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7th International Conference on Computer science and Computational Intelligence, CSCI 2022

作者： Ariansyah, Dedy Pardamean, Bens Caruso, Giandomenico Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers' ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-Traffic condition. © 2022 Elsevier B.V.. All rights reserved.

关键词： Advanced driver assistance systems

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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engineering H2O2 Self-Supplying Platform for Xdynamic Therapies via Ru-Cu Peroxide Nanocarrier: Tumor Microenvironment-Mediated Synergistic Therapy

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ACS Applied Materials and Interfaces 2024年第19期16卷 24172-24190页

作者： Dirersa, Worku Batu Kan, Tzu-Chun Chang, Jungshan Getachew, Girum Ochirbat, Sonjid Kizhepat, Shamsa Wibrianto, Aswandi Rasal, Akash Chen, Hung-An Ghule, Anil Vithal Chou, Tzung-Han Chang, Jia-Yaw Department of Chemical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan International Ph.D. Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Green Nanotechnology Laboratory Department of Chemistry Shivaji University Kolhapur416004 India Department of Chemical and Materials Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan

Of the most common, hypoxia, overexpressed glutathione (GSH), and insufficient H2O2 concentration in the tumor microenvironment (TME) are the main barriers to the advancment of reactive oxygen species (ROS) mediated Xdynamic therapies (X = photo, chemodynamic, chemo). Maximizing Fenton catalytic efficiency is crucial in chemodynamic therapy (CDT), yet endogenous H2O2 levels are not sufficient to attain better anticancer efficacy. Specifically, there is a need to amplify Fenton reactivity within tumors, leveraging the unique attributes of the TME. Herein, for the first time, we design RuxCu1-xO2-Ce6/CPT (RCpCCPT) anticancer nanoagent for TME-mediated synergistic therapy based on heterogeneous Ru-Cu peroxide nanodots (RuxCu1-xO2 NDs) and chlorine e6 (Ce6), loaded with ROS-responsive thioketal (TK) linked-camptothecin (CPT). The Ru-Cu peroxide NDs (RCp NDs, x = 0.50) possess the highest oxygen vacancy (OV) density, which grants them the potential to form massive Lewis’s acid sites for peroxide adsorption, while the dispersibility and targetability of the NDs were improved via surface modification using hyaluronic acid (HA). In TME, RCpCCPT degrades, releasing H2O2, Ru2+/3+, and Cu+/2+ ions, which cooperatively facilitate hydroxyl radical (•OH) formation and deactivate antioxidant GSH enzymes through a cocatalytic loop, resulting in excellent tumor therapeutic efficacy. Furthermore, when combined with laser treatment, RCpCCPT produces singlet oxygen (1O2) for PDT, which induces cell apoptosis at tumor sites. Following ROS generation, the TK linkage is disrupted, releasing up to 92% of the CPT within 48 h. In vitro investigations showed that laser-treated RCpCCPT caused 81.5% cell death from PDT/CDT and chemotherapy (CT). RCpCCPT in cancer cells produces red-blue emission in images of cells taking them in, which allows for fluorescence image-guided Xdynamic treatment. The overall results show that RCp NDs and RCpCCPT are more biocompatible and have excellent Xdynamic the

关键词： Oxidation

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Abby Rasya Angkasa Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta Indonesia Engineering Department Cardiff Metropolitan University - Cardiff School of Technologies Cardiff United Kingdom

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences.

关键词： Emotion recognition TV Statistical analysis Text categorization Speech recognition Linguistics Feature extraction Data augmentation Market research User experience

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Blockchain and API Development to Improve Relational Database Integrity and System Interoperability

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Procedia Computer science 2023年 216卷 151-160页

作者： Andhika Naafi Ramadhan Kharisma Naufal Pane Kun Rifki Wardhana Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.

关键词： Blockchai Cybercrim Database Interoperability

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Environmentally-friendly alkaline ionized water pretreatment and hydrolysis of macroalga via microwave-assisted heating to improve monosaccharide yield for bioethanol production

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Process Safety and Environmental Protection 2024年 189卷 702-713页

作者： Chen, Wei-Hsin Liu, Li-Xuan Khoo, Kuan Shiong Sheen, Herng-Kuang Kwon, Eilhann E. Saravanakumar, Ayyadurai Chang, Jo-Shu Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan International Master Degree Program on Energy National Cheng Kung University Tainan701 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Taiwan Sugar Business Division Taiwan Sugar Corporation Taiwan701 Taiwan Department of Earth Resources and Environmental Engineering Hanyang University Seoul04763 Korea Republic of Directorate of Research SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Chemical Engineering and Materials Science College of Engineering Tunghai University Taichung Taiwan Department of Chemical Engineering National Cheng Kung University Tainan Taiwan

This study synthesizes three sequential steps: pretreatment, acid hydrolysis, and enzyme hydrolysis for bioethanol and hydrochar production from macroalga Gracilaria. The Taguchi method is employed separately in each step to optimize relevant operating factors, aiming to maximize the monosaccharide yield. During pretreatment, environmentally friendly alkaline ionized water is utilized as it does not contain chemical additives. Afterward, microwave-assisted hydrolysis is adopted to enhance the monosaccharide yield of the macroalga for bioenergy production. The results show that the total sugar of Gracilaria undergoing acid hydrolysis alone is 28.72 g‧L−1. The total sugar content after alkaline pretreatment followed by acid hydrolysis is 32.64 g‧L−1, and it increases to 34.76 g‧L−1 after adding enzymes. Meanwhile, the higher heating value of Gracilaria increases from 10.884 MJ‧kg−1 to 12.620 MJ‧kg−1 after undergoing alkaline pretreatment. After the acid and enzyme hydrolysis processes, it increases to 15.164 MJ‧kg−1. The solid biofuel's calorific value increases by 39 % from the three-stage processes. The liquid product combined with Saccharomyces cerevisiae can produce bioethanol, while the produced hydrochar can be used as a solid fuel. This research promotes the development of macroalgal biomass for energy and environmental applications, thereby advancing the circular bioeconomy. © 2024 The Institution of Chemical Engineers

关键词： Ethanol

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Cell segmentation in fluorescence microscopy images

Cell segmentation in fluorescence microscopy images

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IET International Conference on engineering Technologies and Applications (ICETA 2024)

作者： Shao-Yun Yin Wei-Wen Hsu Chih-Hsien Hsia Master Program of Artificial Intelligence National Ilan University Ilan Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (数字)9781837242252

For microscopic cells images, cells’ morphologies are widely varied among different imaging modalities and staining methods. For fluorescence microscopy images, Poisson noises present in images due to the lightening, causing photobleaching, which makes it challenging in analysis with the deep learning models. Traditionally, median filtering can be applied to the original image for denoising, however, it also results in a blurry image, which is harmful to segmentation. In this study, the Haar Wavelet Transform is used to alleviate the photobleaching problem for cell segmentation, integrated to the framework without interfering with feature extraction by the backbone model. For our experiments, instance segmentation for fluorescence-stained cells was performed based on U-Net architecture with the method of gradient flow tracking proposed by Cellpose. And the Haar wavelet downsampling is embedded in the skip connections to verify if it can improve the segmentation performance for fluorescence images. The experimental results show that the approach of Haar wavelet downsampling embedded in the skip connections achieve superior performance among the other settings. Compared with the performance of the baseline model, the performance of the proposed model reached 0.732 in F1-score, which surpassed the one of baseline model (0.6341) by almost 0.1.

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation 9

Creating Mathematical Models to Help Formulate Company Strat...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Pranata, Dewi Sagita BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta13220 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics Strands West MinsterWC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies. © 2024 IEEE.

关键词： Knowledge organization system (KOS)

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"Enhanced Energy Generation Via Composite Catalyst Gasification of In-Situ Plastic Waste"

SSRN

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SSRN 2024年

作者： Abdelrahman, Ameen Erchiqui, Fouad Nedil, Mourad Material science Département School of Engineering Canada Mechanical Engineering Department Université du Québec en Abitibi-Témiscamingue Canada Master of Engineering Program School of Genius

This study responds to the urgent global imperative to address climate change by shifting towards sustainable practices, with a particular focus on mitigating the environmental impact of plastic waste. We delve into the gasification of municipal plastic solid waste as an alternative to traditional disposal methods like incineration or landfilling. Our approach involves the development of composite materials incorporating manganese, titanium, and iron oxides within clay. By optimizing key gasification parameters as heating rate, loading, and reaction time, we aim to maximize gas yields and improve the quality of products. Through characterization techniques XRD, TEM, SEM, GC, and EDX analysis, © 2024, The Authors. All rights reserved.

关键词： Gasification

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT Technology

Enabling Smart Home Energy Management through Gesture-Based ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Khairul Hawani Rambe Chia-Ho Ou International Master Program in Information Technology and Applications Department of Computer Science and Information Engineering National Pingtung University Taiwan

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right.

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