jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junwei Lu | Rongcai Tian | Shuangya Wen | Chunyun Guan
Source: Agronomy (article, 2023-08-26)
Field: Chemistry — Analytical Chemistry

Selection of Agronomic Parameters and Construction of Prediction Models for Oleic Acid Contents in Rapeseed Using Hyperspectral Data High oleic acid oilseed rape is a hot research area in the development of functional oilseed rape. At present, the model of predicting the oleic acid content in rapeseed at the early growth stage based on hyperspectral technology lacks a mechanistic explanation. In this study, based on the data collected at the 5–6 leaf stage of oilseed rape, a one-dimensional linear regression prediction model of the oleic acid content in leaves (x) and the oleic acid content in rapeseed (y) was constructed with the regression equation y = 1.83x + 75.26, and the R2, RMSE, and RPD of the testing set were 0.96, 0.23%, and 4.86, respectively. Then, a support vector regression prediction model of the spectral standard normal transformed feature parameters and the oleic acid content in leaves was constructed, and the R2, RMSE, and RPD of the testing set were 0.74, 0.21%, and 2.01, respectively. Finally, the sensitive parameter transfer model for the prediction of “spectral standard normal transform feature—oleic acid content in leaves—oleic acid content in rapeseed” was validated, and the R2, RMSE, and RPD of the full sample test were 0.71, 0.54%, and 0.54, respectively. The results show that although the accuracy of the prediction model after the introduction of the agronomic parameters was reduced compared with the performance of direct prediction by using spectra, the oleic acid content in oilseed rape leaves, as an important intermediate variable, could better explain the relationship between the reflection spectrum of the leaf and the oleic acid content in rapeseed. This study provides a theoretical basis and technical support for hyperspectral remote sensing technology in the quality prediction of rapeseed. Oleic acid ; Rapeseed ; Hyperspectral imaging ; Feature selection ; Mathematics ; Regression analysis ; Linear regression ; Humic acid ; Predictive modelling ; Chemistry ; Agronomy ; Food science ; Statistics ; Artificial intelligence ; Computer science ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Wei Jiang | Yao Liu | Fu Qiao | Zhongyan Liu | Jianfang Xiong | Shaogeng Zeng
Source: Acta Scientiarum Polonorum Technologia Alimentaria (article, 2023-06-30)
Field: Chemistry — Analytical Chemistry

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning [pdf] Diarrhetic shellfish poisoning (DSP) is one of the marine biological toxins with the highest frequency and the widest distribution in coastal waters around the globe (Blanco et al., 2007).Okadaic acid (OA) is the most important component of DSP, and human consumption of mussels contaminated with high concentrations of OA toxins will cause diarrhetic poisoning, or even death in severe cases.The main symptoms include gastrointestinal disorders, diarrhea, abdominal pain, fever, vomiting and nausea (Corriere et al., 2021;Mak et al., 2005).It has been shown that eating DSPcontaminated mussels may increase the risk of digestive system cancer (Manerio et al., 2008).It should be noted that the nature of DSP toxins will not change for heating or freezing, and the taste of DSP-contaminated mussels will also not be affected.If there is no professional detection instrument, it is usually difficult to find toxin-contaminated mussels.As a result, extensive attention has been given to how to detect DSP toxin-contaminated mussels effectively and quickly. Diarrhetic shellfish poisoning ; Support vector machine ; Shellfish ; Fuzzy logic ; Artificial intelligence ; Computer science ; Biology ; Fishery ; Fish <Actinopterygii> ; Aquatic animal Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Liliana Indrie | Zlatin Zlatev | Julieta Ilieva | IOAN PAVEL OANA
Source: Industria Textila (article, 2023-05-02)
Field: Materials Science — Polymers and Plastics

An algorithm for the analysis of static hanging drape In this paper, an analysis of drape characteristics of textile waste derived from their 2D colour digital images was made. There have been proposed algorithms and procedures for obtaining 3D shapes of real drape. The description of the drape characteristics of fabrics was made by a total of 17 features. The advantages of the proposed methods and tools are that they don't need complicated calculation methods or a lot of time to measure and obtain a three-dimensional model. A predictive model was created from the obtained data for automated prediction of the drape coefficient of textile waste. The proposed methods and procedures can be applied in the analysis of fabric drape on video files obtained after exposure of the textile fabrics at different airflow rates. Textile ; Measure (data warehouse) ; Computer science ; Airflow ; Algorithm ; Engineering drawing ; Engineering ; Data mining ; Mechanical engineering ; Materials science ; Composite material Textile materials and evaluations
Authors: Yingying Wu | Yun Ou | Jinlin Peng | Chihou Lei
Source: Crystals (article, 2023-08-29)
Field: Materials Science — Materials Chemistry

Phase Structures, Electromechanical Responses, and Electrocaloric Effects in K0.5Na0.5NbO3 Epitaxial Film Controlled by Non-Isometric Misfit Strain Environmentally friendly lead-free K1-xNaxNbO3 (KNN) ceramics possess electromechanical properties comparable to lead-based ferroelectric materials but cannot meet the needs of device miniaturization, and the corresponding thin films lack theoretical and experimental studies. To this end, we developed the nonlinear phenomenological theory for ferroelectric materials to study the effects of non-equiaxed misfit strain on the phase structure, electromechanical properties, and electrical response of K0.5Na0.5NbO3 epitaxial films. We constructed in-plane misfit strain (u1−u2) phase diagrams. The results show that K0.5Na0.5NbO3 epitaxial film under non-equiaxed in-plane strain can exhibit abundant phase structures, including orthorhombic a1c, a2c, and a1a2 phases, tetragonal a1, a2, and c phases, and monoclinic r12 phases. Moreover, in the vicinity of a2c−r12, a1c−c, and a1a2−a2 phase boundaries, K0.5Na0.5NbO3 epitaxial films exhibit excellent dielectric constant ε11, while at a2c−r12 and a1c−c phase boundaries, a significant piezoelectric coefficient d15 is observed. It was also found that high permittivity ε33 and piezoelectric coefficients d33 exist near the a2c−a2, a1a2−r12, and a1c−a1 phase boundaries due to the existence of polymorphic phase boundary (PPB) in the KNN system, which makes it easy to polarize near the phase boundaries, and the polarizability changes suddenly, leading to electromechanical enhancement. In addition, the results show that the K0.5Na0.5NbO3 thin films possess a large electrocaloric response at the phase boundary at the a1a2−r12 and a1c−a1 phase boundaries. The maximum adiabatic temperature change ΔT is about 3.62 K when the electric field change is 30 MV/m at room temperature, which is significantly enhanced compared with equiaxed strain. This study provides theoretical guidance for obtaining K1−xNaxNbO3 epitaxial thin films with excellent properties. Materials science ; Phase boundary ; Ferroelectricity ; Condensed matter physics ; Piezoelectricity ; Phase (matter) ; Perovskite (structure) ; Dielectric ; Composite material ; Optoelectronics ; Crystallography ; Chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Shiyu Li | Ho-Chun Lin | Chia Wei Hsu
Source: PubMed (preprint, 2023-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast multi-channel inverse design through augmented partial factorization Computer-automated design and discovery have led to high-performance nanophotonic devices with diverse functionalities. However, massively multichannel systems such as metasurfaces controlling many incident angles and photonic-circuit components coupling many waveguide modes still present a challenge. Conventional methods require <i>M</i><sub>in</sub> forward simulations and <i>M</i><sub>in</sub> adjoint simulations-2<i>M</i><sub>in</sub> simulations in total-to compute the objective function and its gradient for a design involving the response to <i>M</i><sub>in</sub> input channels. Here, we develop a formalism that uses the recently proposed augmented partial factorization method to obtain both the objective function and its gradient for a massively multichannel system in a single or a few simulations, achieving over 2 orders of magnitude speedup and reduced memory usage. We use this method to inverse design a metasurface beam splitter that separates the incident light to the target diffraction orders for all incident angles of interest, a key component of the dot projector for 3D sensing. This formalism enables efficient inverse design for a wide range of multichannel optical systems. Massively parallel ; Inverse ; Computer science ; Factorization ; Channel (broadcasting) ; Splitter ; Photonics ; Nanophotonics ; Beam splitter ; Projector ; Ray ; Topology (electrical circuits) ; Physics ; Algorithm ; Optics ; Parallel computing ; Mathematics ; Telecommunications ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Yizhe Huang | Jiang‐Bo Hu | Jun Wang | Jinfeng Sun | Y.-H. You | Qibai Huang | Enyong Xu
Source: Materials (article, 2023-05-06)
Field: Materials Science — Materials Chemistry

Sound Quality Performance of Orthogonal Antisymmetric Composite Laminates Embedded with SMA Wires Orthogonal antisymmetric composite laminates embedded with shape memory alloys (SMAs) wires have the potential to improve the sound quality of vibro-acoustics by taking advantage of the special superelasticity, temperature phase transition, and pre-strain characteristics of SMAs. In this research, space discretion and mode decoupling were employed to establish a vibro-acoustic sound quality model of SMA composite laminates. The association between the structural material parameters of SMA composite laminates and the sound quality index is then approached through methodologies. Numerical analysis was implemented to discuss the effects of SMA tensile pre-strain, SMA volume fraction, and the ratio of resin-to-graphite in the matrix on the vibro-acoustic sound quality of SMA composite laminates within a temperature environment. Subsequently, the sound quality test for SMA composite laminates is thus completed. The theoretically predicted value appears to agree well with the experimental outcomes, which validates the accuracy and applicability of the dynamic modeling theory and method for the sound quality of SMA composite laminates. The results indicate that attempting to alter the SMA tensile pre-strain, SMA volume fraction, and matrix material ratio can be used to modify loudness, sharpness, and roughness, which provides new ideas and a theoretical foundation for the design of composite laminates with decent sound quality. SMA* ; Composite laminates ; Materials science ; Composite material ; Shape-memory alloy ; Composite number ; Structural engineering ; Computer science ; Engineering Shape Memory Alloy Transformations ; Composite Structure Analysis and Optimization ; Structural Engineering and Vibration Analysis
Authors: Jimy Oblitas | Yuleyci Cieza
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of fresh milk quality by using Artificial Neural Network and Multivariate Regression The objective of this research was to compare the best structure of a Neural Network (ANN) with a multivariate nonlinear regression model (MNLR) to predict the physicochemical quality parameters of milk. To create a predictor model for the livestock sector, 3 input and 6 output variables were used. To achieve this, a Feedforward ANN with Backpropagation training algorithms was applied. For the models, the Matlab 2020a software was used. The lowest mean absolute deviation (MAD) was found to be 0.00715952, corresponding to a Neural Network with 2 hidden layers (18 and 19), with Tansig and log sig type function, respectively. MNLR models had R2values greater than 0.9.Cross-Validation with 10 interactions was used for this purpose.For comparison, a Duncan test was used where it was found that there are no statistically significant differences between the real sample, the MNLR, and the ANN, with a 95.0% confidence level. Multivariate statistics ; Artificial neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Regression ; Bayesian multivariate linear regression ; Regression analysis ; Multivariate adaptive regression splines ; Machine learning ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses
Authors: C. L. Ng | Socheatra Soeung | Sovuthy Cheab | Kin Yuen Leong
Source: Radioengineering (article, 2023-06-19)
Field: Materials Science — Polymers and Plastics

A Modified Vector Fitting Technique to Extract Coupling Matrix from S-parameters In this paper, a modified vector fitting technique to extract coupling matrix from S-parameters is introduced. This work allows designers to extract the coupling matrix of different or any pre-defined topologies from the simulated or measured S-parameter data. A study on vector fitting (VF) equations that can extract the rational polynomial of bandpass filter responses is carried out. The rational polynomials are formed by applying the VF process to S-parameter responses without having to remove the phase offset and de-embedding the transmission lines. The desired coupling matrix configuration is generated directly from the extracted polynomials using unconstrained and finitely bounded non-linear polynomials (NLP) optimization. Without the need for matrix transformation, the matrix elements are still able to show a one-to-one relationship in coupling values of resonators. Two bandpass filters are shown as examples to illustrate the performance of the new variation of VF. Matrix (chemical analysis) ; Coupling (piping) ; Biological system ; Mathematics ; Applied mathematics ; Materials science ; Biology ; Composite material Textile materials and evaluations ; Structural Health Monitoring Techniques
Authors: Alemayehu S. Admasu | Devesh Shah | Devesh Upadhyay | P. Blanchard
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Deep Learning Design of Graphene-Reinforced Polyurethane Foams from SEM Microstructure Images and Style-based Generative Adversarial Networks Journal Article Deep Learning Design of Graphene-Reinforced Polyurethane Foams from SEM Microstructure Images and Style-based Generative Adversarial Networks Get access Alemayehu Admasu, Alemayehu Admasu Ford Motor Company Research & Advanced Engineering, Dearborn, MI, USA Corresponding author: aadmasu@ford.com Search for other works by this author on: Oxford Academic Google Scholar Devesh Shah, Devesh Shah Ford Motor Company Research & Advanced Engineering, Dearborn, MI, USA Search for other works by this author on: Oxford Academic Google Scholar Devesh Upadhyay, Devesh Upadhyay Ford Motor Company Research & Advanced Engineering, Dearborn, MI, USA Search for other works by this author on: Oxford Academic Google Scholar Patrick Blanchard Patrick Blanchard Ford Motor Company Research & Advanced Engineering, Dearborn, MI, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1892–1893, https://doi.org/10.1093/micmic/ozad067.976 Published: 22 July 2023 Polyurethane ; Materials science ; Generative grammar ; Adversarial system ; Microstructure ; Graphene ; Composite material ; Style (visual arts) ; Artificial intelligence ; Nanotechnology ; Computer science ; Art ; Visual arts Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Muneera S. M. Al‐Saleem | Hany W. Darwish | Ibrahim A. Naguib | Mohammed E. Draz
Source: Molecules (article, 2023-10-12)
Field: Chemistry — Analytical Chemistry

Comparative Study of Augmented Classical Least Squares Models for UV Assay of Co-Formulated Antiemetics Together with Related Impurities The classical least squares (CLS) model and three augmented CLS models are adopted and validated for the analysis of pyridoxine HCl (PYR), cyclizine HCl (CYC), and meclizine HCl (MEC) in a quinary mixture with two related impurities: the CYC main impurity, Benzhydrol (BEH), which has carcinogenic and hepatotoxic effects, and the MEC official impurity, 4-Chlorobenzophenone (BEP). The proposed augmented CLS models are orthogonal signal correction CLS (OSC-CLS), direct orthogonal signal correction CLS (DOSC-CLS), and net analyte processing CLS (NAP-CLS). These models were applied to quantify the three active constituents in their raw materials and their corresponding dosage forms using their UV spectra. To evaluate the CLS-based models sensibly, we design a comparative study involving two sets: the training set to construct models and the validation set to assess the prediction abilities of these models. A five-level, five-factor calibration design was established to produce 25 mixtures for the calibration set. In addition, 16 experiments were performed for a test set distributed equally between the in-space and out-space samples. The primary criterion for comparing the models' performance was the validation set's root mean square error of prediction (RMSEP) value. Finally, augmented CLS models showed acceptable results for assaying the three analytes. The results were compared statistically with the reported HPLC methods; however, the DOSC-CLS model proved the best for assaying the dosage forms. CLs upper limits ; Analyte ; Calibration ; Partial least squares regression ; Chemistry ; Test set ; Biological system ; Mean squared error ; Chromatography ; Mathematics ; Statistics ; Medicine Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography
Authors: Lynn Mouawad | Georges Istamboulié | Gaëlle Catanante | Thierry Noguer
Source: Preprints.org (preprint, 2023-12-06)
Field: Chemistry — Analytical Chemistry

Acetylcholinesterase and Butyrylcholinesterase-Based Biosensors for the Detection of Quaternary Ammonium Biocides in Food Industry A sensitive and robust electrochemical cholinesterase-based sensor was developed to detect the quaternary ammonium (QAs) biocides most frequently found in agri-food industry wash waters: benzalkonium chloride (BAC) and didecyldimethylamonium chloride (DDAC). To reach the maximum residue limit of 2.8 10-8 M imposed by the European Union (EU), two types of cholinesterases were tested, acetylcholinesterase (AChE, from Drosophila melanogaster) and butyrylcholinesterase (BChE, from horse serum). The sensors were designed by entrapping AChE or BChE on cobalt-phthalocyanine-modified screen-printed carbon electrodes. The limits of detection (LOD) of the resulting biosensors were 3.8 10-8 M for DDAC and 3.2 10-7 M for BAC, using respectively AChE and BChE. A simple solid phase extraction step was used to concentrate the samples before biosensor analysis, allowing the accurate determination of DDAC and BAC in tap water with limits of quantification (LOQ) as low as 1.3 10-9 M and 5.3 10-9 M, respectively. The biosensor was shown to be stable during 3-months storage at 4°C. Detection limit ; Butyrylcholinesterase ; Chemistry ; Acetylcholinesterase ; Chromatography ; Biosensor ; Benzalkonium chloride ; Biocide ; Ammonium ; Nerve agent ; Paraoxon ; Nuclear chemistry ; Aché ; Biochemistry ; Enzyme ; Organic chemistry Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Environmental Chemistry and Analysis
Authors: Jinfeng Zhong | Elsa Negre
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Context-aware feature attribution through argumentation Feature attribution is a fundamental task in both machine learning and data analysis, which involves determining the contribution of individual features or variables to a model's output. This process helps identify the most important features for predicting an outcome. The history of feature attribution methods can be traced back to General Additive Models (GAMs), which extend linear regression models by incorporating non-linear relationships between dependent and independent variables. In recent years, gradient-based methods and surrogate models have been applied to unravel complex Artificial Intelligence (AI) systems, but these methods have limitations. GAMs tend to achieve lower accuracy, gradient-based methods can be difficult to interpret, and surrogate models often suffer from stability and fidelity issues. Furthermore, most existing methods do not consider users' contexts, which can significantly influence their preferences. To address these limitations and advance the current state-of-the-art, we define a novel feature attribution framework called Context-Aware Feature Attribution Through Argumentation (CA-FATA). Our framework harnesses the power of argumentation by treating each feature as an argument that can either support, attack or neutralize a prediction. Additionally, CA-FATA formulates feature attribution as an argumentation procedure, and each computation has explicit semantics, which makes it inherently interpretable. CA-FATA also easily integrates side information, such as users' contexts, resulting in more accurate predictions. Computer science ; Argumentation theory ; Feature (linguistics) ; Context (archaeology) ; Attribution ; Artificial intelligence ; Argumentation framework ; Machine learning ; Argument (complex analysis) ; Semantics (computer science) ; Fidelity ; Process (computing) ; Task (project management) ; Natural language processing ; Psychology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Jonathan Miller | Roberto Puch‐Solis | Hilary A. S. Buchanan | Niamh Nic Daéid
Source: Data in Brief (article, 2023-01-25)
Field: Chemistry — Spectroscopy

A ground truth data set of gas chromatography mass spectrometry (GCMS) analysed synthesised methylenedioxymethylamphetamine (MDMA) Controlled drug samples are normally chemically analysed to determine their identity and in some cases, their purity. There are also circumstances where a more broad chemical characterisation of drug samples may also be required. This involves investigating the chemical impurities that may be present in a drug sample as a consequence of their synthesis. This impurity or drug profiling can be derived from drugs which are synthesised chemically or extracted from plant materials and then modified chemically. Impurity profiling can provide some insight into the synthetic methods used and sometimes the starting chemicals used. We report on the data generated from repetitive ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>18</mml:mn></mml:mrow> </mml:math> ) synthesis of ecstasy (methylenedioxymethylamphetamine or MDMA) made by three different synthetic methods. Each data sample is expressed in multiple formats. This article uses the template for publishing GCMS data provided in Miller et al.(2022)[1]. The template provides a robust and systematic approach to organise GCMS data that is both useful for practitioners and amenable for automated data manipulation by data scientists. Chromatography ; Profiling (computer programming) ; Chemistry ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Sample preparation ; Computer science Analytical Chemistry and Chromatography ; Psychedelics and Drug Studies ; Advanced Chemical Sensor Technologies