Furthermore, available antibiotics are broad spectrum, often causing off-target effects on host cells and the beneficial microbiome. To overcome these limitations, we used structure-guided design to generate synthetic peptides derived from Andersonin-D1, an antimicrobial peptide (AMP) produced by the odorous frog Odorrana andersonii. We found that both hydrophobicity and net charge were critical for its bioactivity, enabling the design of novel, optimized synthetic peptides. These peptides selectively targeted Gram-negative pathogens in single cultures and complex microbial consortia, showed no off-target effects on human cells or beneficial gut microbes, and did not select for bacterial resistance. Notably, they also exhibited in vivo activity in two preclinical murine models. Overall, we present synthetic peptides that selectively target pathogenic infections and offer promising preclinical antibiotic candidates. © 2025 The Authors

关键词： Invertebrates

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