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Local and global sensitivity analyses on the overflow probability of Abadia de Goiás radioactive near surface repository

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Nuclear Engineering and Design 2025年 439卷

作者： Teixeira, D.G. Frutuoso e Melo, P.F. Alvim, L.G.M. Alvim, A.C.M. Alves, A.S.M. Graduate Program of Nuclear Engineering COPPE Federal University of Rio de Janeiro RJ Rio de Janeiro Brazil Department of Computer Science Multidisciplinary Institute Federal Rural University of Rio de Janeiro UFRRJ RJ Nova Iguaçu Brazil Eletronuclear S. A. RJ Rio de Janeiro Brazil

This work presents a sensitivity analysis of the overflow probability of the near- surface radioactive repository built after the Abadia de Goiás accident, where radioactive 137Cs was released. The purpose of the analysis is to help the repository owner and the regulatory body make decisions on design parameters that affect the overflow probability. The following parameters have been considered: degradation function of the repository ceiling, internal porosity of the repository, internal area of the repository base, repository base and wall thicknesses, repository height, height of the liquid column inside the repository, average rainfall rate, evapotranspiration rate, surface runoff and hydraulic conductivity of the repository concrete. The choice for the range of each parameter is discussed. An investigation of the role of the mentioned parameters is detailed based on results obtained in a previous paper, and the dependencies among the rate parameters on the average rainfall rate is emphasized and modeled. To give a deeper perspective, local and global sensitivity analyses have been performed. In the case of global analysis, two approaches have been followed: in the first, the concept of Normalized Mutual Information and the other one leverages Shapley values derived from Light Gradient Boosting Machine and Shapley values have been used for investigating non-linearities. For some parameters the lowest values are to be considered, e.g., the repository base thickness and the average rainfall rate, while for others the highest value is desirable, like the evapotranspiration rate and the surface runoff in order to obtain lower overflow probabilities. These results should be looked with care because nonlinearities have been identified by the Shapley value analysis. Overall, the degradation function and the evapotranspiration rate emerge as the most influential parameters. The legacy of this work lies in the robust methodological framework it provides, paving the way fo

关键词： Nuclear reactor accidents

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A Multi-Step Multivariate Fuzzy-based Time Series Forecasting on Internet of Things Data

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IEEE Internet of Things Journal 2025年

作者： Bitencourt, Hugo Vinicius Lucas, Patri-Cia de Oliveira Orang, Omid Silva, Petronio C. L. Guimaraes, Frederico Gadelha Universidade Federal de Minas Gerais -UFMG Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Salinas Brazil Universidade Federal de Minas Gerais -UFMG Computer Science MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Januaria Brazil Universidade Federal de Minas Gerais -UFMG Department of Computer Science MG Belo Horizonte Brazil

Multi-step ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision-making. Thus, this study introduces a novel multiple-input single-output (MISO) forecasting method called Multi-Step Embedding-based Fuzzy Time Series (MS-EFTS), designed to predict high-dimensional non-stationary time series data. As a first-order approach, it employs a direct strategy that integrates an embedding transformation with a weighted multivariate FTS (WMVFTS) model. This combination allows for effective predictions over long-term horizons within low-dimensional, learned continuous representations. The effectiveness of the proposed MS-EFTS is assessed using three high-dimensional IoT time series in this investigation. The obtained results showcase the superior performance of the proposed method compared to some deep learning forecasting methods, including LSTM, BiLSTM, TCN, and CNN-LSTM, in terms of accuracy, parsimony, and efficiency. © 2014 IEEE.

关键词： Smart homes

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Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

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IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

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Simulated prosthetic vision identifies checkerboard as an effective raster pattern for retinal implants

arXiv

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arXiv 2025年

作者： Kasowski, Justin M. Beyeler, Michael Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Psychological & Brain Sciences University of California Santa BarbaraCA93106 United States

Objective: This study systematically evaluates the impact of raster patterns-specific spatial arrangements of sequential electrode activation-on performance and perceived difficulty in simulated prosthetic vision (SPV). By addressing a critical gap in the literature, we aimed to identify patterns that optimize functional vision in retinal implants. Approach: Sighted participants completed letter recognition and motion discrimination tasks under four raster patterns (horizontal, vertical, checkerboard, and random) using an immersive SPV system. The simulations employed psychophysically validated models of electrode activation, phosphene appearance, and temporal dynamics, ensuring realistic representation of prosthetic vision. Performance accuracy and self-reported difficulty were analyzed to assess the effects of raster patterning. Main Results: The checkerboard pattern consistently outperformed other raster patterns, yielding significantly higher accuracy and lower difficulty ratings across both tasks. The horizontal and vertical patterns introduced biases aligned with apparent motion artifacts, while the checkerboard minimized such effects. Random patterns resulted in the lowest performance, underscoring the importance of structured activation. Significance: These findings provide the first systematic evaluation of raster patterns, suggesting that the checkerboard configuration may enhance usability and perceptual clarity in retinal implants. While based on simulated environments, this study establishes a foundation for optimizing spatial and temporal electrode activation strategies in next-generation devices. © 2025, CC BY.

关键词： Prosthetics

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ALPINE: An Interpretable Approach for Decoding Phenotypes from Multi-condition Sequencing Data 29th

ALPINE: An Interpretable Approach for Decoding Phenotypes ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Lee, Wei-Hao Li, Lechuan Dannenfelser, Ruth Yao, Vicky Systems Synthetic and Physical Biology Graduate Program Rice University Houston United States Department of Computer Science Rice University Houston United States Ken Kennedy Institute Rice University Houston United States Rice Synthetic Biology Institute Rice University Houston United States

ISBN: (纸本)9783031902512

Advancements in sequencing technology have expanded data availability, capturing diverse phenotypic traits and biological perturbations. However, increased resolution also raises complexity, as studies now examine multiple dimensions, including donor phenotypes, anatomical regions, cell types, and time points. Integrating datasets across studies promises insights into health and disease beyond the scope of individual experiments, but this requires methods that can separate technical artifacts from meaningful biological signals while providing interpretable insights into condition-related genetic factors. Existing approaches tend to focus on either integration or interpretability, rarely addressing both simultaneously. To overcome these challenges, we introduce ALPINE, a joint supervised-unsupervised non-negative matrix factorization framework that disentangles technical and biological variation while directly identifying condition-associated genes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Matrix factorization

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Visual Control and Mapping for UAV-Based Platform Inspection

Visual Control and Mapping for UAV-Based Platform Inspection

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes Carlos M. Soria Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Facultad de Ingeniería Instituto de Automática Universidad Nacional de San Juan Argentine Department of Electric Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Autonomous take-off and landing capabilities are crucial in UAV vision-based missions, ensuring adaptive navigation, especially in challenging environments where realtime identification and interaction with a variety of landing platforms are required. In this context, this paper presents a servo-visual controller that uses pattern detection and color segmentation techniques to identify take-off/landing platforms and estimate their current orientation. The proposed system was subjected to experimental validation with four platforms positioned in different orientations, heights, and positions, demonstrating its versatility in various conditions. Our study addresses the Flying Robots Trial League challenge, which emulates mapping and inspection tasks in offshore platforms.

关键词： Visualization Offshore installations Navigation Color Inspection Autonomous aerial vehicles Aircraft Drones Aerospace control

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Protecting host-based intrusion detectors through virtual machines

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computer NETWORKS 2007年第5期51卷 1275-1283页

作者： Laureano, M. Maziero, C. Jamhour, E. [a]Graduate Program in Applied Computer Science Pontifical Catholic University of Paran¨￠ Brazil

Intrusion detection systems continuously watch the activity on a network or computer, looking for attack and intrusion evidences. However, host-based intrusion detectors are particularly vulnerable, as they can be disabled or tampered by successful intruders. This work proposes and implements an architecture model aimed to protect host-based intrusion detectors, through the application of the virtual machine concept. Virtual machine environments are becoming an interesting alternative for several computing systems due to their advantages in terms of cost and portability. The architecture proposed here makes use of the execution spaces separation provided by a virtual machine monitor, in order to separate the intrusion detection system from the system under monitoring. As a consequence, the intrusion detector becomes invisible and inaccessible to intruders. The prototype implementation and the tests performed show the viability of this solution. (c) 2006 Elsevier B.V. All rights reserved.

关键词： security intrusion detection virtual machines

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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consexpressionR: an R package for consensus differential gene expression analysis

arXiv

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arXiv 2025年

作者： Costa-Silva, Juliana Menotti, David Lopes, Fabricio M. Department of Informatics Federal University of Paraná Rua Coronel Francisco Heráclito dos Santos 100 Paraná81531-990 Brazil Department of Computer Science Bioinformatics Graduate Program Federal University of Technology - Paraná Av. Alberto Carazzai 1640 - Cornélio Procópio ParanáPostal code: 86300-000 Brazil

Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Computational methods have been developed for each stage of the identification of differentially expressed genes. Nevertheless, there are few studies exploring the association between different types of methods. In this study, we evaluated the impact of the association of methodologies in the results of differential expression analysis. By adopting two data sets with qPCR data (to gold-standard reference), seven methods were implemented and assessed in R packages (EBSeq, edgeR, DESeq2, limma, SAMseq, NOISeq, and Knowseq), which was performed and assessed separately and in association. The results were evaluated considering the adopted qPCR data. Results: Here, we introduce consexpressionR, an R package that automates differential expression analysis using consensus of at least seven methodologies, producing more assertive results with a significant reduction in false positives. Availability: consexpressionR is an R package available via Source code and support are available at GitHub (https://***/costasilvati/consexpressionR). © 2025, CC BY-NC-ND.

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