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SSRN 2025年

作者： Chowdhury, Fazlul Habib Matsushima, Takashi Doctoral Program in Engineering Mechanics and Energy University of Tsukuba Tsukuba305-0005 Japan Department of Engineering Mechanics and Energy University of Tsukuba Tsukuba305-0005 Japan

The Depth-Integrated Particle Method (DIPM) is a numerical approach for simulating sediment disasters, such as debris flows, mudflows, and landslides. This study applies DIPM to the 2021 Atami debris flow triggered by heavy rainfall and slope collapse in the Izusan district of Shizuoka Prefecture, Japan. DIPM models the moving mass as soil columns traveling along surface topography, with bottom shear stress calculated using a modified Manning's formula and particle interactions governed by hydraulic gradients. Digital Elevation Models (DEMs) were generated using ArcGIS from pre-disaster 0.5m spaced grid data and processed into 5m and 1m resolution meshes. Manning's coefficient ([[EQUATION]] = 0.1) and the critical deposition angle ([[EQUATION]] = 8.5°) were identified as optimal parameters through sensitivity analyses, closely matching observed flow velocities, and an average sediment deposition height of 1.65 m. The simulations using the 1m DEM resolution detected sediment deposition at the original check dam while validating the design capacity of 10,800 m3 for the newly constructed Izusan check dam. These findings underscore the versatility of DIPM for debris flow modeling and its potential for risk evaluation and disaster mitigation. © 2025, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Harnessing YOLO for Loose Fruits Detection: Boosting Productivity in Palm Oil Plantations

Harnessing YOLO for Loose Fruits Detection: Boosting Product...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Elly Warni Indrabayu Andani Achmad Aldilah Rezki Rhamadani Syahsir Doctoral Program of Electrical Engineering Department Informatics Department Universitas Hasanuddin Makassar Indonesia Electrical Engineering Department Universitas Hasanuddin Makassar Indonesia Informatics Department Universitas Hasanuddin Makassar Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This study explores the use of the YOLOv5 algorithm to detect loose fruits palm oil in plantations, improve collection efficiency, and reduce reliance on manual labor. Loose fruits have high economic value due to their oil content, but they are often not collected, resulting in financial losses and the growth of weeds around oil palm trees. Conventional manual methods are often ineffective; thus, automatic detection is required. The dataset comprises 525 images of loose fruits, covering complex conditions on plantations, and secondary data from Roboflow, including nontarget objects, such as rocks and dry leaves, to train the model to accurately distinguish loose fruits. During training, several parameters were optimized: batch size, learning rate, optimizer type, weight decay, and momentum, which showed that small batch sizes provided higher mAP50 values, whereas large batches increased mAP50:95, indicating the ability to detect objects at higher levels of complexity. Testing in a scenario with loose fruits clearly visible and partially covered with leaves or grass showed that YOLOv5 could detect loose fruits well despite some constraints in the obstructed environment. The results of this study show that, with appropriate parameter settings, YOLOv5 can be a reliable basis for automated loose fruit collection systems, potentially improving operational efficiency in oil palm plantations and contributing to the development of automation in the agricultural sector.

关键词： YOLO Productivity Training Automation Oils Plantations Manuals Rocks Robustness Testing

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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Imran Djafar Indrabayu Syafruddin Syarif Doctoral Program of Electrical Engineering Department Hasanuddin University Dipa Makassar University Makassar Indonesia Department of Informatics Hasanuddin University Makassar Indonesia Department of Electrical Engineering Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities.

关键词： Waste management Deep learning Transfer learning Sea measurements Metals Glass Feature extraction Transformers Convolutional neural networks Drones

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Key factors affecting people's willingness to participate in water resource protection in the agricultural Ba River Basin – in the highlands of Vietnam

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Journal of Water and Climate Change 2025年第4期16卷 1225-1237页

作者： Anh, Nguyen Tuan Can, Le Duy Schmalz, Britta Luu, Tran Le Doctoral Training Program in Sustainable Urban Development Vietnamese-German University Vanh Dai 4 Quarter 4Binh Duong Province Ben Cat Viet Nam Nong Lam University of Ho Chi Minh City Gia Lai Campus Gia Lai Viet Nam Department of Mechatronics and Sensor Systems Technology Vietnamese-German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Binh Duong Province Ben Cat Viet Nam Engineering Hydrology and Water Management Technical University of Darmstadt Darmstadt Germany Master Program in Water Technology Reuse and Management Vietnamese-German University Vanh Dai 4 Quarter 4 Thoi Hoa Ward Binh Duong Province Ben Cat Viet Nam

Willingness to participate (WP) in community-based management depends on various factors such as a sense of community, perceptions, beliefs, and demographic characteristics. This study first evaluates the WP of locals (n = 100) in water management in the Ba River Basin – Highlands of Vietnam, where ethnic minority groups are living and working in the agricultural sector. The multivariable linear regression model is applied to explain WP through the factors including the sense of community (SC), dependance on river water (DW), perceptions of current water resource management (PW), level of income (IN), gender (GE), education levels (ED), and place of residence (PR). The results showed that the final regression model explains 67.9% of the variation in WP levels (R2 = 0.679, p 0.05) in this model. Sense of community and perceptions greatly influence community participation. To boost willingness, periodic training and campaigns are needed. Additionally, improving legal frameworks can encourage greater community involvement in water management programs. © 2025 The Authors.

关键词： Multiple linear regression

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An Ultra-high Sensitivity Sensing Collimation Imager: Design, Modelling, and Assessment

An Ultra-high Sensitivity Sensing Collimation Imager: Design...

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Medical Imaging 2025: Physics of Medical Imaging

作者： Cai, Ling Aggarwal, Samarth Enninful, Kweku Chen, Yunlai Komarov, Sergey Thorek, Daniel Jha, Abhinav K. Tai, Yuan-Chuan Imaging Science Doctoral Program Washington University in St. Louis St. LouisMO United States Department of Biomedical Engineering Washington University in St. Louis St. LouisMO United States Mallinckrodt Institute of Radiology Washington University St. Louis United States

ISBN: (纸本)9781510685888

In this work, we discuss the design and development of an ultra-high sensitivity imaging system, Sensing Collimation Imager (SCI), dedicated to guide and optimize application of alpha particle-emitting radiopharmaceutical therapy (αRPT). Alpha particles are highly potent and unlike conventional external beam therapy can be used in metastatic settings. Currently, the lack of information of radionuclide distribution hinders estimated internal absorbed dose from αRPT, which calls for ultrasensitive SPECT technologies that can report reliable activity distribution under ultra-low count rate imaging conditions. The proposed SCI-SPECT is based on an innovative design that incorporates the geometry of both conventional pinhole collimator and coded aperture. Aimed to decouple the dependence of resolution and sensitivity, the heavy metal collimator is replaced by scintillator crystals to serve dual purposes of increasing detected events and providing spatial information. The detector module consists of an 8×8 array of crystal elements. Each crystal presents an alternating pattern of GAGG scintillator and acrylic. The depth of interaction (DOI) information is obtained through dual-ended readout system. Our experimental results are based on a working prototype SCI module that is fully functional under a broad range of energy (70 keV - more than 511 keV), which is well-suited for alpha particle emitter imaging applications. Furthermore, we modeled the system response function based on analytical derivation and validated it by comparing with Monte Carlo simulations. We carried out a preliminary imaging study of three rod sources filled with Tc99m. The image was successfully reconstructed with the derived system matrix, and the rods at different locations can be clearly resolved. We also compared the performance with Siemens Symbia Intevo scanner for high γ-ray energy imaging with Cu-64, the reconstructed image shows better resolution while potentially achieving 2 orders of magni

关键词： Alpha particles

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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Comparative Analysis of Normal and ASD EEG Data Using Hjorth Parameters

Comparative Analysis of Normal and ASD EEG Data Using Hjorth...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muliyadi Muliyadi Melinda Melinda Yuwaldi Away Syahrul Gazali Doctoral Program School of Engineering Post Graduate Program Universitas Syiah Kuala Banda Aceh Indonesia Electrical Engineering Department Politeknik Negeri Lhokseumawe Aceh Indonesia Dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia Dept. Neurology Universitas Syiah Kuala Banda Aceh Indonesia Mahar Mardjono National Brain Center Hospital Jakarta Indonesia

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The increasing prevalence of autism spectrum disorder (ASD) in recent decades has underscored the need for more accurate and objective diagnostic methods. Electroencephalography (EEG) holds promise as a tool in ASD neurobiological research due to its ability to capture neural oscillatory activity with high temporal resolution. This study aims to analyze the neurophysiological differences between individuals with ASD and neurotypical populations by examining EEG data using Hjorth parameters. The preprocessing stage involved using a Butterworth filter to enhance EEG signal quality by eliminating noise in the frequency range of 4-40 Hz. The Hjorth parameters, including Activity, Mobility, and Complexity, were used to assess the statistical characteristics of the EEG signals. The analysis was conducted on EEG data collected from 5 typical individuals and 5 ASD ASD subjects. Our findings reveal that the Hjorth Activity parameter is notably lower in the ASD group across all regions, with the most significant differences observed in the left and right frontal areas, where average activity measures 0.236 μV 2 and 0.060 μV 2 in ASD individuals, compared to 0.512 μV 2 and 0.138 μV 2 in the neurotypical group. However, the Hjorth Complexity and Mobility values remained consistent between the two groups, implying that the fundamental structure of the EEG signals is relatively preserved. These findings offer insights into the neurobiological mechanisms of ASD and suggest the potential use of Hjorth parameters in developing more targeted interventions for individuals with ASD.

关键词： Human computer interaction Autism Frontal lobe Noise Neural activity Electroencephalography Complexity theory Signal resolution Robots Optimization

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Modeling and Control of PRP-Gantry Crane Systems via Neural IDA-PBC

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Bandong, Steven Jayawardhana, Bayu Plaza, Santiago Sanchez-Escalonilla Nazaruddin, Yul Yunazwin Joelianto, Endra Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia University of Groningen Groningen9712 CP Netherlands Instrumentation Control and Automation Research Group Department of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia

In this paper, we present a novel underactuated gantry crane (GC) systems where an extra degree-of-freedom actuation is introduced to control the trolley position and its sway angle during motion. The proposed gantry crane systems resembles a prismatic-revolute-prismatic (PRP) robotic configuration where the revolute joint corresponds to the sway angle which is not actuated. Firstly, an energy-based modeling of the PRP-GC systems is presented where both Euler-Lagrange and port-controlled Hamiltonian formalisms are used. Secondly, we present the design of a Neural Interconnection and Damping Assignment Passivity-Based Controller (N-IDA-PBC) that allows for an automated learning of an IDA-PBC controller, where the solutions to the corresponding IDA-PBC matching PDE are not trivial for underactuated systems. Finally, the efficacy of the proposed N-IDA-PBC is evaluated through Monte Carlo simulations, where the neural networks training of N-IDA-PBC uses 3,000 randomly generated data points and 20,000 samples of Monte-Carlo simulation are performed, taking into account uncertainties in the systems' parameters, including initial states, physical damping, and payload masses. The Monte-Carlo simulations show that the trained N-IDA-PBC is able to regulate the trolley position and sway angle to the set-point position and it is robust against parameter uncertainties. © 2025 IEEE. All rights reserved.

关键词： Robustness (control systems)

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Lung Cancer Diagnosis Based on Object Detection Technology 14th

Lung Cancer Diagnosis Based on Object Detection Technology

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14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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