jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fanny Courtin | Nicolas THIOLLIÈRE | Xavier Doligez | Marc Ernoult
Source: Progress in Nuclear Science and Technology (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Modeling of Reprocessed Uranium Enrichment for Nuclear Scenarios Simulations Reprocessed uranium recycling in PWR is a key component of the uranium and plutonium multi-recycling strategies in PWR foreseen in the French fleet by the year 2050.Reprocessed uranium (RepU) recovered at UOX spent fuel reprocessing usually contains a significant fraction of 235 U.However, RepU enrichment process also results in an increase of the concentration of other Uranium isotopes, such as 234 U and 236 U. To acquire insights on RepU recycling strategies in PWR and their potential deployment in the French nuclear fleet, nuclear scenario simulations are performed.This paper focuses on the development of a RepU enrichment model for the nuclear scenario simulation code CLASS.This model is based on iterative bisection method calculating the 235 U enrichment in enriched reprocessed uranium (ERU) fresh fuel required to reach a discharge target burn-up in PWR depending on the RepU isotopic composition.This algorithm relies on an artificial neural network estimator of the maximum achievable burn-up depending on ERU fresh fuel composition, based on a PWR ERU depletion calculation database.This algorithm is then applied to a sample of RepU isotopic compositions from spent cooled PWR UOX fuels, highlighting the impact of 236 U fraction in RepU on the required over-enrichment in ERU fresh fuel. Uranium ; Enriched uranium ; Uranium mining ; Radioactive waste Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Radioactive element chemistry and processing
Authors: André Martínez | Alex Krasnok | Sergey Kutsaev | Andrew Seltzman | A. Smirnov
Source: ArXiv.org (preprint, 2025-02-10)
Field: Materials Science — Materials Chemistry

Virtual Critical Coupling in High-Power Resonant Systems Exciting high-power resonators pose challenges such as managing power reflections, which can cause energy losses and damage system components. This is crucial for applications like Lower Hybrid Current Drive (LHCD) systems in tokamaks, where plasma stability and confinement depend on efficient energy transfer. In this work, we introduce the Virtual Critical Coupling mechanism to address reflection-related challenges in S-band resonators. We theoretically designed a complex frequency excitation signal tailored to the resonator's characteristics, facilitating efficient energy storage and minimizing reflections without mechanical modifications. Using a custom low-level RF system, we conducted experiments at 32 mW and 600 kW with a 5 MW S-band klystron, demonstrating a ninefold reduction in reflection coefficients compared to traditional monochromatic excitation in high-power tests. This approach enhances the efficiency and stability of high-power resonant systems, potentially advancing nuclear fusion energy production. Coupling (piping) ; Power (physics) ; Physics ; Computer science ; Engineering ; Mechanical engineering ; Quantum mechanics Solid-state spectroscopy and crystallography ; Acoustic Wave Resonator Technologies ; Gyrotron and Vacuum Electronics Research
Authors: Nayan, Md Mizanur Rahaman | Li, Zheyu | Ponzina, Flavio | Pinge, Sumukh | Rosing, Tajana | Naeemi, Azad J.
Source: ArXiv.org (article, 2025-11-05)
Field: Chemistry — Spectroscopy

HERP: Hardware for Energy Efficient and Realtime DB Search and Cluster Expansion in Proteomics Database search and clustering are fundamental components of many data analytics problems, such as mass spectrometry-driven proteomics. Traditional full clustering and search algorithms suffer from high resource usage and long latencies. We introduce HERP, a lightweight incremental clustering method and a highly parallelizable database (DB) search platform that utilizes 3T2MTJ SOT-MRAM based CAM in 7nm technology for in-memory acceleration. A single hardware initialization using pre-clustered proteomics data allows for continuous DB searching and local re-clustering, providing a more practical and efficient alternative to clustering from scratch. Heuristics derived from the initial pre-clustered data guide the incremental process, accelerating clustering by 20x at a cost of 0.3% increase in clustering error where DB search results overlap by 96% with SOTA algorithms validating search quality. For a 131GB human genome proteomics dataset HERP setup requires 1.19mJ for 2M spectra while 1000 query search consumes only 1.1uJ at SOTA accuracy. Bucket-wise parallelization and query scheduling provides additional 100x speedup. Cluster analysis ; Initialization ; Heuristics ; Efficient energy use ; Scheduling (production processes) ; Analytics ; Cluster (spacecraft) ; Search algorithm ; Firmware ; Bottleneck Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Genomics and Phylogenetic Studies
Authors: Jabatan Kejuruteraan Elektrik, Elektronik dan Sistem, Fakulti Kejuruteraan dan Alam Bina, Universiti Kebangsaan Malaysia, Malaysia. | Ramizi Mohamed | Khalil Azha Mohd Annuar | Jabatan Teknologi Kejuruteraan Elektrik, Fakulti Teknologi dan Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Malaysia. | Yushaizad Yusof | Jabatan Kejuruteraan Elektrik, Elektronik dan Sistem, Fakulti Kejuruteraan dan Alam Bina, Universiti Kebangsaan Malaysia, Malaysia | Muhammad Nasrullah Kassim
Source: Jurnal Kejuruteraan (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Characterization of Thermoelectric Generators for Voltage Control Circuits in Low Power Electronic Devices This research explores the characterization and development of a circuit capable of powering low-power electronic devices using thermoelectric generators (TEG). As global interest in renewable energy sources grows, alternatives such as hydroelectric and solar power have gained attention for their environmental benefits and availability. However, the challenge of energy loss due to insufficient heat persists. TEGs are electronic devices that can convert various forms of thermal energy, including waste heat, into electrical energy. Utilizing the Seebeck effect,TEGs generate voltage when there is a temperature difference between their hot and cold plates. The greater the temperature difference between the two sides, the higher the voltage production. Several factors influence TEG performance, including the construction materials, such as Bi&lt;sub&gt;2&lt;/sub&gt;Te&lt;sub&gt;3&lt;/sub&gt;, and the surface area available for heating. This research aims to identify the best TEG and its characterization that can increase the temperature difference range to produce higher output voltage. Furthermore, it seeks to determine the most suitable type of TEG and the optimal circuit construction for generating sufficient output voltage to power low-power electronic devices. This includes determining the appropriate number of TEG units, the use of a boost-type DC-DC converter, and the number of filter circuits required to smooth the output voltage. Finally, the research aims to identify the optimal voltage, current, and power required to operate low-power electronic devices, such as fans, buzzers, and LEDs. Experimental results show that three TEG units produce a maximum voltage of 0.927V, a current as high as 71.2mA and a highest power of 50.694mW at a surface temperature of 80°C. The efficient energy conversion and voltage stability observed affirm the viability of TEGs as alternative power sources for small-scale sensing systems and IoT applications. Thermoelectric generator ; Voltage ; Renewable energy ; Thermoelectric effect ; Electronic circuit ; Power (physics) ; Characterization (materials science) ; Electronics ; Power electronics Advanced Thermoelectric Materials and Devices
Authors: Xin Fu | Hongbo Yan | Chaohui Ai | Jianxin Wang
Source: Shock and Vibration (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on Chaotic Threshold of Giant Magnetostrictive Actuator With Fractional‐Order Time‐Delay Feedback To reduce the giant magnetostrictive actuator’s (GMA) irregular vibration caused by system parameter changes, we innovatively apply fractional‐order time‐delay feedback to control bifurcation and chaos in the GMA’s nonlinear dynamics. The GMA dynamic equation with feedback control is established using the quadratic domain rotation model, the Jiles–Atherton model, and the structural dynamic. First, the concepts of equivalent stiffness and damping are introduced to deal with the feedback control term. Second, using the Melnikov method, the threshold condition for the controlled system to enter the Smale horseshoe chaos is established, and a numerical solution verifies the accuracy of the analytical solution. Finally, the influence of excitation frequency, damping coefficient, and control parameters on the chaotic threshold of the system is studied by numerical simulation. The results show that complex motion patterns, including jump, period‐doubling bifurcation, and chaos behavior, will occur when the system parameters are improperly chosen. After introducing the controller, the bifurcation and chaos phenomena of the system can be effectively eliminated. By adequately adjusting the feedback parameters, the system’s vibration displacement response can be adjusted from chaotic to stable periodic motion. Control theory (sociology) ; Chaotic ; Order (exchange) ; Magnetostriction ; Actuator ; Computer science ; Mathematics ; Physics ; Economics ; Control (management) Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Magnetic Bearings and Levitation Dynamics
Authors: Ivan Argatov | Vitaly Kocherbitov
Source: Discover Applied Sciences (article, 2025-09-17)
Field: Materials Science — Surfaces, Coatings and Films

Moderately low-Bond-number axisymmetric drop shape analysis and tensiometry for sessile drops Abstract The shape of a liquid drop is governed by both the surface tension of the liquid and the gravity which introduces the hydrostatic pressure gradient inside the liquid. The two factors (along with a characteristic size of the drop) can be combined into one dimensionless parameter, known as the Bond number. The accuracy of asymptotic solutions of the Young–Laplace equation for relatively small drops drastically decreases with increasing the Bond number. To extend their range of applicability, an asymptotic modeling approach is applied leading to simple closed-form approximations for the drop shape profile in a parametric form with the meridian angle used as a parameter. A quantitative comparison with the numerical solutions available in the literature is performed. The developed mathematical modeling framework allows to unify and simplify the consideration of different methods for determination of the surface tension and estimating the contact angle from the axisymmetric drop-shape analysis. Graphical Abstract Surface tension ; Dimensionless quantity ; Rotational symmetry ; Spinning drop method ; Drop (telecommunication) ; Contact angle ; Parametric statistics ; Hydrostatic equilibrium ; Hydrostatic pressure Surface Modification and Superhydrophobicity ; Interactive and Immersive Displays ; Nanomaterials and Printing Technologies
Authors: Hamit Armağan
Source: International Journal of 3D Printing Technologies and Digital Industry (article, 2025-08-30)
Field: Chemistry — Analytical Chemistry

PREDICTION PERFORMANCE OF DECISION TREE INDUCERS ON AUGMENTED BACILLUS CEREUS GROWTH DATA Accurate prediction of microbial growth is of great importance in critical areas such as food safety and environmental sciences. In this study, a hybrid of mathematical methods and machine learning-based approaches are used to model the growth dynamics of foodborne pathogen Bacillus cereus. Since the use of mathematical models alone does not sufficiently cover the non-linear data structure of bacterial systems, better results are obtained when hybrids are used together with machine learning methods. We examine the results of five different tree-based models for predicting the growth of Bacillus cereus, namely Fine Tree, Medium Tree, Coarse Tree, Ensemble Boosted Trees and Ensemble Bagged Trees. We evaluate each model with performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), R² and Mean Absolute Error (MAE). The results show that the Ensemble Bagged Trees model performs the best, with a validation RMSE of 0.0094 and an R² value of 0.9995. Also, the Fine Tree model has an R² value of 0.9990. In general, ensemble methods offer significant advantages in prediction accuracy. Bacillus cereus ; Decision tree ; Inducer ; Computer science ; Biology ; Data mining ; Machine learning ; Biochemistry ; Bacteria ; Genetics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Aneesh M A Aneesh M A | Alan Antony | Dr Uma Nath U Dr Uma Nath U | D R
Source: International Journal of Pharmaceutical Research and Applications (review, 2025-04-01)
Field: Chemistry — Spectroscopy

A Review on Artificial Intelligence in Gas Chromatography Ms Generation One particularly powerful technique, gas chromatography–mass spectrometry (GC-MS) was done by AI is widely used for its high sensitivity and precise quantification. Recent studies have leveraged AI to enhance GC-MS data analysis, particularly in pattern recognition and predictive modeling. However, the performance of such AI approaches heavily depends on access to comprehensive and diverse training datasets— something that can be limited when experimental data is sparse or constrained to specific chemicals(2,3). In this work, we explore the use of AI-driven generative approaches, including Variational Autoencoders (VAE) and Generative Adversarial Networks (GANs), to synthesize highquality data for GC-MS analysis. We also conduct exploratory data analysis (EDA) to investigate the complexity and features of real-world GC-MS datasets, establishing a foundation for evaluating the effectiveness of generated data in downstream tasks.(4) Chromatography ; Gas chromatography ; Artificial intelligence ; Chemistry ; Computer science Analytical Chemistry and Chromatography
Authors: Hernández, N | Choi, Y | Fearn, T
Source: UCL Discovery (University College London) (article, 2025-12-15)
Field: Chemistry — Analytical Chemistry

Bayesian optimization for interval selection in PLS models Robustness (evolution) ; Gaussian process ; Interval (graph theory) ; Bayesian probability ; Bayesian optimization ; Selection (genetic algorithm) ; Model selection ; Monte Carlo method ; Convergence (economics) Spectroscopy and Chemometric Analyses ; Gaussian Processes and Bayesian Inference ; Fault Detection and Control Systems
Authors: Erdal Dіnç | Zehra Ceren Ertekіn | Asiye Üçer
Source: Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi (article, 2025-05-25)
Field: Chemistry — Analytical Chemistry

Simultanuous Quantitation of Amlodipine and Rosuvastatin by Artificial Neural Network Artificial neural network (ANN) is a useful tool in chemometrics to model the complex relationship in spectral data resulting in overlapping spectra, spectral interferences, non-linearity, and deviations from Lambert-Beer law. In this work, a new application of ANN was proposed for the simultaneous spectrophotometric quantification of amlodipine (AML) and rosuvastatin (ROS) in tablets despite their overlapping spectra. The relationship between the actual concentrations and absorption data of binary calibration samples was modeled by ANN. The validity of the method was proved by analyzing AML and ROS in their laboratory-made mixtures, intra and, inter-day samples, as well as standard addition samples. The developed method was then applied to the simultaneous quantitation of AML and ROS in commercial tablets and a good agreement was reported between label claim assay results. Rosuvastatin ; Amlodipine ; Artificial neural network ; Artificial intelligence ; Computer science ; Internal medicine ; Medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: Pascal Pomarède | Nada Miqoi | D. S. Citrin | Fodil Meraghni | Alexandre Locquet
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Quantification des dommages causés par un impact à faible vitesse dans les stratifiés composites à l'aide de l'imagerie par impulsion térahertz International audience Apoptosis ; DNA damage ; Cell culture ; Flow cytometry ; Cell High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Planetary Science and Exploration
Authors: Javier Téllez-García | María‐Teresa Sánchez | Jose Jorge González-Fernández | Dolores Pérez‐Marín
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Evaluation of Nirs Portable Sensors for the Determination of the Physicochemical Quality and Maturity of Intact Mangoes Maturity (psychological) ; Quality (philosophy) ; Chemistry ; Business ; Psychology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Micah Summerlin | Mariano Uberti | Dhananjay Shinde | Emma G. Foster | Brady Sillman | Manjeet Kumar | Baojin Yao | Dongming Peng | Benson Edagwa | Howard E. Gendelman | Yutong Liu | Aditya N. Bade
Source: Frontiers in Pharmacology (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

CEST MRI detects antiretroviral drug toxicities in the developing mouse brain Advances in antiretroviral drugs (ARVs) have achieved remarkable success in preventing gestational human immunodeficiency virus type 1 (HIV-1) transmission from mother to fetus. This is reflected in the rising numbers of HIV-1-exposed uninfected (HEU) children. Worldwide, the number of HEU children exceeds sixteen million, with more than one million children joining this group each year. Although HEU children remain uninfected, they are at an increased risk of neurodevelopmental deficits. Notably, <i>in utero</i> exposure to HIV-1 and ARVs is a causative factor. Both are linked to adverse neurodevelopment, warranting close clinical monitoring and therapeutic intervention. We now demonstrate that chemical exchange saturation transfer (CEST) MRI can be used to successfully monitor <i>in utero</i> ARV-exposure-associated embryo brain metabolomic and macromolecular dysregulations in a mouse model. CEST hyperintensities at -3.5 ppm (nuclear Overhauser effect) and 3.5 ppm (amide/amine protons) are measured in the brains of mouse embryos exposed to dolutegravir (DTG). These reflect DTG-induced alterations in cellular membrane lipids, mobile proteins or peptides, and glutamate levels. All demonstrate impaired neuronal development. Non-targeted metabolomics confirms the CEST results. These support the observations of DTG-induced differential expression of lipids and metabolites that reflect deficits in energy production, cell metabolism, post-translational protein modifications, and transport pathways. Furthermore, CEST MRI demonstrated the therapeutic benefits of long-acting nanoformulation delivery of DTG in mitigating neurodevelopmental impairments. These data, taken together, support the utility of CEST MRI as a non-invasive imaging biomarker for detecting neurodevelopmental deficits. Biomarker ; Drug ; Magnetic resonance imaging ; Metabolomics ; Drug discovery ; Cell ; Glutamate receptor ; Antiretroviral therapy Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Pietro Cappelli | G. Grosso | Marco Letizia | Humberto Reyes-González | M. Zanetti
Source: ArXiv.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

Learning to Validate Generative Models: a Goodness-of-Fit Approach Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman--Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model, known as FlowSim, developed to generate high-energy physics collision events. We demonstrate that the NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data. Interpretability ; Generative grammar ; Benchmark (surveying) ; Generative model ; Interpretation (philosophy) ; Reliability (semiconductor) ; Experimental data Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Gaussian Processes and Bayesian Inference
Authors: Saiful Arifin Ipin | Tukino Tukino | Elfina Novalia
Source: Journal of Artificial Intelligence and Engineering Applications (JAIEA) (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

The Use of Support Vector Machine in Classifying Scrap Metal Goods Based on Physical Characteristics The problem of metal waste management, especially scrap metal, is increasingly complex with the increase in industrial and construction activities that produce various types of waste materials. Scrap metal is one type of waste that still has high economic value if it can be sorted and classified appropriately based on its type and quality. However, manual classification methods are still predominantly used, subjectivity and human error. To overcome these challenges, this study proposes an artificial intelligence-based approach by implementing Support Vector Machine (SVM) as an automatic classification method that is able to identify types of scrap metal items based on their physical characteristics. The characteristics used as input features in this model include surface color, rust rate, material hardness, density, magnetic attraction, and texture. The data is collected directly from UD's scrap metal waste collectors. Cahaya Surya in Karawang, which is one of the largest scrap metal processing centers in the region. This research process includes the stages of data collection, pre-processing, selection of model parameters, training and testing using linear kernels and Radial Base Function (RBF), as well as evaluation of model performance through accuracy, precision, recall, F1-score, and confusion matrix metrics. The results of the tests showed that the SVM algorithm, particularly with the RBF kernel, was able to provide excellent classification performance with an accuracy rate of over 90%, as well as a relatively balanced distribution of predictions between metal classes. This indicates that the physical features used are quite representative in distinguishing different types of scrap metal consistently. Thus, this approach is not only able to improve the efficiency and accuracy of classification, but also contributes to a reduction in reliance on non-objective manual methods. In the future, the effectiveness of these models can be further enhanced through the integration of additional features, such as mild chemical analysis or computer vision-based image processing technology, to support more sophisticated metal classification systems that are adaptive to field conditions. Scrap ; Support vector machine ; Artificial intelligence ; Computer science ; Confusion matrix ; Pattern recognition (psychology) ; Engineering ; Mechanical engineering Material Selection and Properties ; Recycling and Waste Management Techniques ; Mineral Processing and Grinding