jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Se-Jin Kim | Kyung Kiu Kim | Yunseok Seo
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Phase Diagram from Nonlinear Interaction between Superconducting Order and Density: Toward Data-Based Holographic Superconductor We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function $M(F^2)$, which is necessary to understand phase transition behavior. This mass function describes a nonlinear interaction between superconducting order and charge carrier density. We introduce positional embedding layers to improve the learning process in our algorithm, and the Adam optimization is used to predict the critical temperature data via holographic calculation with appropriate accuracy. Consideration of the positional embedding layers is motivated by the transformer model of natural-language processing in the artificial intelligence (AI) field. We obtain holographic models that reproduce borderlines of the normal and superconducting phases provided by actual data. Our work is the first holographic attempt to match phase transition data quantitatively obtained from experiments. Also, the present work offers a new methodology for data-based holographic models. Superconductivity ; Phase diagram ; Holography ; Condensed matter physics ; Order (exchange) ; Nonlinear system ; Diagram ; Physics ; Phase (matter) ; Theoretical physics ; Computer science ; Quantum mechanics ; Database ; Economics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism
Authors: Michel Masella | Fabien Léonforte
Source: The Journal of Chemical Physics (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

The multi-scale polarizable pseudo-particle solvent coarse-grained approach: From NaCl salt solutions to polyelectrolyte hydration We discuss key parameters that affect the reliability of hybrid simulations in the aqueous phase based on an efficient multi-scale coarse-grained polarizable pseudo-particle approach, denoted as pppl, to model the solvent water, whereas solutes are modeled using an all atom polarizable force field. Among those parameters, the extension of the solvent domain (SD) at the solute vicinity (domain in which each solvent particle corresponds to a single water molecule) and the magnitude of solute/solvent short range polarization damping effects are shown to be pivotal to model NaCl salty aqueous solutions and the hydration of charged systems, such as the hydrophobic polyelectrolyte polymer that we have recently investigated [Masella et al., J. Chem. Phys. 155, 114903 (2021)]. Strong short range damping is pivotal to simulate aqueous salt NaCl solutions at moderate concentration (up to 1.0M). The SD extension (as well as short range damping) has a weak effect on the polymer conformation; however, it plays a pivotal role in computing accurate polymer/solvent interaction energies. As the pppl approach is up to two orders of magnitude computationally more efficient than all atom polarizable force field methods, our results show it to be an efficient alternative route to investigate the equilibrium properties of complex charged molecular systems in extended chemical environments. Aqueous solution ; Polarizability ; Chemical physics ; Solvent ; Polyelectrolyte ; Polymer ; Molecular dynamics ; Particle (ecology) ; Materials science ; Chemistry ; Force field (fiction) ; Molecule ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Electrostatics and Colloid Interactions
Authors: A.A. Obydenova | D.M. Myalenko
Source:  (article, 2024-12-11)
Field: Materials Science — General Materials Science

STUDY OF THE STRENGTH PROPERTIES OF A BIODEGRADABLE MATERIAL BASED ON POLYBUTYLENE-ADIPATE TEREPHTHALATE AND POLYLACTIDE WHEN EXPOSED TO ULTRAVIOLET RADIATION Adipate ; Polybutylene terephthalate ; Ultraviolet ; Materials science ; Polyester ; Composite material ; Optoelectronics Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Theodoros Anagnostopoulos | Ioakeim Spiliopoulos
Source: International Journal of Research Publication and Reviews (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

Differentiating PDO Kalamata olive oil in comparison with Crete olive oil based on statistical data analytics Greece is a southeastern Mediterranean country, which cultivates olive oil since the ancient years' agriculture activities.There are several regional areas in Greece which produce high quality olive oil, such a as Kalamata and Crete.Specifically, Kalamata is a city located in southeastern Greece in the Mediterranean basin and it is the capital of the Messenia regional unit.Concretely, Crete is an island located in the southern Greece in the Aegean Sea.Intuitively, both geographical areas are known for the famous olive oil quality they produce.However, Protected Designation of Origin (PDO) Kalamata olive oil, established by Council regulation (EC) No 510/2006, is considered an exceptional extra virgin olive oil variety produced in the Greece region.Subsequently, there is a need to distinguish PDO Kalamata olive oil from other olive oil varieties is Greece such as the Crete olive oil, since PDO Kalamata olive oil is the main variety exported in the global olive oil market.Differentiating PDO Kalamata olive oil compared with Crete olive oil requires experimentation with statistical data analytics models, which are able to distinguish variations of certain chemical characteristics observed in certain regions of Greece.In this paper, we use statistical data analytics models to differentiate the geographical origin of PDO Kalamata olive oil compared with Crete olive oil based on synchronous excitation-emission fluorescence spectroscopy of olive oils.Experimental evaluation as well as data visualization of the adopted statistical models are promising for differentiating the origin of PDO Kalamata olive oil with high values of prediction accuracy thus enabling companies to exploit extra virgin olive oil economic potentiality to global market place. Olive oil ; Statistical analysis ; Analytics ; Computer science ; Data mining ; Statistics ; Food science ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Jajang Japar Sodik | Rusi Rismawanti
Source: International Journal of Health and Medicine (article, 2024-09-23)
Field: Chemistry — Spectroscopy

Method Development for Analysis of Cyclodextrin Inclusion Complexes with HPLC Cyclodextrin inclusion complexes have been widely used in the pharmaceutical industry to improve drug solubility and bioavailability. Analysis of these complexes using HPLC requires the development of appropriate and validated methods. To analyze and compare various HPLC methods used for the analysis of cyclodextrin inclusion complexes, and to identify challenges and trends in method development. A literature review was conducted of articles published in the last 10 years, focusing on HPLC methods for the analysis of cyclodextrin inclusion complexes. Parameters compared included column type, mobile phase composition, flow rate, and detection method. The majority of studies used C18 columns with varying lengths and particle sizes. The composition of the mobile phase varied, reflecting the diversity of properties of the complexes analyzed. The flow rate was generally 1.0 mL/min, with the exception of the HPLC/MS method. Detection methods included UV-Vis, PDA, fluorescence, and mass spectrometry. The main challenges included the dynamic equilibrium of the complexes and stability during analysis HPLC method development for the analysis of cyclodextrin inclusion complexes requires optimization of various parameters to overcome specific challenges. Future trends are towards the use of advanced technologies such as UHPLC and high-resolution mass detection. Cyclodextrin ; Inclusion (mineral) ; High-performance liquid chromatography ; Chromatography ; Chemistry ; Mineralogy Analytical Chemistry and Chromatography ; Protein purification and stability ; Chromatography in Natural Products
Authors: Lia Kamelia | Eki Ahmad Zaki Hamidi | Reno Muhammad Fadilla
Source: IAES International Journal of Artificial Intelligence (article, 2024-10-08)
Field: Chemistry — Analytical Chemistry

Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%. Adaptive neuro fuzzy inference system ; Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Neuro-fuzzy ; Machine learning ; Quality (philosophy) ; Inference system ; Pattern recognition (psychology) ; Data mining ; Fuzzy logic ; Fuzzy control system Spectroscopy and Chemometric Analyses
Authors: B.A. BONDAREV | A.B. BONDAREV | E.V. Basinskikh
Source:  (article, 2024-12-12)
Field: Materials Science — General Materials Science

FATIGUE RESISTANCE OF WOOD-FIBERGLASS COMPOSITE MATERIALS The article discusses the impact of financial risks on the activities of a modern enterprise in a crisis and their management in the system of internal control of the enterprise.A correct assessment of financial risks helps to increase the stability of the enterprise, and the construction of an internal control system at the enterprise also contributes to the minimization of financial risks. Composite material ; Materials science ; Composite number ; Resistance (ecology) ; Impact resistance Material Properties and Applications ; Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites
Authors: Srijana Pandey | D. K. Gautam | Jixin Chen
Source: ChemRxiv (preprint, 2024-03-25)
Field: Chemistry — Spectroscopy

Measuring the Adsorption Cross-Section of YOYO-1 to Immobilized DNA Molecules Many interactions between small molecules and particles occur in solutions. They are surrounded by other molecules that do not react, for example, biological processes in water, chemical reactions in gas or liquid solutions, and environmental reactions in air and water. Predicting the rate of such interactions involves an understanding of a fundamental process called diffusion, the random motion of molecules in solutions exampled by the famous Brownian motion of pollen particles. It has been challenging to conceptualize a mathematical equation to describe the rate, for example calculating the summed probability of all diffusive molecules finding an immobilized target molecule at an interface within a certain period, the elementary step of adsorption and absorption. In this report, we introduced an equation developed very recently to predict the adsorption rate and measured the staining rate of a DNA molecule with a simple organic dye molecule YOYO-1. The adsorption cross-section of the interaction is extracted for the first time to be consistent with the physical sizes of the molecules. This could be an important step toward a genderized method to predict the adsorption rate of many reactions. Adsorption ; Section (typography) ; DNA ; Molecule ; Cross section (physics) ; Chemistry ; Chromatography ; Combinatorial chemistry ; Chemical engineering ; Computer science ; Engineering ; Biochemistry ; Physics ; Organic chemistry Analytical Chemistry and Chromatography ; Mesoporous Materials and Catalysis ; Chemical Synthesis and Characterization
Authors: Ian Bentley | James Tedder | M. Gebran | Ayan Paul
Source: arXiv (Cornell University) (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

High Precision Binding Energies from Physics Informed Machine Learning Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies and three different mass models. Then four machine learning approaches are used to train on each energy difference. The most successful ML technique, both in interpolation and extrapolation, is the least squares boosted ensemble of trees. The best model resulting from that technique utilizes eight physical features to model the difference between experimental atomic binding energy values in AME 2012 and the Duflo Zuker mass model. This resulted in a model that fit the training data with a standard deviation of 17 keV and that has a standard deviation of 92 keV when compared all of the values in the AME 2020. The extrapolation capability of each model is discussed, and the accuracy of predicting new mass measurements has also been tested. Physics ; Computer science Machine Learning in Materials Science ; Computational Physics and Python Applications
Authors: Saumya Vilas Roy | Rajesh Sadanandan | K. Satheesh | Deepak Mishra
Source:  (preprint, 2024-11-27)
Field: Chemistry — Analytical Chemistry

Estimating Non-Uniform Temperature Profiles in Combustion Systems using Laser Absorption Spectroscopy and Multi-Output Gaussian Process Regression Precise temperature profile estimation is indispensable for optimizing combustion efficiency, minimizing pollutant emissions, and enhancing overall system performance. However, existing methods such as Thermocouples 1 suffer from limitations, including slow response times and reduced accuracy, while infrared pyrometry [2] and rapid thermometry [3] offer improved precision but are often hampered by limited spatial resolution. To address these challenges, this study presents a novel approach that integrates Laser Absorption Spectroscopy (LAS) [4] with Multi-Output Gaussian Process Regression (MOGPR), combining the benefits of both techniques to accurately estimate non-uniform temperature profiles in combustion systems. The proposed method leverages a single transition of molecular absorption spectra to infer the parameters of a Boltzmann distribution, thereby representing the temperature profile, and is rigorously evaluated using both simulated and experimental data [1], demonstrating its potential for reliable and efficient temperature estimation in combustion systems. Combustion ; Process (computing) ; Absorption (acoustics) ; Gaussian ; Materials science ; Spectroscopy ; Gaussian process ; Laser ; Absorption spectroscopy ; Kriging ; Analytical Chemistry (journal) ; Optics ; Mathematics ; Chemistry ; Computer science ; Statistics ; Physics ; Chromatography ; Composite material ; Physical chemistry Spectroscopy and Chemometric Analyses ; Combustion and flame dynamics ; Advanced Combustion Engine Technologies
Authors: Jessica Castillo Baltazar | Adolfo Eduardo Obaya-Valdivia | Benjamín Velasco‐Bejarano
Source: American Journal of Educational Research (article, 2024-03-13)
Field: Chemistry — Physical and Theoretical Chemistry

Didactic Sequence for the Introduction of Infrared Absorption Spectrophotometry at the High School Level A didactic sequence is proposed using an infrared puzzle and a didactic questionnaire for the teaching-learning of infrared spectroscopy at the upper secondary level. The didactic sequence that was designed was implemented in person in the subject of Chemistry IV at the high school level, corresponding to the sixth semester of the curriculum of the National School of Sciences and Humanities (CCH), Vallejo Campus, of the National Autonomous University of Mexico. In the didactic sequence, students understand the importance of functional groups where they determine organic compounds, by identifying regularities, properties, and structure of alcohols, aldehydes, ketones, and carboxylic acids. After applying the didactic sequence, a post-test questionnaire was applied, and the results showed that the students significantly improved their knowledge of IR when using the puzzle that allowed them to correctly identify the bands that identify the carbonyl group. Infrared Spectrophotometry ; Spectrophotometry ; Absorption (acoustics) ; Infrared ; Sequence (biology) ; Analytical Chemistry (journal) ; Chemistry ; Remote sensing ; Optics ; Chromatography ; Physics ; Geography ; Biochemistry Various Chemistry Research Topics
Authors: A. S. Kassem | M. M. Ahmed | Sherien Abdelwhab Montaser | Zeinab Hassan | Manal R. Mohammed
Source: Bioscience Journal (article, 2024-12-23)
Field: Chemistry — Organic Chemistry

Olive oil: research model to clarify its health benefits and its unique mechanism of action The uniqueness of Extra Virgin Olive Oil (EVOO) convincing evidence that its intake has many health aspects by increased longevity and prevention of many age-associated non infectious diseases such as cardiovascular and neurodegenerative diseases. In this study, Twenty-four rats were divided into 4 groups (6 rats each), as a following: Control group, EVOO group: rats were oral administrated with EVOO at a dose of 1ml/100g body weight for two weeks, Irradiated group (IR): rats were exposed to 6Gy of whole body γ-radiation; EVOO and IR group: rats were oral administrated with EVOO for two weeks prior to irradiation (6Gy). EVOO successfully reduces cellular destruction, chromosomal aberrations, pro-inflammatory markers interleukin-1β (IL-1β),and ameliorates heart and coronary arteries tissues damages, accompanied by lowering Vascular Endothelial Growth Factor (VEGF),Total Cholesterol (TC) and Triglyceride levels (TG). Therefore, EVOO provided adequate protection for cells against exposure to harmful ionizing irradiation (6 Gy). Mechanism (biology) ; Action (physics) ; Mechanism of action ; Olive oil ; Chemistry ; Biochemical engineering ; Biotechnology ; Food science ; Biology ; Biochemistry ; Engineering ; Physics ; Epistemology ; Philosophy Edible Oils Quality and Analysis ; Nuts composition and effects ; Nutritional Studies and Diet
Authors: Xia Hou
Source: Informatica (article, 2024-11-05)
Field: Materials Science — Polymers and Plastics

Shrinkage and Porosity of Cotton and Hemp Fabrics Based on ELM Algorithm Cotton and linen fabric is a kind of mixed textile of half linen and half cotton, which has the characteristics of both linen and cotton, so it is widely used in summer clothes. Shrinkage and bulk properties are important indicators of the quality of cotton and linen fabrics, so it is necessary to accurately predict their shrinkage and bulk properties. In this study, the ELM is introduced to predict its fluctuation range, and the PSO algorithm is adopted to optimize the ELM. The FIG technology is introduced to realize the dynamic extraction of its characteristics, obtain the FIG-PSO-ELM algorithm, and verify its validity. The experimental results show that within 1500 seconds, the shrinkage rate and elastic viscosity of cotton and linen fabrics change greatly, and the values are in the range of 49.2 to 51.2, and the maximum value is 51.15. In addition, the prediction results of the FIG-PSO-ELM method in Low, R, and Up sequences are basically consistent with the real values, showing good prediction accuracy. Compared with the prediction results of the other four algorithms, the FIG-PSO-ELM method has the best prediction effect, and the errors on the three sequences are the smallest, among which the minimum is 0.0019 and the minimum is 0.0368. Overall, the FIG-PSO-ELM method has a good prediction effect, accurately predicts the polycondensation area and porosity of cotton and linen fabrics, and has a good effect on the actual weaving of cotton and linen fabrics. Shrinkage ; Porosity ; Algorithm ; Materials science ; Composite material ; Computer science Textile materials and evaluations
Authors: Jerzy J. Langer
Source: arXiv (Cornell University) (preprint, 2024-12-13)
Field: Materials Science — Polymers and Plastics

Programmable polyaniline nano neural network. A simple physical model Here we report a working physical model of a programmable neural nanonetwork\nbased on a nanostructured conductive polymer - polyaniline. The device can be\nprogrammed owing to controlled growth of nanowires, a method developed in our\nlaboratory. The response (output signal) of the network depends on the\nactivated input line, but also on the amplitude of the input signals. The\nsystem is able to analyze multiple input signals in parallel and in real time.\nThis is still considered as a challenge.\n Nano- ; Polyaniline ; Artificial neural network ; Simple (philosophy) ; Polyaniline nanofibers ; Computer science ; Physical neural network ; Nanotechnology ; Materials science ; Artificial intelligence ; Recurrent neural network ; Composite material ; Types of artificial neural networks ; Polymer Conducting polymers and applications ; Neural Networks and Applications
Authors: Aixiang Jiang
Source: Preprints.org (review, 2024-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Optimizers in Traditional Statistics and Neural Networks &lt;i&gt;&lt;/i&gt; Phase errors in magnetic resonance (MR) techniques, including Nuclear Magnetic Resonance (NMR) spectroscopy and Magnetic Resonance Imaging (MRI), pose significant challenges to data accuracy and interpretation. As MR technologies advance, the demand for more sophisticated phase correction methods continues to grow, enhancing diagnostic precision and analytical outcomes. This review explores the evolution of phase correction models, beginning with simple global phase shifts, progressing through traditional linear statistical models, and culminating in modern machine learning techniques&amp;mdash;specifically, neural networks. We also examine a range of optimization functions and optimizers, including both MR data-specific and common statistical approaches, applied in phase error correction. Despite notable progress, challenges remain in developing fully automatic phase error correction methods, particularly given the absence of ground truth in real-world MR data. This review highlights key methods, discusses their limitations, and proposes future pathways, including ensemble learning, that could guide further innovation in phase error correction. Artificial neural network ; Computer science ; Phase (matter) ; Statistics ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics Magnetic Properties and Applications ; Nuclear Physics and Applications ; Neural Networks and Applications