jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fan Zhao | Jiawei Zhang | Qiang Liu | Liang Chen | Song Zhang | Mingbao Li
Source: Agriculture (article, 2024-11-30)
Field: Chemistry — Analytical Chemistry

Fast Quality Detection of Astragalus Slices Using FA-SD-YOLO Quality inspection is a pivotal component in the intelligent sorting of Astragalus membranaceus (Huangqi), a medicinal plant of significant pharmacological importance. To improve the precision and efficiency of assessing the quality of Astragalus slices, we present the FA-SD-YOLO model, an innovative advancement over the YOLOv8n architecture. This model introduces several novel modifications to enhance feature extraction and fusion while reducing computational complexity. The FA-SD-YOLO model replaces the conventional C2f module with the C2F-F module, developed using the FasterNet architecture, and substitutes the SPPF module with the Adaptive Inverted Fusion (AIFI) module. These changes markedly enhance the model’s feature fusion capabilities. Additionally, the integration of the SD module into the detection head optimizes parameter efficiency while improving detection performance. Performance evaluation highlights the superiority of the FA-SD-YOLO model. It achieves accuracy and recall rates of 88.6% and 89.6%, outperforming the YOLOv8n model by 1.8% and 1.3%, respectively. The model’s F1 score reaches 89.1%, and the mean average precision (mAP) improves to 93.2%, reflecting increases of 1.6% and 2.4% over YOLOv8n. These enhancements are accompanied by significant reductions in model size and computational cost: the parameter count is reduced to 1.58 million (a 47.3% reduction), and the FLOPS drops to 4.6 G (a 43.2% reduction). When compared with other state-of-the-art models, including YOLOv5s, YOLOv6s, YOLOv9t, and YOLOv11n, the FA-SD-YOLO model demonstrates superior performance across key metrics such as accuracy, F1 score, mAP, and FLOPS. Notably, it achieves a remarkable recognition speed of 13.8 ms per image, underscoring its efficiency and suitability for real-time applications. The FA-SD-YOLO model represents a robust and effective solution for the quality inspection of Astragalus membranaceus slices, providing reliable technical support for intelligent sorting machinery in the processing of this important medicinal herb. Astragalus ; Biology ; Medicine Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Traditional Chinese Medicine Analysis
Authors: M. Ortega | Alexander Saynisch | Bahar-Merve Yurtseven | Thomas Gries
Source: Fibers (review, 2024-04-07)
Field: Materials Science — Polymers and Plastics

A Review on False-Twist Texturing The annual demand for fibres continues to rise worldwide. Consequently, more and more fibres must be produced to meet this demand, most of which are melt-spun polymeric man-made fibres. Smooth filaments made of polymers are mainly used for technical applications in industry. For use in clothing or home textiles, for example, a texturing process is used to give the filaments a crimp and thus a feel like that of natural fibres. In this state, they can be processed together with natural fibres and used in textiles. Partially oriented yarns (POY) are of great importance in texturing. The yarns are mainly crimped with the help of the so-called false-twist texturing process (FTTP). Since POY accounts for about 60% of the melt-spun filament yarn produced worldwide, the FTTP is the most important texturing process in the textile industry. In this paper, the main components of false-twist texturing (FTT) machines are explained, along with the state of the art and research for each component and its influence on the process. Relevant patents are discussed, as well as process optimisation techniques, innovative polymers, and yarn types recently used in FTT, followed by a conclusion and an outlook for the process. Crimp ; Yarn ; Materials science ; Weaving ; Process (computing) ; Textile ; Composite material ; Textile industry ; Polymer ; Tenacity (mineralogy) ; Computer science Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; Natural Fiber Reinforced Composites
Authors: Babu Naik Gugulothu | Sanjay Lakshminarayanan | Madhu Palati | H. L. Suresh | Mahipal Bukya
Source: Engineering Research Express (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

A study of XLPE insulation failure in power cables under electromagnetic stress Abstract Underground cables with cross-linked polyethylene (XLPE) insulation are integral to medium voltage (MV) power transmission systems, ensuring continuous electricity supply amidst operational challenges and environmental conditions. However, the reliability of these cables can be compromised over time due to aging and installation-related factors, particularly at joints and terminations. This study offers a comprehensive analysis of how various defects, including spherical air voids, pinholes, and irregularities in semiconducting layers, affect electric field and potential distributions within cable end terminations using COMSOL Multiphysics software. Through detailed simulations, the study identifies significant variations in electric field strength caused by these defects, highlighting critical stress concentration areas. In this study, it assumed that the cable and termination have the same cross-section. By analyzing these simulations, the study provides insights into optimizing cable design and installation practices to enhance the reliability and lifespan of underground power transmission systems. This study introduces a novel approach by combining advanced COMSOL Multiphysics simulations with a detailed analysis of defect impacts on electric field distributions, offering new insights into stress concentrations and degradation at cable terminations. Simulation outcomes reveal significant variations in electric field strengths due to air voids and pinholes in cables and terminations: for 2 mm voids, up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2.45</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mn>6</mml:mn> </mml:msup> </mml:math> V mm −1 near the conductor and 56.5 V mm −1 at termination. These findings enhance the understanding of XLPE-insulated cable behavior under electromagnetic stress, providing a basis for mitigating failures and improving overall system performance. Stress (linguistics) ; Materials science ; Composite material ; Forensic engineering ; Power (physics) ; Electrical engineering ; Structural engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Lightning and Electromagnetic Phenomena
Authors: Ludwig Horsthemke | Jens Pogorzelski | Dennis Stiegekötter | Frederik Hoffmann | Ann-Sophie Bülter | Sarah Trinschek | Markus Gregor | Peter Glösekötter
Source:  (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Towards Resolving the Ambiguity in Low-Field, All-Optical Magnetic Field Sensing with High NV-Density Diamonds In all-optical magnetic field sensing using nitrogen-vacancy-center-rich diamonds, an ambiguity in the range of 0–8 mT can be observed. We propose a way to resolve this ambiguity using the magnetic-field-dependent fluorescence lifetime. We therefore recorded the frequency response of the fluorescence upon modulation of the excitation intensity in a frequency range of 1–100MHz. The magnetic-field-dependent decay dynamics led to different response characteristics for magnetic fields below and above 3mT, allowing us to resolve the ambiguity. We used a physics-based model function to extract fit parameters, which we used for regression, and compared it to an alternative approach purely based on an artificial neural network. Ambiguity ; Field (mathematics) ; Magnetic field ; Physics ; Remote sensing ; Environmental science ; Computer science ; Geology Diamond and Carbon-based Materials Research ; Magneto-Optical Properties and Applications ; Force Microscopy Techniques and Applications
Authors: Abdennacer El Mrabet | Aimen El Orche | Abderrahim Diane | Lamiae Alami | Amal Ait Haj Said | Mustapha Bouatia | Ibrahim Sbai El Otmani
Source: Flavour and Fragrance Journal (article, 2024-10-10)
Field: Chemistry — Analytical Chemistry

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy ABSTRACT Lavender, widely cultivated in the Mediterranean region, produces essential oil known for its significant biological activities and is a key component of the perfume industry due to its high levels of Linalool and Linalyl acetate, along with low Camphor content, which contributes to its high cost. However, the market is plagued by adulterated lavender oil, often mixed with cheaper alternatives such as eucalyptus and rosemary. Current detection methods, primarily gas chromatography, are expensive, time‐consuming and often fail to detect low levels of adulteration. To address these limitations, this study examines the use of mid‐infrared spectroscopy for the detection and prediction of adulteration levels. A set of 105 samples, comprising pure lavender oil and adulterated lavender oil, was prepared in the laboratory. Principal component analysis (PCA), hierarchical clustering ascending (HCA) and K‐means clustering were applied to the FT‐MIR results for qualitative analysis to effectively discriminate between authentic and adulterated essential oils. For quantitative analysis, partial least squares regression (PLSR) was used to develop accurate calibration models for predicting the percentage of adulteration. The results from PCA, HCA and K‐means demonstrated the efficacy of these techniques in detecting adulteration, even at low levels (2%). Calibration models were developed using the PLSR method with different spectral preprocessing techniques to predict the percentage of adulteration, with results indicating that models generated on the raw data and those using MSC (multiplicative signal correction) pre‐processing are optimal. In addition, the use of interval‐partial least squares (IPLS) variable selection techniques (Forward, Backward) improved the predictive accuracy of the models developed by reducing the number of wavelengths used. Partial least squares regression ; Chemometrics ; Lavender ; Principal component analysis ; Chemistry ; Linalool ; Essential oil ; Linalyl acetate ; Lavender oil ; Calibration ; Linear discriminant analysis ; Hierarchical clustering ; Chromatography ; Biological system ; Cluster analysis ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Adrian Petru Teodoriu | Bogdan Pricop | Nicoleta Monica Lohan | Mihai Popa | Radu Ioachim Comăneci | Ioan Doroftei | Leandru-Gheorghe Bujoreanu
Source: Actuators (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Designing a Robotic Gripper Based on the Actuating Capacity of NiTi-Based Shape Memory Wires In the present study, the capacity of two commercial NiTi and NiTiCu shape memory alloy (SMA) wires to develop work-generating (WG) and constrained-recovery (CR) shape memory effects (SMEs), as well as the capacity of a commercial NiTiFe super-elastic wire to act as cold-shape restoring element, have been investigated. Using differential scanning calorimetry (DSC), the reversible martensitic transformation to austenite of the three NiTi-based wires under study was emphasized by means of an endothermic minimum of the heat flow variation with temperature. NiTi and NiTiCu wire fragments were further tested for both WG-SME and CR-SME developed during the heating, from room temperature (RT) to different maximum temperatures selected from the DSC thermograms. The former tests revealed the capacity to repetitively lift various loads during repetitive heating, while the latter tests disclosed the repetitive development of shrinkage stresses during the repetitive heating of elongated wires. The tensile behavior of the three NiTi-based SMA wires was analyzed by failure and loading–unloading tests. The study disclosed the actuation capacity of NiTi and NiTiCu shape memory wires, which were able to develop work while being heated, as well as the resetting capacity of NiTiFe super-elastic wires, which can restore the initial undeformed shape of shape memory wires which soften while being cooled down. These features enable the design of a robotic gripper based on the development of NiTi-based actuators with repetitive action. Shape-memory alloy ; Nickel titanium ; Materials science ; Mechanical engineering ; Control engineering ; Computer science ; Engineering ; Composite material Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Robot Manipulation and Learning
Authors: Kai Li | Guo Ci Teo | Kevin Yang | Fengchao Yu | Alexey I. Nesvizhskii
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-05-30)
Field: Chemistry — Spectroscopy

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches. Proteomics ; Computer science ; Tracing ; Mass spectrometry ; Ion-mobility spectrometry ; Offset (computer science) ; Phosphoproteomics ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Hye‐Yeon Song | Myounghwan Kim | Kwang Sun Yoo | Ji‐Hyoung Ha
Source: Heliyon (article, 2024-11-28)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive classification of salinity levels in brined kimchi cabbage using hyperspectral imaging In this study, we demonstrate the potential of a non-destructive hyperspectral imaging processing method in the near-infrared (NIR) region (874-1734 nm) for classifying the quality of brined kimchi cabbage. The salinity level of brined kimchi cabbage is an important indicator of consumer preference and the quality of kimchi. Hence, we compared the water content and salinity of brined kimchi cabbage via hyperspectral data. We extracted the optimal wavelengths from the hyperspectral image dataset to classify the salinity level of the predicted brined kimchi cabbage, and thus, established a novel approach for classifying kimchi samples into quality-unacceptable and quality-acceptable groups. Standard normal variate and multiplicative scatter correction (MSC) were used for pathlength correction. The Savitzky-Golay first and second derivatives were used for the deconvolution of the raw spectral data. The experimental results confirmed that the decision tree model combined with MSC pathlength correction and Savitzky-Golay first derivative preprocessing was the best classification model. The proposed hyperspectral image-NIR system can be applied to the detection of salinity during industrial kimchi manufacturing. Hyperspectral imaging ; Salinity ; Environmental science ; Remote sensing ; Biology ; Geology ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Huda Ghassan Hameed | Hayder B. Sahib | Zahraa Sabbar Omran
Source: Pharmacia (article, 2024-06-14)
Field: Chemistry — Organic Chemistry

Investigating the anti-carcinogenic potential action of 1,2,3 triazole core compounds: impact of introducing an aldehyde or Nitro group, integrating cell line studies, and in silico ADME and protein target prediction The development of novel chemotherapeutic drugs begins with the suppression of cancer and angiogenesis. Ringed compounds with one or more heteroatoms are known as heterocyclic compounds. In organic chemistry and the pharmaceutical sector, heterocyclic compounds containing nitrogen atoms are valuable. In pharmaceutical chemistry, molecules containing a 1,2,3-triazole skeleton are particularly favored. They have great stability, making it simple to bind them to biomolecular targets. In this work, two 1,2,3-triazole scaffolds containing new chemical entities were assessed using the MTT assay against two malignant (MCF-7 and HCT116) and one normal (HUVECs) cell lines with the goal of creating a new leading prodrug for cancer treatment. The ligands were well characterized by FTIR and 1HNMR. In silico ADMET studies show acceptable pharmacokinetic properties. With the aid of the ligands’ SWISS target protein prediction, the in silico binding to target proteins was examined. The two compounds exhibited a dose-dependent cytotoxic effect, with the H4 compound demonstrating a favorable selectivity index against MCF-7 breast cancer, indicating its potential as a leading compound for anticancer prodrugs. In silico ; ADME ; Chemistry ; Prodrug ; Combinatorial chemistry ; Stereochemistry ; Aldehyde ; Heteroatom ; Biochemistry ; Organic chemistry ; In vitro ; Ring (chemistry) Click Chemistry and Applications ; Synthesis and biological activity ; Computational Drug Discovery Methods
Authors: William Michael Laprade | Paweł Pięta | Svetlana Kutuzova | Jesper Cairo Westergaard | Mads Eggert Nielsen | Svend Christensen | Anders Bjorholm Dahl
Source:  (article, 2024-06-17)
Field: Chemistry — Analytical Chemistry

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis. Hyperspectral imaging ; Regression ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Statistics ; Mathematics ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chen Qu | Paul L. Houston | Riccardo Conte | Joel M. Bowman
Source: The Journal of Physical Chemistry A (article, 2024-12-03)
Field: Chemistry — Spectroscopy

Dynamics Calculations of the Flexibility and Vibrational Spectrum of the Linear Alkane C<sub>14</sub>H<sub>30</sub>, Based on Machine-Learned Potentials Hydrocarbons are the central feedstock of fuels, solvents, lubricants, and the starting materials for many synthetic materials, and thus the physical properties of hydrocarbons have received intense study. Among these, the molecular flexibility and the power and infrared spectroscopies are the focus of this paper. These are examined for the linear alkane C<sub>14</sub>H<sub>30</sub> using molecular dynamics (MD) calculations and recent machine-learned potentials. All MD calculations are microcanonical and start at the global linear minimum. The radius of gyration, the number of gauche bond conformations and the distributions of all C-C distances are reported as a function of the total internal energy and as a function of time. These are compared to the power spectra and to the double harmonic spectra of stationary points. Spectral features of the double harmonic spectra smoothly track structural differences, measured by the number of gauche conformations in the molecule. Preliminary calculations using the quantum local mode model for the CH-stretch are presented and satisfactorily capture anharmonic effects. Anharmonicity ; Molecular dynamics ; Spectral line ; Radius of gyration ; Alkane ; Chemistry ; Potential energy ; Flexibility (engineering) ; Computational chemistry ; Function (biology) ; Molecule ; Molecular physics ; Statistical physics ; Atomic physics ; Physics ; Quantum mechanics ; Polymer ; Mathematics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Spectroscopy and Chemometric Analyses
Authors: Ghezal Ahmad Jan Zia | Thomas Hanke | Birgit Skrotzki | Christoph Völker | Bernd Bayerlein
Source: Integrating materials and manufacturing innovation (article, 2024-01-18)
Field: Materials Science — Surfaces, Coatings and Films

Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing Abstract High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. Reproducibility ; Dark field microscopy ; Materials science ; Precipitation ; Transmission electron microscopy ; Precipitation hardening ; Microstructure ; Computer science ; Nanotechnology ; Metallurgy ; Microscopy ; Optics ; Physics ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Research Data Management Practices ; Scientific Computing and Data Management