jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. M. Satheesha | K. S. Rajanna | Krishna Prasad K
Source: Zenodo (CERN European Organization for Nuclear Research) (review, 2023-04-29)
Field: Chemistry — Analytical Chemistry

A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing <strong>Background/Purpose: </strong><em>These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article.</em> <strong>Objective: </strong><em>The development of a system for the automated categorization of Arecanut using images is limited by difficulties. To assess the value of computer vision application for Arecanut, it is critical to taken as account the traditional and economic significance of Arecanut.</em> <strong>Design/Methodology/Approach</strong>: <em>Several types of Arecanut are prone to great variation in color, texture, and form depending on the category and the area in which they are cultivated. Arecanuts are processed utilizing a variety of techniques, with an emphasis on the finished product's exterior. Here, the color, size, and texture of Arecanut are used to construct a classification or grading system.</em> <strong>Findings/Result: </strong><em>With reference to the cited significant work that has been done on other fruits as well as Arecanuts from the standpoint of computer vision. This article provided a thorough introduction to Arecanuts, computer vision, and the uses and benefits of vision-aided technologies in the grading of Arecanuts and categorization.</em> <strong>Result Limitations/Implications: </strong><em>This review is based on the detection and classification of the Arecanuts done using computer vision and AI techniques.</em> <strong>Originality Value: </strong><em>Several inline resources including review papers on Arecanut, research articles, technical books, and website resources.</em> <strong>Paper Type: </strong><em>Literature</em> <em>Review paper on smart auto Arecanut Sorting and Grading of Arecanut using Computer Vision and Image Processing</em> Grading (engineering) ; Sorting ; Computer vision ; Image processing ; Computer science ; Artificial intelligence ; Computer image ; Computer graphics (images) ; Pattern recognition (psychology) ; Image (mathematics) ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advances in Cucurbitaceae Research
Authors: Daniel R Kick | Jacob D. Washburn
Source: Research Square (preprint, 2023-04-06)
Field: Chemistry — Analytical Chemistry

Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone Ensemble learning ; Best linear unbiased prediction ; Artificial intelligence ; Machine learning ; Yield (engineering) ; Computer science ; Deep learning ; Statistics ; Econometrics ; Mathematics ; Selection (genetic algorithm) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Genetics and Plant Breeding
Authors: Ahmed Ahmed | Saman Q. Mawlud
Source: Research Square (preprint, 2023-11-10)
Field: Materials Science — Ceramics and Composites

Spectroscopic Characteristics and Radiative Parameters of Er+3 Doped Ternary Lead Bismuth Tellurite Glass: Judd-Ofelt Analysis Chromaticity ; Analytical Chemistry (journal) ; Materials science ; Doping ; Bismuth ; Ternary operation ; Oscillator strength ; Radiative transfer ; Glass transition ; Absorption spectroscopy ; Quantum efficiency ; Absorption (acoustics) ; Spectral line ; Chemistry ; Optics ; Physics ; Optoelectronics ; Polymer Glass properties and applications ; Luminescence Properties of Advanced Materials ; Phase-change materials and chalcogenides
Authors: A. Bolyachkin | Ekaterina Dengina | Н. А. Кулеш | Xin Tang | H. Sepehri‐Amin | Tadakatsu Ohkubo | K. Hono
Source: Research Square (preprint, 2023-12-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tomography-based Digital Twin of Nd-Fe-B Permanent Magnets Abstract Many functional materials are designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models capable of replicating the microstructure with nm-level accuracy are required. This is the case for permanent magnets, for which there is a long-standing problem of a significant offset between simulated and experimental coercivity. To overcome this problem and resolve the Brown paradox, we developed a novel approach to construct large-scale finite element models based on the microstructure tomography. It was applied to ultrafine-grained Nd-Fe-B magnets for which, besides the shape, size, and packing of the grains, we reconstructed individual regions of thin intergranular phase separated by triple junctions. Such micromagnetic model managed to reproduce not only experimental coercivity but also its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal was revealed. Proposed digital twins of Nd-Fe-B permanent magnets can assist their optimization towards an ultimate coercivity, while the developed tomography-based approach can be adapted to a wide range of polycrystalline materials. Magnet ; Materials science ; Tomography ; Mechanical engineering ; Physics ; Engineering ; Optics Magnetic Properties of Alloys ; Superconducting Materials and Applications ; Magnetic Field Sensors Techniques
Authors: Sékou-Oumar Kaba | Siamak Ravanbakhsh
Source: arXiv (Cornell University) (preprint, 2023-12-14)
Field: Materials Science — Materials Chemistry

Symmetry Breaking and Equivariant Neural Networks Using symmetry as an inductive bias in deep learning has been proven to be a principled approach for sample-efficient model design. However, the relationship between symmetry and the imperative for equivariance in neural networks is not always obvious. Here, we analyze a key limitation that arises in equivariant functions: their incapacity to break symmetry at the level of individual data samples. In response, we introduce a novel notion of 'relaxed equivariance' that circumvents this limitation. We further demonstrate how to incorporate this relaxation into equivariant multilayer perceptrons (E-MLPs), offering an alternative to the noise-injection method. The relevance of symmetry breaking is then discussed in various application domains: physics, graph representation learning, combinatorial optimization and equivariant decoding. Equivariant map ; Symmetry (geometry) ; Symmetry breaking ; Perceptron ; Artificial neural network ; Computer science ; Representation (politics) ; Graph ; Mathematics ; Topology (electrical circuits) ; Artificial intelligence ; Pure mathematics ; Theoretical computer science ; Physics ; Combinatorics ; Particle physics ; Geometry Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Graph Neural Networks
Authors: Fangning Ren | Fang Liu
Source: ChemRxiv (preprint, 2023-09-11)
Field: Chemistry — Analytical Chemistry

Data-Driven Insights into the Fluorescence of Asphaltene Aggregates Using Extended Frenkel Exciton Model The origin of the fluorescence redshift during asphaltene aggregation remains debated due to the great diversity of asphaltene molecules, while the extended Frenkel exciton model provided a theoretical framework for studying multi-chromophore systems such as asphaltene aggregates. We investigated the fluorescence energy of hundreds of asphaltene dimers based on 132 experimentally determined asphaltene monomer structures. Our result shows that the dimer’s fluorescence energy is always lower than both of its monomers regardless of its intermolecular conformation, with its redshift dominated by superexchange. The dimer oscillator strength predominantly depends on the monomer with the lower fluorescence energy, and the involvement of charge-transfer states and the cancellation between monomer transition dipole moments suppressed the fluorescence especially when two monomers have similar fluorescence energy. The above findings hold for all asphaltene dimers despite their diversity, which offers a theoretical interpretation for comprehending the relationship between asphaltene aggregation and its fluorescence variation. Furthermore, this work provides theoretical insights into other research areas related to organic planar conjugated systems with multiple chromophores. Chromophore ; Monomer ; Asphaltene ; Fluorescence ; Intermolecular force ; Dimer ; Chemical physics ; Exciton ; Chemistry ; Materials science ; Photochemistry ; Molecule ; Polymer ; Physics ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Spectroscopy and Quantum Chemical Studies
Authors: Raja Zaidatul Akhmar Raja Jamaluddin | Musa Ahmad | Yook Heng Lee | Ling Ling Tan
Source: Sains Malaysiana (article, 2023-05-31)
Field: Chemistry — Physical and Theoretical Chemistry

Filem Mikrosfera Akrilik Terpegun Alizarin Merah S untuk Pengesanan Pendarfluor Optik Asid Bori Mikrosensor pendarfluor optik untuk kuantifikasi asid borik dalam sampel makanan telah dibangunkan berdasarkan filem mikrosfera poli(n-butil akrilat) [poli(nBA)]. Pencirian optik reagen alizarin merah S (ARS) terpegun pada filem mikrosfera poliakrilat dengan asid borik telah dilakukan melalui pendekatan transduksi pendarfluor. Reagen ARS telah dijerap secara fizikal pada filem mikrosfera akrilik hidrofobik untuk membentuk mikropolimer kelat dengan sokongan matriks sol-gel untuk mengelakkan larut lesap molekul ARS yang bersifat larut air. Pengkelat ARS yang terpegun pada filem mikrosfera poli(nBA) memberikan tindak balas pengesanan optik yang cepat dalam masa 1 minit. Julat rangsangan linear dinamik yang berguna bagi mikrosensor pendarfluor optik asid borik telah diperoleh antara 1.6 µM dan 32.0 µM asid borik dengan had pengesanan (LOD) pada 1 μM. Mikrosensor pendarfluor optik asid borik berasaskan mikropolimer akrilik menghasilkan prestasi kebolehulangan yang baik dengan nilai sisihan piawai relatif (RSD) yang dicapai dalam julat 3.3-3.6%. Mikrosensor optik yang dibangunkan telah digunakan untuk pengukuran pendarfluor asid borik dalam sampel mi kuning dan mi beras leper ('hor fun'/'kway teow') dan tidak menunjukkan perbezaan yang ketara berbanding dengan kaedah rujukan tradisional spektrometri pemancaran plasma-optik berganding secara induktif (ICP-OES). Physics ; Nuclear chemistry ; Chemistry Various Chemistry Research Topics ; Educational Methods and Technology
Authors: Taiki Yanagishima | John Russo | Roel P. A. Dullens | Hajime Tanaka
Source: The Journal of Chemical Physics (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

From ultra-fast growth to avalanche growth in devitrifying glasses During devitrification, pre-existing crystallites grow by adding particles to their surface via a process that is either thermally activated (diffusive mode) or happens without kinetic barriers (fast crystal growth mode). It is yet unclear what factors determine the crystal growth mode and how to predict it. With simulations of repulsive hard-sphere-like (Weeks-Chandler-Andersen) glasses, we show for the first time that the same system at the same volume fraction and temperature can devitrify via both modes depending on the preparation protocol of the glass. We prepare two types of glass: conventional glass (CG) via fast quenching and uniform glass (UG) via density homogenization. First, we bring either glass into contact with a crystal (X) and find the inherent structure (CGX/UGX). During energy minimization, the crystal front grows deep into the CG interface, while the growth is minimal for UG. When thermal noise is added, this behavior is reflected in different crystallization dynamics. CGX exhibits a density drop at the crystal growth front, which correlates with enhanced dynamics at the interface and a fast growth mode. This mechanism may explain the faster crystal growth observed below the glass transition experimentally. In contrast, UGX grows via intermittent avalanche-like dynamics localized at the interface, a combination of localized mechanical defects and the exceptional mechanical stability imposed by the UG glass phase. Devitrification ; Materials science ; Crystallization ; Crystal (programming language) ; Crystal growth ; Crystallite ; Glass transition ; Homogenization (climate) ; Chemical physics ; Crystallography ; Chemistry ; Composite material ; Thermodynamics ; Physics ; Polymer Material Dynamics and Properties ; Glass properties and applications ; Metallic Glasses and Amorphous Alloys
Authors: William Harbutt Dawson | Eisuke Kawashima | Laura E. Ratcliff | Muneaki Kamiya | Luigi Genovese | Takahito Nakajima
Source: Bristol Research (University of Bristol) (preprint, 2023-01-16)
Field: Materials Science — Materials Chemistry

Complexity Reduction in Density Functional Theory: Locality in Space and Energy We present recent developments of the NTChem program for performing large scale hybrid Density Functional Theory calculations on the supercomputer Fugaku. We combine these developments with our recently proposed Complexity Reduction Framework to assess the impact of basis set and functional choice on its measures of fragment quality and interaction. We further exploit the all electron representation to study system fragmentation in various energy envelopes. Building off this analysis, we propose two algorithms for computing the orbital energies of the Kohn-Sham Hamiltonian. We demonstrate these algorithms can efficiently be applied to systems composed of thousands of atoms and as an analysis tool that reveals the origin of spectral properties. Locality ; Exploit ; Density functional theory ; Computer science ; Hamiltonian (control theory) ; Reduction (mathematics) ; Orbital-free density functional theory ; Theoretical computer science ; Space (punctuation) ; Algorithm ; Computational science ; Hybrid functional ; Computational chemistry ; Mathematics ; Chemistry ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Mina M. Aljuboury | Hussein A. Jebur
Source: Bionatura (article, 2023-03-15)
Field: Chemistry — Analytical Chemistry

Effect of traditional and automated sorting on some tomato's properties This study was conducted in a laboratory experiment at the University of Baghdad, College of Science, computing Department, 5 km from the center of Baghdad city, in 2021 to evaluate the sorting method for the tomato crop. The experiments were conducted in a factorial experiment under a complete randomized design with three replications and using SAS analysis, artificial neural network, image processing, the study of external characteristics, and physical features; fruit surface area and fruit circumference were 1334.46 cm2,57.53 cm2 and free diseases. The error value was less than zero, while training with outputs recorded the highest value and which was 5. The neural network's performance between the input and the mean square of the regression, as recorded as the best value to validate the performance, was 58.11 in the second period. The importance of fruit circumference is attributed to the sorting and grading of fruits, especially in packing boxes and marketing. Keywords: Automated grading and sorting, traditional grading and sorting, image processing, computer vision, Artificial neural networks Sorting ; Artificial neural network ; Circumference ; Grading (engineering) ; Artificial intelligence ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Statistics ; Algorithm ; Engineering ; Geometry Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Junmin Jia | Fei Hu | Xubo Zhang | Zongyou Ben | Yifan Wang | Kunjie Chen
Source: Agriculture (article, 2023-08-31)
Field: Chemistry — Analytical Chemistry

Method of Attention-Based CNN for Weighing Pleurotus eryngii Automatic weight detection is an essential step in the factory production of Pleurotus eryngii. In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R2, RMSE, MAE, and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii. Pleurotus eryngii ; Artificial intelligence ; Pattern recognition (psychology) ; Feature (linguistics) ; Mathematics ; Set (abstract data type) ; Computer science ; Statistics ; Machine learning ; Botany ; Biology ; Mushroom Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Infrared Thermography in Medicine
Authors: Anour Shebani | Hussein Etmimi | Ebtesam Ahmed | Omaya Abujarir | Abdalbary Rhab
Source: مجلة الجامعة الأسمرية (article, 2023-03-09)
Field: Materials Science — Polymers and Plastics

Low and High Molecular Weight Polyethylene Glycol as A Treating and Coupling Agent in High Density Polyethylene/Date Palm Tree Fiber Composites nical properties and water absorption (WA) behavior of high density polyethylene/date palm tree fiber (HDPE/DPTF) composites were investigated. Two different parts of the date palm tree (mesh and leaflets) were used. The focus was on studding the combination effect of using both low and/or high molecular weight polyethylene glycol (PEG) as a treatment and coupling agent, respectively on shore D hardness, impact strength and water absorption behavior of the composites. Morphological properties of the prepared composites were also investigated. Results showed that treating the fibers with low molecular weight PEG had a significant effect, which resulted in composites with better shore D hardness and impact strength compared to pure HDPE and composites made with untreated fibers. Furthermore, the use of high molecular weight PEG as a coupling agent resulted in better impact strength and shore D hardness, indicating the presence of a strong interaction between the fibers and the HDPE matrix. Morphological observations of composites made with untreated fibers by scanning electron microscope (SEM) revealed the formation of some fiber aggregation, fiber debonding and fiber pullout in composites made with untreated fibers. Contrary, the composites made with PEG as a treatment and/or coupling agent showed no fiber aggregation or fiber debonding. Composite material ; Materials science ; High-density polyethylene ; Fiber ; Polyethylene glycol ; Scanning electron microscope ; Absorption of water ; Polyethylene ; Coupling (piping) ; Shore durometer ; PEG ratio ; Chemical engineering Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies
Authors: Yuki Nagai | Akio Tomiya
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Materials Science — Materials Chemistry

Self-learning Monte Carlo with equivariant Transformer Machine learning and deep learning have revolutionized computational physics, particularly the simulation of complex systems. Equivariance is essential for simulating physical systems because it imposes a strong inductive bias on the probability distribution described by a machine learning model. However, imposing symmetry on the model can sometimes lead to poor acceptance rates in self-learning Monte Carlo (SLMC). Here, we introduce a symmetry equivariant attention mechanism for SLMC, which can be systematically improved. We evaluate our architecture on a spin-fermion model (\textit{i.e.}, double exchange model) on a two-dimensional lattice. Our results show that the proposed method overcomes the poor acceptance rates of linear models and exhibits a similar scaling law to large language models, with model quality monotonically increasing with the number of layers. Our work paves the way for the development of more accurate and efficient Monte Carlo algorithms with machine learning for simulating complex physical systems. Monte Carlo method ; Computer science ; Scaling ; Statistical physics ; Equivariant map ; Artificial intelligence ; Quantum Monte Carlo ; Lattice (music) ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Quantum many-body systems ; Topic Modeling
Authors: Muhammad Ashfaq | Imran Khan
Source: Research Square (preprint, 2023-05-26)
Field: Chemistry — Analytical Chemistry

Crop Yield Prediction of Wheat using CNN and Remote Sensing multispectral imagery, a hybrid approach Abstract The authors have requested that this preprint be removed from Research Square. Multispectral image ; Yield (engineering) ; Remote sensing ; Crop ; Aerial imagery ; Satellite imagery ; Environmental science ; Multispectral pattern recognition ; Agricultural engineering ; Computer science ; Agronomy ; Geography ; Forestry ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Ade Kurniawan | Andi Sunyoto | Alva Hendi Muhammad
Source: JAIA - Journal of Artificial Intelligence and Applications (article, 2023-06-06)
Field: Chemistry — Inorganic Chemistry

Detection of Palm Fruit Maturity Using Convolutional Neural Network Method Palm oil has an important role as a source of foreign exchange in the economy in Indonesia. Oil palm is one of the vegetable oil-producing plants that has the highest economic value compared to other crops such as soybeans, olives, coconuts or sunflowers. Palm oil quality is also influenced by water content, dirt content, free fatty acid content and the level of maturity of the palm fruit. Maturity of palm fruit is a very important factor in determining the quality of crude oil produced by palm fruit. In determining the maturity of oil palm, sorting is necessary to get quality palm fruit with the appropriate level of maturity. The use of image processing technology (ImageProcessing) can facilitate the process of analyzing objects. Meanwhile, the implementation of deep learning using the Convolutional Neural Network method can help identify the maturity level of oil palm fruit with a high level of accuracy. The results showed a very good effectiveness with an accuracy reaching 99% and a precision level reaching 99.8%. Maturity (psychological) ; Palm ; Palm oil ; Convolutional neural network ; Agricultural engineering ; Horticulture ; Mathematics ; Environmental science ; Agricultural science ; Biology ; Computer science ; Artificial intelligence ; Engineering Coconut Research and Applications ; Oil Palm Production and Sustainability ; Agricultural and Environmental Management