jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jairo A GUERRERO D | Datar P.A | S. V.
Source: International Journal of Research Publication and Reviews (article, 2025-04-01)
Field: Chemistry — Spectroscopy

Overview And Analytical Methods For Determination Of Abiraterone This paper presents an innovative approach to enhancing vehicular safety through the development of a weather-aware car speed control system.The system utilizes advanced sensors to detect adverse weather conditions, such as fog, and dynamically adjusts the vehicle's speed to ensure compliance with safe thresh-olds.It incorporates GPS tracking for location monitoring, accident coverage for emergency response, and proximity sensors leveraging ultrasonic or LiDAR technology to measure the distance to nearby vehicles.The proximity-based mechanism dynamically adjusts the speed and braking system, preventing colli-sions and maintaining safe driving conditions.By integrating these technologies, the project aims to improve road safety and reduce accident rates caused by envi-ronmental and situational factors.This survey examines existing systems, their limitations, and the potential of the proposed model to set a new benchmark in automotive safety and intelligent driving systems. Abiraterone ; Abiraterone acetate ; Chromatography ; Medicine ; Chemistry ; Internal medicine ; Prostate cancer Analytical Chemistry and Chromatography ; Radiopharmaceutical Chemistry and Applications ; Inorganic and Organometallic Chemistry
Authors: Turki Al Hagbani | Rami M. Alzhrani | Majed A. Algarni
Source: Scientific Reports (article, 2025-07-12)
Field: Chemistry — Spectroscopy

Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method Over the last decades, industrial employment of supercritical fluids (SCFs) as a trustworthy alternative of organic solvents has increased substantially. SCFs (mainly CO<sub>2</sub>SCF) have illustrated great potential to improve the solubility of poorly water-soluble drugs. Compared to traditional procedures, SCF-based processes possess irrefutable advantages like good sustainability, eco-friendliness, affordability, safety during application, thermodynamic stability, low consumption of energy and obtaining new products with better purity. This research was done with the aim of modeling the solubility of febuxostat (FBX) drug with the help of machine learning methods. Temperature and pressure are the input values on which the modeling is done. Regression models including GPR, KNN, and a voting regression model using these two basic models are employed in this research. As an innovative aspect of this research, in addition to the Voting model, the HHO algorithm has been used to tune the hyper-parameters of the models. The final models obtained were then evaluated and compared using different criteria. With the R<sup>2</sup> criterion, the GPR predictive model has a score of 0.819 and the KNN model has a score of 0.854, but the voting model has a score of 0.980, which shows that the combined voting method using the other two models has a better result than both of them. Also, the RMSE error rate of the Voting model is 2.78 × 10<sup>-1</sup> and with MAPE metric the error of this model is 3.81 × 10<sup>-2</sup>. Febuxostat ; Computer science ; Solubility ; Supercritical fluid ; Machine learning ; Artificial intelligence ; Chemistry ; Organic chemistry ; Uric acid ; Biochemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies
Authors: Kamyar Barakati | Alexander Molak | Chris Nelson | Xiaohang Zhang | Ichiro Takeuchi | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

LLM-Assisted Causal Discovery: Mapping Hidden Patterns in Crystalline Solids Materials science ; Computer science ; Computational biology ; Biology Machine Learning in Materials Science
Authors: Swetha Bandaru | J. Jayaprabakar | K. Vijetha | Ashish Kumar | G. Hariharan | Man Mohan | Abhishek Sharma | Hawa Singh
Source: Scientific Reports (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Optimization of selective catalytic reduction for nox emission control: an experimental and CFD analysis of mixer designs This study focuses on optimizing the selective catalytic reduction (SCR) system to reduce urea deposits and enhance overall system performance. Both computational fluid dynamics (CFD) simulations and experimental analyses were conducted to evaluate an S-bend inlet SCR design and an improved configuration featuring multiple mixer setups. The enhanced design demonstrated superior performance, with the flap mixer reducing the accretion rate to 3.6 × 10⁻⁴ kg/m²s, representing a 60.9% reduction in urea deposition at an injection rate of 41.9 mg/s under acceleration conditions, compared to the no-mixer SCR setup. Furthermore, the thickness of the urea deposit was substantially reduced, with experimental measurements showing a decrease from 5.6 × 10⁻⁴ microns (no-mixer SCR setup) to 2.6 × 10⁻³ microns (flap mixer), marking a 53.6% reduction. The experimental and CFD results exhibited strong agreement, with deviations of 8% for the accretion rate and 7-9% for urea deposit thickness, reinforcing the reliability of the simulation model. The close alignment between experimental and numerical findings highlights the effectiveness of the optimized mixer in promoting uniform droplet breakup and reducing localized deposition. The implementation of optimized mixer geometries improved overall system performance, facilitated compliance with stringent emission regulations, and mitigated operational complexities related to urea accumulation. This study supports the ability of current SCR systems to meet future NOx emission limitations at reduced cost. Computational fluid dynamics ; NOx ; Selective catalytic reduction ; Urea ; Reduction (mathematics) ; Catalysis ; Acceleration ; Thermal Catalytic Processes in Materials Science ; Combustion and flame dynamics ; Odor and Emission Control Technologies
Authors: Jinjin Rong | Xin Fang
Source: International Journal of Advanced Computer Science and Applications (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Innovative Design Algorithm of Huizhou Bamboo Weaving Patterns Based on Deep Learning In the field of innovative design of Huizhou bamboo weaving patterns, traditional deep learning algorithms cannot fully capture the fine structure and subtle changes of patterns, resulting in distorted or blurred results, and require a lot of computing resources and time during the training process. This paper constructs an improved ViT (Vision Transformer) model to collect diverse Huizhou bamboo weaving pattern data covering different styles and forms. In the data enhancement stage, common enhancement techniques such as rotation, scaling, flipping, and color perturbation are used to increase the diversity of training data. Based on the traditional ViT model, a local self-attention mechanism is applied to replace the traditional global self-attention mechanism. Mixed precision training and distributed training strategies are used to effectively accelerate the training process while maintaining high accuracy. The model automatically generates innovative designs by learning the style and structural characteristics of Huizhou bamboo weaving patterns, and adds a detail repair module in the generation process to enhance the detail expression of the pattern. The experimental results show that the improved ViT model tends to 0.95 after 50 training rounds, indicating that it performs well in detail preservation and structural similarity; with a sample data volume of 5000, the training time of the improved ViT model is 47.4 seconds, and the GPU memory usage is 37.1GB, providing higher computing efficiency. The experimental results prove the effectiveness of this paper’s research on the innovative design algorithm of Huizhou bamboo weaving patterns. Weaving ; Computer science ; Bamboo ; Artificial intelligence ; Algorithm ; Machine learning ; Mechanical engineering Textile materials and evaluations
Authors: Nuradin Zhailkhan | Jiyenbeсk Sugirov | Mermurat Nigmetov | Г.Г. Байсарова | Kalyy Yerzhanov
Source: Civil Engineering and Architecture (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Study of the Non-Linear Static Elastic Stress-Strain State of Rod Elements Rod elements have wide application in the engineering structures including bridges, buildings, drilling platforms and other types of infrastructure. The ability to predict accurately the behaviour of these elements under various loads is crucial to the safety and reliability of above-mentioned structures. The aim of this work is to improve the reliability prediction of the offshore platform. One important aspect of this prediction is the study of the non-linear static elastic stress state of the rod elements. To solve these problems, designers and engineers use a number of techniques including the finite element analysis to ensure that the platform can withstand the expected loads and perform its intended function safely and reliably. The design of drilling platforms in the Caspian Sea is a complex and non-trivial task that requires careful consideration of various factors including environment, safety regulations and operational requirements. Severe environmental conditions such as strong winds, waves and currents can have a significant impact on the structural integrity of the platform, while the presence of hydrocarbons in the area poses a risk of explosions and fires. This study presents the calculation of amplitude-frequency characteristics of free oscillations of the offshore drilling platforms on an anisotropic layered base using the finite element method. The findings will help to understand better the design of the Structural engineering ; Stress–strain curve ; Materials science ; Stress (linguistics) ; Linear elasticity ; Strain (injury) ; State (computer science) ; Mechanics ; Finite element method ; Physics ; Engineering ; Mathematics Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Engineering Diagnostics and Reliability
Authors: Lorenzo Villa | Till Frömling | Karsten Albe
Source: ChemRxiv (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

Influence of donor and acceptor doping on conductivity in potassium niobate Lead-free ferroelectrics based on the (K,Na)NbO3 solid solution have emerged as strong candidates to replace lead-containing ceramics due to their excellent piezoelectric and thermal properties. However, their practical application is hindered by high leakage currents and low ceramic density. Doping provides a strategy for enhancing material performance, highlighting the need for a deeper understanding of defect chemistry. For this purpose, we investigated the equilibrium of intrinsic defects and the doping behaviour of Ca and Fe in the boundary phase KNbO3. We found all elements to be prone to vacancy formation. Moreover, Ca acts as a donor and leads to a large concentration of charge carriers and therefore larger electronic conductivity. On the other hand, Fe behaves as an acceptor and shows oxidation states ranging from 2+ to 5+, whose concentrations depend on the total Fe concentration. As a consequence, the number of charge carriers and the electronic conductivity are reduced. Doping ; Conductivity ; Acceptor ; Potassium ; Potassium niobate ; Materials science ; Inorganic chemistry ; Condensed matter physics ; Chemistry ; Optoelectronics ; Physical chemistry ; Physics ; Metallurgy Ferroelectric and Piezoelectric Materials ; Glass properties and applications
Authors: Jaza Anwar Sayyed | Ansari Novman Nabeel | Ansari Ammara Firdaus
Source: International Journal of Environmental Sciences (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Exploring Thermal Conductivity: Experimental Insights Into Heat Transfer Properties Of Common Materials This study investigates the thermal conductivity of various materials, focusing on their heat transfer capabilities under controlled laboratory conditions. The research employs extensive experimental analysis, including measurements of heat flux, temperature gradients, and material dimensions, to determine thermal conductivities for materials such as aluminum, copper, glass, and wood. Using Fourier’s law of heat conduction as the theoretical framework, the experiment involved precise instrumentation, including thermocouples and data acquisition systems, to ensure accurate data collection. Error propagation analysis was conducted to quantify uncertainties in the measurements. The results reveal significant differences in the thermal conductivities of these materials, providing insights into the physical mechanisms governing heat transfer and their practical applications in engineering, thermal management systems, and material science. This comprehensive approach bridges theoretical concepts with real-world applications, making the findings valuable for designing efficient heat transfer and insulation systems. Thermal conductivity ; Heat transfer ; Materials science ; Thermal conduction ; Thermal ; Thermal effusivity ; Thermodynamics ; Thermal resistance ; Composite material ; Thermal contact conductance ; Physics Thermal properties of materials ; Heat Transfer and Optimization
Authors: Shiyu Peng | Donnie Pinkston | Jia Yao | Sergei Kliavinek | Ivan Maliyov | Marco Bernardi
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Efficient GPU Parallelization of Electronic Transport and Nonequilibrium Dynamics from Electron-Phonon Interactions in the Perturbo Code The Boltzmann transport equation (BTE) with electron-phonon (e-ph) interactions computed from first principles is widely used to study electronic transport and nonequilibrium dynamics in materials. Calculating the e-ph collision integral is the most important step in the BTE, but it remains computationally costly, even with current MPI+OpenMP parallelization. This challenge makes it difficult to study materials with large unit cells and to achieve high resolution in momentum space. Here, we show acceleration of BTE calculations of electronic transport and ultrafast dynamics using graphical processing units (GPUs). We implement a novel data structure and algorithm, optimized for GPU hardware and developed using OpenACC, to process scattering channels and efficiently compute the collision integral. This approach significantly reduces the overhead for data referencing, movement, and synchronization. Relative to the efficient CPU implementation in the open-source package Perturbo (v2.2.0), used as a baseline, this approach achieves a speed-up of 40 times for both transport and nonequilibrium dynamics on GPU hardware, and achieves nearly linear scaling up to 100 GPUs. The novel data structure can be generalized to other electron interactions and scattering processes. We released this GPU implementation in the latest public version (v3.0.0) of Perturbo. The new MPI+OpenMP+GPU parallelization enables sweeping studies of e-ph physics and electron dynamics in conventional and quantum materials, and prepares Perturbo for exascale supercomputing platforms. Non-equilibrium thermodynamics ; Collision ; Overhead (engineering) ; Central processing unit ; Acceleration ; Code (set theory) ; Supercomputer ; Scattering ; Computation Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Bakura MaiBunu | Abubakar Hamisu | Idriss Mohammed Godowolli
Source: International Journal of Advances in Engineering and Management (review, 2025-03-01)
Field: Materials Science — Materials Chemistry

Detecting and Monitoring Insulation Failure: A Review of Partial Discharge (PD) Detection Techniques The reliability and efficiency of electrical systems vitally depend on electrical insulation to prevent electricity leakage during transmission. However, due to various operational and environmental factors such as humidity, thermal stress, and others, insulation materials degrade over time. Therefore, detecting and monitoring insulation degradation at an early stage is essential to prevent catastrophic losses, ensure system reliability, and extend the operational life of electrical equipment. This paper reviews the relevance of recognizing the early stages of insulation failure and deterioration. The focus is on Partial Discharge (PD) analysis, a widely used technique, and the various test configurations employed to evaluate insulation performance, highlighting the importance of predictive maintenance to increase the lifespan and safety of insulation systems by summarizing experimental case studies that investigated the PD method detection of a 30 MVA transformer and 6/10kV XPLE cable. Partial discharge ; Reliability engineering ; Forensic engineering ; Environmental science ; Computer science ; Engineering ; Materials science ; Electrical engineering High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Fire Detection and Safety Systems
Authors: Gyeong-Yeol Lee | Gyung‐Suk Kil | Sung‐Wook Kim
Source: Journal of Electrical Engineering (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Partial discharge defect classification in cast-resin transformers using machine learning-based algorithms Abstract Cast-resin transformers have been widely installed in a power system due to excellent arc-extinguishing characteristics, easy installation, and low expense of maintenance. From the perspective of condition monitoring, identification and classification techniques of internal defects based on partial discharge (PD) measurements are getting more important. This paper studies PD defect classification using two kinds of machine learning (ML) algorithms, random forest (RF) and artificial neural network (ANN) models. Four typical PD defect models were designed: metal protrusion, a particle on insulator, delamination, and a void. PD single pulses and phase-resolved partial discharge (PRPD) patterns at each partial discharge inception voltage (PDIV) were measured by the printed circuit board (PCB) based Rogowski-type PD sensor. Various kinds of PD features were extracted from each PD single pulse and PRPD pattern. From the experimental results, the two different ML algorithms, used in this paper, could classify the PD defects with over 90%, and the PD classification rate using RF model was slightly higher than that of the ANN model. Partial discharge ; Artificial neural network ; Transformer ; Voltage ; Backpropagation ; Pattern recognition (psychology) ; High voltage ; Random forest High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: S. Kim | D.K. Park | Hyung-Joo Choi | Chul Hee Min | Jae Joon Ahn
Source: Energies (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies The increase in the number of nuclear power plants utilized for achieving carbon neutrality has emphasized the importance of managing high-radiation spent nuclear fuel (SNF). Traditional inspection methods, such as gamma emission tomography (GET), have limitations in terms of detecting partial defects within SNF assemblies. This study aims to increase the accuracy of SNF defect detection by optimizing artificial intelligence (AI)-based classification algorithms. By using emission tomography image data acquired from 3 × 3 nuclear fuel assemblies, we compare the performance of neural network models (AlexNet, ResNet, and the squeeze-and-excitation network (SENet)) and tree-based ensemble models (extreme gradient boosting (XGBoost), random forest model, and light gradient boosting machine (LightGBM)). Our results show that the neural network models, particularly ResNet and SENet, achieve superior classification accuracy with limited training data. SENet in particular demonstrates high performance with fewer samples, indicating its defect detection effectiveness in scenarios with minimal data. Tree-based models such as XGBoost and LightGBM also exhibit high accuracy but are slightly less effective than the neural networks are. In conclusion, AI-based classification systems, especially those utilizing advanced neural networks, can significantly improve the inspection and management processes related to SNFs, ensuring safety and compliance in nuclear energy operations. Future work should explore these methodologies for larger configurations beyond 3 × 3 assemblies to further validate their effectiveness. Image (mathematics) ; Computer science ; Nuclear engineering ; Artificial intelligence ; Engineering Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Nuclear Materials and Properties
Authors: Akihiro Fujii | Anh Khoa Augustin Lu | Kōji Shimizu | Satoshi Watanabe
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A straightforward gradient-based approach for designing superconductors with high critical temperature: exploiting domain knowledge <i>via</i> adaptive constraints Gradient-based design of compositions for high- T c superconductors: optimising T c and formation energy while adaptively integrating domain knowledge—controlling element and enforcing small integer stoichiometry—via masks and a special loss function. Domain (mathematical analysis) ; Energy (signal processing) ; Integer (computer science) ; Constraint (computer-aided design) ; Element (criminal law) ; Topology (electrical circuits) Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Inorganic Chemistry and Materials
Authors: Abdul Adheem Zaily Hameed | Qeethara Kadhim Al-Shayea | Muzhir Shaban Al-Ani | Bintang Annisa Bagustari
Source: International Journal of Advances in Soft Computing and its Applications (article, 2025-03-30)
Field: Materials Science — Surfaces, Coatings and Films

Bio-composite Material Characterization Based on Field Emission Scanning Electron Microscopy Images Measuring the texture properties of the sample surface is an important matter for bio composite materials. In this research, the samples were prepared from egg shells and palm fronds, and a set of pictures were taken in the Field Emission Scanning Electron Microscopy (FESEM) of many samples before and after hydrochloric acid immersion. Then, the image processing and analysis phase begins by taking images of different sizes and measuring the texture properties of the images. The statistical measurement results were performed on the samples to specify their effect on the acid solution. Many factors were applied, including; contrast, dissimilarity, homogeneity, angular second moment, maximum probability, entropy, energy, mean, variance, and correlation. After studying and comparing the samples' results and examining the acid effect on them, it was found that the two samples are resistant to acid immersion; the palm frond sample is more resistant to acid immersion than the eggshell sample. The results are quite acceptable compared with the previous studies. Characterization (materials science) ; Field electron emission ; Scanning electron microscope ; Materials science ; Composite number ; Field (mathematics) ; Scanning confocal electron microscopy ; Electron ; Nanotechnology ; Composite material ; Physics ; Mathematics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques