jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lihui Zhao | Yang An | Yurong Li | Lizhen Fu | Shihan Liu | Lu Wang
Source: IET Image Processing (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MDM‐TLI: Multi‐Defect Detection Model for Transmission Line Insulators ABSTRACT In recent years, significant progress has been made in detecting defects in transmission line insulators using aerial imagery. However, the application of these methods in complex power environments continues to face challenges such as intricate backgrounds, small target sizes, multiple scales and varied morphologies. To address these issues, this study introduces a multi‐defect detection model for transmission line insulators (MDM‐TLI). Firstly, MDM‐TLI incorporates a hybrid attention model based on a channel separation strategy, which enhances the model's focus on defect regions, effectively mitigating the difficulty of extracting small target features amidst complex backgrounds. Secondly, the model employs a scale‐balanced loss function, that introduces a scale balance factor designed to eliminate discrepancies in the position and shape calculations of bounding boxes across different scales. Lastly, an adaptive feature fusion module is utilised, allowing the model to dynamically adjust the fusion ratios of various features during the integration of multi‐level features. This reinforces the model's capability to represent multi‐morphological features comprehensively. Experimental results demonstrate that MDM‐TLI significantly enhances the performance of insulator defect detection. Specifically, the detection accuracies for insulator shedding, damage, and flashover defects reached 97.6%, 84.5% and 80.7%, respectively. These results outperform those of existing models, highlighting the effectiveness of MDM‐TLI and providing robust technical support for the intelligent maintenance of transmission lines. The project code is released at https://github.com/albertsolus/powercv . Transmission line ; Line (geometry) ; Computer science ; Transmission (telecommunications) ; Optoelectronics ; Algorithm ; Materials science ; Telecommunications ; Mathematics High voltage insulation and dielectric phenomena ; Power Line Inspection Robots ; Thermal Analysis in Power Transmission
Authors: Christophe Reboud | Roberto Miorelli | Nicolas Leymarie | TRANSVALOR | Frédéric Costes | Chengdan Xue | Édouard Demaldent
Source: e-Journal of Nondestructive Testing (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Simulating ultrasonic inspections of welds with complex microstructure: a contribution to the IWELD European project Within the IWELD European project, a global model-based approach is set up and aims at enhancing welds inspections by means of ultrasonic imaging techniques. This paper presents in particular a strategy based on Artificial Intelligence (AI) to build a model describing texture orientation maps in welds. This model relies on a simple phenomenological model called MINA. The objective is to generate realistic descriptions of ultrasonic properties that will be considered in the CIVA software for the inspection simulation and the optimization of ultrasonic imaging algorithms. A perspective of this work is to handle also data coming from several sources, like experimental characterizations, ab initio modelling of the welding process using the Transweld software. Ultrasonic sensor ; Welding ; Process (computing) ; Set (abstract data type) ; Software ; Orientation (vector space) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Ultrasonics and Acoustic Wave Propagation
Authors: Muhammad Ikhsan | Majid Rahardi
Source: Journal of Applied Informatics and Computing (article, 2025-10-08)
Field: Chemistry — Organic Chemistry

Image-Based Classification of Indonesian Traditional Houses Using a Hybrid CNN-SVM Algorithm The diversity of Indonesian traditional houses represents a cultural heritage that must be preserved. However, the lack of interest among younger generations and the difficulty in recognizing the distinctive architectural characteristics of traditional houses present challenges to preservation efforts. This study aims to develop an image classification model for Indonesian traditional houses using a hybrid CNN-SVM approach to improve recognition accuracy. The dataset consists of 3,919 images from five classes of traditional houses, namely gadang, joglo, panjang, tongkonan, and honai, with an 80% training split, 10% validation, and 10% testing. The data were processed through resizing, augmentation, and normalization before being trained using a CNN architecture with five convolutional layers as a feature extractor and an SVM serving as a multi-class classifier. The experimental results show that the hybrid CNN-SVM model achieved an accuracy of 96.68%, with consistently high precision, recall, and F1-score across all classes. These findings demonstrate that integrating CNN as a feature extractor and SVM as the final classifier can enhance the model’s generalization capability in distinguishing images of Indonesian traditional houses. Extractor ; Indonesian ; Classifier (UML) ; Support vector machine ; Pattern recognition (psychology) ; Feature (linguistics) ; Feature extraction ; Normalization (sociology) Wood and Agarwood Research ; Cultural and Artistic Studies ; Cultural Heritage Management and Preservation
Authors: Senior Board Test Engineer, NVIDIA, CA, USA. | Karan Lulla | Reena Chandra | Tools and Automation Engineer, Amazon, CA, USA. | Karthik Sirigiri | Software Developer, Redmane Technology, IL, USA
Source: The American Journal of Applied Sciences (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Proxy-Based Thermal and Acoustic Evaluation of Cloud GPUs for AI Training Workloads The use of cloud-based Graphics Processing Units (GPUs) to train and deploy Deep Learning models has grown rapidly in importance, with the demand to learn more about their thermal and acoustic behavior under real-world workloads. A normal cloud cannot make direct telemetry like temperature, fan speed, or acoustic emissions. To overcome such shortcomings, this study quantifies GPU workloads' thermal and acoustic output with a proxy-based model derived from available metrics such as GPU utilization, memory provisioning, power consumption, and empirical Thermal Design Power (TDP) values. They compare the two typical AI tasks, BERT on natural language processing and YOLOv5 on real-time object detection, on Colab-based NVIDIA GPUs (T4, V100, P100). The nvidia-smi was used to gather runtime logs, and the specifications of the GPUs have been obtained in the form of public Kaggle datasets. Proxy statistics, including TDP-per-MHz and thermal load (Power * Duration), were calculated to model heat loss due to workload. To measure the degree of acoustic impact, a threshold of TDP was applied to approximate the level of fan-driven acoustics. The visual analytics, such as boxplot, scatterplot, and bubble plot, demonstrated certain considerable distinctions in the stress patterns of GPUs: the BERT jobs demanded extremely high cumulative thermal load and medium acoustic effect, whereas the YOLOv5 demonstrated bursty power footprint and substantial acoustic imprint on high-TDP GPUs. The findings reveal that proxy estimation is reproducible, interpretable, and a lightweight substitute for determining the GPU thermal and acoustic behavior of a machine used in the cloud setting. Such a solution facilitates making thermal-aware schedules, optimizing the infrastructure, and deploying AI models with reduced energy consumption in multi-tenant GPU environments. Proxy (statistics) ; Cloud computing ; Computer science ; Training (meteorology) ; Thermal ; Operating system ; Meteorology ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Neural Network Applications ; Neural Networks and Applications
Authors: Juan Shu | Yuxin Guo | Julio A. Chirinos | Zirui Fan | Yilin Yang | Yujue Li | Xiaochen Yang | Cameron Beeche | Bao‐Lian Su | Iris Horng | Rong Zheng | Peristera Paschou | Li‐San Wang | Joan M. O’Brien | Ruben C. Gur | Jinjie Lin | Walter R. Witschey | Daniel J. Rader | Anthony Rosenzweig | Bingxin Zhao
Source: medRxiv (preprint, 2025-05-27)
Field: Chemistry — Spectroscopy

Contextualizing molecular and structural aging across human organs Organ-specific aging clocks have shown promise as predictors of disease risk and aging trajectories; however, the underlying biological mechanisms they reflect remain largely unexplored. Here, we use large-scale proteomic and imaging data to investigate the relationships among organ-specific and modality-specific aging clocks and to uncover the biological processes they represent. By estimating paired protein-based and imaging-based aging clocks across 8 major organs, we demonstrate that these omics and structural profiles exhibit distinct phenotypic and genetic signatures, each potentially quantifying different stages and playing complementary roles within a unified biological aging process. Furthermore, context-specific aging clocks from multiple organs often converge and jointly capture established biological and disease pathways. For example, 65.7% of the KEGG Alzheimer's disease pathway is enriched by at least one of 11 protein- and imaging-based aging clocks, with each clock representing different components of the pathway. These results underscore the importance of a pan-organ multi-modal perspective for quantifying the mechanisms underlying age-related diseases. Additionally, we identify modality-specific links between aging clocks and complex diseases and lifestyle factors. In summary, we uncover intricate relationships among molecular and structural aging clocks across human organs, providing novel insights into their context-specific roles in capturing consequences of aging biology and their implications for disease risk. Context (archaeology) ; Biology ; Computational biology ; Disease ; Perspective (graphical) ; Evolutionary biology ; Systems biology ; Neuroscience ; Computer science ; Medicine ; Pathology ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Genetics, Aging, and Longevity in Model Organisms
Authors: Balaji Sriram | Nandini Gupta
Source:  (preprint, 2025-10-28)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for the Estimation of the Total Accumulated Charge in Nanodielectrics from PEA Measurements (Pre-print to be submitted to IEEE-Transactions on Dileectrics and Electrical Insulation) The Total Accumulated Charge (TAC) inside a nanodielectric is closely associated with the insulation quality of the material. The Q(t) method and the Pulsed-Electro-Acoustic (PEA) method are widely used to estimate the TAC in these materials. However, the accuracy of the PEA method is significantly affected by the presence of electrode charges, the low spatial resolution of the PEA equipment, and the attenuation/dispersion of acoustic signals, which is worsened due to the presence of nanofillers. In this work, Physics-Informed Linear Transformation (PI-LT) is adopted to remove the effect of image charges from PEA measurements and estimate the TAC inside LDPE-silica nanocomposite samples. PI-LT model is trained with PEA measurement data as input, and the TAC (estimated from the Q(t) method) as output. The PI-LT model is simultaneously trained with physics information required for the conversion of experimental space charge distributions to the TAC. Finally, the PI-LT model is put to test against space charge measurements gathered from untrained experimental conditions. The accuracy of the proposed PI-LT model is observed to be better than the conventional methods and a proposed physics-based approximation. Charge (physics) ; Quality (philosophy) ; Pattern recognition (psychology) ; Charge-coupled device ; Support vector machine High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Liliana Liliana | Zulfatri Aini | Sepannur Bandri | Naufal Zaki Akbar
Source: Engineering and Technology Journal (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Evaluation and Improvement of Insulation Resistance in Induction Motor Stators Using Corrective Maintenance Methods The reliability of induction motors is strongly influenced by the condition of stator insulation, which commonly deteriorates due to thermal stress, humidity, contamination, and aging. Severe degradation of insulation can cause unexpected breakdowns, power losses, and reduced equipment lifetime. This study aims to evaluate and restore the insulation resistance of a three-phase induction motor through systematic measurement and corrective maintenance.The research was conducted in several stages, beginning with the measurement of insulation resistance using a megohmmeter. The initial results showed values of 0 MΩ for both phase-to-phase and phase-to-ground across all sessions, indicating that the insulation system had completely failed. These values were then corrected to a reference temperature of 40 °C in accordance with IEEE Std 43-2013 to allow accurate comparison. The causes of degradation were identified as contamination, moisture ingress, high operating temperature, vibration, and natural aging of insulation materials. Corrective maintenance actions were applied, including cleaning, drying, varnish impregnation, reinforcement, and complete rewinding. After maintenance, the corrected insulation resistance values improved significantly, reaching up to 2000 MΩ in all sessions, far exceeding the IEEE threshold of 100 MΩ. This improvement demonstrates that the applied methods successfully restored the dielectric integrity of the stator insulation. In conclusion, systematic testing combined with corrective maintenance ensures the recovery of insulation performance, supports reliable motor operation, and extends service life. Induction motor ; Engineering ; Reliability engineering ; Materials science ; Structural engineering ; Automotive engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Machine Fault Diagnosis Techniques
Authors: Airlie J. McCoy | Randy J. Read
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

Xtricorder: A likelihood-enhanced self-rotation function and application to a machine-learning enhanced Matthews prediction of asymmetric unit copy number Abstract Analysis of crystallographic diffraction data before phasing gives the crystallographer a ‘first look’ at the nature of the problem and the context in which the structure determination will be performed. We here report the development of Xtricorder , an application that targets analysis of crystallographic data specifically for likelihood-based phasing. As well as porting many of the analyses previously available but relatively inaccessible in our Phaser codebase, Xtricorder offers a likelihood-enhanced self-rotation function. A novel and intuitive graphical representation of the self-rotation function presents the results for user inspection, and has the added advantage that, in an adapted form, is appropriate for training a convolutional neural network to enhance the standard Matthews analysis and more accurately predict the number of copies in the asymmetric unit. We investigate the usefulness of the likelihood-enhanced self-rotation function in ‘first look’ analyses, exploring the circumstances under which the self-rotation function results are useful, and discuss the application to AI-generated structure prediction. Synopsis Xtricorder is a new tool for analysing crystallographic data prior to phasing, featuring a likelihood-enhanced self-rotation function and graphical output that aids both user interpretation and machine learning-based prediction of asymmetric unit content. Unit (ring theory) ; Rotation (mathematics) ; Artificial intelligence ; Function (biology) ; Computer science ; Machine learning ; Mathematics ; Mathematics education ; Biology Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Protein Structure and Dynamics
Authors: Malick Fall | Mody Abdoulaye Camara | Omar Niang | Fatimata Mbaye | Ahmadou Dem | Sidy Ka | Mamadou Diallo | Julie HARDOUINE | Silly Touré | Ndongo Dia | Mbacké Sembène | Pascal Cosette | Emmanuel Cornillot
Source: International Journal of Health Sciences (article, 2025-12-16)
Field: Chemistry — Spectroscopy

Proteomic mapping associated with clinical and sociodemographic parameters of breast cancer in Senegal Breast cancer refers to a malignant tumour resulting from the uncontrolled proliferation of epithelial cells in the mammary gland. It is the leading cause of cancer in women. In Senegal, regional disparities remain marked by differences in access to screening, diagnosis, and treatment. Proteomics provides a direct reflection of the functional state of tissues and biological pathways and captures the functional effects of molecular alterations. In order to better understand the relationship between the pathogenesis of breast cancer and the existence of potential biomarkers based on each underlying clinical and sociodemographic parameter, this study performs correlation analyses. Proteins were extracted from healthy and cancerous tissues. The analytical workflow showed 30 proteins that were statistically deregulated between those under and over 50 years of age, 5 proteins between married and unmarried patients, 37 proteins between women with fewer than 7 children and those with more than 7 children, six proteins between the early stage and the locally advanced stage, and treatment response showed that 17 proteins were statistically deregulated. The results of this study have identified numerous proteins with high prognostic value associated with robust statistics and significantly overexpressed according to the parameters. Breast cancer ; Proteomics ; Cancer ; Pathogenesis ; Biomarker ; Stage (stratigraphy) Advanced Proteomics Techniques and Applications ; Biological Research and Disease Studies ; Cancer and biochemical research
Authors: Masoud Mohammadi-Arzanagh | Andrey Grankin | Victor Galitski | Mohammad Hafezi
Source: ArXiv.org (preprint, 2025-02-04)
Field: Materials Science — Materials Chemistry

Enhanced Cooper pairing in nano-patterned metals Nano-patterning has been shown to be a powerful tool for manipulating the vibrational modes of elastic structures, with applications such as optical-mechanical mode coupling. Inspired by these recent developments in phononic band engineering, we propose a nano-patterning scheme to enhance the superconducting transition temperature $T_c$ in phonon-mediated nano-film superconductors, such as aluminum. Using the finite element method, we simulate the lattice vibrational modes of nano-patterned films within the Debye model. Our results show that periodic nano-patterning softens the lattice vibrational modes compared to bulk films. It also increases the density of states at high energies, resulting in a couple of percent enhancement in $T_c$. Moreover, we investigate connections to Weyl's law and provide an experimental design prescription to optimize nano-patterning for further enhancement of the superconducting transition temperature. Pairing ; Nano- ; Cooper pair ; Nanotechnology ; Materials science ; Physics ; Condensed matter physics ; Superconductivity ; Composite material Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Copper Interconnects and Reliability
Authors: Benjamin D. Harding | Barry E. DeZonia | Rajat Garg | Ziling Hu | Frank Delaglio | Timothy Grant | Chad M. Rienstra
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Top-Down Scoring of Spectral Fitness by Image Analysis for Protein Structure Validation Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for protein structure determination, but traditional approaches require extensive manual assignment of hundreds to thousands of resonances. Here we present NMRFAM-BPHON, a novel "top-down" approach that treats experimental NMR spectra as continuous grayscale images and quantitatively scores the agreement with simulated spectra generated from candidate protein structures. This method does not require complete resonance assignments, though it can incorporate experimental chemical shifts when available to improve performance. The simulated spectra are generated from postulated resonance assignments, which can be derived either from empirical database predictions, direct interpretation, or a hybrid combination. BPHON employs a physics-based approximate polarization transfer model to predict cross-peak intensities from the internuclear distances in the decoy structure, and models the peak lineshapes using empirical, bulk T <sub>2</sub> relaxation rates and literature values for scalar couplings. The resulting simulated spectra are scored relative to the experimental data by normalized cross correlation, yielding a fitness score between 0 and 1. We demonstrate BPHON's ability to discriminate structural models, particularly in the case of <sup>13</sup> C-detected magic angle spinning solid-state NMR spectra. The software is packaged with a user-friendly graphical user interface for ChimeraX, enabling advanced NMR analysis accessible without requiring extensive manual analysis. Computer science ; Image (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) Enzyme Structure and Function ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: James L. Wei | Guy P. Nason
Source: Electronic Journal of Statistics (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Fully NonParametric MIDAS: A new approach for nonparametric mixed frequency time series regression We introduce Fully NonParametric MIDAS (FNP-MIDAS), a new approach for estimating mixed frequency regressions that extends the widely-used MIxed DAta Sampling (MIDAS) group of models to incorporate nonlinear component functions in parsimonious fashion. We present an implementation that handles multiple regressors, allowing MIDAS models to capture nonlinearity in both the weighting of lags and the dependencies between the input and output time series. In the FNP-MIDAS model, all high-frequency lags of a given time series regressor are transformed by the same component function, but rescaled by different magnitudes using MIDAS coefficients. To estimate the parameters of the model, we propose a simple backfitting algorithm that alternates between coefficient estimation and component function estimation at each iteration. For the latter problem, we apply the recently-developed trend filtering method, which allows for local adaptivity without a substantial increase in computational complexity compared to standard spline regression methods. We demonstrate the outperformance of FNP-MIDAS compared to linear MIDAS models by performing experiments on simulated and real-world data, the latter involving the forecasting of urban atmospheric pollution levels using meteorological conditions. Nonparametric statistics ; Mathematics ; Nonparametric regression ; Series (stratigraphy) ; Statistics ; Semiparametric regression ; Regression ; Time series ; Econometrics Spectroscopy and Chemometric Analyses ; Time Series Analysis and Forecasting ; Fault Detection and Control Systems