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Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCd industry

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International Journal of Production Research 2025年第6期63卷 2090-2113页

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Chiang, Chih-Hsiung Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei Taiwan Division of Quality Control AUO Corporation Taichung Taiwan

In the thin film transistor-liquid crystal display (TFT-LCd) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCd industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liquid crystal displays

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Eff-SAM: SAM-based Efficient Method for Brain Tumor Segmentation in Multimodal 3d MRI Scans

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IEEE Access 2025年 13卷 90063-90087页

作者： Ahmad, Nisar Chen, Yao-Tien Ming Chi University of Technology International Ph.D. Program in Innovative Technology of Biomedical Engineering Medical Devices New Taipei City243303 Taiwan

Accurate brain tumor segmentation in MRI scans is crucial for effective diagnosis and treatment planning, as even minor segmentation errors can lead to significant clinical concerns. However, this task is challenged by the complex anatomy of the brain, variable tumor shapes, and low contrast between tumor sub-regions. Moreover, existing methods often lack generalizability across diverse segmentation tasks and imaging modalities. Large Vision Model (LVM) like the Segment Anything Model (SAM) represents a foundational advancement in tumor segmentation. While SAM has shown impressive performance on natural images, its effectiveness in medical imaging, especially brain tumor segmentation, is limited due to domain differences and indistinct boundaries in MRI scans. To address these challenges, we propose Eff-SAM, a 3d brain tumor segmentation framework that adapts SAM for medical applications through Parameter-Efficient Fine-Tuning (PEFT) technique. The framework incorporates PEFT with adapters into SAM’s encoder to fine-tune the model effectively and optimize its performance for medical imaging while maintaining computational efficiency. Additionally, a Cross-Sliced Attention (CSA) mechanism captures semantic relationships, improves tumor localization, and is introduced into the encoder. Robust data preprocessing further enhances the model’s generalization across datasets. Eff-SAM demonstrates state-of-the-art performance and outperforms benchmark methods on the BraTS 2020 and BraTS 2021 datasets. It achieves dice scores of 0.884 for Whole Tumor (WT), 0.853 for Tumor Core (TC), and 0.818 for Enhancing Tumor (ET) on the BraTS 2020 dataset, and 0.880 (WT), 0.861 (TC), and 0.821 (ET) on the BraTS 2021 dataset. This work highlights the potential of integrating vision models like SAM with lightweight, domain-specific modules to deliver accurate and efficient brain tumor segmentation offering a clinically valuable tool, especially in scenarios with limited data and complex tu

关键词： Semantic Segmentation

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deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (dBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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Innovation in Geriatric Care: An AI Assistant with LLM Integration Based on Health Guidelines

Innovation in Geriatric Care: An AI Assistant with LLM Integ...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Basulo-Ribeiro, Juliana Matos, Nádia C. G. Araujo, Sabrina Magalhães Capela, Nuno Bischoff, Francisco Teixeira, Leonor Cruz-Correia, Ricardo University of Aveiro Aveiro Portugal Faculty of Medicine University of Porto Porto Portugal Faculty of Medicine of the University of Porto Health Data Science Ph.D. Program Porto Portugal CINTESIS - Center for Health Technology and Services Research Faculty of Medicine University of Porto Porto Portugal VirtualCare Portugal

With the advance of artificial intelligence and natural language processing technology, a new tool is standing out in the field of understanding and generating natural language in a sophisticated way: the Large Language Model (LLM). According to several authors, LLMs can be used for various types of medical cases, providing access to different sources of information, and have opened up countless opportunities in the healthcare sector. This work aims to share the lessons learned during the process of developing an LLM-based assistant aimed at specific pathologies that are more prevalent in the elderly in order to support caregivers, whether they are private individuals, home care organizations, nursing homes or others. This study has a significant potential impact on the community by providing access to detailed information on developing an LLM-based assistant. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Electronic health record

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Bioactive compounds from tropical fruit by-products: Extraction, characterization and therapeutic potential

Journal of Agriculture and Food Research

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Journal of Agriculture and Food Research 2025年 21卷

作者： Otero-Guzman, Niza Andrade-Pizarro, Ricardo Ph.D. Program in Food Science and Technology Universidad de Córdoba Cra. 6 #77-305 Córdoba Montería Colombia

The processing of tropical fruits generates large amounts of residues, such as peels or seeds, with great nutraceutical potential due to their high content of bioactive compounds: polyphenols, carotenoids, anthocyanins, flavonoids, tannins, and phenolic acids with antioxidant, anti-inflammatory, antimicrobial, anticancer and anti-obesity properties. However, there are several challenges associted with the efficient extraction and accurate characterization of these compounds, including the complexity of plant matrices, variability in compound polarity, and the need for scalable, eco-friendly methods that preserve their bioactivity. This review highlights the bioactive composition, the most important and studied therapeutic properties of the by-products of different tropical fruits such as mango, pineapple, and avocado, and the main extraction, identification, and characterization techniques of these compounds. A scientific surveillance analysis of studies published on the last 5 years revealed a 67.6 % increase in research activity, with a growing emphasis on green extraction technologies—such as ultrasound-assisted, microwave-assisted, and supercritical fluid extraction— and the use of emerging solvents like natural deep eutectic solvents. Moreover, there is increasing interest in optimizing extraction conditions and in validating the functional potential of bioactive compounds through in vitro digestion and bioavailability assays. These trends underscore the strategic relevance of tropical fruit by-products as a sustainable source of functional compounds. The integration of clean technologies, optimization and scale-up strategies, and advanced analytical methods supports their valorization and promotes the development of high value-added nutraceuticals aligned with circular economy principles and public health objectives. © 2025 The Authors

关键词： Antioxidants Bioaccessibility Green technologies Health benefits phytochemicals

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Impact of transient liquid phase bonding on CuFeNiMnTi high entropy alloy: A microstructural and mechanical perspective

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physica B: Condensed Matter 2025年 712卷

作者： Hosseini, S.H. Adabavazeh, Z. Karimzadeh, F. Abbasi, M.H. Department of Materials Engineering Isfahan University of Technology Isfahan*** Iran Ph.D. Program Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung411030 Taiwan

High-entropy alloys exhibit unique properties but face challenges in permanent joining, particularly in maintaining mechanical integrity. Transient liquid phase bonding offers an efficient solution using a controlled solidifying interlayer. This study investigates the effect of bonding temperature on the microstructure and mechanical properties of CuFeNiMnTi HEA joints using AWS BNi-2 and pure copper interlayers. Bonding was conducted at 980 °C, 1030 °C, and 1080 °C for 30 min. The HEA was cast using vacuum induction melting and heat-treated at 940 °C for 5 h. Samples underwent polishing, cleaning, and bonding in a furnace. Mechanical performance was assessed through microhardness and shear strength tests, while SEM and XRd characterized the diffusion-affected and isothermally solidified zones. AWS BNi-2 improved microstructural uniformity and increased joint strength from 200 MPa to over 350 MPa, achieving 91 % of the base metal's shear strength. These results confirm its effectiveness in optimizing HEA bonding for high-strength applications. © 2025

关键词： Microhardness

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PEARL: projection-estimation accelerated Richardson-Lucy deconvolution for rapid volumetric imaging

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Optics Express 2025年第11期33卷 22685-22698页

作者： Shunan Wu Wenhao Liu Xuanwen Hua Jiajia Luo Xunbin Wei Shu Jia Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta Georgia 30332 USA Department of Biomedical Engineering College of Future Technology Peking University Beijing 100871 China PKU-GT-Emory Joint Ph.D. Program China xwei@*** shu.jia@gatech.edu

Three-dimensional imaging of biological specimens requires high spatiotemporal resolution, but the intensive computational demands of processing continuously recorded images hinder the real-time reconstruction of millisecond cellular events. Here, we introduce projection-estimation accelerated Richardson-Lucy (PEARL) deconvolution, an accelerated 3d reconstruction technique that significantly reduces iteration requirements for image reconstruction based on Richardson-Lucy deconvolution. We validate PEARL's capabilities by imaging immuno-stained mitochondria, GFP-labeled peroxisomes, and human pluripotent stem cell-derived organoids, demonstrating enhanced reconstruction speed and accuracy across diverse biological applications.

关键词： Biomedical imaging Image reconstruction Machine learning phase retrieval Three dimensional imaging Three dimensional reconstruction

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Chemical Characteristics of Stack Particulate Matter Emitted from Sintering Process of an Integrated Steel Mill and Resolution of Source Contributors on Boundary PM_2.5

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Aerosol and Air Quality Research 2025年第6期25卷 1-14页

作者： Shao, Cheng-Tsung Tseng, Yu-Lun Yuan, Chung-Shin Cheng, Wen-Hsi Li, Nian-Jie Zhao, Jia-Yi Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung City Taiwan Net Zero Emissions and Resource Recycling Technology Research Center National Sun Yat-Sen University Kaohsiung City Taiwan Environmental Protection Department China Steel Corporation Kaohsiung City Taiwan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

Background: This study investigated the chemical characteristics of PM2.5 emitted from the sintering process of an integrated steel mill and assesses the contribution of emission sources to boundary PM2.5. Methods: PM... 详细信息

Background: This study investigated the chemical characteristics of PM_2.5 emitted from the sintering process of an integrated steel mill and assesses the contribution of emission sources to boundary PM_2.5. Methods: PM_2.5 samples were simultaneously collected from three stacks of the sintering process and two boundary sites located at the upstream (Site N) and downstream (Site S) of the steel mill. The chemical compositions of PM_2.5, including water-soluble ions (WSIs), metallic elements, carbonaceous matter, anhydrosugars, and organic acids, were further analyzed. The chemical mass balance (CMB) receptor model was then employed to identify emission sources and quantify their contributions. Results and discussion: Stack emissions revealed that the dominant anions (SO₄²⁻, NO₃⁻, Cl⁻) and cations (Na⁺, NH₄⁺, and Ca²⁺) were similar across TPM, FPM, and CPM fractions. Fe, Al, and Ca were the most abundant metals, while organic carbon (OC) dominated the carbonaceous content, primarily from coke oven gas and solid fuel combustion. At the boundary sites, PM_2.5 concentrations were generally higher at Site S, especially in fall and winter, due to fugitive dust from raw material handling and heavy-duty truck activities. Seasonal variation of PM_2.5 concentrations followed the order: winter > fall > spring > summer. dominant WSIs included NO₃⁻, SO₄²⁻, NH₄⁺, Cl⁻, and Na⁺, and the key metals were Fe, Ca, Mg, Al, and Zn. The OC/EC ratios ranged from 1.49–1.88 and were slightly higher at Site N. Source apportionment showed that the steel mill contributed 28.3% and 36.6% of PM_2.5 at Sites N and S, respectively, with the sintering process being the largest contributor (3.37% at Site N and 12.4% at Site S). Conclusions: The sin

关键词： Chemical characteristics Particulate matter Source apportionment Spatiotemporal variation Stack and field samplings

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Cytocompatible and antibacterial Fe-, Cu- and Zn-substituted calcium hydroxyapatite materials for skin applications

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Ceramics International 2025年第9期51卷 11286-11296页

作者： Lukaviciute, L. Lukowiak, A. Stankeviciute, Z. Junka, A. Mortimer, M. Zarkov, A. Yang, J.-C. Ganceviciene, R. Kareiva, A. Faculty of Chemistry and Geosciences Vilnius University Naugarduko st. 24 Vilnius03225 Lithuania Institute of Low Temperature and Structure Research Polish Academy of Sciences Okolna 2 Wroclaw50422 Poland Department of Pharmaceutical Microbiology and Parasitology Wroclaw Medical University Borowska 211A Wroclaw50556 Poland Laboratory of Environmental Toxicology National Institute of Chemical Physics and Biophysics Akadeemia tee 23 Tallinn12618 Estonia Graduate Institute of Nanomedicine and Medical Engineering International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Research Center of Biomedical Device Research Center of Digital Oral Science and Technology Taipei Medical University Taiwan Faculty of Medicine Vilnius University M.K. Ciurlionio st. 21 Vilnius03101 Lithuania

Calcium hydroxyapatite (C10(PO4)6(OH)2;CHAp) and metal-substituted samples (Fe:CHAp, Cu:CHAp, and Zn:CHAp) were synthesized using a recently developed method. This method involved the wet precipitation of amorphous calcium phosphate (ACP), followed by solid-state reaction synthesis of α-tricalcium phosphate (α-TCP), and finally hydrothermal synthesis of CHAp. The effect of Fe3+, Cu2+, and Zn2+ substitution on the phase purity and antimicrobial properties of CHAp are summarized in this study. Notably, it highlights the phase purity of CHAp and Zn:CHAp (in the presence of Fe3+ and Cu2+ ions, the α- and β-TCP phases have formed) as well as the antimicrobial activity of Zn:CHAp and Cu:CHAp against Staphylococcus aureus and Candida albicans. The synthesized nanosized CHAp, Fe:CHAp, Cu:CHAp, and Zn:CHAp samples were used as additives in the preparation of skin creams. In vitro biological studies of the resulting skin creams demonstrated their antimicrobial activity against Pseudomonas aeruginosa, and showed a reduction in osteoblasts cells viability of in the range of 20–40 %. The addition of zinc enhances antimicrobial efficacy but reduces biocompatibility, while copper maintains both high antimicrobial activity and biocompatibility, making it more suitable for the development of medical or cosmetic materials. dermal creams containing CHAp with copper and iron ions also show significant antimicrobial activity while preserving good cell viability. These results highlight the importance of optimizing the balance between antimicrobial effectiveness and biocompatibility in these materials. © 2024

关键词： Biocompatibility

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Identifying moral foundations and disciplinary frameworks of engineering ethics 125

Identifying moral foundations and disciplinary frameworks of...

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125th ASEE Annual Conference and Exposition

作者： Beever, Jonathan Pinkert, Laurie A. University of Central Florida Texts and Technology Ph.D. Program United States

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