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14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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A Neuroelectronic Interface with Microstructured Substrates for Spiral Ganglion Neurons Cultured In Vitro: Proof of Concept

Biosensors

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Biosensors 2025年第4期15卷 224-224页

作者： Delipetar, Boris Žarković Krolo, Jelena Bedalov, Ana Kovačić, Damir Department of Physics Faculty of Science University of Split Ruđera Boškovića 33 Split21000 Croatia University of Split Ruđera Boškovića 31 Split21000 Croatia The Doctoral Program in Mechanical Engineering Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture University of Split Ruđera Boškovića 32 Split21000 Croatia The Doctoral Program in Biophysics Faculty of Science University of Split Ruđera Boškovića 33 Split21000 Croatia Bedalov d.o.o for Research Development and Consulting Ulica T. Antunovića 17 Kaštel Sućurac21212 Croatia

In this study, we present a proof-of-concept neuroelectronic interface (NEI) for extracellular stimulation and recording of neurophysiological activity in spiral ganglion neurons (SGNs) cultured in vitro on three-dimensional, micro-patterned substrates with customized microtopographies, integrated within a 196-channel microelectrode array (MEA). This approach enables mechanotaxis-driven neuronal contact guidance, promoting SGN growth and development, which is highly sensitive to artificial in vitro environments. The microtopography geometry was optimized based on our previous studies to enhance SGN alignment and neuron-electrode interactions. The NEI was validated using SGNs dissociated from rat pups in the prehearing period and cultured for seven days in vitro (DIV). We observed viable and proliferative cellular cultures with robust neurophysiological responses in the form of local field potentials (LFPs) resembling action potentials (APs), elicited both spontaneously and through electrical stimulation. These findings provide deeper insights into SGN behavior and neuron-microenvironment interactions, laying the groundwork for further advancements in neuroelectronic systems. © 2025 by the authors.

关键词： Neurons

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Zaenal Abidin Akmal Junaidi Wamiliana Favorisen Rosyking Lumbanraja Dian Kurniasari Rohmat Indra Borman Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Department of Computer Science Universitas Lampung Bandar Lampung Indonesia Department of Mathematics Universitas Lampung Bandar Lampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandar Lampung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages.

关键词： Measurement Sentiment analysis Text analysis Electronic learning Morphology Linguistics Data science Information retrieval Machine translation Information systems

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Cigarette butt filter as membrane material with tannic acid and FeCl₃ additives for improve antifouling properties

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Luthfiana, Anisa Mulyati, Sri Arahman, Nasrul Bilad, Muhammad Roil Aulia, Muhammad Prayogie Doctoral Program School of Engineering Science Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical and Process Engineering Faculty of Integrated Technologies Universiti Brunei Darussalam Bandar Seri Begawan Brunei Darussalam Department of Chemical Science and Engineering Kobe University Kobe Japan

Membrane fouling remains a critical challenge in wastewater treatment, particularly in applications involving oil-water emulsions. This study addresses this issue by fabricating antifouling membranes from repurposed cigarette filter waste, modified with tannic acid and FeCl₃ (ferric chloride) coatings. These modifications enhance membrane hydrophilicity, antifouling properties, flux recovery, and selectivity through an environmentally friendly approach. The membranes were prepared using the non-solvent induced phase separation (NIPS) method and subsequently coated through vacuum filtration. Key performance metrics included pure water flux, oil emulsion selectivity, and antifouling properties. The antifouling mechanism was attributed to the hydrophilic and protective layers formed by the tannic acid and FeCl₃ modifications, which reduced fouling and improved flux recovery. Characterization revealed that the tannic acid and FeCl₃ modifications created a hydrophilic layer with uniform pore distribution, leading to an oil rejection rate of up to 97 % and an increased flux recovery ratio of 85 %, compared to 65 % in unmodified membranes. The results highlight the potential of waste-derived membranes as a sustainable alternative for industrial wastewater treatment, aligning with the principles of circular economy and green chemistry. Future work should explore long-term stability, surface charge effects, and optimization of additive concentrations to enhance performance and antifouling efficiency further. © 2025 The Authors

关键词： Antifouling membrane Cigarette filter waste FeCl3 Oil-water emulsion tannic acid

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Enhancing polyethersulfone membrane durability and stability through Eco-friendly green silica from natural kaolin for water treatment applications

Results in Engineering

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Results in engineering 2025年 26卷

作者： Mulyati, Sri Muchtar, Syawaliah Razi, Fachrul Ambarita, Aulia Chintia Luthfiana, Anisa Aulia, Muhammad Prayogie Bilad, Muhammad Roil Department of Chemical Engineering Faculty of Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Doctoral Program School of Engineering Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemical Science and Engineering Kobe University 1-1 Rokkodai Nada Kobe 647-8501 Japan Faculty of Integrated Technologies Universiti Brunei Darussalam Jalan Tungku Link BE1410 Brunei Darussalam

Membrane technology is pivotal in addressing global water scarcity;however, issues such as membrane fouling and chemical degradation significantly hinder its operational efficiency and longevity. This study investigates the use of eco-friendly green silica derived from natural kaolin as an additive to enhance the performance of polyethersulfone (PES) membranes fabricated via the phase inversion method. Seven membrane compositions were developed, incorporating varying concentrations of both green silica and commercial silica, to allow a direct performance comparison. The membranes were systematically characterized for hydrophilicity, porosity, pore size distribution, and mechanical stability, while their filtration performance was evaluated in terms of water flux, humic acid (HA) rejection, fouling resistance, and chemical *** findings revealed that green silica enhanced membrane hydrophilicity and porosity at optimal concentrations, leading to improved fouling resistance and cleaning efficiency. Notably, the membrane containing 1 % green silica (M-K3) exhibited a high HA rejection rate of 98.37 % with a moderate water flux of 10.85 L/m²·h, balancing selectivity and permeability. In contrast, membranes containing 1 % commercial silica (M-S3) showed higher water flux (86.83 L/m²·h) but lower HA rejection (73.75 %), highlighting a trade-off. Green silica-modified membranes also demonstrated superior chemical stability, maintaining consistent performance under extended filtration and exposure to cleaning agents. These results underscore the potential of kaolin-derived green silica as a cost-effective, sustainable, and high-performance alternative to commercial additives in water treatment membranes. © 2025 The Author(s)

关键词： Chemical stability Humic acid rejection antifouling Pes membrane Water flux

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Polystyrene nanoplastics cause developmental abnormalities, oxidative damage and immune toxicity in early zebrafish development

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Comparative Biochemistry and Physiology Part - C: Toxicology and Pharmacology 2025年 295卷 110216页

作者： Saputra, Febriyansyah Pramata, Azzah Dyah Soegianto, Agoes Hu, Shao-Yang Department of Biology Faculty Sciences and Technology Universitas Airlangga Campus C Surabaya 60115 Indonesia Mathematics and Natural Sciences Doctoral Program Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya 60116 Indonesia Department of Biological Science and Technology National Pingtung University of Science and Technology Pingtung 912301 Taiwan

Aquatic organisms are increasingly affected by polystyrene nanoplastics (PSNPs), which have the potential to disrupt development, induce oxidative stress, and impair immune function. This study examined the effects of PSNPs on zebrafish (Danio rerio) embryos exposed to 0.01, 0.1, 1, and 10 μg/mL from 2 to 120 h post-fertilization (hpf). The findings indicated that developmental abnormalities occurred in a dose-dependent manner, including delayed hatching, decreased survival rates, reduced body length, smaller eye diameter, and enlarged yolk sac area. PSNPs accumulated in the chorion and yolk sac as early as 6 hpf and were detected in vital tissues, such as the eyes, heart, yolk sac, liver, pancreas, intestine, neuromasts, and tail, immediately after hatching. By 120 hpf, PSNPs significantly reduced swimming distance and velocity. Exposure to PSNPs induced oxidative damage evidenced by altered expression of antioxidant-related genes (CAT1, GPX1A, SOD1, NRF2, KEAP1, HSP70, MT), disrupting cellular homeostasis and causing structural and organ defects. Immune toxicity was marked by dysregulated expression of immune-related genes (IL-1β, IL-6, NF-κB, TNF-α, C3B, TLR-1, TLR-3, TLR-4), indicating inflammation and innate immune activation triggered by oxidative damage. This study highlights the interconnected impacts of developmental abnormalities, oxidative damage, and immune toxicity caused by PSNPs exposure, highlighting the systemic impacts of nanoplastics contamination in aquatic environments. © 2025 Elsevier Inc.

关键词： Developmental abnormalities Immune toxicity Oxidative damage Polystyrene nanoplastics Zebrafish embryos

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

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arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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Unique motif Sequences for early diagnosis of preeclampsia

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Clinica Chimica Acta 2025年 574卷 120339页

作者： Humardani, Farizky Martriano Endharti, Agustina Tri Ningrum, Ratih Asmana Arsana Wiyasa, I Wayan Mulyanata, Lisa Thalia Antonius, Yulanda Jonathan, Jonathan Dwi Putra, Sulistyo Emantoko Doctoral Program in Medical Science Faculty of Medicine Universitas Brawijaya Malang Indonesia Research Center for Genetic Engineering National Research and Innovation Agency Bogor Cibinong Indonesia Department of Obstetrics and Gynecology Faculty of Medicine Universitas Brawijaya Malang Indonesia Faculty of Medicine University of Surabaya Surabaya Indonesia Faculty of Biotechnology University of Surabaya Surabaya Indonesia

Preeclampsia (PE) is a disease that significantly impacts both maternal and infant health with its prevalence varying across different ethnicities. Current diagnostic methods for PE typically identify the condition after 20 weeks of gestation, often when the disease has already manifested and reached an advanced stage. The situation underscores the urgent need for early biomarkers capable of effective screening and diagnosis. Our review addresses this challenge by utilizing bioinformatics approaches as an alternative method prior to preclinical and clinical studies. Specifically, we focus on FRAGmentomics-based Methylation Analysis (FRAGMA), targeting the CGCGCGG sequence motif for methylation studies in cell-free DNA (cfDNA). Since cfDNA is largely derived from the placenta, the FRAGMA approach is particularly promising, given that the primary pathophysiology of PE originates in the placenta, and methylation patterns are unique to specific tissues. In the previous research, we identified 66 genes containing this sequence motif that are implicated in the pathophysiology of PE, and only six genes – FN1, ITGA2, ITGA5, ITGB1, ITGB3, and VWF – show potential as early detection biomarkers for PE. These genes still require further investigation to confirm their utility as biomarkers for PE in the future studies. © 2025 Elsevier B.V.

关键词： Early detection Gene candidates Methylation Preeclampsia

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