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10th International Conference on Applied System Innovation, ICASI 2024

作者： Li, Yun-Qi Chen, Hsuan-Jung Yang, Hsi-Wen Chen, Chih-Yuan Chang, Shao-Fu Chen, Ssu-Ying Chen, Chien-Chon College of Engineering and Science Ph.D. Program in Materials and Chemical Engineering National United University Miaoli Taiwan Michigan State University Department of Chemistry MI48824 United States National United University Department of Materials Engineering Miaoli Taiwan Graduate Institute of Intellectual Property National Taipei University of Technology Taiwan National United University Department of Energy Engineering Miaoli Taiwan

ISBN: (纸本)9798350394924

For how to store and transport hydrogen, the first thought is to store hydrogen directly in the cylinder. However, this method is not desirable, because generally at least 700 atm high pressure, the hydrogen contained in the cylinder to be useful. Therefore it is very unsafe at such high pressures. In This paper, we design and production of a new generation of solid-state hydrogen storage and discharge container. The goal of 'development of solid-state hydrogen storage technology' is to break through the bottlenecks of hydrogen storage technology safety, hydrogen storage density, high-efficiency hydrogen absorption and release, transportation safety, and engineering practice. © 2024 IEEE.

关键词： Aluminum

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Cytometry in the Short-Wave Infrared

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ACS Nano 2024年第28期18卷 18534-18547页

作者： Liu, Te-I Wang, Jhih-Shan Nguyen, Ai-Phuong Raabe, Marco Quiroz Reyes, Carlos Jose Lin, Chih-Hsin Lin, Ching-Wei Institute of Atomic and Molecular Sciences Academia Sinica Taipei City106319 Taiwan Department of Materials Science and Engineering National Taiwan University Taipei City106319 Taiwan Department of Physics University of Stuttgart Stuttgart70174 Germany Department of Chemistry National Tsing Hua University Hsinchu300044 Taiwan International Ph.D. Program in Biomedical Engineering Taipei Medical University New Taipei City235603 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University New Taipei City235603 Taiwan

Cytometry plays a crucial role in characterizing cell properties, but its restricted optical window (400-850 nm) limits the number of stained fluorophores that can be detected simultaneously and hampers the study and utilization of short-wave infrared (SWIR;900-1700 nm) fluorophores in cells. Here we introduce two SWIR-based methods to address these limitations: SWIR flow cytometry and SWIR image cytometry. We develop a quantification protocol for deducing cellular fluorophore mass. Both systems achieve a limit of detection of ∼0.1 fg cell-1 within a 30 min experimental time frame, using individualized, high-purity (6,5) single-wall carbon nanotubes as a model fluorophore and macrophage-like RAW264.7 as a model cell line. This high-sensitivity feature reveals that low-dose (6,5) serves as an antioxidant, and cell morphology and oxidative stress dose-dependently correlate with (6,5) uptake. Our SWIR cytometry holds immediate applicability for existing SWIR fluorophores and offers a solution to the issue of spectral overlapping in conventional cytometry. © 2024 The Authors. Published by American Chemical Society.

关键词： Oxygen

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ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE UNIVERSITY RESTAURANT-UNB 32nd

ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE...

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32nd European Biomass Conference and Exhibition, EUBCE 2024

作者： Ramos, Arthur Augusto Frasson De Brites, Eneida Campos Felipe Soares, Raquel Moraes Silveira, Edgar A. Contreras, Francisco University of Brasilia Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil University of Brasilia Graduate Program on Environmental Technology and Water Resources Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil Federal Institute of Education Science and Technology of Brasília Estrutural Campus DF Brasília71250-000 Brazil University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and Environment DF Brasilia70910-900 Brazil

With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the environmental repercussions associated with nutrient-rich waste. Inappropriate disposal of such residues contributes to nitrogen release into the environment, impacting aquatic ecosystems and precipitating the generation of greenhouse gases. This study tackles the global challenge of effectively managing nitrogen in food waste by utilizing Material Flow Analysis (MFA) as a tool to comprehend this cycle. The analysis uncovered that vegetables, legumes, and fruits constitute the primary sources of waste generation, while meats, despite their lower mass, account for a substantial proportion of total nitrogen depletion. In the surveyed month of October 2023, 174,834 meals were served, resulting in an average food consumption N-footprint of 0.003 kg of nitrogen discarded per individual meal within the restaurant’s organic waste. These findings indicate that 65% of nitrogen is consumed in meal form, while 35% is discarded as organic solid waste. However, 53% of the nitrogen in the residues originates from food preparation processes, with food preparation responsible for over half of this figure. A deeper process analysis reveals that vegetables have low nitrogen concentrations, although they significantly contribute to waste at all stages. In contrast, meats and eggs, with higher nitrogen concentrations, emerge as noteworthy contributors to the overall nitrogen content in waste. Vegetables and meats contribute about 50% and 45% of the total nitrogen, respectively. These outcomes substantially enhance our comprehension of waste generation dynamics and nutrient utilization at the university restaurant, assisting with waste management, design of sustainable menus, reduction of food waste, and optimized resource utilization, contributing to sustainability and re

关键词： Effluent treatment

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Prototype development of VR based personal zone defense practice equipment 4

Prototype development of VR based personal zone defense prac...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Honma, Hiroki Iida, Yoshiaki Okumura, Yasuyuki Fujii, Katsuyuki Umehira, Masahiro Nanzan University Graduate Program of Mechatronics Graduate School of Science and Engineering Nagoya Japan Nanzan University Physical Education Center Nagoya Japan Nanzan University Faculty of Science and Engineering Department of Mechatronics Nagoya Japan Nanzan University Faculty of Science and Engineering Department of Electronics and Communication Technology Nagoya Japan

ISBN: (纸本)9781665419048

In a basketball game, players must act appropriately according to the position and movement of the player. This needs to be learned through team practice. Therefore, if we can build an environment that allows even one person to practice as a team, the efficiency of practice can be greatly improved. It is effective to use virtual reality (VR) technology to realize team practice alone. This paper proposes a VR-based basketball team practice learning system that provides VR environments similar to the actual team practice environments by arranging players in a virtual space. Focusing on zone defense practice, which is said to be difficult to learn even in team practice, we propose a practice system for orthodox 3-2 zone tactics. In addition, because the distance between the player and the surrounding VR player is important for this system, this paper presents the evaluation result of the 3D virtualization accuracy measured by the experiment. © 2022 IEEE.

关键词： Virtual reality

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices

Random Number Generator for Securing Data Exchange in Smart ...

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Computer Technologies (ICCTech), International Conference on

作者： Andi Sama Meyliana Yaya Heryadi Taufik Roni Sahroni Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent Information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption.

关键词： Industries Systematics Market research Data transfer Control systems Generators Encryption

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Towards a Healthy AI Tradition: Lessons from Biology and Biomedical science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

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Light(science & Applications) 2022年第11期11卷 2817-2831页

作者： Yueming Li Ying Jiang Lu Lan Xiaowei Ge Ran Cheng Yuewei Zhan Guo Chen Linli Shi Runyu Wang Nan Zheng Chen Yang Ji-Xin Cheng Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Graduate Program for Neuroscience Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Chemistry Boston UniversitBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Division of Materials Science and Engineering Boston UniversityBostonMA 02215USA Department of Biological Engineering Massachusetts Institute of TechnologyCambridgeMA 02142USA

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological *** non-invasive neuromodulation methods offer limited precision at the milimeter ***,we report opticallygenerated focused ultrasound(OFUS)for non-invasive brain stimulation with ultrahigh *** is generated by a soft optoacoustic pad(SOAP)fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane *** generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83μm,which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound(tFUS).Here,we show effective OFUS neurostimulation in vitro with a single ultrasound *** demonstrate submillimeter transcranial stimulation of the mouse motor cortex in *** acoustic energy of 0.6 mJ/cm?,four orders of magnitude less than that of tFUS,is suffcient for successful OFUS *** offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision noninvasively.

关键词： SOAP stimulation precision

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