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One-step flow synthesis of size-controlled polymer nanogels in a fluorocarbon microfluidic chip

RSC Advances 2024年第16期14卷 11258-11265页

作者： Montalbo, Reynaldo Carlos K. Wu, Meng-Jie Tu, Hsiung-Lin Institute of Chemistry Academia Sinica Taipei11529 Taiwan Nanoscience and Technology Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Department of Engineering and System Science National Tsing-Hua University Hsinchu300044 Taiwan Department of Chemistry National Cheng-Kung University Tainan70101 Taiwan Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taiwan

Synthetic polymer nanoparticles (NPs) with biomimetic properties are ideally suited for different biomedical applications such as drug delivery and direct therapy. However, bulk synthetic approaches can suffer from poor reproducibility and scalability when precise size control or multi-step procedures are required. Herein, we report an integrated microfluidic chip for the synthesis of polymer NPs. The chip could sequentially perform homopolymer synthesis and subsequent crosslinking into NPs without intermediate purification. This was made possible by fabrication of the chip with a fluorinated elastomer and incorporation of two microfluidic mixers. The first was a long channel with passive mixing features for the aqueous RAFT synthesis of stimuli-responsive polymers in ambient conditions. The polymers were then directly fed into a hydrodynamic flow focusing (HFF) junction that rapidly mixed them with a crosslinker solution to produce NPs. Compared to microfluidic systems made of PDMS or glass, our chip had better compatibility and facile fabrication. The polymers were synthesized with high monomer conversion and the NP size was found to be influenced by the flow rate ratio between the crosslinker solution and polymer solution. This allowed for the size to be predictably controlled by careful adjustment of the fluid flow rates. The size of the NPs and their stimuli-responses were studied using DLS and SEM imaging. This microfluidic chip design can potentially streamline and provide some automation for the bottom-up synthesis of polymer NPs while offering on-demand size control. © 2024 The Royal Society of Chemistry.

关键词： Microfluidic chips

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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Computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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IoT: Monitoring Temperature, Water pH, and Automatic Water Drainage systems for Gurami Cultivation

IoT: Monitoring Temperature, Water pH, and Automatic Water D...

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2023 International Conference on Networking, Electrical engineering, Computer Science, and Technology, IConNECT 2023

作者： Sulistiani, Heni Saputra, Wijaya Darwis, Dedi Isnain, Auliya Rahman Rimanda Putra, Ardy Khairunisa, Yosi Universitas Teknokrat Indonesia Study Program of Accounting Information System Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Computer Engineering Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Informatics Lampung Indonesia Regional Research and Development Agency Lampung Indonesia

ISBN: (纸本)9798350331172

Gurami is a native fish from Indonesian waters that is much-loved by Indonesian people for daily food consumption. The gurami inhabits still waters such as swamps, lakes, and ponds. Besides that, carp cannot live in a water content that is too alkaline or acidic, the oxygen content in the water must be stable. Water quality is a very important factor and can affect the survival of cultivated fish. Several parameters must be considered in improving the development of carp, one of which is the temperature and pH of the water and automatic drainage of water. In this paper, we conduct IoT-based research that focuses on controlling water temperature and pH and automatically draining pool water. Water temperature and pH control such as a pH sensor temperature sensor and a water turbidity sensor will later be connected to the ESP 32 microcontroller which already has a wi-fi module. A system that is linked to electrical power and the internet then the sensor will read the water quality including the pH temperature value and water turbidity from inside the fish pond, after getting the data, the data will go to ESP 32. This research can help gurami cultivating farmers to make it easy to control the pH temperature and perform automatic pool water draining. The results of sensor testing with actual data are pretty accurate with the average error value of the ds18b20 sensor being 0.05% while the value of the pH sensor itself is 0.07%. © 2023 IEEE.

关键词： Temperature sensors

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Evaluation of negative stiffness mechanism on a pitch-type wave energy converter 33rd

Evaluation of negative stiffness mechanism on a pitch-type w...

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33rd International Ocean and Polar engineering Conference, ISOPE 2023

作者： Poguluri, Sunny Kumar Kim, Dongeun Bae, Yoon Hyeok Mechanical and System Design Engineering Hongik University Seoul Korea Republic of Multidisciplinary Graduate School program for Wind Energy Jeju National University Jeju Korea Republic of

ISBN: (纸本)9781880653807

The current study focuses on the application of the nonlinear negative stiffness mechanism to a Salter's duck, which is a well-known wave energy conversion (WEC) device known for its efficiency of 90% in 2D regular waves. Despite the proven efficiency of WEC devices, they face the challenge of producing a stable system that can effectively extract energy from constantly changing wave conditions. Recent studies have shown that the use of negative stiffness can address this issue, and this approach has been adopted in the present study for the pitch-type WEC-rotor (Salter’s duck). The study employed a time domain analysis based on linear potential theory to examine the impact of factors such as spring length and stiffness on wave energy extraction in both regular and irregular wave conditions. The results of the study showed that the incorporation of negative stiffness improves the extraction of wave energy. © 2023 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Extraction

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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The One-vs-Rest Method for a Multilabel Patent Classification Machine Learning Approach using a Regression Model

The One-vs-Rest Method for a Multilabel Patent Classificatio...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Widodo, Slamet Ermatita Setiawan, Deris Pradnyana, I Wayan Widi Universitas Sriwijaya Politeknik Negeri Sriwijaya Faculty of Engineering Department of Computer Engineering Palembang30139 Indonesia University of Sriwijaya Faculty of Engineering Palembang30139 Indonesia University of Pembangunan Nasional Veteran Jakarta Information System Program Jakarta Indonesia

ISBN: (纸本)9798350307306

Redundancy in patent abstracts, that is, the repetition of an International Patent Classification tag or patent class information across documents, poses a challenge in patent classification, which is essential for efficiently managing the growing number of patent documents. This study aims to develop a model to analyze the weights and index values of summary sentences to improve the efficiency and accuracy of patent classification. This model can predict relevant patent data types. Utilizing logistic regression and the one-vs-rest method, this study focuses on the development of models to analyze the weights and index values of summary sentences, with the goal of enhancing the precision and efficiency of patent classification. The main contribution of this research is the creation of predictive models for new patent documents to streamline patent citations based on document type. This study also serves as a reference for evaluating Indonesian patent document classification models, thereby facilitating the patent evaluation process. © 2023 IEEE.

关键词： Efficiency

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Bioprinting Methods for Fabricating In Vitro Tubular Blood Vessel Models

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Cyborg and Bionic systems 2023年第1期4卷 178-195页

作者： Seon-Jin Kim Min-Gyun Kim Jangho Kim Jessie SJeon Jinsoo Park Hee-Gyeong Yi Department of Rural and Biosystems Engineering College of Agriculture and Life SciencesChonnam National UniversityGwangjuRepublic of Korea Department of Convergence Biosystems Engineering College of Agriculture and Life SciencesChonnam National UniversityGwangjuRepublic of Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National UniversityGwangju 61186Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Department of Mechanical Engineering Chonnam National UniversityRepublic of Korea

Dysfunctional blood vessels are implicated in various diseases,including cardiovascular diseases,neurodegenerative diseases,and *** studies have attempted to prevent and treat vascular diseases and understand interactions between these diseases and blood vessels across different organs and *** studies were conducted using 2-dimensional(2D)in vitro and animal ***,these models have difficulties in mimicking the 3D microenvironment in human,simulating kinetics related to cell activities,and replicating human pathophysiology;in addition,3D models involve remarkably high ***,in vitro bioengineered models(BMs)have recently gained *** created through biofabrication based on tissue engineering and regenerative medicine are breakthrough models that can overcome limitations of 2D and animal *** can also simulate the natural microenvironment in a patient-and target-specific *** this review,we will introduce 3D bioprinting methods for fabricating bioengineered blood vessel models,which can serve as the basis for treating and preventing various vascular ***,we will describe possible advancements from tubular to vascular ***,we will discuss specific applications,limitations,and future perspectives of fabricated BMs.

关键词： Blood organs printing

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