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International Conference on Computing and Networking technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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New ent-kaurene and germacrene derivatives from Mesona procumbens Hemseley and their biological activity

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Natural Product Research 2023年第13期37卷 2172-2180页

作者： Ganzon, Jerome G. Liaw, Chia-Ching Lin, Yu-Chi Lin, Zhi-Hu Wang, Chang-Hung Chen, Che-Yi Chuang, Kuo-Hsiang Kuo, Yao-Haur Division of Chinese Materia Medica Development National Research Institute of Chinese Medicine Taipei Taiwan Department of Biochemical Science and Technology National Chiayi University Chiayi Taiwan Ph.D. Program in Clinical Drug Development of Herbal Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei Taiwan Graduate Institute of Integrated Medicine College of Chinese Medicine China Medical University Taichung Taiwan Department of Research and Development Starsci Biotech Co. Ltd Taoyuan Taiwan

Mesona procumbens Hemseley is a well-known traditional herbal medicine used for heat-related ailments. In Taiwan, boiled extracts of M. procumbens are also used as desserts called grass jelly. In this study, the hexane extract from 75% EtOH of M. procumbens showed potent activities on inhibition of E. coli β-glucuronidase (eβG) and NO production and cytotoxicity against MCF-7 and HepG2 cancer cell lines. Furthermore, using various flash columns and HPLC chromatography on the bioactive layer led to the isolation of twelve compounds (1–12), including a new ent-kaurene, mesokaurol A (1), and a new germacrene derivative, mesogermapene A (2). Their structures were elucidated by extensive spectroscopic analyses, especially 2 d NMR and mass data. Biological assays showed that compound 9 (linolenic acid) had specific activity on inhibition of eβG (68.27%) at 100 µg/mL but was non-inhibitory to human β-glucuronidase. Compound 1 possessed significant cytotoxicity against MCF-7 (EC₅₀ = 9.76 µM) and HepG2 (EC₅₀ = 8.64 µM) cancer cell lines. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Anti-β-glucuronidase cytotoxicity germacrene Kaurane-type diterpene Mesona procumbens Hemseley

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Characterization of VOCs during diesel oil composting process

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Bioresource technology Reports 2023年 22卷

作者： Lin, Tachen Kuo, Shulung Hussain, Adnan Chen, Zhigui Huang, Manlin Lin, Chitsan School of Food and Chemical Engineering Liuzhou Institute of Technology Guangxi Liuzhou545000 China Kelee Environmental Consultants Corporation Kaohsiung City806040 Taiwan Ph.D. Program of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan Continental Water Engineering Corporation Taipei City103018 Taiwan School of Food Science and Engineering Beibu Gulf University Guangxi Qinzhou535011 China Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung City811213 Taiwan

By composting kitchen waste, deisel-contaminated soil was treated and volatile organic compound (VOC) emissions were assessed. Composting kitchen waste reduces contamination volume due to gas emissions. An aerobic treatment period of 12 days was conducted for the measurements of VOC emissions from different phases, such as gaseous, solid, and leachate, were conducted throughout the experiment. The results show that VOC concentrations were high during the mesophilic phase and reduced with increasing temperature during the composting period. It has been observed that at least 314 VOCs have been detected during the mesophilic, thermophilic, and cooling phases. Compost odor was mainly caused by NH3, methanethiol, carbon disulfide, dimethyl sulfide, and dimethyl disulfide, and most of these compounds appeared in thermophilic phase. Furthurmore, emissions of VOCs from composting kitchen waste reduces total diesel concentration in the soil to achieve a green and clean environment. © 2023 Elsevier Ltd

关键词： Gas emissions

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Sensing property of Ga2O3-based extended-gate field-effect transistors for a living cell viability sensor

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Sensors and Actuators A: physical 2023年 349卷

作者： Chou, Hsin-Yu Chiang, Jung-Lung Yu, Chang-Tze Ricky Chen, Jo-Mei Maureen Wuu, dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Chemistry National Chi-Nan University Nantou54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In this study, gallium oxide (Ga2O3) nanorods were deposited onto an indium tin oxide (ITO) glass substrate to develop a real-time living cell viability sensor. Ga2O3 nanorods had characteristics of cell population sensing based on cellular metabolism to detect cell viability. The ph sensing characteristics of Ga2O3-based extended-gate field-effect transistors (EGFETs) with and without the annealing process were measured from ph 1–11. Three different sensor heads, ITO/glass, GaOOH/ITO/glass, and Ga2O3/ITO/glass, were produced as EGFETs and immersed into a ph standard buffer and high-glucose dulbecco's modified Eagle's medium (dMEM) to investigate hydrogen ion sensing. Results showed that the Ga2O3/ITO/glass probe had the best hydrogen ion sensing sensitivity and stability in different sample buffers. In contrast, GaOOH/ITO/glass had instability and lower sensitivity due to interference with other components in the medium. The drift characteristic of sensors was analyzed to detect long-term stability. The Ga2O3/ITO/glass probe showed lower drift properties, which can potentially monitor the dMEM situation in real-time for a long-term period. during in vitro detection, the Ga2O3/ITO/glass probe revealed a 70 μA/cells change in IdS-VdS analysis and a 10 μV/cells change in IdS-VGS analysis, which was superior linearly dependent within 10,000 cells. The biocompatibility of the Ga2O3/ITO/glass probe was also detected by 3-(4,5-dimethylthiazol-2-yl)− 2,5-diphenyltetrazolium bromide assay and showed no cytotoxicity. Overall, the Ga2O3/ITO/glass EGFET sensor has potential applications as a living cell viability device in microenvironmental studies in the future. © 2022 Elsevier B.V.

关键词： Nanorods

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Nanoscale Viscoelasticity Characterization of Engineered Organometallic Regenerative Tissue Scaffold Nanocomposites for Biomedical Applications

SSRN

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SSRN 2022年

作者： Yuya, philip A. Patel, Nimitt G. Department of Mechanical and Aerospace Engineering Clarkson University PotsdamNY13699 United States Materials Science and Engineering Ph.D. Program Clarkson University PotsdamNY13699 United States

This work is focused on nanoscale viscoelastic properties characterization of gold nanoparticles (AuNPs) reinforced chitosan nanocomposites using nanoindentation. Chitosan nanocomposite films reinforced with gold nanoparticles concentration (w/v) up to 0.25 mg/ml in chitosan were prepared via a solution evaporation technique. Creep and stress relaxation responses were modeled using the generalized Voigt model and the generalized Maxwell model, respectively. Nanoindentation experimental data were analyzed using these models to obtain model constants. Both displacement- and load-controlled ramp functions were used to indent the samples with a 100 µm conospherical indenter tip. The moduli increased with an increase in AuNPs concentration in chitosan. The plasticity index increased with an increase in AuNPs concentration indicating that the deformation became more plastic with an increase in the filler. The results also show the time-dependent viscous response of the nanocomposite is mainly governed by the response of the matrix because the characteristic time scales of the nanocomposite films are independent of the AuNPs concentration. © 2022, The Authors. All rights reserved.

关键词： Creep

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Integrating physical Wheelchairs and Virtual Basketball for Enhanced Access and Enjoyment in Wheelchair Basketball

Integrating Physical Wheelchairs and Virtual Basketball for ...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Yu-Hung Hsu Iuan-Kai Fang Chun Wang Jui-Chien Chen Min-Chun Hu Department of Computer Science National Tsing Hua University Hsinchu Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan School of Information University of Texas at Austin USA

Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice.

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Prediction tools for assessing functional impacts of gene mutations in Clear Cell Renal Cell Carcinoma: A comparative study

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Computers in Biology and Medicine 2025年第Pt A期192卷 110187-110187页

作者： Yang, Cheng-Hong Lin, Guan-Cheng Wu, Chih-Hsien Chen, Jin-Bor Chuang, Li-Yeh Department of Information Management Tainan University of Technology Tainan Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Drug Development and Value Creation Research Center Kaohsiung Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taiwan Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering I-Shou University Kaohsiung Taiwan Taiwan

Renal cell carcinoma (RCC) is a well-known malignancy characterized by specific gene mutations that elevate its occurrence. It can be categorized into clear cell RCC (ccRCC), papillary RCC, and chromophobe RCC, which account for approximately 85 % of all primary RCC cases. The mutations typically involve single-nucleotide variants (SNVs) that lead to amino acid substitutions, which influence various biological functions, including gene expression. Therefore, predicting the functional consequences of RCC-related single-nucleotide polymorphisms (SNPs), including substitutions, insertions, deletions, and duplications, is crucial for the effective clinical management of RCC. In recent years, the accessibility and popularity of tools for predicting SNP functional variations have grown, especially in research concerning the potential risks associated with key gene mutations in cancer. Accordingly, this study focused on commonly mutated genes in ccRCC, namely VHL, BAP1, PBRM1, and SETd2. Public data from sources such as The Cancer Genome Atlas and the National Center for Biotechnology Information were used to identify 61 gene mutation positions. Nine nonsynonymous mutation prediction tools were used for analysis, namely SIFT, Polyphen-2, Mutation Assessor, Fathmm, MutPred, CHASM, Revel, Provean, and SNP&GO. The tools were evaluated by their statistical performance, prediction distribution, and receiver operating characteristic curves. The three tools with the highest accuracy for predicting the functional consequences of mutations in the four frequently mutated genes associated with ccRCC were SIFT (accuracy of 0.75), Provean (0.7), and Polyphen-2 (0.69). These findings offer valuable insights into predicting ccRCC-related gene mutation effects, but further research and validation are essential to support their clinical applications. © 2025

关键词： Transcription

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The intelligent optimization of AI/ML framework to detect Network Abnormalities via Time Series Analysis

The intelligent optimization of AI/ML framework to Detect Ne...

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International Conference on Computing and Networking technology (ICCNT)

作者： Aruna Sri Rongali K. Jagannayaki Paramita Sarkar Sumedh Vithalrao dhole Ch. Mohan Sai Kumar Nanditha S Matad Information Technology Emphasis/Information Technology Ph.D University of The Cumberlands US INSTITUTE OF AERONAUTICAL ENGINEERING HYDERABAD India Department of Computer Science and Engineering JIS UNIVERSITY Kolkata India Department of Electronics & Communication Engineering College of Engineering Pune Pune-satara road Bharati Vidyapeeth (Deemed to be University) Dhankawadi Pune Maharashtra India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of Mathematics New Horizon college Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents an intelligent optimization of the AI/ML framework to detect network anomalies via time collection analysis. The proposed framework entails leveraging an $\mathrm{AI} / \mathrm{ML}$ version to manner the time series facts and to identify community anomalies. Traditional methods of describing the information through time variety or histogram analysis are then improved by introducing a supervised schooling set of rules for higher accuracy. The methodology is similarly validated using a real-global dataset. The result of the preliminary analysis famous that the $\mathrm{AI} / \mathrm{ML}$ model trained with the proposed framework enables efficient detection of extraordinary network sports. The findings of this examination underscore the benefits of using AI/ML models for the timely detection of community anomalies. The painting affords an intelligent optimization of AI/ML frameworks to locate community abnormalities via time collection evaluation. The proposed framework is evolved through leveraging a hard and fast of features, consisting of topology, via graph-primarily based strategies, an excellent way to seize the worldwide behavior of the community and trigger alarms in case of ability network anomalies. The time series evaluation is then achieved to extract and model features from the monitored traffic information to discover patterns and insights, also used in labeling and predicting a suitable elegance for each traffic file. In the end, the AI/ML model skilled the use of the categorized statistics to locate any ability community abnormalities and cause alarms to inform of such unusual behavior. The proposed framework, as a result, presents an automated solution for network anomaly detection and acts as a powerful tool in mitigating security threats for businesses and companies that rely heavily on their community infrastructure…

关键词： Accuracy Time series analysis Predictive models Feature extraction Topology Security Protection Optimization Monitoring Sports

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Application of the Internet of Everything in Educational Institutions

Application of the Internet of Everything in Educational Ins...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Yuan Chou, Tsung Lai, Wen-Hsing National Kaohsiung University of Science and Technology Ph.D.Program In Enginering Seience and Technology College of Enginering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665419512

developments in network infrastructure and progress in sensor devices have brought the rise of the Internet of Everything (IoE). The technologies and applications are characterized by overall perception, tight connection, and intelligent responses. This study explored the application of the IoE in education such that these features can be utilized in educational institutions and areas designated for educational services. Focus was placed on the use of the Smart Manager in school attendance tracking, notification system, and security monitoring system for more broad and comprehensive campus safety. © 2021 IEEE.

关键词： Internet of Everything

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Estimating the volume of penumbra in rodents using dTI and stack-based ensemble machine learning framework

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European Radiology Experimental 2024年第1期8卷 59页

作者： Kuo, duen-Pang Chen, Yung-Chieh Li, Yi-Tien Cheng, Sho-Jen Hsieh, Kevin Li-Chun Kuo, Po-Chih Ou, Chen-Yin Chen, Cheng-Yu Department of Medical Imaging Taipei Medical University Hospital No.250 Wu Hsing Street Taipei Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan

Background: This study investigates the potential of diffusion tensor imaging (dTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PdM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PdM at 30 and 90 min after occlusion. We used 11 dTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PdM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PdM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PdM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions: Our study confirmed that PV can be estimated using dTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PdM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement: The proposed dTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points: • Penumbral volume can be estimated by dTI combi

关键词： Animals diffusion tensor imaging Infarction (middle cerebral artery) Ischemic stroke Machine learning

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