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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tang, Haoning Wang, Yiting Ni, Xueqi Watanabe, Kenji Taniguchi, Takashi Fan, Shanhui Mazur, Eric Yacoby, Amir Cao, Yuan School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States

ISBN: (纸本)9781957171395

We introduce the first on-chip, microelectromechanical system for the in situ tuning of twisted 2D materials, enabling tunable interfacial properties, synthetic topological singularities, and adjustable-polarization light sources for advanced quantum material manipulation in 2D-3D devices. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Degrees of freedom (mechanics)

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Enhancing the Repair of Substantial Volumetric Muscle Loss by Creating Different Levels of Blood Vessel Networks Using Pre-Vascularized Nerve Hydrogel Implants

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Advanced Healthcare materials 2024年第13期13卷 2303320-2303320页

作者： Wei, Shih-Yen Chen, Po-Yu Tsai, Min-Chun Hsu, Ting-Lun Hsieh, Chia-Chang Fan, Hsiu-Wei Chen, Tzu-Hsuan Xie, Ren-Hao Chen, Guan-Yu Chen, Ying-Chieh Department of Materials Science and Engineering National Tsing-Hua University Hsinchu300044 Taiwan Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15289 United States Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300193 Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300193 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300193 Taiwan National Yang Ming Chiao Tung University Hsinchu300193 Taiwan

Volumetric muscle loss (VML), a severe muscle tissue loss from trauma or surgery, results in scarring, limited regeneration, and significant fibrosis, leading to lasting reductions in muscle mass and function. A promising approach for VML recovery involves restoring vascular and neural networks at the injury site, a process not extensively studied yet. Collagen hydrogels have been investigated as scaffolds for blood vessel formation due to their biocompatibility, but reconstructing blood vessels and guiding innervation at the injury site is still difficult. In this study, collagen hydrogels with varied densities of vessel-forming cells are implanted subcutaneously in mice, generating pre-vascularized hydrogels with diverse vessel densities (0–145 numbers/mm2) within a week. These hydrogels, after being transplanted into muscle injury sites, are assessed for muscle repair capabilities. Results showed that hydrogels with high microvessel densities, filling the wound area, effectively reconnected with host vasculature and neural networks, promoting neovascularization and muscle integration, and addressing about 63% of the VML. © 2024 Wiley-VCH GmbH.

关键词： Muscle

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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A Low Complexity ML-Based Methods for Malware Classification

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computers, materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Micromachined Flexible Silicon Solar Cells as a Power Supply for Smart Contact Lenses 21

Micromachined Flexible Silicon Solar Cells as a Power Supply...

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21st International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications, PowerMEMS 2022

作者： Pourshaban, Erfan Karkahnis, Mohit U. Deshpande, Adwait Hasan, Md. Rabiul Rock, Nathan D. Banerjee, Aishwaryadev Ghosh, Chayanjit Kim, Hanseup Mastrangelo, Carlos H. University of Utah Department of Electrical and Computer Engineering Salt Lake CityUT84112 United States University of Utah Department of Materials Science and Engineering Salt Lake CityUT84112 United States

ISBN: (纸本)9781665493062

This article reports the fabrication, characterization, implementation, and microsystem integration of micromachined flexible silicon solar cells to supply electric power to smart contact lenses. Single silicon solar cell shows the open circuit voltage (Voc) of 0.5 and 0.55 V Under indoor and outdoor lighting conditions, respectively. The Voc enhanced to 1.25 and 1.65 V after making series connections between three cells. The maximum power output of 50 μW and 2.7 mW are recorded under indoor and outdoor lighting conditions. Furthermore, a power management IC is used to boost up the voltage to 3.3 V and efficiently store or use the generated energy. © 2022 IEEE.

关键词： Open circuit voltage

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Yanfi, Yanfi Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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An enzyme-free interdigitated electrode for ketamine detection

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第5期46卷 441-452页

作者： Phan, Thien Luan Van Hieu, Nguyen Lin, Jing-Jenn Wu, Hsing-Ju Chu, Pei-Yi Hsiao, Chin-Sung Yao, Fiona Yan-Dong Lin, Yung-Kai Ching, Congo Tak Shing Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung Taiwan Viet Nam Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Taiwan Research Assistant Center Show Chwan Memorial Hospital Taiwan Department of Medical Research Chang Bing Show Chwan Memorial Hospital Taiwan Department of Pathology Show Chwan Memorial Hospital Taiwan School of Medicine College of Medicine Fu Jen Catholic University Taiwan Computer Science and Information Engineering Asia University Taiwan Division of Science Engineering and Health Studies College of Professional and Continuing Education The Hong Kong Polytechnic University Hong Kong Department of Food Science National Taiwan Ocean University Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan

Ketamine is an anesthetic, which when used wrongly would affect judgment and behavior, increasing risks to individual such as unwanted or risky sexual behavior. This study reports the development of an admittance- and capacitance-spectroscopy-based sensor to detect ketamine in solution, such as whiskey, a popular alcoholic drink. The result confirms the proposed method is capable of detecting the presence of ketamine in whiskey, in concentration as low as 6.25 ng/μL, by using admittance and capacitance measured at 6.75 MHz, as well as quantifying the measured admittance (sensitivity: 300 μΩ−1/Log (ng/μL);limit of detection (LOD): 0.16 ng/μL) or capacitance data (sensitivity: 2 nF/Log (ng/μL);LOD: 0.73 ng/μL) at 18 kHz and 7.69 MHz, respectively. © 2023 The Chinese Institute of Engineers.

关键词： Capacitance

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Atomic Insights into Strain-Induced Nanoscopic Compositional Fluctuation in AlGaN Quantum Well Epitaxy

SSRN

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SSRN 2025年

作者： Huang, Chia-Yen Chao, Ying-Chun Huang, Cheng-Yao Yen, Hung-Wei Department of Photonics College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei Taiwan Department of Materials Science and Engineering

Two identical UVC light-emitting diode AlGaN epitaxy were grown on high-quality AlN buffer layers with different strain states. We characterized the AlGaN epi by high-resolution scan transmission electron microscopy (HR-STEM) and atom probe tomography (APT). Geometry phase analysis with HR-STEM revealed the sample grown on the more compressively strained template possesses a more pronounced local lattice constant distortion in the multiple-quantum-well (MQW) region. APT further verified the local lattice distortion originated from the strain-induced Ga segregation in the MQW. The comparative compositional analysis revealed the nanoscopic compositional fluctuation favors further incorporation of Ga during epitaxial growth, which triggered the AlGaN growth front destabilization and new defect nucleation. The 1-D composition profiling from both electron dispersive microscopy and APT suggested nitrogen desorption during the AlGaN stress relaxation. © 2025, The Authors. All rights reserved.

关键词： Stress relaxation

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Localization of Crop Damages Utilizing a Wake-Up Gas Sensor Network 22

Localization of Crop Damages Utilizing a Wake-Up Gas Sensor ...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Noh, Seungbeom Tope, Sayali Sium, Farhan Sadik Khan, Shakir-Ul Haque Karkhanis, Mohit Wang, Leo Deshpande, Adwait Dalapati, Rana Mural, Ravi V. Mastrangelo, Carlos H. Ji, Mingyu Zang, Ling Schnable, James C. Kim, Hanseup University Of Utah Electrical And Computer Engineering Salt Lake CityUT84112 United States University Of Utah Materials Science And Engineering Salt Lake CityUT84112 United States University Of Nebraska Department Of Agronomy And Horticulture LincolnNE68588 United States

ISBN: (纸本)9784886864352

This paper reports the initial demonstration of on-field crop damage prediction through a network of low power gas sensors that were deployed in an actual sorghum field and detected crop stress molecules, specifically hexanal. Such detection could replace current farm-scouting methods that are mostly manual and help reduce production loss of biofuel crops during a farming period. The initially-deployed gas sensor network consisted of a 2×3 array with a 7-meter interspace. When the leaves of sorghums were cut at a random location within the network, three sensors in the gas sensor network responded at 45, 62, and 72 minutes, respectively, allowing us to estimate an origination zone within an area of 21.7 m2 out of the total monitoring farm area of 294 m2. This demonstration proves the potential of precise localization of plant damages through the use of the gas sensor network. © 2023 IEEJ.

关键词： biofuel and sorghum gas sensor network Localization of crop damage smart farming

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