jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikidadi Abubakar | Peter Wasswa | Esther Masumba | Patrick Obia Ongom | Geoffrey Mkamilo | Edward Kanju | Wilfred Abincha | Richard Edema | Karoline Leonard Sichalwe | Phinehas Tukamuhabwa | Siraj Ismail Kayondo | Ismail Rabbi | Heneriko Kulembeka
Source: Scientific Reports (article, 2024-07-25)
Field: Chemistry — Analytical Chemistry

Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R<sup>2</sup>, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R<sup>2</sup> = 0.56 and R<sup>2</sup> = 0.51 respectively while breakdown had calibration and validation accuracy of R<sup>2</sup> = 0.66 and R<sup>2</sup> = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R<sup>2</sup> = 0.65 and R<sup>2</sup> = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties. Context (archaeology) ; Computer science ; Quality (philosophy) ; Near-infrared spectroscopy ; Calibration ; Spectrum analyzer ; Throughput ; Spectrometer ; Data mining ; Mathematics ; Statistics ; Biology ; Telecommunications ; Wireless ; Physics Spectroscopy and Chemometric Analyses ; Cassava research and cyanide ; Food composition and properties
Authors: Yiming Chen | Jianwei Li | Xiaobing Hu | YiRui Liu | Jiankai Ma | Xing Chen | Junjie Li | Zhijun Wang | Jincheng Wang
Source: Science China Technological Sciences (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Instance segmentation from small dataset by a dual-layer semantics-based deep learning framework Computer science ; Segmentation ; Scalability ; Artificial intelligence ; Reliability (semiconductor) ; Dual (grammatical number) ; Deep learning ; Standardization ; Process (computing) ; Noise (video) ; Semantics (computer science) ; Similarity (geometry) ; Data mining ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Image (mathematics) ; Database Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Wonjun Shin | Seongwoo Jang | Yunhyoung Hwang | Jihoon Han
Source: Research Square (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA Nanocrystalline material ; Convolutional neural network ; Extrapolation ; Principal component analysis ; Artificial intelligence ; Grain boundary ; Artificial neural network ; Curse of dimensionality ; Computer science ; Materials science ; Machine learning ; Voronoi diagram ; Pattern recognition (psychology) ; Nanotechnology ; Mathematics ; Geometry ; Statistics Machine Learning in Materials Science ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Sophie Blundell | Thomas Radford | Idris A. Ajia | Daniel Lawson | Xingzhao Yan | Mehdi Banakar | David J. Thomson | Ioannis Zeimpekis | Otto L. Muskens
Source: arXiv (Cornell University) (preprint, 2024-09-19)
Field: Materials Science — Materials Chemistry

Ultracompact programmable silicon photonics using layers of low-loss phase-change material Sb$_2$Se$_3$ of increasing thickness High-performance programmable silicon photonic circuits are considered to be a critical part of next generation architectures for optical processing, photonic quantum circuits and neural networks. Low-loss optical phase change materials (PCMs) offer a promising route towards non-volatile free-form control of light. Here, we exploit direct-write digital patterning of waveguides using layers of the PCM Sb$_2$Se$_3$ with a thickness of up to 100 nm, demonstrating the ability to strongly increase the effect per pixel compared to previous implementations where much thinner PCM layers were used. We exploit the excellent refractive index matching between Sb$_2$Se$_3$ and silicon to achieve a low-loss hybrid platform for programmable photonics. A five-fold reduction in modulation length of a Mach-Zehnder interferometer is achieved compared to previous work using thin-film Sb$_2$Se$_3$ devices, decreased to 5 $μ$m in this work. Application of the thicker PCM layers in direct-write digital programming of a multimode interferometer (MMI) shows a three-fold reduction of the number of programmed pixels to below 10 pixels per device. The demonstrated scaling of performance with PCM layer thickness is important for establishing the optimum working range for hybrid silicon-PCM devices and holds promise for achieving ultracompact programmable photonic circuits. Silicon ; Photonics ; Materials science ; Phase (matter) ; Phase change ; Optoelectronics ; Silicon photonics ; Phase-change material ; Optics ; Engineering physics ; Engineering ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Jing Yan | Yingling Zhang | Hui Liang | Zibin Jiao | Lingling Song | Ji Liu | Qingming Zhang | Yaowen Liu | Wen Qin
Source: LWT (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

Physicochemical characterization of thermally oxidized rapeseed oil: An insight into combining acoustic diagnostic technique and chemometrics This study aimed to apply an ultrasonic pulse-echo system to characterize thermally oxidized rapeseed oil. After 60 h of heating treatment (165 ± 5°C), the physicochemical (density, viscosity, acid value, iodine value and polar compounds) and acoustic properties (velocity, acoustic impedance, maximum amplitude of the first echo, difference in amplitude, and area under the curve) of rapeseed oil were measured. Support vector machine, random forest (RF), and backpropagation neural network algorithms were used to establish quantitative prediction models for viscosity and polar compounds based on the acoustic properties of rapeseed oil. The results indicated significant correlations between acoustic impedance and viscosity (R=0.70), as well as between acoustic impedance and polar compounds (R=0.79). Heating treatment reduced oil unsaturation and led to the formation of oxidative and polymeric compounds, which in turn increased the velocity and impedance, while decreasing the other three acoustic features. The RF model yielded the best performance in predicting viscosity (R 2 =0.7944) and polar compounds (R 2 =0.8385). These findings highlight that ultrasonic technology not only accurately predicts key quality parameters, but also provides a rapid, non-destructive, and cost-effective alternative to traditional methods for characterizing thermally oxidized rapeseed oil. • Significant correlations between acoustic and physicochemical indicators were found • Variations in ultrasonic indicators are linked to intermolecular structure changes • Random forest model exhibits the highest accuracy for viscosity and polar compounds • Ultrasonic technology could characterize thermally oxidized rapeseed oils Chemometrics ; Rapeseed ; Characterization (materials science) ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Chromatography ; Biochemical engineering ; Nanotechnology ; Engineering ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: X.Y. Li | Jianxiang Tian
Source: Fluid Phase Equilibria (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Determining state points through the radial distribution function of Yukawa fluids at equilibrium Chemistry ; Yukawa potential ; Radial distribution function ; Function (biology) ; Thermodynamics ; Equation of state ; State (computer science) ; Distribution function ; Statistical physics ; Computational chemistry ; Physics ; Molecular dynamics ; Quantum mechanics Material Dynamics and Properties ; Advanced Thermodynamics and Statistical Mechanics ; Quantum Electrodynamics and Casimir Effect
Authors: Graziele Feltrin Dias Wendling | Rita de Cássia Mota Monteiro | Ruan Bernardy | Karine Von Ahn Pinto | Romário de Mesquita Pinheiro | Gizele Ingrid Gadotti
Source: Brazilian Archives of Biology and Technology (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Soybean Seed Classification Using NIR and Machine Learning To assess the physiological quality of soybean seeds, regulations require germination levels of at least 80% for a batch of seeds to be approved. With the evolution of technology and computing, spectrophotometric methods are advancing, making it possible to analyze the molecular vibrations present in seed samples and correlate them with germination results. The aim of this work was to analyze the use of Near Infrared (NIR) spectrometry with the aid of machine learning, in conjunction with physiological attributes, to predict the classification of soybean seed samples into accepted (above 80% germination) and rejected (below 79% germination), according to the legislation. A total of 134 soybean samples were analyzed for germination on paper, vigor by first count and tetrazolium. The NIR equipment was used to obtain the spectral curves of these samples. The Savitzky-Golay filter was used for pre-processing and data processing showed that the algorithm with the highest precision and accuracy was Random Forest, generating 96.1% recall and 98% average precision for the class of accepted lots, and 98.8% and 97.5% for the class of rejected lots. After using the algorithms, it was found that the range of 807.18 to 817.65nm can express information on physiological attributes and can be used to rank batches of soybean seeds, allowing for rapid estimation of the physiological quality of batches. Spectroscopy can be a complementary methodology with a quick response, with the potential to be used analogously to physiological quality analysis. Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Huda Faeq Hasan Aldabag | Abdullah Kamaluldeen | Saleh A. Ahmed | Talib abbas Alzurfi
Source: Indonesian Journal on Health Science and Medicine (article, 2024-10-05)
Field: Chemistry — Analytical Chemistry

Innovations in Drug Spectroscopy Methods for Pharmaceutical Compounds General Background: Spectroscopy is a pivotal analytical technique in pharmaceutical research, enabling precise analysis of molecular structures and compositions through the interaction of light with matter. Specific Background: Spectroscopic methods have significantly advanced pharmaceutical analysis since the mid-20th century, addressing challenges like drug resistance and the need for improved drug purity and stability. Techniques such as UV-Visible, Fourier Transform Infrared (FTIR), and Nuclear Magnetic Resonance (NMR) spectroscopy are widely employed for drug discovery, quality control, and structural elucidation. Knowledge Gap: Despite their extensive use, limitations persist, such as spectral complexity, overlapping bands, and the need for costly instrumentation, leaving room for improvement in sensitivity, efficiency, and cost-effectiveness. Aims: This study aims to consolidate the principles, applications, and advancements of key spectroscopic methods in pharmaceutical analysis while highlighting emerging trends to overcome existing limitations. Results: The analysis demonstrates that UV-Visible spectroscopy provides high-speed, non-destructive analysis for drug quantification; FTIR offers qualitative and quantitative insights into functional groups; and NMR remains unparalleled for structural determination. Recent advancements include the integration of spectroscopy with machine learning for enhanced data interpretation and the development of hybrid techniques to improve sensitivity. Novelty: The study underscores the evolution of spectroscopy into a cornerstone of modern pharmaceutical analysis, driven by technological innovation and regulatory emphasis on accurate, reliable methods. Novel applications in drug design, impurity profiling, and real-time monitoring further exemplify its versatility. Implications: These findings emphasize the necessity of continued research into cost-effective, automated spectroscopic techniques to enhance pharmaceutical quality and safety, meeting the growing demands of global healthcare. Spectroscopy's integration into regulatory frameworks and novel drug development processes cements its role as a critical tool for advancing pharmaceutical science. Highlights: Spectroscopy ensures drug quality, purity, and safety in pharmaceuticals. UV-Vis, FTIR, and NMR are key for structural and impurity analysis. Innovations improve accuracy, efficiency, and broaden applications in drug analysis. Keywords: pectroscopy, Pharmaceutical Analysis, UV-Vis, FTIR, NMR Drug ; Pharmaceutical sciences ; Spectroscopy ; Nanotechnology ; Materials science ; Biochemical engineering ; Chemistry ; Pharmacology ; Medicine ; Engineering ; Physics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Inorganic and Organometallic Chemistry
Authors: Francesca Venturini | Silvan Fluri | Manas Mejari | Michael Baumgärtner | Dario Piga | Umberto Michelucci
Source:  (article, 2024-06-20)
Field: Chemistry — Analytical Chemistry

Machine learning-enhanced fluorescence spectroscopy for the quality assessment of extra virgin olive oil during ageing The safeguarding of extra virgin olive oil (EVOO) quality throughout its shelf life is imperative due to its vulnerability to quality deterioration caused by auto-oxidation and photo-oxidation. This study investigates machine learning (ML) capabilities applied to fluorescence spectroscopy to detect the ageing of EVOO. The quality of EVOO was assessed by UV-absorption spectroscopy measurements as mandated by European Regulations. In parallel, excitation-emission matrices (EEMs) were measured to determine the predictive potential of ML approaches applied to fluorescence data. First, two excitation wavelengths (480 nm and 300 nm) are identified as exhibiting the maximum relative change in fluorescence intensity, serving as potential indicators of EVOO ageing. Then, ML algorithms were developed to predict olive oil quality based on highly aggregated spectral data at these excitation wavelengths. The algorithms successfully identify still good EVOOs from aged EVOOs with over 90% accuracy, proposing an innovative approach that foregoes the need for detailed chemical analysis. This work shows the potential of ML-based approaches applied to fluorescence to replace traditional, labourintensive analyses. Therefore, it paves the way for the development of a compact, field-deployable fluorescence sensing device for rapid and objective quality control in olive oil production and early detection of oxidation or adulteration, and aiding in the classification of olive oils for market purposes. Ageing ; Fluorescence spectroscopy ; Spectroscopy ; Fluorescence ; Quality (philosophy) ; Olive oil ; Computer science ; Environmental science ; Materials science ; Artificial intelligence ; Chemistry ; Food science ; Optics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Masafumi Uchida | Tatsuya Yokoi | Yusuke Ogura | Katsuyuki Matsunaga
Source: Physical Review Materials (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Artificial-neural-network descriptor enhancing accuracy of machine-learning interatomic potential and its application to lattice defects in Si Trainable descriptors based on artificial neural networks (ANNs) are presented with the goal of accurately predicting the structure-property relationship in the presence of various lattice defects for Si. In this architecture, interatomic distances and bond angles are directly fed to the ANNs instead of empirical analytic functions. The ANNs are hierarchically connected with another ANN that outputs the atomic energy involving many-body terms of the potential energy. All ANNs are then simultaneously trained using density-functional-theory data, which enables updating the entire functional forms numerically. For training and test datasets containing point defects, surfaces, and grain boundaries in Si, the ANN descriptor exhibits systematically smaller errors in total potential energy than those based on the symmetry function and the Chebyshev polynomials, which are commonly used in ANN potentials. The outputs of the trained descriptor each have flexible functional forms that are difficult to construct using a single separation-of-variable function in terms of the interatomic distance and the bond angle. This indicates that the ANN descriptor can acquire a better representation of diverse atomic environments via the training, which leads to the decreased error. The present study also discusses an efficient way of training the ANN descriptor in a stepwise manner while maintaining its flexibility. Furthermore, the computational time is found to be comparable to that of the symmetry function, which will pave the way for large-scale molecular simulations involving multiple lattice defects with high accuracy. Materials science ; Artificial neural network ; Lattice (music) ; Interatomic potential ; Artificial intelligence ; Condensed matter physics ; Machine learning ; Computer science ; Computational chemistry ; Molecular dynamics ; Acoustics Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis ; Semiconductor materials and interfaces
Authors: Jiahao Cui | Xue Han | Guochao Shi | Kuihua Li | Wenying Zhou | Zelong Li | Zengshan Yu | Mingli Wang
Source: Plasmonics (article, 2024-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman Scattering Biosensor by Combining Rare Earth Elements: Integration with Machine Learning Tools for Diosgenin and Tectorigenin Detection and Classification Diosgenin ; Biosensor ; Materials science ; Raman scattering ; Surface (topology) ; Nanotechnology ; Rare earth ; Raman spectroscopy ; Optics ; Chemistry ; Physics ; Metallurgy ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Muhammad Shalahuddin Shalahud Al Jafarawy | Vo Thi Nhat Linh | Jun-Young Yang | ChaeWon Mun | Seunghun Lee | Sung‐Gyu Park | In Woong Han | Samjin Choi | Min‐Young Lee | Dongho Kim | Ho Sang Jung
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Whole Urine-Based Multiple Cancer Diagnosis and Metabolite Profiling Using 3D Evolutionary Gold Nanoarchitecture Combined with Machine Learning-Assisted SERS Profiling (computer programming) ; Metabolite ; Metabolite profiling ; Urine ; Computer science ; Computational biology ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Materials science ; Biology ; Biochemistry Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Radiomics and Machine Learning in Medical Imaging
Authors: Kinjal Mondal | Jeffery B. Klauda
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-18)
Field: Materials Science — Materials Chemistry

Physically Interpretable Performance Metrics for Clustering Abstract Clustering is a type of machine learning (ML) technique which is used to group huge amounts of data based on their similarity into separate groups or clusters. Clustering is a very important task which is nowadays used to analyze the huge and diverse amount of data coming out of molecular dynamics (MD) simulations. Typically, the data from the MD simulations in terms of their various frames in the trajectory are clustered into different groups and a representative element from each group is studied separately. Now a very important question coming in this process is what is the quality of the clusters that are obtained. There are several performance metrics that are available in literature like Silhouette index and Davies-Bouldin Index that are often used to analyze the quality of clustering. However, most of these metrics focus on the overlap or the similarity of the clusters in the reduced dimension that is used for clustering and do not focus on the physically important properties or the parameters of the system. To address this issue, we have developed two physically interpretable scoring metrics that focus on the physical parameters of the system that we are analyzing. We have used and tested our algorithm on three different systems (1) Ising model, (2) peptide folding and unfolding of WT HP35, (3) a protein-ligand trajectory of an enzyme and substrate and (4) a protein-ligand dissociated trajectory. We show that the scoring metrics provide us clusters that match with our physical intuition about the systems. Cluster analysis ; Computer science ; Artificial intelligence ; Data mining ; Machine learning Machine Learning in Materials Science ; Anomaly Detection Techniques and Applications ; Neural Networks and Applications