jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamed Islam Bentaleb | Tao Zhang | Yanzhang Geng | El Moundher Aouiche | Wajid Khan | Muhammad Zain Yousaf | Basem Abu Zneid | Олена Рубаненко
Source: Scientific Reports (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Band level similarity reduction and deep dual signal fusion for reliable diagnosis of power insulator defects Effective monitoring of electrical insulators is critical for maintaining power system reliability, because insulator defects can trigger partial discharges and service interruptions. However, state-of-the-art diagnostic methods are computationally inefficient, require extensive hyperparameter tuning and perform poorly on complex scenarios-particularly perforated insulators without contamination (Signal d). This study addresses these limitations by proposing a novel, hyperparameter-free framework that applies a Similarity Reduction (SR) technique to remove redundant frequency bands through bandpass filtering and feature extraction, then fuses classifier outputs from acoustic (0-20 kHz) and ultrasonic (21-250 kHz) signal components using MFCC features (which outperform FFT, wavelet and time-domain alternatives). By reducing inter-signal redundancy via SR, the framework enables FFT features to be effectively used and allows a transition from a complex CNN-RNN architecture to a lightweight CNN while maintaining high performance. The resulting SRMF + CNN model achieved perfect classification (100% true positive and true negative rates) and dramatically shorter training times, with about a 20 times speedup on perforated cases and a 10 times speedup on contaminated cases compared with the SRMF + CNN-RNN baseline; it also resolved previous failures in Signal d classification, achieving 96.8% accuracy for perforation and 96.9% for contamination. Comparative benchmarks confirm that the SRMF approach outperforms XGBoost, Random Forest and MLP classifiers in both accuracy and efficiency, without increasing sensitivity to class imbalance. These results highlight the SRMF framework as a scalable, generalisable solution for real-time, embedded insulator diagnostics in modern power systems. Speedup ; Fast Fourier transform ; Classifier (UML) ; Signal processing ; Random forest ; Pattern recognition (psychology) ; Feature extraction ; Insulator (electricity) ; Reduction (mathematics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Neural Network Applications
Authors: Pavitra Sokke Rudraiah | Louisa Herbsleb | Michaela Saláková | Henriette Gröger | Anna M. Steyer | Frauke Alves | Claus Feldmann | Andreas Walter
Source:  (preprint, 2025-05-23)
Field: Materials Science — Biomaterials

Cryogenic Super-Resolution Fluorescence, Electron, and X-ray Microscopy -- A Novel Correlative Nanoscale Imaging Workflow to Reveal Nanoparticle Internalization into Cancer Cells Understanding the intracellular fate of nanoparticles (NPs) is critical for advancing nanomedicine, particularly in targeted drug delivery for cancer therapy. Here, we present a multimodal cryogenic microscopy workflow (correlative light, electron, an Internalization ; Correlative ; Nanoscopic scale ; Resolution (logic) ; Nanotechnology ; Microscopy ; Workflow ; Nanoparticle ; Fluorescence ; Fluorescence microscope ; Materials science ; X-ray ; Electron microscope ; Optics ; Chemistry ; Computer science ; Physics ; Cell ; Artificial intelligence ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced Electron Microscopy Techniques and Applications ; Cancer Research and Treatments
Authors: Taiwo Olaleye
Source:  (article, 2025-09-15)
Field: Chemistry — Analytical Chemistry

Sequential Minimal Optimization of Herbal Plants and Bias Treatment of Multi-Feature Attributes for Quantitative Ethnobotany The unprecedented endemic nature of viral infections and diseases posing global medical challenges in recent time calls for home-grown and inclusive interventions. Nonetheless current clinical efforts by medical and research virologists through orthod Ethnobotany ; Malaria ; Minimal model ; Endemic diseases Chromatography in Natural Products
Authors: Alexis Delabrière | Coline Gianfrotta | Sylvain Déchaumet | Annelaure Damont | Thaïs Hautbergue | Pierrick Roger | Emilien L. Jamin | Olivier Puel | Christophe Junot | François Fenaille | Etienne Thévenot
Source: ChemRxiv (preprint, 2025-06-24)
Field: Chemistry — Spectroscopy

mineMS2: Annotation of spectral libraries with exact fragmentation patterns Identification is a major challenge in metabolomics due to the large structural diversity of metabolites. Tandem mass spectrometry is a reference technology for studying the fragmentation of molecules and characterizing their structure. Recent instruments can fragment large amounts of compounds in a single acquisition. The search for similarities within a collection of MS/MS spectra is a powerful approach to facilitate the identification of new metabolites. We propose an innovative de novo strategy for searching for exact fragmentation patterns within collections of MS/MS spectra. This approach is based on i) a new representation of spectra as graphs of m/z differences, and ii) an efficient frequent-subgraph mining algorithm. We demonstrate both on a spectral database from standards and on acquisitions in biological matrices that these new fragmentation patterns capture similarities that are not extracted by existing methods, and facilitate the structural interpretation of molecular network components and the elucidation of unknown spectra. The mineMS2 software is publicly available as an R package (https://github.com/odisce/mineMS2). Fragmentation (computing) ; Annotation ; Geography ; Computer science ; Artificial intelligence ; Programming language Mass Spectrometry Techniques and Applications
Authors: Yerru Kalyana Krishna | B. Karthikeyan | Sameer Kumar Devarakonda
Source: International Research Journal of Multidisciplinary Technovation (article, 2025-05-29)
Field: Materials Science — Polymers and Plastics

Assessment of Tribological Behaviour of Flax/Bagasse Composites Using GRA Based Statistical Studies A combined Taguchi-grey Relational Analysis (GRA) has been used to improve the wear resistance of Flax, Bagasse and Epoxy F-B-E composites that have been treated with NaOH. The study investigates Dry sliding wear behaviour using Taguchi's L27 orthogonal array experimental design in compliance with ASTM G99 standards. Additionally, this study assesses the influence of independent variables such as usual load (2N,3N,4N), Sliding velocity (1m/s,2m/s,3m/s) and proportion of fibers (F75-B25, F50-B50, F25-B75) on the wear behaviour of epoxy resin reinforced with flax and bagasse powder composites, adopting a statistical methodology. Composite samples were made by hand layup technique using an epoxy resin mixed with flax fiber and bagasse powder. Dry sliding wear tests were done in a pin-on-disc setup, with adherence to Taguchi’s experimental design L27. This means scheming the “Signal-to-noise” (S/N) ratio and performing analysis of variances (ANOVA) to identify the optimal factors to minimize the wear rate to 98.30%. A better wear resistance rating was obtained from a composite material created by epoxy reinforced with 25% flax and 75% bagasse. Regression analysis's multi-response rate was 98.57%. The results showed that A1 (Load), B1 (Sliding Velocity), and C3 (Fibre Content Percentage) produced the best outcomes for wear rate and co-efficient of friction. The observed composite specimens were the primary variable influencing the wear rate, with sliding velocity and normal load also being major factors. ANOVA on the grey relational grade (GRG) showed that sliding velocity was the most important factor affecting how well the F-B-E composites resist wear. Tribology ; Bagasse ; Composite material ; Materials science ; Pulp and paper industry ; Engineering Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Engineering and Vibrations Research
Authors: Priti Prakash Patil | Vijay Patil
Source: Jordan Journal of Agricultural Sciences (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy for Rapid Freshness Assessment and Quality Classification of Chicken Eggs Eggs undergo significant alterations during storage, which results in a loss of quality. To keep an eye on the freshness and quality of the eggs, it is essential to predict these changes. This study's objective was to assess the use of visible Near infrared (NIR) spectroscopy, which is a quick, non-destructive, online method for evaluating the quality of eggs. During the study six hundred sixty whole fresh eggs with white shells produced by the same group of hens fed a typical feed was acquired. They were placed in temperature-controlled environments whose temperature was 20°C and 30°C respectively and observed their spectra for 25 days of storage. The spectra of 40 eggs were collected for 0, 4,7,10,14,17,19,21,25 days within the NIR spectral range of 902 to 1810 nm; the absorption spectrum data was found to be collected for every 4nm span. The spectral non-destructive data was contrasted with the Haugh Units (HU) of the egg sample in terms of freshness and to the quantity of storage days in terms of quality. This study explores the potential of Near-Infrared (NIR) spectroscopy combined with chemometric analysis for non-destructive egg quality assessment, focusing on predicting Haugh Units (HU) and storage duration. The research involved systematic data collection, preprocessing of NIR spectra, and developing predictive models using Partial Least Squares (PLS) regression. Results demonstrated a high accuracy in predicting HU values and storage duration, with an R² value of 0.986 for calibration. Eggs stored at 20°C maintained higher HU values than those stored at 30°C, significantly impacting freshness assessment. Principal Component Analysis (PCA) effectively reduced data dimensionality, enhancing model precision. Combining shell measurement areas and preprocessing techniques improved PLS-DA model performance, achieving up to 95.75% accuracy in classifying egg freshness. The findings highlight the practical application of NIR spectroscopy and chemometric analysis in the food industry for ensuring egg quality and freshness. Quality assessment ; Quality (philosophy) ; Environmental science ; Computer science ; Food science ; Chemistry ; Business ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Christopher J. Forman | David Onofrei | Dillan Stengel | Julian Aldana | Christopher Paolini | Nathan C. Gianneschi | Gregory P. Holland
Source: PNAS Nexus (article, 2025-12-01)
Field: Materials Science — Biomaterials

Sequence-encoded tubular architectures in disordered spider silk proteins revealed by multiscale simulations and NMR Spider silk proteins (spidroins) are large, block-copolymer-like proteins that must remain soluble at high concentration in the spinning dope while being primed for rapid fiber formation. Understanding how these intrinsically disordered proteins organize in solution is key to explaining the transformation from soluble dope to solid fibers with exceptional strength and toughness. Here, we show that major ampullate (Ma) spidroins from the black widow spider form dynamic ensembles that include metastable tubular substructures. Multiscale molecular dynamics (MD) simulations reveal compact, anisotropic monomers with tubular geometries ∼3-4 nm in diameter and 50 nm in contour length. Small-angle X-ray scattering (SAXS) ensemble fitting confirms that a minority population of tubular conformers is required to reproduce experimental scattering profiles. Complementary atomistic MD and solution NMR chemical shift and relaxation analyses show that these tubular conformers are enriched in β-turn and bend motifs, maintaining local flexibility while promoting overall compaction. Mutational simulations further demonstrate that alternating poly(Ala) and Gly-Gly-X sequence patterning drives amphiphilic packing that stabilizes the tubular morphology. Together, these findings reveal that spider silk proteins form dynamic, disordered ensembles with sequence-encoded tubular substructure. This model reconciles SAXS and NMR observations and provides a mechanistic framework for how amphiphilic patterning, metastability, and disorder collectively enable spider silk proteins to remain soluble yet preorganized for hierarchical self-assembly into one of nature's toughest materials. Spider silk ; Intrinsically disordered proteins ; Small-angle X-ray scattering ; Amphiphile ; SILK ; Spider ; Molecular dynamics ; Conformational isomerism ; Monomer ; Polymer Silk-based biomaterials and applications ; Collagen: Extraction and Characterization ; Polymer Surface Interaction Studies
Authors: Xiaojing Liu | Lei Zheng | Zhengxing Men
Source: Coatings (article, 2025-12-07)
Field: Materials Science — Biomaterials

In Situ Neutron and Synchrotron X-Ray Analysis of Structural Evolution on Plastically Deformed Metals During Annealing This review highlights the significance of modern quantum-beam techniques, particularly neutron and synchrotron radiation sources, for advanced microstructural characterization of metallic systems. Following a brief introduction to neutron and synchrotron diffraction, selected studies demonstrate their application in probing thermally induced structural evolution in plastically deformed metals. Additively manufactured CoCrFeNi alloys and 316L stainless steels subjected to high-pressure torsion (HPT) were investigated by in situ neutron diffraction during heating, revealing the sequential regimes of recovery, recrystallization, and grain growth. Coupled with mechanical measurements, the results show that HPT followed by controlled thermal treatment improves the mechanical performance, offering strategies for designing engineering materials with enhanced properties. The thermal anisotropy behavior of Ti-45Al-7.5Nb alloys under in situ neutron diffraction is defined as anisotropic ordering upon heating, while the HPT-processed alloy displayed isotropic recovery of order at earlier temperatures. Complementary in situ synchrotron studies in rolled-sheet magnesium alloys unveiled microstructural rearrangement, grain rotation, recovery, and precipitate dissolution during annealing. And phase transformation, recovery, and recrystallization processes were detected in steel using HEXRD. This work emphasizes the complementary strengths of the neutron and synchrotron methods and recommends their broader application as powerful tools to unravel microstructure–property relationships in plastically deformed metals. Synchrotron ; Neutron diffraction ; Synchrotron radiation ; Alloy ; Annealing (glass) ; Recrystallization (geology) ; Neutron ; Neutron scattering ; Anisotropy Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Metallurgical and Alloy Processes
Authors: Qian Li | Sixu Wang | Jie Yang | Hanbin Gao | K. Okamoto | Rui Liu | Yunpeng Ma | Tao Zhou | Martin V. Holt | Qiang Zheng | Hiroshi Funakubo | Shi Liu | Jing‐Feng Li
Source: Research Square (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

Millimeter-wave dielectric tunability driven by topological polar structure switching in PbTiO3/SrTiO3 superlattices Superlattice ; Dielectric ; Extremely high frequency ; Polar ; Millimeter ; Materials science ; Optoelectronics ; Topology (electrical circuits) ; Physics ; Optics ; Electrical engineering ; Engineering ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Photonic Crystals and Applications ; Microwave Dielectric Ceramics Synthesis
Authors: Adroaldo Santos Soares | Marcelo A. Moret | Oberdan Rocha Pinheiro | Fernando Luiz Pellegrini Pessoa
Source: Journal of Bioengineering, Technologies and Health (article, 2025-11-10)
Field: Materials Science — Biomaterials

Computational Model to Optimize the Prediction of Fouling in the Deposition Process During Oil Pre-Processing in Heat Exchanger Networks Based on Machine Learning The accumulation of deposits in heat exchangers during oil pre-processing, known as fouling, is a reality in the oil and gas industry. This deposition, caused by the presence of suspended solids, organic, and mineral compounds, compromises the thermal and hydraulic efficiency of heat exchangers, resulting in less efficient operations and increased maintenance and energy costs. The implementation of predictive computational models aims to ensure the functioning of heat exchangers and is essential for maintaining refinery operations. The objective of this work was to analyze models for managing deposition in heat exchangers during oil pre-processing, in order to maximize operational efficiency and minimize costs associated with maintenance and energy, using Artificial Intelligence with machine learning models capable of processing sequential data, which is particularly useful in deposition processes that evolve, as in the network of heat exchangers used in oil pre-processing. The computational models were developed using historical measurement data from a network of 25 heat exchangers at a refinery in southeastern Brazil, spanning from September 1, 2014, to July 25, 2021, and comprising a total of 57,225 records stored in a CSV (Comma-Separated Values) file. For prediction, the independent variables were the operating parameters of the exchangers, and as dependent variables, the fouling factor (Rfs), which quantifies the resistance to thermal exchange due to deposition. The prediction models were evaluated based on error metrics, and the DNN (Deep Neural Network) model presented MSE (Mean Squared Error) of 0.01835, RMSE (Root Mean Squared Error) of 0.13549, MAE (Mean Absolute Error) of 0.10743, and R² (Coefficient of Determination) of 0.3049. The LSTM (Long Short-Term Memory) model presented MSE of 0.01863, RMSE of 0.13649, MAE of 0.10895, and R² of 0.29458. The Hybrid Model presented an MSE of 0.01856, an RMSE of 0.13624, an MAE of 0.10663, and an R² of 0.29720. We concluded that predicting the deposition coefficient is critical for operational planning. Computational fouling prediction models can be utilized to minimize costs and risks in the heat exchanger network during oil pre-processing, offering an efficient approach to process optimization. Heat exchanger ; Fouling ; Artificial neural network ; Refinery ; Mean squared error ; Oil refinery ; Deposition (geology) ; Process (computing) Calcium Carbonate Crystallization and Inhibition ; Solar-Powered Water Purification Methods ; Erosion and Abrasive Machining
Authors: Gang Yang | Jiyuan Chen | Chunai Gong | Rong Wang | Lu Han | Jing Wang | M S Chen | Yongfang Yuan
Source: Cancer Cell International (article, 2025-11-04)
Field: Chemistry — Inorganic Chemistry

Astragaloside IV-loaded zeolitic imidazolate framework-8 based on supercritical fluid as anti-solvent technology to improve bioavailability and anti-tumor activity against non-small cell lung cancer Astragaloside (AST) has shown therapeutic potential against non-small cell lung cancer (NSCLC). However, its poor water solubility and low bioavailability limit its clinical application. To overcome these challenges, we developed an AST-loaded zeolitic imidazolate framework-8 (AST@ZIF) using supercritical fluid carbon dioxide (SCF-CO2) technology. This approach aimed to enhance the solubility, bioavailability, and anti-tumor efficacy of AST. Notably, this is the first study to employ SCF-CO2 as an anti-solvent through solution-enhanced dispersion by supercritical fluids (SEDS) for preparing drug-loaded ZIF-8. The resulting AST@ZIF-SEDS displayed a uniform hexagonal or cubic morphology, with AST transitioning from a crystalline to an amorphous state. Compared to AST@ZIF prepared using traditional methods (one-pot synthesis and solvent adsorption), AST@ZIF-SEDS demonstrated superior drug loading capacity, dispersibility, reduced residual solvent content, and improved stability. As a novel carrier, ZIF-8 effectively enhanced the solubility and bioavailability of AST while maintaining favorable biosecurity. In vivo studies further confirmed that AST@ZIF-SEDS significantly improved tumor inhibition compared with AST powder. In conclusion, SEDS technology represents a promising strategy for maximizing the therapeutic potential of ZIF-8 as a drug carrier. AST@ZIF-SEDS exhibited strong anti-tumor activity and holds potential as an effective treatment for NSCLC. Bioavailability ; Zeolitic imidazolate framework ; Supercritical fluid ; Solubility ; Supercritical carbon dioxide ; Solvent ; Drug ; In vivo Metal-Organic Frameworks: Synthesis and Applications ; Tannin, Tannase and Anticancer Activities ; Lung Cancer Treatments and Mutations
Authors: K Nikhil
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-05-07)
Field: Chemistry — Analytical Chemistry

Fruit Ripeness Detection Using CNN Model Abstract—Fruit ripeness detection is crucial in the agricul- ture field to determine the time of harvest and fruit quality. Substantial crop loss might result from timing slips or delays, consequently affecting income rates. Manual detection of ripeness in fruits can be inefficient for large scale implementation and pose issues such as requiring lots of time and intensive labor. Hence, in this project, fruit ripeness is detected utilizing computer vision and machine learning technologies. The method being used will recognize the fruits and categorize their ripeness using the CNN algorithm, on the basis of the dataset it is trained on. Our system will detect ripeness of 6 different types of fruits. The convolutional layers applied different filters as feature detectors to create a feature map representing the detected input features. Then the features were shaped into one-dimensional features and sent to an Neural Network for the ripeness classification. Data augmentation techniques are used to expand the dataset’s size prior to building a trained model. This technology can be integrated with robotics to implement smart agriculture and help farmers view their crops in real time, know the number of mature fruits, plan harvesting and assist in fruit picking and sorting. Index Terms—Automated Detection, Machine Learning, Deep Learning, Computer Vision, Agriculture 4.0, Smart Farming. Ripeness ; Computer science ; Artificial intelligence ; Horticulture ; Ripening ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yogeshwari F. Patel | Mohammad Izadi
Source: Journal of Nonlinear Mathematical Physics (article, 2025-10-10)
Field: Chemistry — Inorganic Chemistry

Dynamic Modeling and Parameter Sensitivity in Singular Non-Isothermal Reaction-Diffusion Systems: A DTM-Padé Solution for Spherical Catalysts In this study, we address the mathematical modeling and solution of a non-isothermal reaction diffusion model involving a spherical biocatalyst and catalyst. The system is shaped by an intricate balance between diffusion processes and chemical reactions. We investigate the Lane-Emden equation as governing equation of the model, which accounts for the spatial distribution of reactants and products. To tackle the singularity challenges associated with this dynamic system, we employ the semi-analytical approach, namely Differential Transform Method (DTM), enhanced by Padé approximation. In order to establish the reliability and effectiveness of proposed scheme, we conduct a comprehensive comparison with solutions available in the existing literature. Furthermore, a comprehensive investigation of sensitivity of the parameters is also conducted. Combining DTM and Padé approximation offers a promising robust computational technique for further study in the field of mathematical modeling of chemical and biological processes affected by singularities. Sensitivity (control systems) ; Singularity ; Field (mathematics) ; Diffusion ; Reliability (semiconductor) ; Differential equation ; Diffusion equation ; Mathematical model Zeolite Catalysis and Synthesis ; Catalysts for Methane Reforming ; Fractional Differential Equations Solutions