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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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Recommendation of Resource Allocation decision Based on Bipartite Graph Network Structure

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 128-139页

作者： Pei, Wen Pan, Wen-An Huang, Jui-Chan College of Management Chung Hua University Hsinchu30012 Taiwan Ph.D Program of Management Chung Hua University Hsinchu30012 Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan

In resource allocation decisions in business, fully understanding customers’ needs and preferences helps to maximise benefits. As a result, in the modern business environment, the design of customized recommendation systems has gained a lot of attention. To this end, the study designs a recommendation algorithm for resource allocation decision based on improved two-part graph network structure. In this algorithm, an improved K-means clustering algorithm is introduced to deeply mine potential information. The calculation of similarity between users is also optimised to assist the target user to find the real neighbouring users. The findings demonstrate that, in comparison to the other algorithms, the resource allocation recommendation algorithm based on improved bipartite graph suggested in the study has a greater hit rate. The hit rate of the suggested algorithm can reach 32.5% when the recommendation list length is 10, which is a 21.5% improvement over the collaborative filtering algorithm. The suggested algorithm’s popularity is only 39.1, which is 69.3 less than the collaborative filtering algorithm when the suggestion list length is 10. Furthermore, the suggested algorithm for resource allocation decision-making created by the research has a greater recommendation accuracy, more personalization, and diversity, as seen by the proposed algorithm’s mean Hamming distance of 0.976. Through an improved bipartite graph network, the algorithm can fully analyze the historical preference information of users, effectively capture the complex relationship between users and products, generate personalized recommendation lists, and improve user satisfaction and purchase conversion rates. It provides an effective recommendation role for resource allocation decisions in modern business and helps to create greater economic benefits. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Collaborative filtering

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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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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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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

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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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High-Energy Lithium-Ion Capacitors Using the NiS2@MXene Electrode: Evidence of dual Faradaic Reactions driven by the Choice of Electrolytes

ACS Applied Engineering Materials

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ACS Applied Engineering Materials 2025年第5期3卷 1277-1291页

作者： Keerthana dileep Pamangadan C. Sharafudeen Mani Govindasamy Pin-Yi Chen Perumal Elumalai Electrochemical Energy Storage Lab Department of Green Energy Technology Madanjeet School of Green Energy Technologies Pondicherry University Puducherry 605014 India International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 24301 Taiwan；Research Centre for Intelligence Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 24301 Taiwan；Department of Mechanical Engineering Ming Chi University of Technology New Taipei City 24301 Taiwan；Department of Mechanical Engineering Chang Gung University Taoyuan City 33302 Taiwan

Transition metal sulfides (TMSs) are emerging as promising supercapacitor electrodes due to their excellent pseudocapacitive properties and substantial theoretical charge density. The combination of TMS and layered MXene synergistically enhances the energy and power, making them ideal for advanced energy storage applications. Here, NiS2@MXene composites were successfully synthesized via a simple probe sonication, and their formation, morphology, and composition were scrutinized using materials characterization techniques. Electrochemical analyses of the NiS2@MXene electrode in the KOH electrolyte revealed the occurrence of a Faradaic reaction involving (Ni3+/Ni2+) redox. Consequently, lithium-ion capacitors (LICs) using the NiS2@MXene and reduced graphene oxide (rGO) as respective positive and negative electrodes in aqueous and different nonaqueous electrolytes were fabricated, and their performances were analyzed in the form of coin cells. It turned out that the aqueous LIC (NiS2@MXene|1 M LiClO4(aq.)|rGO) delivered a specific energy of 48.4 W h kg–1at a specific power of 800 W kg–1. Notably, the nonaqueous LIC (NiS2@MXene|1 M LiClO4(EC/dMC)|rGO) achieved an increased specific energy of 64 W h kg–1at a specific power of 1500 W kg–1and exhibited excellent capacity retention of 80% over 20,000 long cycles. Excitingly, the nonaqueous LIC (NiS2@MXene|1 M LiPF6(EC/dEC)|rGO) delivered a remarkable specific energy of 117.3 W h kg–1at a specific power of 2100 W kg–1and successfully powered a commercial LEd bulb and a stopwatch, showcasing the practicality of the electrode. The electrochemical measurements and the ex situ X-ray diffraction analyses confirmed the simultaneous occurrence of the conversion/reconversion and the intercalation/deintercalation reactions, facilitated on the electrode that led to such a high Li+charge storage performance. The conductive MXene matrix contributed to enhancing the charge transport by reducing the diffusion path and led to more active s

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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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Too hot to handle? Governance, markets, and the slow burn of geothermal energy in Taiwan

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Energy Research and Social Science 2025年 125卷

作者： Hsieh, Chun-Po Liu, Yuwen International Intercollegiate Ph.D. Program National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan Institute of Technology Management National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

despite significant resource potential and ambitious targets, geothermal energy development in Taiwan remains limited. This study applies the Technological Innovation System (TIS) framework to examine why diffusion has stalled, using process analysis and stakeholder interviews. Findings reveal that weak market incentives, high costs, regulatory barriers, and social resistance, especially in Indigenous areas, have created a proto-virtuous cycle where progress is sustained but insufficient for large-scale adoption. The study introduces this concept to explain incomplete innovation dynamics and proposes policy reforms, including risk sharing, streamlined governance, and inclusive stakeholder engagement. These insights highlight the need to align policy, market, and social factors to advance geothermal energy in Taiwan and inform similar energy transitions elsewhere. © 2025 Elsevier Ltd

关键词： Energy policy Geothermal energy Renewable energy Taiwan Technological innovation system

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Pulmonary entrapping predicts occult diaphragmatic injury in blunt thoracic trauma with rib fractures

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Asian Journal of Surgery 2025年第5期48卷 2859-2865页

作者： Chang, Po-Chih Chen, Chao-Wen Liu, Yu-Wei Chou, Shah-Hwa Chang, Ting-Wei Li, Hsien-Pin Chiang, Hung-Hsing School of Medicine College of Medicine National Sun Yat-sen University Kaohsiung City Taiwan Division of Thoracic Surgery Department of Surgery Kaohsiung Medical University Hospital/Kaohsiung Medical University Kaohsiung City Taiwan Weight Management Center Kaohsiung Medical University Hospital/Kaohsiung Medical University Kaohsiung City Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung City Taiwan Ph. D. Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung City Taiwan Faculty of Medicine College of Medicine Kaohsiung Medical University Kaohsiung City Taiwan Division of Trauma and Surgical Critical Care Department of Surgery Kaohsiung Medical University Hospital/Kaohsiung Medical University Kaohsiung City Taiwan Division of Thoracic Surgery Department of Surgery Kaohsiung Medical University Ganshan Hospital/Kaohsiung Medical University Kaohsiung City Taiwan Ph.D. Program in Environmental and Occupational Medicine College of Medicine Kaohsiung Medical University Kaohsiung City Taiwan

Background: Blunt thoracic trauma (BTT) often leads to rib fractures (RFs), with an increasing trend in surgical stabilization of rib fractures (SSRF) combined with thoracoscopic exploration, revealing previously underestimated occult diaphragmatic injuries (OdIs) that can result in hernias if not promptly addressed. We present our experience managing OdIs following BTT with complex RFs in a tertiary hospital in Southern Taiwan. Methods: We conducted a retrospective study including patients undergoing SSRF for BTT with complex RFs from January 2018 to March 2024. We analyzed demographic data, trauma details, clinical presentations, intraoperative findings, radiological features, and attempted to identify predictive factors for OdIs using logistic regression analysis. We also reviewed previous case series on OdIs. Results: We identified 11 OdIs, primarily detected during thoracoscopic exploration performed concurrently with SSRF (n = 8) or excessive bloody pleural effusion (n = 3). One hundred and twenty-two patients underwent SSRFs for their complex RFs simultaneously. All these patients with incidental OdI findings during SSRF for complex RFs underwent successful diaphragm repair via thoracoscopy. Multivariate logistic regression analysis revealed a significant association between trapped lung and OdI occurrence in patients undergoing SSRF after BTT (odds ratio: 24.423;95 % confidence interval: 2.77–215.369;p = 0.004). Conclusions: Our findings emphasize the importance of considering pulmonary entrapment as a significant predictive factor for OdI after BTT with complex RFs. Routine thoracoscopy during SSRF is crucial for early detection and management of this rare condition. © 2025 Asian Surgical Association and Taiwan Society of Coloproctology

关键词： Blunt injury diaphragm Fracture fixation Pulmonary entrapping Rib fractures Thoracoscopy

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