jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D. Goll | Ralf B. Loeffler | Marius Boettle | J. Buschbeck | Gerhard Schneider
Source: Materials (article, 2024-06-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring Sintered Fe-(Ce, Nd)-B with High Degree of Cerium Substitution as Potential Gap Magnet The more effective use of readily available Ce in FeNdB sintered magnets is an important step towards more resource-efficient, sustainable, and cost-effective permanent magnets. These magnets have the potential to bridge the gap between high-performance FeNdB and hard ferrite magnets. However, for higher degrees of cerium substitution (>25%), the magnetic properties deteriorate due to the lower intrinsic magnetic properties of Fe<sub>14</sub>Ce<sub>2</sub>B and the formation of the Laves phase Fe<sub>2</sub>Ce in the grain boundaries. In this paper, sintered magnets with the composition Fe70.9-(Ce<sub>x</sub>Nd<sub>1-x</sub>)18.8-B5.8-M4.5 (M = Co, Ti, Al, Ga, and Cu; with Ti, Al, Ga, and Cu less than 2.0 at% in total and Co<sub>bal</sub>; <i>x</i> = 0.5 and 0.75) were fabricated and analyzed. It was possible to obtain coercive fields for higher degrees of Ce substitution, which previous commercially available magnets have only shown for significantly lower degrees of Ce substitution. For <i>x</i> = 0.5, coercivity, remanence, and maximum energy product of <i>µ</i><sub>0</sub><i>H</i><sub>c</sub> = 1.29 T (<i>H</i><sub>c</sub> = 1026 kA/m), <i>J</i><sub>r</sub> = 1.02 T, and (<i>BH</i>)<sub>max</sub> = 176.5 kJ/m<sup>3</sup> were achieved at room temperature for <i>x</i> = 0.75 <i>µ</i><sub>0</sub><i>H</i><sub>c</sub> = 0.72 T (<i>H</i><sub>c</sub> = 573 kA/m), <i>J</i><sub>r</sub> = 0.80 T, and (<i>BH</i>)<sub>max</sub> = 114.5 kJ/m<sup>3</sup>, respectively. Cerium ; Substitution (logic) ; Materials science ; Degree (music) ; Magnet ; Metallurgy ; Electrical engineering ; Physics ; Engineering ; Computer science Magnetic Properties of Alloys ; Magnetic properties of thin films ; Rare-earth and actinide compounds
Authors: Philipp Schmidt | Florian Marquardt | Naeimeh Mohseni
Source: arXiv (Cornell University) (preprint, 2024-05-25)
Field: Materials Science — Materials Chemistry

Transfer learning in predicting quantum many-body dynamics: from physical observables to entanglement entropy Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compelling area of research across diverse fields. One interesting open question is how beneficial representation learning can be for quantum many-body physics, with its notouriosly high-dimensional state space. In this work, we showcase the capacity of a neural network that was trained on a subset of physical observables of a many-body system to partially acquire an implicit representation of the wave function. We illustrate this by demonstrating the effectiveness of reusing the representation learned by the neural network to enhance the learning process of another quantity derived from the quantum state. In particular, we focus on how the pre-trained neural network can enhance the learning of entanglement entropy. This is of particular interest as directly measuring the entanglement in a many-body system is very challenging, while a subset of physical observables can be easily measured in experiments. We show the pre-trained neural network learns the dynamics of entropy with fewer resources and higher precision in comparison with direct training on the entanglement entropy. Observable ; Quantum entanglement ; Statistical physics ; Quantum ; Physics ; Entropy (arrow of time) ; Quantum discord ; Quantum mechanics ; Transfer entropy ; Classical mechanics ; Theoretical physics ; Mathematics ; Principle of maximum entropy ; Statistics Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Thermodynamics and Statistical Mechanics
Authors: Yessi Jusman | A.A. Putri | Anna Nur Nazilah Chamim | Yudhi Ardiyanto
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Comparison of Extracted Haar Wavelet Features for Herbal Leaf Type Classification Plants are incredibly beneficial to human survival in various ways. Leaves are part of plants widely used as medicine. They are similar in shape but have different advantages. Leaf types can only be identified by experts. This study aims to create a classification system for herbal leaves based on the Haar wavelet transform and machine learning. The study was carried out to assist ordinary people in recognizing herbal leaves. The results revealed that Haar wavelet level 1 was better suited to the leaf data. The Quadratic SVM model yielded the highest result with an accuracy of 77%, a precision of 83%, a recall of 83%, a specificity of 82%, and an F-score of 73%. Pattern recognition (psychology) ; Haar ; Artificial intelligence ; Traditional medicine ; Haar wavelet ; Type (biology) ; Wavelet ; Mathematics ; Computer science ; Wavelet transform ; Discrete wavelet transform ; Biology ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Haoxiang Zhang | Chao Liu | Jianguang Ma | Hui Sun
Source: Mathematics (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS). Computer science ; Bipartite graph ; Credibility ; Matching (statistics) ; Artificial intelligence ; Metric (unit) ; Fidelity ; Feature (linguistics) ; Graph ; Machine learning ; Pattern recognition (psychology) ; Data mining ; Theoretical computer science ; Engineering ; Mathematics Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Suphachai Phawiakkharakun | Sunee Pongpinigpinyo
Source: International Journal of Applied Science and Engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Enhanced non-destructive of degree of pineapple juiciness using ensemble learning model based on tapping sound sensing This research proposes to enhance the non-destructive method for classifying the juiciness of pineapples using tapping sound sensing. Ten statistical features were extracted from the waveform signals by waveform analysis. These features were then separated into a spectral feature set (3 features) and a temporal feature set (7 features). Each feature set was calculated with the weight of important features and selected features for 15 training datasets using 10 machine learning classifiers. Ten machine learning classifiers were Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Ensemble Voting, Adaboost, Ensemble Bagging, and Ensemble Stacking. The classifiers were evaluated with accuracy and the kappa coefficient. Grid search was used to determine various important hyperparameters for Machine Learning classifiers. The experiment results showed that the Ensemble Voting (soft), Ensemble Stacking, and MLP outperformed other classifiers. They can obtain an accuracy of 92.08%, and kappa coefficients are 0.8811, 0.8808 and 0.8808, respectively. Artificial intelligence ; Ensemble learning ; AdaBoost ; Pattern recognition (psychology) ; Random subspace method ; Random forest ; Support vector machine ; Gradient boosting ; Boosting (machine learning) ; Machine learning ; Computer science ; Weighted voting ; Hyperparameter ; Feature (linguistics) ; Mathematics ; Voting Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Zhi Tan | Xiang Lv | Jie Xing | Shaoxiong Xie | Hui Zhang | Jianguo Zhu
Source: arXiv (Cornell University) (preprint, 2024-11-05)
Field: Materials Science — Polymers and Plastics

Transverse Bending Mimicry of Longitudinal Piezoelectricity The origin of frequently observed ultrahigh electric-induced longitudinal strain, ranging from 1% to 26%, remains an open question. Recent evidence suggests that this phenomenon is linked to the bending deformation of samples, but the mechanisms driving this bending and the strong dependence of nominal strain on sample thickness have yet to be fully understood. Here, we demonstrate that the bending in piezoceramics can be induced by non-zero gradient of d31 acrcoss thickness direction. Our calculations show that in standard perovskite piezoceramics, such as KNbO3, a 0.69% concentration of oxygen vacancies results in a 6.3 pC/N change in d31 by inhibiting polarization rotation, which is sufficient to produce ultrahigh nominal strain in thin samples. The gradients of defect concentration, composition, and stress can all cause sufficient inhomogeneity in the distribution of d31, leading to the bending effect. We propose several approaches to distinguish true electric-induced strain from bending-induced effects. Our work provides clarity on the origin of nominal ultrahigh electricinduced strain and offers valuable insights for advancing piezoelectric materials. Mimicry ; Bending ; Transverse plane ; Physics ; Structural engineering ; Geology ; Engineering ; Biology ; Ecology Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Mechanical Behavior of Composites
Authors: Nurul Hidayah | Lina Permatasari | Agriana Rosmalina Hidayati | Handa Muliasari
Source: JURNAL FARMASI DAN ILMU KEFARMASIAN INDONESIA (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

In Silico Analgesic and Toxicity Analysis of Modified Paracetamol on COX-2 Receptor (PDB ID: 3LN1) Background: Paracetamol is often used as the main analgesic in Indonesia. The use of more than 4 g/day or a single dose above 10 g can cause hepatotoxicity. This can be overcome by modifying the structure through a computer-aided drug design (CADD) approach, particularly molecular docking, which aims to produce compounds with greater potency and fewer side effects. Objective: This study aimed to determine the analgesic activity and toxicity of paracetamol derivatives modified using the Topliss method. Methods: Analgesic activity was tested by molecular docking of the COX-2 receptor (PDB ID 3LN1) using AutoDock Tool 4.2 and toxicity testing using pkCSM and Protox Online Tool. Results: The results of docking showed that the free binding energy values ​​for test compounds 1 to 5 are -10.59 kcal/mol, -10.17 kcal/mol, -8.79 kcal/mol, -10.01 kcal/mol, and -9.32 kcal/mol, respectively, with corresponding inhibition constants of 17.29 nM, 35.21 nM, 360.88 nM, 46.36 nM, and 146.65 nM. These values are lower than paracetamol, which has a free binding energy of -6.21 kcal/mol and an inhibition constant of 28,043 nM. The results showed that the test compound was more stable in ligand-receptor binding. Toxicity tests showed that all the test compounds and paracetamol belonged to toxicity class IV. The test compound had an LD50 value of 1551 mg/kg, which was higher than that of paracetamol (338 mg/kg), indicating better effectiveness. Conclusions: Compound 2 was predicted to have the best biological activity and potential as an alternative to paracetamol. In silico ; Analgesic ; Protein Data Bank (RCSB PDB) ; Pharmacology ; Toxicity ; Chemistry ; Computational biology ; Medicine ; Biology ; Internal medicine ; Stereochemistry ; Biochemistry Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Siska Anraeni | Muhid Mustari | Ramdaniah Ramdaniah | Nia Kurniati | Syahrul Mubarak
Source: Bulletin of Social Informatics Theory and Application (article, 2024-12-05)
Field: Chemistry — Analytical Chemistry

Innovative CNN approach for reliable chicken meat classification in the poultry industry In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a model adept at distinguishing between fresh and rotten chicken meat. This endeavor holds immense potential in augmenting food safety and elevating quality control standards within the poultry industry. Our research methodology entails meticulous data collection, which includes acquiring high-resolution images of chicken meat. This meticulously curated dataset serves as the bedrock for both training and testing our CNN model. To optimize the model, we employ the 'adam' optimizer, while critical performance metrics, such as accuracy, precision, recall, and the F1-score, are methodically computed to evaluate the model's effectiveness. Our experimental findings unveil the remarkable success of our CNN model, with consistent accuracy, precision, and recall metrics all reaching an impressive pinnacle of 94%. These metrics underscore the model's excellence in the realm of object classification, with a particular emphasis on its proficiency in distinguishing between fresh and rotten chicken meat. In summation, our research concludes that the CNN model has exhibited exceptional prowess in the domains of object recognition and classification. The model's high accuracy signifies its precision in furnishing accurate predictions, while its elevated precision and recall values accentuate its effectiveness in differentiating between object classes. Consequently, the CNN model stands as a robust foundation for future strides in object classification technology. As we peer into the horizon of future research, myriad opportunities beckon. Our CNN model's applicability extends beyond chicken meat classification, inviting exploration across diverse domains. Furthermore, the model's refinement and adaptation for specific challenges represent an exciting avenue for future work, promising heightened performance across a broader spectrum of object recognition tasks. Context (archaeology) ; Categorization ; Computer science ; Artificial intelligence ; Convolutional neural network ; Object (grammar) ; Machine learning ; Quality (philosophy) ; Set (abstract data type) ; Identification (biology) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Christophe Domain | Antoine Ambard | Gilles Adjanor | Andrée De Backer | Ludovic Thuinet | Charlotte Becquart | Alexandre Legris
Source: EPJ Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

HPC atomic simulations of defect formation and kinetic Monte Carlo simulation of material ageing Kinetic Monte Carlo (KMC) simulations are developed to simulate microstructure evolution under irradiation of structural materials of nuclear power plants. Methods based on rigid lattice atomic KMC and object KMC, despite some approximations, present the advantage to reach significant time, hence irradiation doses. The physical inputs such as the primary damage and defect cluster properties are the results of intensive atomic simulation on high performance computers. Here, atomic KMC is applied to model microstructure evolution of reactor pressure vessel steels and object KMC is applied to zirconium cladding materials. Kinetic Monte Carlo ; Monte Carlo method ; Kinetic energy ; Statistical physics ; Ageing ; Physics ; Materials science ; Computational physics ; Engineering physics ; Classical mechanics ; Mathematics ; Medicine Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Takashi Kurono | Jinjia Zhang | Yasushi Kamimura | Keiichi Edagawa
Source: arXiv (Cornell University) (preprint, 2024-09-12)
Field: Materials Science — Materials Chemistry

Large-scale database analysis of anomalous thermal conductivity of quasicrystals and its application to thermal diodes One long-standing and crucial issues in the study of quasicrystals has been to identify the physical properties characteristic of quasicrystals. The large positive temperature coefficient of thermal conductivity at temperatures above room temperature, which has been observed in several quasicrystals, is one such characteristic property. Here, we show that this is indeed a very distinct property of quasicrystals through analysis using a large physical property database "Starrydata". In fact, several quasicrystals ranked nearly first among more than 10,000 samples of various materials (metallic alloys, semiconductors, ceramics, etc.) in terms of the magnitude of the positive temperature coefficient of thermal conductivity. This unique property makes quasicrystals ideal for use in composite thermal diodes. We searched the database for the most suitable materials that can be combined with quasicrystals to create high-performance composite thermal diodes. Analytical calculations using a simple one-dimensional model showed that by selecting the optimal material, a thermal rectification ratio of 3.2 can be obtained. Heat transfer simulations based on the finite element method confirmed that this can be achieved under realistic conditions. This is the highest value of the thermal rectification ratio reported to date for this type of thermal diode. Thermal conductivity ; Quasicrystal ; Materials science ; Diode ; Scale (ratio) ; Thermal ; Database ; Engineering physics ; Optoelectronics ; Condensed matter physics ; Physics ; Computer science ; Thermodynamics ; Composite material Quasicrystal Structures and Properties ; Advanced Semiconductor Detectors and Materials
Authors: Bashar S. Falih | Łukasz Gierz | Mustafa Ahmed Jalal Al-Sammarraie
Source: Journal of Applied Mathematics and Computational Mechanics (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Fruit classification by assessing slice hardness based on RGB imaging. Case study: apple slices Journal of Applied Mathematics and Computational Mechanics, Prace Naukowe Instytutu Matematyki i Informatyki, Politechnika Częstochowska, Scientific Research of the Institute of Mathematics and Computer Science, Czestochowa University of Technology Artificial intelligence ; RGB color model ; Computational mechanics ; Computer science ; Materials science ; Engineering ; Structural engineering ; Finite element method Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Johanna Moeller | Nina G. Bozhanova | Markus Voehler | Jens Meiler | Clara T. Schoeder
Source: Biomolecular NMR Assignments (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Backbone chemical shift and secondary structure assignments for mouse siderocalin The lipocalin protein family is a structurally conserved group of proteins with a variety of biological functions defined by their ability to bind small molecule ligands and interact with partner proteins. One member of this family is siderocalin, a protein found in mammals. Its role is discussed in inflammatory processes, iron trafficking, protection against bacterial infections and oxidative stress, cell migration, induction of apoptosis, and cancer. Though it seems to be involved in numerous essential pathways, the exact mechanisms are often not fully understood. The NMR backbone assignments for the human siderocalin and its rat ortholog have been published before. In this work we describe the backbone NMR assignments of siderocalin for another important model organism, the mouse - data that might become important for structure-based drug discovery. Secondary structure elements were predicted based on the assigned backbone chemical shifts using TALOS-N and CSI 3.0, revealing a high content of beta strands and one prominent alpha helical region. Our findings correlate well with the known crystal structure and the overall conserved fold of the lipocalin family. Lipocalin ; Bcl-2 family ; Small molecule ; Protein family ; Protein secondary structure ; Biology ; Organism ; Protein structure ; Chemical shift ; Chemistry ; Computational biology ; Cell biology ; Programmed cell death ; Gene ; Biochemistry ; Apoptosis ; Genetics Enzyme Structure and Function ; Protein Structure and Dynamics ; Hemoglobin structure and function
Authors: Abdellatef A. A. Radowan | Marwa Anter | Alaa Omar Abdel-Aziz | Farida Saad Eldin El-Dars | Abdel Fattah Bastawi Farag | Reda E. Abdelhameed
Source: Egyptian Journal of Chemistry (article, 2024-10-02)
Field: Chemistry — Inorganic Chemistry

Tailoring Metal-Organic Frameworks for Polyurethane Foams to Improve Tetracycline Removal from Aqueous Solutions For the effective removal of tetracycline (TC) from aqueous solutions, a novel Grafted Polyurethane Foam (GPUF) functionalized with iron or copper metal-organic frameworks (FeMOF-PUF and CuMOF-PUF, respectively) was created. By adjusting experimental parameters such as adsorbent dosage, initial pH, contact time, initial TC concentration, and temperature, the adsorption performance of both GPUF materials was examined. Based on the Langmuir isotherm model, the FeMOF-PUF showed a higher maximum adsorption capacity of 500.0 mg g-1 than the CuMOF-PUF which is 375.0 mg g-1. This finding suggests that the incorporation of MOF into the PUF matrix not only preserves but also improves the base material's adsorption capacity. The spontaneous and endothermic nature of the adsorption process, as demonstrated by thermodynamic measurements, points to a chemically driven interaction between TC and FeMOF-PUF. The synergistic combination of porosity, Lewis acidity of the iron sites within the MOF structure, and electrostatic interactions with TC molecules is responsible for FeMOF-PUF's excellent adsorption capability. The result shows that GPUFs, especially FeMOF-PUF, have a promising future as effective and useful adsorbents for the removal of pharmaceutical pollutants in water treatment applications. Polyurethane ; Aqueous solution ; Metal-organic framework ; Tetracycline ; Chemical engineering ; Materials science ; Metal ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy ; Adsorption ; Engineering ; Antibiotics Metal-Organic Frameworks: Synthesis and Applications