jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ting-Ju Wei | Chuin-Shan
Source: arXiv (Cornell University) (preprint, 2024-11-10)
Field: Materials Science — General Materials Science

Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images via masked image reconstruction. The pretrained MMAE learns latent representations that capture essential microstructural features and are broadly transferable across tasks. We demonstrate two key applications: (i) predicting homogenized stiffness components through fine-tuning on limited data, and (ii) inferring physically interpretable parameters by coupling MMAE with an interaction-based material network (IMN), thereby enabling extrapolation of nonlinear stress-strain responses. These results highlight the promise of microstructure foundation models and lay the groundwork for future extensions to more complex systems, such as 3D composites and experimental datasets. Foundation (evidence) ; Composite number ; Materials science ; Composite material ; History ; Archaeology Material Properties and Applications
Authors: Duc-Tri Vo | Ionela Prodan | Laurent Lefèvre | Vincent Vanel | Sylvain Costenoble | Binh Dinh
Source: arXiv (Cornell University) (preprint, 2024-02-06)
Field: Chemistry — Inorganic Chemistry

PSO-Based Adaptive NMPC for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process This paper addresses the particularities of adaptive optimal control of the uranium extraction-scrubbing operation in the PUREX process. The process dynamics are nonlinear, high dimensional, and have limited online measurements. In addition, analysis and developments are based on a qualified simulation program called PAREX, which was validated with laboratory and industrial data. The control objective is to stabilize the process at a desired solvent saturation level, guaranteeing constraints and handling disturbances. The developed control strategy relies on optimization-based methods for computing control inputs and estimates, i.e., Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimation (NMHE). The designs of these two associated algorithms are tailored for this process's particular dynamics and are implemented through an enhanced Particle Swarm Optimization (PSO) to guarantee constraint satisfaction. Software-in-the-loop simulations using PAREX show that the designed control scheme effectively satisfies control objectives and guarantees constraints during operation. Data scrubbing ; Extraction (chemistry) ; Uranium ; Spent nuclear fuel ; Process (computing) ; Nuclear fuel ; Nuclear engineering ; Enriched uranium ; Environmental science ; Waste management ; Computer science ; Chemistry ; Engineering ; Materials science ; Chromatography ; Metallurgy Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Parsa Kharazi Esfahani | Hadi Mahdavi Basir | Ahmad Reza Rabbani
Source: Scientific Reports (article, 2024-09-02)
Field: Chemistry — Analytical Chemistry

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock-Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock-Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock-Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods. Vitrinite reflectance ; Workflow ; Persian ; Reflectivity ; Computer science ; Data mining ; Geology ; Database ; Paleontology ; Optics ; Source rock Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Remote-Sensing Image Classification
Authors: Jiyao Zhang | Yue Hu | A. Lazarian
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability of Faraday Rotation Measure sources. To address the challenge, we propose a novel method that employs Convolutional Neural Networks (CNNs) alongside synchrotron emission observations to estimate magnetic field strengths in galaxy clusters. Our CNN model is trained on either magnetohydrodynamic (MHD) turbulence simulations or MHD galaxy cluster simulations, which incorporate complex dynamics such as cluster mergers and sloshing motions. The results demonstrate that CNNs can effectively estimate magnetic field strengths with mean squared error of approximately \SI{0.135}{\micro G}$^2$, \SI{0.044}{\micro G}$^2$, and \SI{0.02}{\micro G}$^2$ for $β= 100$, 200, and 500 conditions, respectively. Additionally, we have confirmed that our CNN model remains robust against noise and variations in viewing angles with sufficient training, ensuring reliable performance under a wide range of observational conditions. We compare the CNN approach with the traditional magnetic field strength estimates method that assumes equipartition between cosmic ray electron energy and magnetic field energy. Different from the equipartition method, this CNN approach relies on the morphological feature of synchrotron images, offering a new perspective for complementing traditional estimates and enhancing our understanding of cosmic ray acceleration mechanisms. Synchrotron ; Galaxy ; Field (mathematics) ; Magnetic field ; Synchrotron radiation ; Physics ; Astrophysics ; Optics ; Mathematics Machine Learning in Materials Science
Authors: Bohan Li | C Wang | Junjie Yan
Source:  (preprint, 2024-05-21)
Field: Materials Science — Materials Chemistry

Three-dimensional numerical investigation on the transient response characteristics of DIR-SOFC during increasing electrical load process Transient (computer programming) ; Process (computing) ; Materials science ; Transient analysis ; Mechanics ; Transient response ; Computer science ; Electrical engineering ; Physics ; Engineering Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Amit Kumar | A. Sarangi | D.K. SINGH | Indra Mani | Kakali Bandhyopadhyay | Sukanta Dash | Manoj Kumar Khanna
Source: Journal of Agrometeorology (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Evaluation of soft-computing techniques for pan evaporation estimation Estimation of pan evaporation (Epan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75&lt;NSE 1; PBias&lt; 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86), RMSE (0.93 to 1.22), PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of Epan for the study region. Soft computing ; Pan evaporation ; Evaporation ; Estimation ; Environmental science ; Computer science ; Meteorology ; Agricultural engineering ; Artificial intelligence ; Engineering ; Geography ; Artificial neural network ; Systems engineering Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Advanced Sensor Technologies Research
Authors: Antonin Louiset | Georg Daniel Förster | Elisabetta Medina | Saïd Tahir | Vincent Jourdain | Christophe Bichara | Hanako Okuno
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structural characterization of single wall carbon nanotubes via AI assisted transmission electron microscopy International audience Characterization (materials science) ; Transmission electron microscopy ; Carbon nanotube ; Materials science ; Nanotechnology ; Electron microscope ; Energy filtered transmission electron microscopy ; Scanning transmission electron microscopy ; Optics ; Physics Carbon Nanotubes in Composites ; Graphene research and applications ; Diamond and Carbon-based Materials Research
Authors: Saadia Makni | Imen Fourati Kallel | M. A. Triki
Source: Journal of Applied Research and Technology (article, 2024-10-30)
Field: Chemistry — Analytical Chemistry

Intelligent inspection and quality control of table olives Quality is a key factor of product-marketing in the field of agriculture and food industry. A number of researches have suggested the use of vision computer systems and machine learning (ML) techniques essentially to inspect imperfection surfaces and defects in fruit products. Following the trend, this paper still relies on the use of vision computer system by assuring an intelligent detection of table olive defect, with a certainty of its quality control. The specifically exploited computer vision system consists of automatically extracting the texture, colour, and shape features mainly from the global thresholding segmented image. With reference to the extracted features, the newly introduced system has the capacities of distinguishing between the defected and the healthy olive fruits rapidly and effectively at the same time. Subsequently, it is capable of identifying and specifying the diseased table olive. This system is also helpful for estimating the table olive size automatically. The experimental findings are indicative of the high accuracy of the binary classification algorithm, reaching 99, 32%, the average processing time for just one olive is about 0.4 s, which could meet the requirements of the real-time applications, and the error in estimating the size of the table olives does not exceed 10%. Quality (philosophy) ; Table (database) ; Control (management) ; Computer science ; Reliability engineering ; Engineering drawing ; Operations management ; Engineering ; Data mining ; Artificial intelligence ; Philosophy Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Date Palm Research Studies
Authors: Agustami Sitorus
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Referee report. For: Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling [version 2; peer review: 3 approved with reservations, 1 not approved] Quality (philosophy) ; Regression analysis ; Polynomial regression ; Linear regression ; Polynomial ; Regression Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Banana Cultivation and Research
Authors: Federico Napoli | Jia‐Ying Guan | Charles‐Adrien Arnaud | Pavel Macek | Hugo Fraga | Cécile Breyton | Paul Schanda
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-08)
Field: Chemistry — Spectroscopy

Deuteration of proteins boosted by cell lysates: high-resolution amide and Hα MAS NMR without re-protonation bottleneck Amide-proton detected magic-angle spinning NMR of deuterated proteins has become a main technique in NMR-based structural biology. In standard deuteration protocols that rely on D 2 O-based culture media, non-exchangeable amide sites remain deuterated, making these sites unobservable. Here we demonstrate that proteins produced with H 2 O-based culture medium doped with deuterated cell lysate allow to overcome this “reprotonation bottleneck”, while retaining a high level of deuteration (ca. 80 %) and narrow line widths. We quantified coherence life times of several proteins prepared with this labelling pattern over a range of MAS frequencies (40-100 kHz). We demonstrate that under commonly used conditions (50-60 kHz MAS), amide 1 H line widths with our labelling approach are comparable to those of perdeuterated proteins and better than those of protonated samples at 100 kHz. For three proteins in the 33-50 kDa size range many previously unobserved amides become visible. We report how to prepare the deuterated cell lysate for our approach from fractions of perdeuterated cultures which are usually discarded, and show that such media can be used identically to commercial media. The residual protonation of Hα sites allows for well-resolved Hα-detected spectra and Hα resonance assignment, exemplified by the de novo assignment of 168 Hα sites in a 39 kDa protein. The approach based on this H 2 O/cell-lysate deuteration and MAS frequencies compatible with 1.3 or 1.9 mm rotors presents a strong sensitivity benefit over 0.7 mm/100 kHz MAS experiments. Protonation ; Deuterium ; Amide ; Chemistry ; Magic angle spinning ; Lysis ; Proton NMR ; Analytical Chemistry (journal) ; Nuclear magnetic resonance spectroscopy ; Crystallography ; Stereochemistry ; Chromatography ; Biochemistry ; Organic chemistry ; Physics ; Atomic physics Advanced NMR Techniques and Applications ; Ion channel regulation and function ; Advanced MRI Techniques and Applications
Authors: Erica Walker | Amanda Bridges
Source: Color and Imaging Conference (article, 2024-10-28)
Field: Materials Science — Polymers and Plastics

Color Accuracy and Durability for Printed, Branded Textiles Branded athletic apparel is a multi-billion dollar business and it continues to expand year over year. Sports teams and their fans expect brand accuracy and durability across all types of textile-based products from clothing to home decor. Color is a vital part of a brand&#x2019;s identity and printing consistent, durable, and accurate brand colors across different materials can be challenging for manufacturers. This study examines the accuracy and durability of two brand colors, specified by the brand as Pantone and CMYK values and measured as LAB values using a spectrophotometer, and printed on three textiles commonly used for clothing. The study examined the three most common printing processes for manufacturing apparel&#x2013;dye sublimation, direct-to-garment (DTG), and screen printing. Study results show that the screen printing process is able to provide the most accurate color replication on any of the three textiles when compared to the target brand color. The DTG process in combination with a polyester or poly-blend material provides the best durability of the color after the first cycle of laundering has been completed. However, dye sublimation on blend or polyester textiles provides the most color stability after the full five treatment cycles. Durability ; Materials science ; Composite material ; Pulp and paper industry ; Business ; Engineering Textile materials and evaluations ; Color Science and Applications ; Color perception and design
Authors: Segar VP Nithya | K P Ragunath | S Pazhanivelan | R. Kumaraperumal | Paramanandham Latha
Source: Plant Science Today (article, 2024-11-15)
Field: Chemistry — Inorganic Chemistry

Mapping coconut plantation in Western Agro-Climatic zone using object-based classification and machine learning technique Coconut (Cocos nucifera), a key crop for over 10 million farming families in India, is vital in the agricultural economies of southern states like Tamil Nadu. However, traditional methods of monitoring coconut plantations are challenging due to the crop's geographical dispersion and seasonal variations. This study focuses on mapping coconut plantations in the Western Agro-Climatic Zone of Tamil Nadu using Object-Based Classification (OBC) and machine learning techniques. A ten-year time series of Landsat 7 optical satellite data (2012-2013 and 2022-2023) was employed, combined with ground truth surveys across the region. The study utilized Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy. The RF classifier achieved an accuracy of 91.7% in 2012-2013 and 90.3% in 2022-2023, outperforming SVM, which hovered around 70%. The research also conducted a change detection analysis, revealing a net increase of 3,270 hectares of coconut plantations over the decade, with the Coimbatore district contributing the most significant growth of 2,560 hectares. This study underscores the effectiveness of integrating OBC and machine learning, mainly RF, for accurate and efficient mapping of coconut plantations using Landsat satellite data. Cocos nucifera ; Object based ; Geography ; Object (grammar) ; Agroforestry ; Artificial intelligence ; Computer science ; Environmental science ; Biology ; Horticulture Coconut Research and Applications ; Remote Sensing in Agriculture ; Date Palm Research Studies
Authors: Arturo Martin Jimenez | M Baltes | Jackson Cornelius | Neşet Aközbek | Zachary Coppens
Source: arXiv (Cornell University) (preprint, 2024-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-Shot Phase Diversity Wavefront Sensing in Deep Turbulence via Metasurface Optics Free-space optical communication (FSOC) systems offer high-bandwidth and secure communication with minimal capital costs. Adaptive optics (AO) are typically added to these systems to decrease atmospheric channel losses; however, the performance of traditional AO wavefront sensors degrades in long-range, deep turbulence conditions. Alternative wavefront sensors using phase diversity can successfully reconstruct wavefronts in deep turbulence, but current implementations require bulky setups with high latency. In this work, we employ a nanostructured birefringent metasurface optic that enables low-latency phase diversity wavefront sensing in a compact form factor. We prove the effectiveness of this approach in mid-to-high turbulence (Rytov numbers from 0.2 to 0.6) through simulation and experimental demonstration. In both cases an average 16-fold increase in signal from the corrected beam is obtained. Our approach opens a pathway for compact, robust wavefront sensing that enhances range and accuracy of FSOC systems. Wavefront ; Optics ; Diversity (politics) ; Single shot ; Phase (matter) ; Turbulence ; Adaptive optics ; Shot (pellet) ; Physics ; Materials science ; Political science ; Meteorology Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Optical Wireless Communication Technologies
Authors: Zhen Liu | Mohamed A. Ettabib | Bethany M. Bowden | Philip N. Bartlett | James S. Wilkinson | Michalis N. Zervas
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-01-21)
Field: Chemistry — Analytical Chemistry

Multiframe-based non-local means denoising for Raman spectra A method for denoising Raman spectra is presented in this paper. The approach is based on the principle that the original signal can be restored by averaging pixels based on structure similarity. Similarity searching and averaging are not limited to the neighbouring pixels but extended throughout the entire signal range across different frames. This approach is distinguished from the conventional single-frame neighbour pixel-based filtering. The effectiveness and robustness of the proposed method are demonstrated through denoising simulated and experimental Raman data sets with fixed denoising parameters. Several denoised results and statistical indicators are presented for the simulated data. Recovery of the experimental Raman spectrum from our newly developed cost-effective waveguide-enhanced Raman spectroscopy system is also presented and compared to the spectrum from a conventional expensive Raman microscope for the same analyte. Raman spectroscopy ; Noise reduction ; Pixel ; Robustness (evolution) ; SIGNAL (programming language) ; Chemistry ; Artificial intelligence ; Pattern recognition (psychology) ; Analyte ; Similarity (geometry) ; Optics ; Biological system ; Computer science ; Image (mathematics) ; Physics Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods ; Spectroscopy Techniques in Biomedical and Chemical Research