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SSrN 2023年

作者： Thankachan, darieo Anbazhagan, rajeshkumar Tsai, Hsieh Chih Van Thi Thuy, dinh Gebrie, Hailemichael Tegenu Chen, Wei-Liang Chen, Jem-Kun Department of Materials Science and Engineering National Taiwan University of Science and Technology 43 Sec. 4 Keelung Road Taipei106 Taiwan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106 Taiwan R&D Centre for Membrane Technology Chung Yuan University Taoyuan320 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine Chung Shan Medical University Hospital Taichung City402 Taiwan

Covalent organic polymers (COP) are a special class of two-dimensional and three-dimensional cross-linked polymeric networks with repeating organic units. It is ideally suited for drug delivery applications due to its larger surface area, porous nature, and tunable properties. Furan-2,5-dicarbohydrazide and 1,3,5-triformylbenzene were used to synthesize a pH-responsive hydrazone linked furan based spherical COP;(FrT-COP) at room temperature. The formulated FrT-COP were later stabilized by hyaluronic acid and encapsulated anticancer drug doxorubicin (dOX) by utilizing the porous structure of the synthesized COP. The newly developed FrT-COP have pore sizes of 10 nm, and FrT-COP-dOX-HA exhibits drug loading capacity of 34.4% and an encapsulating efficiency of 52.32%. The NIH 3T3 cells' 90.01% vitality and the CT2A cells' 88.47% viability even at a higher concentration of 100 µg/mL of the carrier system proves the outstanding biocompatibility of the carrier system for chemotherapeutic drug delivery administration. The FrT-COP-dOX-HA-treated NIH-3T3 normal cell line exhibits a viability of 80.59% at a material concentration of 25 µg/mL. Furthermore, CT2A cancer cell lines show a viability of 33.1% at a material concentration of 25 µg/mL. These results unequivocally demonstrate the proposed drug delivery carrier system's exceptional suitability for applications requiring efficient and targeted anti-tumor chemotherapeutic drug administration. © 2023, The Authors. All rights reserved.

关键词： Biocompatibility

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Controlling Factors and Involved Mechanisms on Forming Alginate Like Extracellular Polymers in Flocculent Sludge

SSRN

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SSrN 2022年

作者： Hao, Xiaodi Li, Ji Gan, Wei van Loosdrecht, Mark M.C.M. Wu, Yuanyuan Sino-Dutch R&D Centre for Future Wastewater Treatment Technologies Beijing Advanced Innovation Centre of Future Urban Design Beijing University of Civil Engineering & Architecture Beijing100044 China Department of Biotechnology Delft University of Technology van der Maasweg 9 HZ Delft 2629 Netherlands

Alginate like extracellular polymers (ALE) recovered from excess sludge have been evaluated as an eco-friendly, cost effective and sustainable alternative to highly valued materials. However, the ALE extraction from flocculent sludge ranges normally from 90 to190 mg/g VSS, which is only equivalent to the lowest edge of the ALE production from aerobic granular sludge (AGS). As flocculent sludge is much higher in production than AGS, it is worth investigating the key factors controlling the ALE formation in flocculent sludge for potential applications. For this reason, some influent characteristics & operational parameters and the associated mechanisms were experimentally investigated with flocculent sludge in lab-scale sequencing batch bioreactors (SBrs). The results indicate that flocculent sludge with starch being a substrate contained the highest ALE production (220.3±8.0 mg/g VSS). Low temperature was more favorable to enriching ALE, up to 303.3±21.5 mg/g VSS at 12 oC. Moreover, ALE reached up to 137.8±13.2 mg/g VSS at C:N=5:1 and slightly declined with increased or decreased C/N. The specific ALE yield was 63.7 mg ALE/(g BOd5) at a low organic load, which was twice as high as those with high organic loadings. However, SrT had a minor effect on the ALE formation. Although a band of the factors controlling the ALE formation in flocculent sludge needs to be further studied, different parameters would exert significant influence on the composition and chemical properties of ALE, which also implies that the parameters could be controlled to directionally cultivate ALE with the unique structures. In a word, adjusting or optimizing the influent characteristics and operational parameters could improve the formation, production and characteristics of ALE for wide applications. © 2022, The Authors. All rights reserved.

关键词： Temperature

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Enhancing Oral Cancer Screening with deep Learning Algorithms

Enhancing Oral Cancer Screening with Deep Learning Algorithm...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Attarde Viren Bhaskar A Soujanya P. Janaki ramal S. SanthanaLakshmi K Nithyakalyani Bharathi ramesh Kumar Department of Critical Care Medicine Dr. D.Y. Patil Medical College Hospital & Research Centre Pimpri Pune India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam Telangana India Department of Computer Science Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Mohamed Sathak A.J.college of Engineering Siruseri Chennai India Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

The majority of the conventional approaches used in current oral cancer screening methods, such as biopsy and visual inspection, result in late-stage detection, high false-positive/negative rates, and higher healthcare expenses. These techniques need specialized staff and are labor-intensive and subjective. To improve diagnostic speed and accuracy, the research offers a unique approach that makes use of deep learning methods, particularly Transformer models and Hybrid Neural Networks (HNNs). To enhance input quality, the system incorporates sophisticated image preparation methods such as data augmentation and normalization. While HNNs integrate Convolutional Neural Networks (CNNs) and recurrent Neural Networks (rNNs) for robust feature extraction and temporal analysis, Transformers use attention techniques to extract contextual information. Its potential for early and accurate oral cancer detection is highlighted by the results, which show a significant improvement in detection accuracy (94% vs. 80-85% in existing systems), sensitivity (90% vs. 70-78%), and specificity (92% vs. 75-82%), along with a reduction in processing time to just 2 minutes.

关键词： deep learning Visualization Accuracy Sensitivity Computational modeling Medical services Transforms Transformers Feature extraction Cancer

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A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues

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Intelligence-Based Medicine 2025年 11卷 100223-100223页

作者： Murugavel, Kiruthiga devi r, Parthasarathy Mathivanan, Sandeep Kumar Srinivasan, Saravanan Shivahare, Basu dev Shah, Mohd Asif Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai 600026 India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computing Science and Engineering Galgotias University Greater Noida 203201 India Department of Economics Kardan University Parwane Du Kabul 1001 Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Punjab Phagwara 144001 India

Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. deep denoising Autoencoders (ddAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces deep denoising Autoencoders (ddAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-rOC of 0.85 & less interface time of 6.5 ms/sample scores the effectiveness of integrating mult

关键词： Audio-visual modality Bipolar disorder deep denoising autoencoders (ddAEs) Fisher vectors (FVs) Multimodal

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Power Conversion Efficiency Improvement of Photovoltaic Panels by Means of Thermal Analysis and Effective Cooling

SSRN

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SSrN 2022年

作者： Malik, Abdul Haleem Smend, Joshua Moursi, Mohamed El Chiesa, Matteo Xiao, Weidong El-Fouly, Tarek H.M. R&D Engineer DEWA Dubai United Arab Emirates Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Centre for Future Energy Networks School of Electrical and Information Engineering University of Sydney Australia

Insecure petroleum-based energy supplies and environmental calamitous have escalated the penetration of renewable Energy Sources(rES) in the primary energy supplies. The low Levelized cost of electricity(LCOE) of the Photovoltaic(PV) system places it on the top of the rES list. However, the energy efficiency of the PV system is still progressing. The power output of a photovoltaic module depends mainly on two parameters;temperature and irradiance. A rise in the temperature (above 25°C), is associated with the absorbed sunlight that is converted into heat resulting in the module output power decreasing, affecting the life span and energy efficiency of the panel, and in arid climates e.g. middle east, etc. where the operating temperatures of the panels can rise to as high as 70°C. which immensely affects the PV panel efficiency. So, it is imperative to have appropriate cooling schemes to improve efficiency, and *** paper presents an effective active-passive cooling strategy (with little water and paraffin wax) to improve the power conversion efficiency of photovoltaic modules and their overall performance. A detailed thermal analysis for both techniques (active & passive) has been presented, simulated, validated experimentally, and compared with the reference PV panel. The active scheme involves spraying a controlled thin film of water onto the front surface of the module using a combination of seawater and freshwater to keep the front surface clean and cool whereas the passive scheme involves paraffin wax as a phase change material (PCM) sealed at the back of the module to help reduce the module’s temperature further. The experimental platform is set up in parallel with simulation built by Comsol Multiphysics and Matlab. The results and the efficiency enhancement are examined and discussed. © 2022, The Authors. All rights reserved.

关键词： Phase change materials

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Synthesis of AC@CuO-NWs and removal of basic dye from wastewater

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Materials Today: Proceedings 2022年 53卷 336-338页

作者： Lakkaboyana, Sivarama Krishna Soontarapa, K. Nabel Kale, A. Sreenath reddy, A. Kumar, Vinay Karthik, K. M. Hanafiah‬, ‪Marlia Wan Zuhairi, W.Y. Department of Chemical Technology Chulalongkorn University Pathumwan Bangkok10330 Thailand Center of Excellence on Petrochemical and Materials Technology Chulalongkorn University Pathumwan Bangkok10330 Thailand A.P Vidyanagar-524413 India Department of Biosciences and Bioengineering Indian Institute of Technology Roorkee Roorkee247667 India Department of Physics Bharathidasan University Tiruchirappalli620 024 India Center for Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor UKM Bangi43600 Malaysia Centre for Tropical Climate Change System Institute of Climate Change Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Faculty of Science Depatment of Chemistry Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi-Vel Tech Road Tamil Nadu Chennai600062 India Building and Construction Technology Engineering Northern Technical University Mosul41002 Iraq

The AC@CuO-NWs nanocomposite was synthesized and examined using various spectroscopic methods. The Xrd results established with the monoclinic structure, average crystalline size ∼ 17.48 nm of CuO-NWs. This material FE-SEM image established the wire shaped and EdX examination approves the CuO nanomaterial. The FE-SEM image demonstrated that nanowires are agglomerated to each other and formed flower form on composite. The adsorption and photocatalytic degradation studies were examined under natural sunlight irradiation in contrast to the Methylene blue dye. © 2019

关键词： Copper oxides

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Flower-shaped covalent organic framework synthesis and its anticancer drug delivery application

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Materials Chemistry and Physics 2024年 312卷

作者： Anbazhagan, rajeshkumar dinh, Thi Thuy Van krishnamoorthi, rajakumari Thankachan, darieo Tsai, Hsieh-Chih Chang, Yen-Hsiang Yang, Jen-Ming Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106 Taiwan Advanced Membrane Materials Center National Taiwan University of Science and Technology Taipei106 Taiwan Department of Material Science and Engineering National Taiwan University of Science and Technology Taipei106 Taiwan R&D Centre for Membrane Technology Chung Yuan University Taoyuan320 Taiwan Department of Chemical and Materials Engineering Chang Gung University Taoyuan333 Taiwan Department of General Dentistry Chang Gung Memorial Hospital Taoyuan333 Taiwan Innovation of Life & Technology CO LTD No. 53 Ln. 447 Sec. 1 Wenhua 3rd Rd. Linkou Dist. New Taipei City244015 Taiwan

Fabrication of covalent organic frameworks (COFs) at room temperature is necessary due to their easy preparation, practical scale-up, and low cost to overcome the limitations of solvothermal and high-temperature synthesis. Herein, we fabricated flower-shaped COFs (FSCOFs) that were synthesized in various ratios of ortho-xylene and butanol mixture solvents. The FSCOFs exhibited excellent crystallinity with a pore size of approximately 10 nm, which was confirmed by PXrd and BET measurements. To utilize the FSCOFs as a drug carrier, doxorubicin (dOX) was loaded, followed by encapsulation of hyaluronic acid to increase the colloidal stability of FSCOFs in a physiological environment (FSCOFs-dOX-HA). The encapsulation efficiency and the drug loading capacity of FSCOFs-dOX-HA were 21.23 % and 29.82 %, respectively. Moreover, 92 % and 59 % of dOX is released in acidic environments and elevated GSH environments, proving the sensitivity of the FSCOFs as carriers. The excellent drug release mechanism of FSCOFs-HA-dOX is demonstrated by FSCOFs imine nitrogen protonation under acidic environment confirmed by its zeta potential changes under various physiological environments. The designed FSCOFs-dOX-HA was demonstrated to be a pH-sensitive drug carrier to deliver anticancer drugs into cancer cells. The MTT and drug internalization data demonstrated sustained dOX release from FSCOFs-dOX-HA. Finally, the real-time fluorescence demonstrates the effectiveness of the FSCOFs-dOX-HA upon cellular uptake and release of the anticancer drug. Thus, the FSCOFs system reported here opens a new path in COF research for drug carrier systems. © 2023

关键词： Hyaluronic acid

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deformation dynamics of Bubbles in Asymmetric Semicircle Porethroat Microchannels: Experimental and Theoretical Insights

SSRN

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SSrN 2023年

作者： Yang, Yuqi Wang, Zizhen Wan, Guangze Liu, Zhengxiao Zhang, Haipeng Fu, Yuequn Ariane, Essouma E.F.B. Liu, Jian Xing, Tingyan Zhang, Shiling China University of Petroleum–Beijing at Karamay Karamay834000 China Institute of Unconventional Oil and Gas Science and Technology China University of Petroleum Beijing102249 China School of Information Engineering China University of Geosciences Beijing100083 China CNPC Engineering and Technology R&D Company Limited Beijing Beijing102200 China PoreLab The Njord Centre Department of Physics University of Oslo Oslo0371 Norway State Key Laboratory of Heavy Oil Processing China

Multi-phase microfluidic transport is a topic of great interest in both academic research and industry. This study focuses on the deformation dynamics of bubbles generated during oil transport in microchannels. We investigate the deformation behavior of bubbles in square microchannels with two semi-cylinders. Our findings show that bubble deformation occurs in two modes: breakup deformation and elastic nonbreakup deformation. By evaluating the minimum width of the microchannel neck, we divide both modes into four stages, which exhibit power-law variation. Additionally, we investigate the effects of presetting factors on viscosity in the microchannel. To gain deeper insights into the deformation mechanism of bubbles, we use micro-PIV to describe the velocity patterns and dimensional distribution of viscosity in the flow. We successfully summarize a dynamical scaling law that specifies the size of bubbles formed in the pore throat microchannels. It reveals that the fluid characteristic of the continuous phase dominates the deformation of bubbles in such microdevices. Moreover, we developed the rosin-rammler regression models to predict droplet size and neck width formation in the continuous liquid at microchannels, which we compare with experimental data. results of the analytical model show that microchannel shape and droplet volume play important roles in the droplet deformation process. © 2023, The Authors. All rights reserved.

关键词： Microchannels

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rNN-Based Models for Predicting Seizure Onset in Epileptic Patients

RNN-Based Models for Predicting Seizure Onset in Epileptic P...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Mathan Kumar Mounagurusamy V S Thiyagarajan Abdur rahman Shravan Chandak d. Balaji Venkateswara rao Jallepalli Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of CSE Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Accounting and Technology School of Business & Technology Master of Science in Information Technology Emporia State University United States of America Symbiosis Centre for Management Studies Pune Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Pharmacy Practice MB School of Pharmaceutical Sciences Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel recurrent Neural Network (rNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. results show a considerable reduction in false alarm rates (average of $\mathbf{6. 8 \%}$) and an improvement in prediction accuracy $(90.2 \%$ sensitivity, 88.9% specificity, and AUC-rOC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed rNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment.

关键词： Training Accuracy Epilepsy Brain modeling Feature extraction Electroencephalography real-time systems Hardware data mining Long short term memory

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rNN-Based Models for Predicting Seizure Onset in Epileptic Patients

arXiv

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arXiv 2024年

作者： Mounagurusamy, Mathan Kumar Thiyagarajan, V.S. rahman, Abdur Chandak, Shravan Balaji, d. Jallepalli, Venkateswara rao Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of CSE Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Master of Science in Information Technology Department of Accounting and Technology School of Business & Technology Emporia State University United States Symbiosis Centre for Management Studies Pune Symbiosis International [Deemed University Pune India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Pharmacy Practice School of Pharmaceutical Sciences Mohan Babu University Andhra Pradesh Tirupati India

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel recurrent Neural Network (rNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. results show a considerable reduction in false alarm rates (average of 6.8%) and an improvement in prediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-rOC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed rNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment. © 2024, CC BY.

关键词： Electroencephalography

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