jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M.C. Girish Babu | Padma M.C
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Noisy and Mixed Pixel Aware Hyperspectral Crop Classification Hyperspectral imaging (HSI) is composed of both feature noise and label noise; thus, makes HSI crop classification extremely challenging. Recently, various feature noise tolerant machine learning and Deep learning mechanism have been presented for HSI crop classification considering; however, very l Hyperspectral imaging ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Noise (video) ; Pixel ; Pattern recognition (psychology) ; Machine learning ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification
Authors: Inostroza, Diego
Source: theses.fr (ABES) (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Proposition d'une méthode accélérée utilisant des descripteurs de réactivité pour explorer les surfaces d'énergie potentielle dans les amas atomiques La complexité isomorphique croissante des amas atomiques avec l'augmentation de leur taille a incité la communauté scientifique à créer une variété de techniques et de stratégies innovantes pour analyser efficacement la surface d'énergie potentielle (SEP). Dans ce contexte, la présente étude introduit un ensemble d'outils de calcul hautement optimisés conçus pour accélérer l'identification des minima énergétiques dans la SEP des systèmes moléculaires et des amas atomiques. Ces outils se distinguent par leur efficacité de calcul, ce qui en fait une option attrayante pour la recherche future. Des évaluations complètes des techniques stochastiques et dirigées précédemment décrites dans la littérature scientifique ont été réalisées. L'algorithme AUTOMATON, en particulier, a été mis à jour avec des implémentations significatives qui ont permis l'identification de nouveaux systèmes atomiques, tels que le premier carbone hexacoordonné dans une configuration planaire. 1 Les techniques stochastiques ont été abordées par l'incorporation de descripteurs de réactivité, tels que la fonction Fukui(i), qui ont permis de guider avec précision la recherche de minima énergétiques et de structures stables. En outre, grâce à l'intégrale NCI (ii), nous avons pu démontrer une forte corrélation entre les valeurs d'intégration et l'énergie du système en question. Cette découverte constitue un point de départ prometteur pour de futures recherches dans ce domaine. La combinaison stratégique des techniques d'essaimage de particules et des permutations atomiques a considérablement amélioré notre capacité à explorer la SEP, facilitant l'identification d'un large éventail de solutions. Ces outils sont allés au-delà de la simple reproduction de résultats antérieurs, découvrant au passage de nouveaux minima globaux et des structures stables. En fusionnant des concepts chimiques fondamentaux avec des méthodes d'optimisation avancées et des algorithmes stochastiques, ce travail de thèse a établi une approche hautement prédictive de l'exploration chimique et de la conception de nouveaux matériaux. Statistical analysis ; Agrégation ; Homogeneous Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Hà Thanh Tùng | Dieu An Nguyen Thi | Nguyễn Đoàn Quốc Anh
Source: Bulletin of Electrical Engineering and Informatics (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

The effects of Ca&lt;sub&gt;14&lt;/sub&gt;Mg&lt;sub&gt;2&lt;/sub&gt;(SiO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;8&lt;/sub&gt;:Eu&lt;sup&gt;2+&lt;/sup&gt; phosphor on white light emission quality of LED-phosphor packages Ca14Mg2(SiO4)8: Eu2+ (CMS:Eu2+) green phosphor is used for creating the white-light emitting diode (W-LED) packages with conversion phosphor materials. The phosphor shows the broadband green emission that peaks at 505 nm in the blue wavelength. The phosphor introduces the improvement in the blue and green emission spectra, which helps to heighten the luminous flux. Moreover, that the concentration of CMS:Eu2+ increases, the scattering events are enhanced to benefits the color blending for lower color variations or better color uniformity. The color renditions reduce with the rising green-phosphor concentration. The green-light amount becomes surplus and redundant for balancing color elements of white light emission. Thus, it should adjust and keep the concentration of CMS:Eu2+ in the range of ~2–8 wt%, to get the average number of color rendering index (CRI) (73–75), and color quality scale (CQS) (60–64). The CMS: Eu2+, hence, is suitable for white light realization, and the W-LED aiming at high-luminescence white light emission with improved color uniformity, and average rendering performances. Phosphor ; Color rendering index ; Color temperature ; High color ; Luminous efficacy ; Materials science ; Luminous flux ; Luminescence ; Optoelectronics ; Diode ; Light-emitting diode ; Optics ; Physics ; Nanotechnology ; Light source ; Computer science ; Artificial intelligence ; Layer (electronics) Luminescence Properties of Advanced Materials
Authors: Samuel P. Gleason | Alexander Rakowski | Jim Ciston | Colin Ophus
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Random Forest Prediction of Crystal Structure from Diffraction Patterns Journal Article Random Forest Prediction of Crystal Structure from Diffraction Patterns Get access Samuel P Gleason, Samuel P Gleason National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United StatesDepartment of Chemistry, University of California, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Alexander Rakowski, Alexander Rakowski National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Jim Ciston, Jim Ciston National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Colin Ophus Colin Ophus National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 698–699, https://doi.org/10.1093/micmic/ozad067.344 Published: 22 July 2023 Random forest ; Diffraction ; Crystal structure prediction ; Materials science ; Statistical physics ; Crystallography ; Crystal structure ; Computer science ; Artificial intelligence ; Optics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jessica Araujo Sciammarelli
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-05-29)
Field: Materials Science — General Materials Science

POTENTIAL FUTURE OF EMERGING TECHNOLOGIES There is no longer any doubt that technology is evolving exponentially and that it will be part of people’s lives in all ways, whether at home, at work, in transport and even in moments of entertainment. Technology has advanced a lot during these years, 20 years ago we never imagined that we would have the facility to access the internet anywhere simply like a minicomputer that is now known as a smartphone. Currently, there are several new emerging technologies that seek to solve different types of problems, bringing agility and efficiency both in different business models, as well as residential and personal. It is possible to affirm that within a parameter of a few years, these technologies will mature and evolve more over time. With the evolution of hardware, allowing for faster processing power and greater memory, Big Data emerged, which is data accumulating in sizes like terabytes, zettabytes, and we currently generate more than 2.5 quintillion bytes daily. With all this volume and accumulation of data, Data Science and Artificial intelligence has improved, creating an ocean of opportunities for the development and expansion of applications in technology. As you can see, technology contains a complex cycle, where each technology connects, merges and complements each other in a set. Even with this evolution and data explosion, the IoT (internet of things) emerged, which are sensors that can be developed for different solutions to solve N problems in different sectors, both in the industrial, residential, logistics, transport and so on. The concept is that these sensors collect data in real time, so this is a perfect storm for data science and artificial intelligence, as they need data for analysis or training, as good data quality is important for their accuracy applications. Within the concept of Robotics, which was already known in the industrial and factory field, currently gave rise to the context called Industry 4.0, which aims to improve their business models using IoT and especially artificial intelligence to improve the robotics intelligence in developing tasks, these that are getting more and more complex. These emerging technologies will be approached in a general and introductory context, mainly in the technical part, because to develop at a more technical level it requires more time and more content from the most diverse sources to exercise engineering with responsibility and excellence. The thesis was divided into modules that are the introduction, description, general analysis, current information, discussions, conclusions, and bibliography at the end. Emerging technologies ; Computer science ; Artificial intelligence Material Properties and Applications ; Industrial Engineering and Technologies
Authors: Maria Ameziane | Joonatan Huhtasalo | Lukáš Flajšman | Rhodri Mansell | Sebastiaan van Dijken
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solid-state lithium-ion supercapacitor for voltage control of skyrmions Ionic control of magnetism gives rise to high magneto-electric coupling efficiencies at low voltages, which is essential for low-power magnetism-based non-conventional computing technologies. However, for on-chip applications, magneto-ionic devices typically suffer from slow kinetics, poor cyclability, impractical liquid architectures or strong ambient effects. As a route to overcoming these problems, we demonstrate an LiPON-based solid-state ionic supercapacitor with a magnetic Pt/Co$_{40}$Fe$_{40}$B$_{20}$/Pt thin-film electrode which enables voltage control of a magnetic skyrmion state. Skyrmion nucleation and annihilation are caused by Li ion accumulation and depletion at the magnetic interface under an applied voltage. The skyrmion density can be controlled through dc applied fields or through voltage pulses. The skyrmions are nucleated by single 60-$μ$s voltage pulses and devices are cycled 750,000 times without loss of electrical performance. Our results demonstrate a simple and robust approach to ionic control of magnetism in spin-based devices. Supercapacitor ; Lithium (medication) ; Ion ; Voltage ; Materials science ; Solid-state ; Skyrmion ; Physics ; Engineering physics ; Electrical engineering ; Condensed matter physics ; Electrode ; Engineering ; Capacitance ; Psychology Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Electric Power Systems and Control
Authors: Dianika Lestari | Listya Citra Suluhingtyas | Danu Ariono
Source: Food Research (article, 2023-06-30)
Field: Chemistry — Inorganic Chemistry

The effect of natural antioxidant from clove (Syzygium aromaticum L.) on theoxidative stability of crude oil produced by the traditional wet rendering processof coconut (Cocos nucifera L.) Coconut oil produced by the traditional wet rendering process may contain free fatty acids and water which lead to rancidity and shorter shelf life. Previous studies proved that cloves (Syzygium aromaticum L.), as a natural source of antioxidant eugenol, can increase coconut oil oxidation stability. This research aimed to investigate the effects of clove addition during the wet rendering process on the Peroxide Value of coconut oil and to estimate shelf life using Accelerated Shelf Life Testing (ASLT) and Artificial Neural Network (ANN). Clove powder was added into coconut milk prior to oil rendering or into coconut oil, followed by oil storage at 28, 63, and 100°C. ASLT method predicted that adding 1 g clove/kg coconut milk and 5 g clove/kg coconut oil gave the best result by improving oil shelf life from approximately 11 days (control) to 14 days, while the ANN method predicted that adding 10 g clove/kg coconut milk gave the best result by improving estimated oil shelf life to 47 days at 28oC. Coconut oil produced from clovetreated coconut milk had the best oxidative stability during storage. Activation energies and activation enthalpies of coconut oil oxidation increased due to clove addition and therefore, improved stability during storage. Coconut oil ; Syzygium ; Cocos nucifera ; Rendering (computer graphics) ; Shelf life ; Peroxide value ; Coco ; Food science ; Antioxidant ; Chemistry ; Botany ; Biology ; Organic chemistry Coconut Research and Applications ; Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis
Authors: Yu-Tao Xu | James Taylor | Hao-Cheng Liu | Niklas Dreyer | Qian-Qian Cho | Yu Zhang | Shi-Feng Guo | Saskia Brix | Yue Him Wong
Source: Research Square (preprint, 2023-09-26)
Field: Materials Science — Biomaterials

The shell proteome of the deep-sea barnacle Bathylasma hirsutum and the convergency in barnacle and molluscan shell proteins Abstract Background As a group of sessile crustaceans that were being misclassified as mollusks by Carl Linnaeus, barnacles produce calcareous shell plates which, in most species, are permanently attached to the substratum. As biomineralization has independently evolved in multiple marine invertebrate taxa, a key question is how biomineralization has driven the evolution of genetic toolkits underlying shell formation. Here, we explore the shell proteome of the deep-sea acorn barnacle Bathylasma hirsutum (Hoek, 1883) using an integrated transcriptomic-proteomic approach and compare the properties of barnacle shell proteins with molluscan shell matrix proteins. Results We identified 31 B. hirsutum barnacle shell proteins (BSPs), including a series of key biomineralization proteins, such as carbonic anhydrase and C-type lectin. More than half of barnacle specific shell proteins (BSSPs) exhibit unknown functions. The amino acid composition of these BSSPs were biased toward A, D, E, G, S, P and Q, and were acidic and hydrophilic. Almost all BSSPs were detected with repetitive low complexity domains. Similar to molluscan shell matrix proteins, RLCDs in D-, and E-rich BSSPs constituted up to 50% amino acid of the whole protein. RLCDs in Q-rich proteins also exhibited similarity to a Q-rich abalone shell matrix protein and an insect cuticle protein. Conclusion From the B. hirsutum shell proteome, certain proteins such as carbonic anhydrase, C-type lectin, and peroxidase were implicated in shell formation or protein cross-linking across sessile invertebrate taxa. Despite the lack of sequence homology, D- and Q-rich BSSPs share similar features with molluscan shell matrix proteins in sequence redundancy, amino acid bias and thereby protein isoelectric point and hydropathy. Such convergence may reflect that similar selection pressures shape the molecular evolution of biomineralization and shell formation genes in marine invertebrates. Barnacle ; Biomineralization ; Proteome ; Biology ; Shell (structure) ; Marine invertebrates ; Echinoderm ; Biochemistry ; Ecology ; Paleontology ; Crustacean Calcium Carbonate Crystallization and Inhibition ; Marine Biology and Environmental Chemistry ; Crustacean biology and ecology
Authors: Rasool Ahmad | Wei Cai
Source: arXiv (Cornell University) (preprint, 2023-08-18)
Field: Materials Science — Materials Chemistry

Accelerating force calculation for dislocation dynamics simulations Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformation and work-hardening behavior of metals by explicitly modeling the evolution of dislocation lines under stress. However, the computational cost associated with calculating forces due to the long-range elastic interactions between dislocation segment pairs is one of the main causes that limit the achievable strain levels in DDD simulations. These elastic interaction forces can be obtained either from the integral of the stress field due to one segment over the other segment, or from the derivatives of the elastic interaction energy. In both cases, the results involve a double-integral over the two interacting segments. Currently, existing DDD simulations employ the stress-based approach with both integrals evaluated either from analytical expressions or from numerical quadrature. In this study, we systematically analyze the accuracy and computational cost of the stress-based and energy-based approaches with different ways of evaluating the integrals. We find that the stress-based approach is more efficient than the energy-based approach. Furthermore, the stress-based approach becomes most cost-effective when one integral is evaluated from analytic expression and the other integral from numerical quadrature. For well-separated segment pairs whose center distances are more than three times their lengths, this one-analytic-integral and one-numerical-integral approach is more than three times faster than the fully analytic approach, while the relative error in the forces is less than $10^{-3}$. Because the vast majority of segment pairs in a typical simulation cell are well-separated, we expect the hybrid analytic/numerical approach to significantly boost the numerical efficiency of DDD simulations of work hardening. Stress field ; Quadrature (astronomy) ; Dislocation ; Range (aeronautics) ; Stress (linguistics) ; Integral equation ; Work (physics) ; Strain hardening exponent ; Statistical physics ; Mathematics ; Physics ; Mathematical analysis ; Finite element method ; Materials science ; Quantum mechanics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Harun Çuğ | Rajab Elkilani | Mehmet Akif Erden
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

INVESTIGATION OF THE TRIBOLOGICAL PROPERTIES OF THE ADDITION OF DIFFERENT AMOUNTS OF CHROMIUM TO MOLYBDENUM-CONTAINING POWDER METAL STEEL AFTER QUENCHING This study focuses on the wear and hardness properties of Cr and quenching heat treatment of Mo steel produced by powder metallurgy.Mo steel containing different amounts of chromium (Cr) was pressed unidirectionally at 700MPa. It was sintered at 1400°C in an argon atmosphere for 2 hours. The results obtained showed that the addition of chromium and heat treatment increased the mechanical properties. Molybdenum ; Chromium ; Materials science ; Tribology ; Metallurgy ; Quenching (fluorescence) ; Metal Metal Alloys Wear and Properties ; Polymer Science and Applications ; Surface Treatment and Coatings
Authors: Hafiz Muhammad Ijaz
Source: Lahore Garrison University Research Journal of Computer Science and Information Technology (article, 2023-09-05)
Field: Chemistry — Analytical Chemistry

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset Crops are the most essential and beneficial food source in Pakistan. The demand for food hasincreased in Pakistan due to population growth. Pakistan produced 7,410 million tons of riceaccording to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304hectares of agricultural land and exported worldwide. Rice is also increased by 0.6% of Pakistan'sGross Domestic Product (GDP) (FYS-2020). The old and manual process of rice classification is moreexpensive and time-consuming. In this study, we describe a machine vision approach for riceidentification. We use four different rice varieties for the experimental process such as Pakei_Kaynat,Kaynat_Kauchei, Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100images dataset have been used for practical work and the total calculated 400 (4 x 100) rice image. Thedifferent process has been deployed on available datasets, such as introduction, preprocessingmethodology, and result discussion. A quality enhancement technique has been implementedtoclarifythe difference between rice color and shape sampling, and it is also converted color imagestoagray scale level. Every image has employed six different non-overlapping regions of interest (ROI’s)and calculated 2400 (6 x 400) ROI’s. Binary (B), Histogram (H), and Texture (T) features have beenimplemented and extracted 43 features on each ROI’s and total calculated 103,200 (2400 x 43)machine learning (ML) features. Best First Search (BFS) Algorithm was used for featureoptimization. Different ML classifiers are implemented for the experimental process: FunctionMulti-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression andMeta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describeda betteraccuracy result is 99.8333%. Artificial intelligence ; Region of interest ; Local binary patterns ; Preprocessor ; Population ; Histogram ; Hectare ; Mathematics ; Computer science ; Geography ; Agriculture ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Ehab S. Elkhayat | Mohamed E. Abouelela | Reda A. Abdelhamid | Mohammad S Alorainy | Khaled Shaaban
Source: Bulletin of Pharmaceutical Sciences Assiut (review, 2023-09-16)
Field: Chemistry — Organic Chemistry

Macrolactones and Macrolides from Plant Endophytic Fungi, Chemical Scaffolds, Biological Activities and Spectroscopy: A Comprehensive Review Over the preceding ten years, 91 macrolides with 80 novel compounds with cytotoxic, antibacterial, antifungal, and α-glucosidase inhibitory activities. Unfortunately, the number of novel chemicals identified from marine or bacterial endophytes in the same period is substantially lower. Accordingly, further study on plant endophytes, which are critical for drug research and the development of novel medicines, including antitumors, antivirals, antibacterials, and antimalarials, should be conducted. A report of the <sup>13</sup>C NMR data of several endophytic macrolides are reported as a supplementary according to ring sizes and based on a united numbering built on literature search. Plant use of endophytic fungi in defense ; Spectroscopy ; Botany ; Chemistry ; Biology ; Materials science ; Physics Chemical synthesis and alkaloids