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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Kaito Watanabe Hirotaka Nakayama Atsushi Shiraki Tomoyoshi Ito Katsumi Hirata Naoto Hoshikawa Advanced Course General Engineering Program National Institute of Technology Oyama College Oyama Japan Center for Computational Astrophysics National Astronomical Observatory of Japan Mitaka Japan Graduate School of Engineering Chiba University Chiba Japan Department of Innovative Electrical and Electronic Engineering National Institute of Technology Oyama College Oyama Japan Graduate School of Science and Tecnology University of Tsukuba Tsukuba Japan

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined.

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An ANN-Based Electronic Nose for Monitoring Pollutant Gases in Landfills

An ANN-Based Electronic Nose for Monitoring Pollutant Gases ...

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IEEE Instrumentation and Measurement Technology Conference

作者： Juliene dos Santos Davi Muniz Francisco Auriberto Ferreira Marques Junior Cleonilson Protasio De Souza William De Paiva Post-Graduate Program of Electrical Engineering Federal University of Paraíba João Pessoa PB Brazil Post-Graduate Program of Environmental Science and Technology State University of Paraíba Campina Grande PB Brazil Department of Electrical Engineering Federal University of Paraíba João Pessoa PB Brazil Department of Sanitary and Environmental Engineering State University of Paraíba João Pessoa PB Brazil

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Biogas generated from urban solid waste degradation in landfills is composed of various types of gases, some of them are very toxic and harmful to the health of both humans and animals, and contribute directly to global warming as well. In this way, the monitoring of biogas concentration in landfills and their surroundings is very important because this can assess the air contamination around landfills, as well as to propose means to minimize it. In this work, an electronic nose for gas identification and measurement of gases present in landfill biogas using an Artificial Neural Network (ANN) is introduced. The ANN training data have been collected by the developed electronic nose, which consisted on a set of low-cost gas sensors and on a signal acquisition system controlled by the LabViewsoftware. After the ANN training, a neural architecture has been obtained and implemented in a microcontrolled digital system to measure different gas concentrations, mainly, methane. The main results of the proposed ANN-based Electronic Nose show that it was possible to measure the gases concentrations, with RMSE less than 1%, for the most of target gases, confirming the accuracy of the developed system.

关键词： Gases Semiconductor device measurement Accuracy Landfills Artificial neural networks Electronic noses Biogas

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network

arXiv

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arXiv 2024年

作者： Nam, Jong Woo Choi, Eun Young Ailshire, Jennifer A. Chiang, Yao-Yi Neuroscience Graduate Program University of Southern California United States Leonard Davis School of Gerontology University of Southern California United States Department of Computer Science and Engineering University of Minnesota United States

Environmental hazards place certain individuals at disproportionately higher risks. As these hazards increasingly endanger human health, precise identification of the most vulnerable population subgroups is critical for public health. Moderated multiple regression (MMR) offers a straightforward method for investigating this by adding interaction terms between the exposure to a hazard and other population characteristics to a linear regression model. However, when the vulnerabilities are hidden within a cross-section of many characteristics, MMR is often limited in its capabilities to find any meaningful discoveries. Here, we introduce a hybrid method, named regression-guided neural networks (ReGNN), which utilizes artificial neural networks (ANNs) to non-linearly combine predictors, generating a latent representation that interacts with a focal predictor (i.e. variable measuring exposure to an environmental hazard). We showcase the use of ReGNN for investigating the population heterogeneity in the health effects of exposure to air pollution (PM2.5) on cognitive functioning scores. We demonstrate that population heterogeneity that would otherwise be hidden using traditional MMR can be found using ReGNN by comparing its results to the fit results of the traditional MMR models. In essence, ReGNN is a novel tool that enhances traditional regression models by effectively summarizing and quantifying an individual’s susceptibility to health risks. © 2024, CC BY-NC-SA.

关键词： Multiple linear regression

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Q-Learning-Based Multi-Objective Global Path Planning for UGV Navigation*

Q-Learning-Based Multi-Objective Global Path Planning for UG...

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IEEE Latin American Robotics Symposium, LARS

作者： Hiago O. B. Batista Thayron M. Hudson Kevin B. de Carvalho Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa - MG Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

This paper presents an offline path planning strategy for unmanned ground vehicles (UGVs) using Q-learning. The proposed method addresses path optimization in warehouse-like environments, where tasks involve item pickup and delivery to specific locations. The Q-learning algorithm trains an agent to determine the most efficient routes, with validation conducted in an $8 \times 5$ meter workspace equipped with an Optitrack motion capture system. The workspace was discretized into a $16 \times 10$ grid, allowing the Q-learning to effectively navigate through complex obstacle-laden scenarios. Experimental results indicate that the Q-learning approach outperforms traditional methods such as Dijkstra, A-star, and Breadth-First Search in terms of path length, number of turns, planning time, and overall success rate; being up to 7 times faster to plan a path and reducing the number of bends by up to 41%. The Q-learning based paths feature more linear segments, which contribute to energy savings and improved navigational efficiency. Future work will explore applications in heterogeneous multi-agent systems and enhancements in training time and agent collaboration.

关键词： Training Meters Q-learning Navigation Path planning Motion capture Planning Robots Optimization Multi-agent systems

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Study on triphase of polymorphs TiO2 (anatase/rutile/brookite) for boosting photocatalytic activity of metformin degradation

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Chemosphere 2024年 351卷

作者： Eddy, Diana Rakhmawaty Nur Sheha, Geometry Amal Permana, Muhamad Diki Saito, Norio Takei, Takahiro Kumada, Nobuhiro Irkham Rahayu, Iman Abe, Ikki Sekine, Yuta Oyumi, Tomoki Izumi, Yasuo Department of Chemistry Faculty Mathematics and Natural Sciences Universitas Padjadjaran Sumedang45363 Indonesia Special Educational Program for Green Energy Conversion Science and Technology Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi Kofu400-8511 Japan Center for Crystal Science and Technology University of Yamanashi Kofu400-8511 Japan Department of Chemistry Graduate School of Science Chiba University Chiba263-8522 Japan

The elution of pharmaceutical products such as metformin at higher concentrations than the safe level in aquatic systems is a serious threat to human health and the ecosystem. Photocatalytic technology using TiO2 semiconductors potentially fixes this problem. This study aims to synthesize triphasic anatase–rutile–brookite TiO2 using ultrasound assisted sol–gel technique in the presence of acid and its application to photodegradation of metformin under UV light irradiation. Based on X-ray diffraction analysis, a TiO2 sample consisted of anatase (76%), rutile (7%), and brookite (17%) polymorph (A76R7B17) that was fully crystallized. Scanning electron microscopy (EM)–energy dispersive X-ray spectra results showed agglomerated triphasic A76R7B17 with irregular spherical clusters. Transmission EM results revealed that the crystal size of A76R7B17 was 4–14 nm. The Brunauer–Emmett–Teller analysis showed the sample's specific surface area of 149 m2 g−1. The degradation test of metformin demonstrated that the A76R7B17 exhibited a 75.4% degradation efficiency after 120 min under UV light irradiation, significantly higher than using biphasic and single-phase TiO2 photocatalysts. This difference could be attributed to the heterojunction effect of triphasic materials that effectively reduced electron–hole recombination rate as well as the combination of effective electron transfer from conduction band of brookite and anatase and the utilization of wider range of UV–visible light using rutile. © 2024 Elsevier Ltd

关键词： Titanium dioxide

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UV-VIs Spectrophotometry Study of Escherichia Coli Bacteria and Microfluidic Channel Chip Fabrication For Optical Biosensing Application

UV-VIs Spectrophotometry Study of Escherichia Coli Bacteria ...

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National Conference on Radio science (NSRC)

作者： Sara Mohamed Amira A.M. Ahmed Ahmed Kreta Amany Khalifa Mai Mostafa Shaimaa Ahmed Heba Refaat A. M. Faramawy Ahmed Moustafa Mohamed A. Swillam Department of Physics School of Science and Engineering The American University in Cairo New Cairo Egypt Faculty of Engineering May University in Cairo Cairo Egypt Biotechnology Graduate Program Department of Biology The American University in Cairo New Cairo Egypt Department of Physics Faculty of Science Ain Shams University (ASU) Cairo Abbassia Egypt

One of the most significant foodborne organisms that cause serious foodborne illnesses is Escherichia coli (E. coli). In this work, we demonstrated that the characterization of E. coli suspensions can be facilitated using conventional absorption spectroscopy in the ultraviolet and visible spectral ranges. With increasing concentrations of E. coli, a red shift in the UV-Vis spectra along with an obvious rise in intensity were observed. The results showed that the calculated cell count of E. coli was found in Luria-Bertani (LB) broth in the range of $4.08 \times 10^{8}$ to $1.41 \times 10^{9}$ $\mathrm{CFU} / \mathrm{mL}$. Besides, the optical density was theoretically calculated to be in the range of 1.22 to 3.18. An E. coli suspension in a minute volume needed to be employed, so a microfluidic channel chip was fabricated. To evaluate the suitability and validity of the microfluidic channel for the optical-based measurements, various solutions with different flow rates, including water, acetone, and ethanol, were pumped through the tubes and into the microfluidic channels.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Steady-state phases and interaction-induced depletion in a driven-dissipative chirally-coupled dissimilar atomic array

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Physical Review Research 2024年第2期6卷 023232-023232页

作者： Shao-Hong Chung I Gusti Ngurah Yudi Handayana Yi-Lin Tsao Chun-Chi Wu G.-D. Lin H. H. Jen Department of Physics and Center for Quantum Science and Engineering Taipei 10617 Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Department of Physics Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Trapped-Ion Quantum Computing Laboratory Hon Hai Research Institute Taipei 11492 Taiwan

A nanophotonic waveguide coupled with an atomic array forms one of the strongly coupled quantum interfaces to showcase many fascinating collective features of quantum dynamics. In particular, for a dissimilar array of two different interparticle spacings with competing photon-mediated dipole-dipole interactions and directionality of couplings, we study the steady-state phases of atomic excitations under a weakly driven condition of laser field. We identify a partial set of steady-state phases of the driven system composed of combinations of steady-state solutions in a homogeneous array. We also reveal the intricate role of the atom at the interface of the dissimilar array in determining the steady-state phases and find an alteration in the dichotomy of the phases strongly associated with steady-state distributions with crystalline orders. We further investigate in detail the interaction-induced depletion in half of the dissimilar array, where the blockaded region results from two contrasting interparticle spacings near the reciprocal coupling regime. This can be further evidenced from the analytical solutions under the reciprocal coupling. Our results can provide insights in the driven-dissipative quantum phases of atomic excitations with nonreciprocal couplings and pave the avenues toward quantum simulations of exotic many-body states essential for quantum information applications.

关键词： Collective behavior Dissipative dynamics Nanophotonics Phase diagrams Dipole approximation Two-level models

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