jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Long Chen | Qiong Li | Guohua Long | Yang Zou | Jian Deng
Source: Scientific Reports (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Cable partial discharge identification network based on adaptive residual diffusion denoising and morphological attention In power systems, the accurate identification of partial discharge (PD) in cables is crucial for the early detection of insulation defects and for maintaining power grid reliability. However, the low amplitude of PD signals and their susceptibility to noise interference increase the complexity of the identification process. To address this challenge, this paper proposes a novel deep learning architecture: the Adaptive Residual Diffusion Denoising Morphological Attention Network (ARDDMA-Net). ARDDMA-Net employs a two-stage design. In the first stage, an adaptive residual diffusion denoising module dynamically suppresses noise, while a morphological attention mechanism preserves critical features of PD signals, such as pulse width and edge structures. This effectively mitigates the masking and misclassification of weak PD pulses caused by noise. In the second stage, a multi-scale attention-enhanced ResNet-1D convolutional neural network extracts multi-scale features and performs precise classification, thereby addressing the limitations of traditional convolutional neural networks (CNNs) in handling low signal-to-noise ratio (SNR) conditions and multi-scale characteristics. Experimental results demonstrate that ARDDMA-Net maintains stable recognition performance under various noise levels and data loss conditions, outperforming traditional machine learning methods and existing deep learning models in terms of identification accuracy. Residual ; Noise reduction ; Noise (video) ; Convolutional neural network ; Partial discharge ; Identification (biology) ; Pattern recognition (psychology) ; Masking (illustration) High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Dielectric materials and actuators
Authors: M. C. Abhinav | K. P. Shabeer | Athulya C. K
Source: International Journal of Research Publication and Reviews (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

A Comparative Study for Price Forecasting Models of Paddy (Oryza sativa): A Conventional and Hybrid Approach Agriculture serves as a cornerstone for economic growth in developing nations, playing significant position in generating employment, ensures food security, support GDP. Agricultural price forecasting has remained a crucial area of study, particularly with the increasing use of historical price trends, weather conditions, and data quality indicators.This study employed a combination of statistical and machine learning techniques for forecasting, specifically the ARIMAX, neural network autoregression and a hybrid ARIMAX-NNAR model.The structure of the NNAR model obtained for the study was (4,1,4)12.The NNAR model showed lowest value for MAPE (1.09%) followed by hybrid and ARIMA(x).The highest forecasting efficiency was found for NNAR model based on MAPE, MAE and RMSE criteria.Based on MAE criteria, the hybrid ARIMA(x)-NNAR model showed a loss of efficiency of 246.35% compared to the single NNAR model. Oryza sativa ; Econometrics ; Economics ; Computer science ; Biology Spectroscopy and Chemometric Analyses
Authors: Ezinne U. Ekwujuru | Moses G. Peleyeju | Cornelius Cano Ssemakalu | Mzimkhulu Ephraim Monapathi | Michael J. Klink
Source: International Journal of Molecular Sciences (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Characterization and Antimicrobial Assessment of Cadmium Sulfide Nanoparticles Resistance to conventional antibiotics remains a global health challenge. The search for more effective antimicrobial agents has led to the consideration of nanoparticles due to their potential biocidal activities. This study synthesized, characterized, and evaluated the antimicrobial behavior of cadmium sulfide nanoparticles (CdS NPs) during incubations at 37 °C and at room temperature (rt; 23 to 27 °C). XRD results showed that the synthesized nanoparticles had a cubic zinc blende structure, while microscopic investigations confirmed the particle size to be 7.236 nm on average. UV-Vis spectroscopy showed that the nanoparticles are active in the visible light region. Raman spectroscopy results showed peaks at 302.3 cm<sup>-1</sup> and 601 cm<sup>-1</sup>, which represent the first- and second-order longitudinal optical phonon. Agar well diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) assays were conducted to investigate the antimicrobial activity of CdS NPs (50 mg/mL, 25 mg/mL, and 10 mg/mL) against <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>. CdS NPs were effective against both test organisms. However, they were more effective against Gram-negative <i>E. coli</i>. The higher the concentration of CdS NPs, the more effective they were against the test organisms. Furthermore, MBC results showed greater bactericidal activity of CdS NPs at 37 °C. With increasing incidences of antimicrobial resistance against conventional antimicrobial agents, especially in wastewater treatment, nanoparticles are considered promising alternatives and the next generation of antimicrobial agents. Antimicrobial ; Nanoparticle ; Cadmium sulfide ; Cadmium ; Minimum inhibitory concentration ; Zinc ; Particle size ; Raman spectroscopy Quantum Dots Synthesis And Properties ; Nanoparticles: synthesis and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Kieran D. Richards | Ella Comish | Rachel C. Evans
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computer vision for high-throughput analysis of pickering emulsions The quanitative analysis of solid-particle stabilized emulsions, known as Pickering emulsions, is crucial for their application in food, cosmetics, and pharmaceuticals. However, size analysis of these emulsion droplets, with diameters ranging from 5 to 500 μm, is challenging due to their non-uniform spatial and polydisperse size-distribution. Here, we investigate the application of the circle-Hough transform (CHT), a well-established computer-vision technique characterised by its ability to detect circular features in noisy images, for the seldom explored quantitative assessment of droplet size from optical microscopy images. This is particularly relevant to images where emulsions are captured in a single 2D focal plane. To implement the CHT with optical images, we have developed an open-source software application ("Hough-Scan"), which incorporates a user-friendly graphical interface for ease of use, and a tiling algorithm allowing localised regions of circles to be processed in parallel and improving computational efficiency. Using Hough-Scan, we demonstrate that the CHT has superior precision, recall and accuracy for the identification of Pickering emulsion droplets and determination of their size, compared to both manual identification and established computer vision methods. Our study demonstrates that CHT implementation using Hough-Scan can significantly increase the ease of image analysis for a diverse range of Pickering emulsion systems of varying spatial and size distribution, as well as visual artefacts common to example microscopy images. Pickering emulsion ; Hough transform ; Throughput ; Microscopy ; Artificial intelligence ; Computer vision ; Computer science ; Materials science ; Nanotechnology ; Optics ; Image (mathematics) ; Physics ; Nanoparticle Pickering emulsions and particle stabilization ; Proteins in Food Systems ; African Botany and Ecology Studies
Authors: Hou‐Yong Yu | Tenglong Cong | Jie Zhang
Source: Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Calculation Simulation of Equivalent Thermomechanical Properties of Dispersion Nuclear Fuel The equivalent performance parameters of dispersion fuels are critical indicators for reactor safety analysis and fuel element evaluation. This study develops a numerical method to simulate the thermomechanical coupling behavior of metal matrix dispersion fuel rods at the mesoscopic scale and to calculate their macroscopic equivalent properties. Based on a fission gas migration model and considering irradiation effects, a thermomechanical-fission gas migration coupling method is established for metal matrix dispersion fuels. The effects of particle volume fraction, particle size, temperature, and burnup on the equivalent performance parameters are systematically analyzed and fitting formulas for the equivalent properties are provided. The results show the following: (1) The equivalent elastic modulus and shear modulus increase with particle volume fraction but decrease with temperature, and they exhibit a decreasing-then-increasing trend with burnup. (2) The equivalent thermal expansion coefficient increases with both particle volume fraction and temperature, while particle size has little effect. This study provides a theoretical basis for the optimization of dispersion fuel design and contributes to enhancing reactor core safety. Volume fraction ; Burnup ; Dispersion (optics) ; Nuclear fuel ; Particle (ecology) ; Thermal expansion ; Elastic modulus ; Volume (thermodynamics) ; Rod Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Runzhou Xu | Yinshui Liu | Xianchun Jiang | Yipan Deng
Source: Corrosion Reviews (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Effect of seawater intrusion in water-glycol hydraulic fluid on the electrochemical corrosion behavior of carbon steel and stainless steel Abstract Water-glycol hydraulic fluid (HFC) has been applied in deep-sea hydraulic systems owing to its flame retardant and environmental performance. However, the corrosion characteristics of metals in HFC have not been widely investigated. The electrochemical corrosion behavior of 45# steel and 17-4PH stainless steel in HFC containing four concentrations of seawater (0 %, 3 %, 11 %, 19 %) were evaluated by potentiodynamic polarizations (−800 mV∼1,500 mV vs. Hg/HgO), electrochemical impedance spectroscopy (EIS), potentiostatic polarizations (0.5 V Hg/HgO ) and immersion test (240 h). The research results indicate that a corrosion-resistant carbon film is formed on the surface of 17-4PH stainless steel and 45# steel in HFC. The infrared spectroscopy results suggest that the formation of the carbon film is due to the adsorption of the benzene ring of tolyltriazole (TTA) in HFC by the metal C. 45# steel exhibited stronger corrosion resistance than 17-4PH stainless steel due to the formation of a denser carbon film through high carbon content adsorption. The infiltration of seawater into HFC enhanced its corrosiveness by enhancing its conductivity and Cl − pitting on the C film. This research is significant as it sheds light on the corrosion behavior of metals in HFC, a crucial aspect in the design and maintenance of deep-sea hydraulic systems. Materials science ; Seawater ; Carbon steel ; Electrochemistry ; Corrosion ; Metallurgy ; Carbon fibers ; Composite material ; Electrode ; Composite number Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Dhanush Ghate D | Pramukh Subrahmanya Hegde | H Saishma | K N Pallavi | Sudeep D. Ghate
Source: Research Square (preprint, 2025-01-20)
Field: Chemistry — Analytical Chemistry

Enhancing Arecanut Quality Grading: A Comparison of Custom CNNs and Transfer Learning Models Grading (engineering) ; Transfer of learning ; Transfer (computing) ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Physics ; Operating system ; Civil engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Blind Source Separation Techniques
Authors: Irvin Séchepée | Hiroaki Matsumoto | Hugo Rousseaux | Vincent Velay | Vanessa Vidal
Source: Mechanics of Materials (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

CRSS evolution with changing grain size for a dual-phase Ti-6Al-2Sn-4Zr-2Mo-Si alloy having a martensite structure Materials science ; Alloy ; Martensite ; Grain size ; Metallurgy ; Dual (grammatical number) ; Titanium alloy ; Phase (matter) ; Microstructure Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Aluminum Alloy Microstructure Properties
Source:  (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

HyperProphet: An AI/ML Framework in Hypervelocity Impact Studies for Space Applications Extensive research into hypervelocity impact testing (HVIT) is necessary to develop protective measures for spacecraft and satellites due to the increasing threat posed by orbital debris in low Earth orbit (LEO).To replicate such impacts, two-stage light gas guns (2SLGGs) are frequently used; however, each trial entails significant time and costs.To offer a more affordable substitute for conventional trial-based testing, a machine learning (ML) based application, HyperProphet is put forth in this study to forecast the projectile velocity of 2SLGGs using experimental parameters.A dataset of 96 samples from past trials was used to assess regression-based, ensemble, and kernel-based regression models, which were further optimized.ElasticNet and Support Vector Regression (SVR) showed the highest post-optimization accuracy (~85%), showcasing their resilience to nonlinear trends and multicollinearity in small datasets.The findings advocate the potential of ML as an adjunct to physical HVIT systems, especially for initial testing and parameter estimation. Hypervelocity ; Space (punctuation) ; Space debris ; Aerospace High-Velocity Impact and Material Behavior ; Gamma-ray bursts and supernovae ; Rock Mechanics and Modeling
Authors: Shekhar Kondibhau Rahane | Krupal Prabhakar Pawar
Source: Metallurgical and Materials Engineering (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Parametric Comparative Analysis of Advance Concretes for Green Construction Practices On another hand, in the construction field, enhancement of concrete technology has played a vital role in meeting the needs of better performance, sustainability, and durability of the infrastructure. In this article we will provide a detailed comparison of five of the most advanced concrete materials used today: Ultra High Performance Concrete (UHPC), Self-Healing Concrete, Geopolymer Concrete, High Performance Concrete (HPC) and Fiber Reinforced Concrete (FRC). These materials have unique characteristics that make them suitable for specific forms of engineering applications. Self-Healing Concrete is an experimental technique that involves the remarkable concept of healing structural integrity over a long time, while Ultra High Performance Concrete is well known for exceptional strength and accuracy. Being a low carbon footprint, eco-friendlier alternative Geopolymer Concrete uses about 50% to 90% less energy than the traditional cementitious binding matrix. Because of its high strength and durability, HPC is suitable for demanding applications, whereas FRC enhances concrete’s resistance to cracking and dynamic loads. This review, therefore, delves into the specific attributes, advantages, obstacles, and wide-ranging use cases associated with these advanced materials with the objective of informing the future direction of research and development in the concrete technology industry. Materials science ; Parametric statistics ; Construction engineering ; Engineering ; Statistics ; Mathematics Structural mechanics and materials ; BIM and Construction Integration
Authors: Kok Swee Sim | Iksan Bukhori | Dominic Chee Yong Ong | Kok Beng Gan
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Surfaces, Coatings and Films

Signal-to-Noise Ratio in Scanning Electron Microscopy: A Comprehensive Review Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field. Interpretability ; Scanning electron microscope ; Image quality ; Image resolution ; Resolution (logic) ; Noise (video) ; Signal-to-noise ratio (imaging) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Yi Luo | Jin Liu | Yanyi Wang | Zijie Mei | Xuandong Liu
Source: Frontiers in Electronics (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Research on insulator contamination component identification based on neural network Glass suspension insulators in power transmission lines are vulnerable to surface contamination over time, especially in harsh environments like metallurgical plants. Analysis of such contamination revealed significant metal deposits, primarily iron particles sized between 2 μm and 20 μm. To study the impact of this metallic contamination on flashover behavior, researchers created artificial pollution using NaCl, diatomaceous earth, and iron powder. Leakage current tests demonstrated that metal content fundamentally alters the current waveform, causing it to exhibit AC superimposed impulses. Key findings include: metal lowers the voltage threshold for impulse inception, shortens the impulse rise and fall times, and increases critical impulse parameters (frequency, maximum amplitude, and discharge magnitude) as the metal proportion rises. Furthermore, a ResNet18-SA deep learning model was developed, integrating a self-attention mechanism. This architecture demonstrates exceptional robustness in interpreting pulsed current signals while accurately classifying levels of metallic contamination, providing a reliable and automated solution for insulator condition assessment. Contamination ; Impulse (physics) ; Insulator (electricity) ; Robustness (evolution) ; Electric power transmission ; Voltage ; Arc flash ; Artificial neural network High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Yingying Li | Qiuxiang Yi | Yaoliang Chen
Source: Forests (article, 2025-05-11)
Field: Chemistry — Analytical Chemistry

The Hybrid Retrieval of Leaf Anthocyanin Content Using Four Machine Learning Methods Leaf anthocyanins are essential for plants to resist biotic and abiotic stresses. The timely and accurate estimation of leaf anthocyanin content (Lanth) plays a vital role in supporting agriculture and forestry management. To date, numerous satisfactory results have been obtained using hybrid methods for vegetation trait estimation. However, the feasibility of the hybrid retrieval of Lanth is underexplored. In this study, four typical machine learning algorithms—an artificial neural network (ANN), a support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF)—were investigated to estimate Lanth with a hybrid scheme. The results showed that satisfactory accuracy (R2 &gt; 0.57 and RMSE &lt; 2.97 μg/cm2) was obtained with all four machine learning algorithms. Among all constructed models, GPR showed superior performance. The best GPR model utilized the first three principal components derived from the logarithmic transformation of reflectance (log(1/reflectance)) as independent variables, achieving an R2 value of 0.76 and an RMSE of 2.24 μg/cm2. However, compared to empirical models directly built from the in situ dataset, the hybrid scheme had reduced accuracy owing to the uncertainty between the simulated and in situ datasets. Nevertheless, the present study further verifies the potential of hybrid retrieval for Lanth and supports its future application in Lanth mapping. Anthocyanin ; Computer science ; Content (measure theory) ; Artificial intelligence ; Botany ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: D. Bissenbay | Abilio Ten | V. К. Yu | Zh.M. Zhaxibayeva | Valery M. Dembitsky
Source: Chemical Journal of Kazakhstan (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

In silico EVALUATION OF THE PHARMACOKINETICS AND TOXICITY OF NOVEL PIPERIDINE DERIVATIVES Introduction. Modern computer modeling methods make it possible to predict the biological activity and pharmacokinetic properties of compounds at early stages of drug development, thereby accelerating the identification of promising candidates. Piperidine derivatives are of particular interest due to their high potential pharmacological value. The aim of this study is to evaluate the biological activity, pharmacokinetic parameters, and toxicity of novel piperidine derivatives using in silico methods. Results and discussion. Four new compounds were synthesized via aminomethylation of 1-(2- ethoxyethyl)-4-ethynylpiperidin-4-ol esters, isolated by column chromatography on Al2O3, and characterized using physicochemical analysis methods. Biological activity was assessed using the PASS program, pharmacokinetics via SwissADME, and toxicity via ProTox-III. The compounds demonstrated a high probability of antibacterial, anti-inflammatory, and analgesic activity, as well as potential effectiveness in the treatment of osteoporosis, diabetic neuropathy, and neurological disorders. Pharmacokinetic analysis indicated good absorption and the ability to cross the blood–brain barrier. According to ProTox-III, the compounds showed predominantly low toxicity (lowest for compound 5, LD50 = 2935 mg/kg). Conclusion. Compounds 2, 3, 6, and 7 are considered especially promising for further research due to their high bioavailability and low toxicity. The results support the feasibility of continued in vitro and in vivo screening of the synthesized compounds. Piperidine ; In silico ; Pharmacokinetics ; Toxicity ; Pharmacology ; Chemistry ; Medicine ; Organic chemistry ; Biochemistry Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods