jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. I. Shveykin | П. В. Трусов | Kirill Romanov
Source: Metals (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Stability of Crystal Plasticity Constitutive Models: Observations in Numerical Studies and Analytical Justification In designing accurate constitutive models, it is important to investigate the stability of the response obtained by means of these models to perturbations in operator and input data because the properties of materials at different structural-scale levels and thermomechanical influences are stochastic in nature. In this paper, we present the results of an application of the method developed by the authors to a numerical study of the stability of multilevel models to different perturbations: perturbations of the history of influences, initial condition perturbations, and parametric operator perturbations. We analyze a two-level constitutive model of the alpha-titanium polycrystal with a hexagonal closed packed lattice under different loading modes. The numerical results obtained here indicate that the model is stable to perturbations of any type. For the first time, an analytical justification of the stability of the considered constitutive model by means of the first Lyapunov method is proposed, and thus the impossibility of instability in models with modified viscoplastic Hutchinson relations is proved. Plasticity ; Constitutive equation ; Crystal plasticity ; Stability (learning theory) ; Materials science ; Statistical physics ; Geology ; Thermodynamics ; Computer science ; Physics ; Finite element method ; Composite material High-Velocity Impact and Material Behavior ; Geotechnical and Geomechanical Engineering ; Elasticity and Material Modeling
Authors: Saman Zohrabi | Seyed Sadegh Seiiedlou | Iman Golpour | Mark Lefsrud | Raquel P. F. Guiné | Barbara Sturm
Source: Journal of Food Process Engineering (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques ABSTRACT Contamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum . NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non‐visible reflectance images of wheat kernels in the ultraviolet and near‐infrared ranges were applied to develop the multi‐layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals. Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Artificial neural network ; Feature selection ; Component (thermodynamics) ; Selection (genetic algorithm) ; Computer science ; Feature (linguistics) ; Computer vision ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Silvia Arduini | Fabio Chinnici
Source: Applied Sciences (article, 2024-09-09)
Field: Chemistry — Analytical Chemistry

Advancements in Analytical Strategies for Authentication and Quality Control of Grappa and Wine Brandy with Geographical Indications In recent years, food authentication has acquired significant importance due to the increase in the incidence of fraud and counterfeiting. Alcoholic beverages are among the food products most susceptible to these kinds of illicit practices due to their high commercial value. In the EU alone, there are 47 categories of spirit drinks and approximately 250 geographical indications (GIs). The production and labeling of GIs are strictly regulated, and developing analytical procedures that can ensure compliance with the legislation is essential to guarantee the typicality of these products. The aim of this review is to summarize the most relevant analytical techniques used for the authentication and quality control of two well-renowned GIs: “Grappa” and wine brandy. It considers the last decade of advancements for both conventional targeted chromatographic techniques and less common methods mainly based on spectrometry coupled with chemometrics for quick and non-destructive discrimination of samples. Novel approaches and future perspectives are also highlighted. Wine ; Geographical indication ; Quality (philosophy) ; Authentication (law) ; Computer science ; Business ; Geography ; Food science ; Computer security ; Chemistry ; Regional science ; Philosophy ; Epistemology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Varun Ananth | Justin J. Sanders | Melih Yilmaz | Sewoong Oh | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-30)
Field: Chemistry — Spectroscopy

A learned score function improves the power of mass spectrometry database search Abstract One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing employ machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesize that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. Our results show that, at a 1% peptide-level false discovery rate threshold, Casanovo-DB outperforms existing hand-designed score functions by 35% to 88%. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides. Database search engine ; Benchmark (surveying) ; Computer science ; Function (biology) ; Sequence database ; Database ; Artificial intelligence ; Data mining ; Search engine ; Biology ; Information retrieval ; Geography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Aaron J. Nessler | Okimasa Okada | Yuya Kinoshita | Koki Nishimura | H. Nagata | Kaori Fukuzawa | Etsuo Yonemochi | Michael J. Schnieders
Source: Crystal Growth & Design (article, 2024-03-09)
Field: Materials Science — Materials Chemistry

Crystal Polymorph Search in the <i>NPT</i> Ensemble via a Deposition/Sublimation Alchemical Path The formulation of active pharmaceutical ingredients involves discovering stable crystal packing arrangements or polymorphs, each of which has distinct pharmaceutically relevant properties. Traditional experimental screening techniques utilizing various conditions are commonly supplemented with in silico crystal structure prediction (CSP) to inform the crystallization process and mitigate risk. Predictions are often based on advanced classical force fields or quantum mechanical calculations that model the crystal potential energy landscape but do not fully incorporate temperature, pressure, or solution conditions during the search procedure. This study proposes an innovative alchemical path that utilizes an advanced polarizable atomic multipole force field to predict crystal structures based on direct sampling of the <i>NPT</i> ensemble. The use of alchemical (i.e., nonphysical) intermediates, a novel Monte Carlo barostat, and an orthogonal space tempering bias combine to enhance the sampling efficiency of the deposition/sublimation phase transition. The proposed algorithm was applied to 2-((4-(2-(3,4-dichlorophenyl)ethyl)phenyl)amino)benzoic acid (Cambridge Crystallography Database Centre ID: XAFPAY) as a case study to showcase the algorithm. Each experimentally determined polymorph with one molecule in the asymmetric unit was successfully reproduced via approximately 1000 short 1 ns simulations per space group where each simulation was initiated from random rigid body coordinates and unit cell parameters. Utilizing two threads of a recent Intel CPU (a Xeon Gold 6330 CPU at 2.00 GHz), 1 ns of sampling using the polarizable AMOEBA force field can be acquired in 4 h (equating to more than 300 ns/day using all 112 threads/56 cores of a dual CPU node) within the Force Field X software (https://ffx.biochem.uiowa.edu). These results demonstrate a step forward in the rigorous use of the <i>NPT</i> ensemble during the CSP search process and open the door to future algorithms that incorporate solution conditions using continuum solvation methods. Force field (fiction) ; Chemistry ; Lattice energy ; Crystal structure prediction ; Sublimation (psychology) ; Crystal (programming language) ; Crystal structure ; Crystallography ; Statistical physics ; Algorithm ; Materials science ; Physics ; Computer science ; Quantum mechanics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Enzyme Structure and Function
Authors: Saumya Suvarna | Madhu Priya
Source: AIP Advances (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Role of range of interaction potential on structure and dynamics of a one-component system of particles interacting via Mie potential The thermodynamic and transport properties of a fluid depend significantly on the particle interactions at the molecular level. The range of these interparticle interactions thus plays a crucial role in determining its phase behavior. In the present work, we study the role of the range of particle–particle interaction potential on the structure and dynamics of a two-dimensional fluid in the proximity of the liquid–solid phase transition using molecular dynamics simulations. The particles are considered to interact via a Mie (2n, n) potential, and the range of this potential is varied by changing the attractive exponent n from 4 to 20. As the range of interaction potential is increased, a decrease in the local structural order is observed, while the sixfold orientational order remains almost invariant. Some exceptions are observed for the longest range of interaction potential at the liquid–solid phase transition temperature. Furthermore, we observe a temperature-dependent crossover in the relaxation of the nearest-neighbor correlations and the self-diffusion coefficient with a change in the interaction range. This results in a similar crossover in the Stokes–Einstein relation of the two-dimensional system of particles. In addition, we observe a deviation from the conventional Stokes–Einstein relation in systems interacting via these Mie potentials. Molecular dynamics ; Chemical physics ; Phase transition ; Pair potential ; Particle (ecology) ; Range (aeronautics) ; Relaxation (psychology) ; Physics ; Crossover ; Diffusion ; Thermodynamics ; Materials science ; Quantum mechanics Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Theoretical and Computational Physics
Authors: Lars Nieradzik | Henrike Stephani | Jördis Sieburg-Rockel | Stephanie Helmling | Andrea Olbrich | Stephanie Wrage | Janis Keuper
Source: Forests (article, 2024-10-30)
Field: Chemistry — Organic Chemistry

WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally. Detector ; Object (grammar) ; Computer vision ; Artificial intelligence ; Object detection ; Computer science ; Pattern recognition (psychology) ; Remote sensing ; Environmental science ; Physics ; Optics ; Geography Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Visual Attention and Saliency Detection
Authors: Muhammad Raza Khowja | Gaurang Vakil | Syed Shahjahan Ahmad | Ramkumar Ramanathan | Chris Gerada | Maamar Benarous
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Life Characterization of PEEK and Nanofilled Enamel Insulated Wires Under Thermal Ageing This paper characterizes and develops life models for two different wire insulating materials: Poly-ether-ketone and nanofilled enamel (Allotherm wire). The article focuses on predicting the lifespan of PEEK and Allotherm wire insulation materials, targeting low voltage electrical machines. The study investigates the effects of thermal ageing on factors such as dissipation factor, insulation capacitance, and partial discharge inception voltage (PDIV). Delamination of the insulation coating is observed in both wires during the ageing process, resulting in an increase in the differential dissipation factor and insulation capacitance. With respect to its unaged condition, Allotherm wire exhibits faster degradation, showing 2.4- and 4.5-times higher changes in the differential dissipation factor and insulation capacitance respectively compared to PEEK wire after the 16th ageing cycle. In addition, Allotherm wire experiences faster deterioration of the PDIV, with a 42% reduction compared to 32% in PEEK after the same ageing cycle. Using a single-stress Arrhenius life model, the study estimates the relative thermal index (RTI) for both materials as 245°C for PEEK and 226.25°C for Allotherm wire. These results indicate a 72.1% decrease in the lifetime of PEEK and a 25.5% decrease in the lifetime of Allotherm wire when compared to the manufacturer’s RTI specification. Peek ; Materials science ; Composite material ; Capacitance ; Dissipation factor ; Delamination (geology) ; Dissipation ; Accelerated aging ; Dielectric ; Polymer ; Electrode ; Optoelectronics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: D. Nolasco-Altamirano | Alejandro Alonso‐Sotolongo | J.F. Benavente | Ricardo García‐Salcedo | Olivia Amanda García‐Garduño | J. Zárate‐Medina | V. Correcher | Teodoro Rivera‐Montalvo
Source: physica status solidi (b) (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Kinetic Parameters Analysis of GdAlO<sub>3</sub> Based on Thermoluminescent Phenomenon Herein, it is reported on calculating thermoluminescence (TL) kinetic parameters determined from the TL emission of synthetic GdAlO 3 (GAO) phosphors prepared by the coprecipitation method. The sample characterized employing X‐ray diffraction with an orthorhombic phase structure (space group Pnma (62)) shows complex glow curves consisting of at least four groups of components peaked at 100, 140, 240, and 290 °C where the two lower overlapped temperature peaks are difficult to identify using the TM‐Tstop. The coexistence of a continuum in the trap distribution (linked to the lower temperature peaks) and a discrete trap system (associated with the components at temperatures higher than 200 °C) can be distinguished. The estimation of the TL kinetic parameters is performed using GlowFit, computing glow curve deconvolution (CGCD), and various heating rate methods. However, only CGCD appears as the suitable technique for such purpose since it provides information on the TL physical process supported by mathematical models based on a linear combination of functions related to the first order kinetic approach. Thermoluminescence ; Orthorhombic crystal system ; Kinetic energy ; Phosphor ; Coprecipitation ; Analytical Chemistry (journal) ; Materials science ; Deconvolution ; Chemistry ; Luminescence ; Crystal structure ; Physics ; Crystallography ; Optics Luminescence Properties of Advanced Materials ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Marco M. Z. Sharkawi | Nehal F. Farid | M. Hassan M. Hassan | Said A. Hassan
Source: BMC Chemistry (article, 2024-11-19)
Field: Chemistry — Analytical Chemistry

New chemometrics-assisted spectrophotometric methods for simultaneous determination of co-formulated drugs montelukast, rupatadine, and desloratadine in their different dosage combinations Two accurate, precise and robust multivariate chemometric methods were developed for the simultaneous determination of montelukast sodium (MON), rupatadine fumarate (RUP) and desloratadine (DES). These methods provide a cost-effective alternative to chromatographic techniques by utilizing spectrophotometry in pharmaceutical quality control. The proposed approaches, partial least squares-1 (PLS-1) and artificial neural network (ANN), were optimized using genetic algorithm (GA) to select the most influential wavelengths, enhancing model performance. A five-level, three-factor design was employed to construct a calibration set with 25 mixtures, utilizing concentration ranges of 3-19, 5-25, and 4-20 µg.mL<sup>-1</sup> for MON, RUP, and DES, respectively. An independent validation set was employed to assess the performance of the models. GA significantly improved the PLS-1 and ANN models for RUP and DES, though minimal enhancement was observed for MON. These methods were successfully applied to the simultaneous quantification of the compounds in pharmaceutical formulations and proved useful as stability-indicating assays for RUP, given that DES is a known degradation product. The developed methods offer a valuable tool for impurity profiling and quality control in pharmaceutical analysis. Desloratadine ; Chemometrics ; Partial least squares regression ; Chromatography ; Chemistry ; Aceclofenac ; Quality by Design ; Computer science ; Machine learning ; Pharmacology Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Sensors
Authors: Roberto Romaniello | Antonietta Eliana Barrasso | Claudio Perone | Antonia Tamborrino | Antonio Berardi | Alessandro Leone
Source: Foods (article, 2024-01-26)
Field: Chemistry — Analytical Chemistry

Optimisation of an Industrial Optical Sorter of Legumes for Gluten-Free Production Using Hyperspectral Imaging Techniques The market demand for gluten-free food is increasing due to the growing gluten sensitivity and coeliac disease (CD) in the population. The market requires grass-free cereals to produce gluten-free food. This requires sorting methods that guarantee the perfect separation of gluten contaminants from the legumes. The objective of the research was the development of an optical sorting system based on hyperspectral image processing, capable of identifying the spectral characteristics of the products under investigation to obtain a statistical classifier capable of enabling the total elimination of contaminants. The construction of the statistical classifier yielded excellent results, with a 100% correct classification rate of the contaminants. Tests conducted subsequently on an industrial optical sorter validated the result of the preliminary tests. In fact, the application of the developed classifier was able to correctly select the contaminants from the mass of legumes with a correct classification percentage of 100%. A small proportion of legumes was misclassified as contaminants, but this did not affect the scope of the work. Further studies will aim to reduce even this small share of waste with investigations into optimising the seed transport systems of the optical sorter. Hyperspectral imaging ; Gluten free ; Population ; Food industry ; Sorting ; Computer science ; Biotechnology ; Biochemical engineering ; Environmental science ; Artificial intelligence ; Gluten ; Food science ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Plant and soil sciences