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Geotechnical Frontiers 2025: Geotechnics of Natural Hazards

作者： Akarsh, Pattan K. Chaudhary, Babloo Sajan, Manu K. Sah, Babita Kumar, S. Geo-Disaster Prevention Laboratory Dept. of Civil Engineering National Institute of Technology Karnataka Karnataka Surathkal India Dept. of Civil Engineering National Institute of Technology Karnataka Karnataka Surathkal India

ISBN: (数字)9780784485996

During past earthquakes, many breakwaters were found unstable due to the loss of seabed foundation stability and the deformation of its components. Limited studies are available on the seismic stability of rubble mound breakwaters. Hence, in this study, earthquake effects on RM breakwater were investigated. A series of shake table tests were conducted, applying sinusoidal input motion at the model’s base. The conventional model has seabed soils and breakwater mound. In addition, a reinforcing technique employing geosynthetic materials for mitigating the earthquake-induced damage of RM breakwater was developed. The geosynthetic reinforcing elements like geotextile sand-filled bags and geogrids were utilized at various locations of the model. The performance of the developed reinforcing model was compared with the responses of the conventional model using various parameters. The settlement and horizontal displacement of the developed model were reduced by 45% and 43%, respectively, during the mainshock. The developed model can be utilized for real-world applications. © ASCE.

关键词： Breakwaters

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Applying Vertical Photovoltaic system for Increasing Hosting Capacity in the Distribution system

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 190-197页

作者： Park, Seong-Jun Nam, Jun-Hyuk Ryu, Ki-Hwan Kim, Byung-Ki Moon, Won-Sik Dept. of Electrical Engineering Soongsil University Korea Republic of Korea Electric Association Korea Republic of Electric Power System Research Team Korea Institute of Energy Research Korea Republic of

South Korea plans to achieve a 20% renewable energy share by 2030, entailing significant expansion of wind and photovoltaic system capacity. Rapid integration of distributed renewable sources into the distribution network leads to challenges intensified by high-output bifacial photovoltaic systems: overvoltage, line capacity limitations, and increased reverse power flow during peak photovoltaic system generation. Existing solutions focus on voltage constraints but have drawbacks like reduced equipment lifespan, higher costs, and do not address thermal capacity limitations. To overcome these challenges, this paper proposes an algorithm that recalculates the photovoltaic system hosting capacity by considering both voltage and thermal constraints through power flow analysis. Additionally, we suggest integrating vertical photovoltaic system installations to mitigate the reduced hosting capacity caused by bifacial modules. © 2025 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Photovoltaics

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Pathology Image Classification for Colorectal Cancer Prediction Using Experience Replay

Pathology Image Classification for Colorectal Cancer Predict...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Kumar, Penumacha Naveen Krishna, Ch. Nanda Lukka, Sai Lasya Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331512965

Pathology image classification helps diagnosis and monitoring as well as treatment of a disease. Thus, it can help the decision-making of a health worker. Still, conventional machine learning models struggle with medical data that often changes, as they typically need retraining on the full dataset to learn new information on a frequent basis. This retraining requires a lot of computations and sometimes loses what it 'knew' previously. This is called catastrophic forgetting. To solve this problem, we propose a continual learning framework, using Experience Replay to gradually add new pathology image data, while retaining knowledge of older tasks. Using Experience Replay, the model replays a sample of past data while training so that it does not forget any new information. The proposed framework is validated on the PathMNIST dataset, where it shows better classification accuracy and robustness than the classical retraining. This indicates that continual learning has the potential to be used for pathology image classification, and as a result, it can be scalable and adaptive to the needs of large-scale medical data. Through such advancements diagnostic accuracy will increase, resource use will become more efficient, and patient outcomes will improve in medicine. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing the Resilience of Prestressed Girder Bridges against Over-Height Vehicle Collisions

Enhancing the Resilience of Prestressed Girder Bridges again...

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Structures Congress 2025

作者： Elshazli, Mohamed T. ElGawady, Mohamed Ibrahim, Ahmed Dept. of Civil and Environmental Engineering Univ. of Missouri United States Civil Architectural and Environmental Engineering Dept. Missouri Univ. of Science and Technology United States Dept. of Civil and Environmental Engineering Univ. of Idaho United States

ISBN: (纸本)9780784486085

Accidental collisions between over-height vehicles and bridge superstructures pose significant risks, often resulting in damaged girders and, in severe cases, structural failure. Beyond lives lost and the costs of replacement, the failure of a single bridge can disrupt the mobility of people and goods for extended periods, highlighting the need for improved resilience in bridge design. This study employs high-fidelity finite element simulations using LS-DYNA to assess the dynamic response of prestressed girder bridges subjected to various impact scenarios. The validated model provides a comprehensive understanding of bridge behavior under high-speed impacts, capturing critical damage patterns such as longitudinal cracking, shear push-out, and strand severing. The increase in impact speed significantly amplifies the severity of damage. At lower speeds, the damage is more distributed and characterized by deflection and cracking, whereas at higher speeds, the damage becomes more localized and intense, with greater structural failures concentrated near the impact zone. © ASCE.

关键词： Cracks

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Bird Repeller Using Opencv 4

Bird Repeller Using Opencv

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Upendra, M. Anuradha, T. Prakash, L. Dept. of Information Technology Siddhartha Engineering College Velagapudi Ramakrishna Vijayawada India

This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person's presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used;YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds. © 2025 The Authors. Published by Elsevier B.V.

关键词： Bird Repellent Cascade Classifier Computer Vision Distress Call image detection Open CV pi camera Raspberry pi YOLO

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Assessing the Potential of Montmorillonite Clay Mineral for Containment of Pharmaceutical Waste Using Molecular Dynamics Simulation

Assessing the Potential of Montmorillonite Clay Mineral for ...

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Geo-EnvironMeet 2025: Geoenvironmental Contamination and Waste Containment

作者： Dadhich, Sharad Bharat, Tadikonda Venkata Dept. of Civil Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (数字)9780784485699

Montmorillonite clay mineral has been vastly used in many construction and pharmaceutical applications because of its high specific surface area (70-120 m2/g) and cation exchange capacity (66-123 meq/100 g). These properties enable the ability of montmorillonite to bind with cations. Recently, post-COVID-19 pandemic, a significant amount of pharmaceutical waste has been disposed in the municipal solid waste (MSW) landfills, which are made of compacted clay liners or geosynthetics clay liners. Recent studies have proven the MSW landfills are susceptible to leachate under high chemo-mechanical loadings. Therefore, it becomes mandatory to study the interaction between the clay and pharmaceutical waste at molecular level. In this study, the potential of montmorillonite clay mineral has been investigated using molecular dynamics simulation. The system consists of montmorillonite surface, paracetamol molecule, and water. The simulations are performed for 20 ns time step under NTP conditions using NAMD software. The stability of the simulation has been assured by using three different statistical parameters i.e., Root Mean Squared Deviation (RMSD), Root Mean Squared Fluctuation (RMSF), and Radius of Gyration (Rg). The simulation study reveals the various inter particle interactions (bonded and non-bonded) between the montmorillonite surface and paracetamol particle. The interaction energy between montmorillonite and paracetamol particle is -22 kcal/mol (-ive means attraction i.e., adsorption energy), which justifies the possible positive interaction between the particles. Various H-bond formations have been observed in the simulation, which confirms the ability of montmorillonite to hold the paracetamol molecule. Apart from these interactions, the study reveals the density profile and diffusion coefficient of the paracetamol particle. The insights gained from these simulations have practical implementation for design of containment facility of bio-medical waste. © 2025 Ame

关键词： Municipal solid waste

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Machine Learning-based Soil Analysis for Crop Forecasting

Machine Learning-based Soil Analysis for Crop Forecasting

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Raji Singh, Shiv Prakash Gupta, Rishabh Saurabh Agarwal, Shikha Ajay Kumar Garg Engineering College Dept. of Information Technology Ghaziabad India

ISBN: (纸本)9798331508685

Agriculture is a crucial part of India's economy, contributing over 17% to the nation's GDP (Gross Domestic Product), engaging over 60% of the workforce but still the effort-to-yield ratio of the sector remains significantly low. Analyzing soil properties, such as temperature, pH, humidity, water retention capacity, etc., helps determine soil quality, which is crucial for optimal agricultural productivity. This assessment later recommended crops that align with soil conditions and external environments, maximizing yield potential, and sustainable land management thereby preventing degradation. Similar to most studies aimed at advancing agriculture, the main objective of this research is to drive development by implementing ML-based models for crop classification. This paper presents a comparative analysis of various machine learning including KNN, SVC, Random Forest, etc on a single dataset. The comparative analysis highlights the advantages of each algorithm over others, helping to select the best-suited model to be implemented for real life applications. Our findings prove that the Random Forest algorithm outperforms the others, achieving the highest accuracy of 99.24%. © 2025 IEEE.

关键词： agriculture crop nutrient prediction soil analysis

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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