jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeqing Yang | Zhimeng Li | Ning Hu | Mingxuan Zhang | Wenbo Zhang | Lingxiao Gao | Xiangyan Ding | Zhengpan Qi | Shuyong Duan
Source: Agriculture (article, 2023-01-25)
Field: Chemistry — Analytical Chemistry

Multi-Index Grading Method for Pear Appearance Quality Based on Machine Vision The appearance quality of fruits affects consumers’ judgment of their value, and grading the quality of fruits is an effective means to improve their added value. The purpose of this study is to transform the grading of pear appearance quality into the classification of the categories under several quality indexes based on industry standards and design effective distinguishing features for training the classifier. The grading of pear appearance quality is transformed into the classification of pear shapes, surface colors and defects. The symmetry feature and quasi-rectangle feature were designed and the back propagation (BP) neural network was trained to distinguish standard shape, apical shape and eccentric shape. The mean and variance features of R and G channels were used to train support vector machine (SVM) to distinguish standard color and deviant color. The surface defect area was used to participate in pear appearance quality classification and the gray level co-occurrence matrix (GLCM) features of defect area were extracted to train BP neural network to distinguish four common defect categories: tabbed defects, bruised defects, abraded defects and rusty defects. The accuracy rates of the above three classifiers reached 83.3%, 91.0% and 76.6% respectively, and the accuracy rate of pear appearance quality grading based on grading rules was 80.5%. In addition, the hardware system prototype for experimental purpose was designed, which have certain reference significance for the further construction of the pear appearance quality grading pipeline. PEAR ; Artificial intelligence ; Grading (engineering) ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Artificial neural network ; Mathematics ; Computer vision ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Shuhei Aramaki | S. Tsuge | Ariful Islam | Fumihiro Eto | Takumi Sakamoto | Soho Oyama | Wenxin Li | Chi Zhang | Shinichi Yamaguchi | Daiki Takatsuka | Yuko Hosokawa | A. S. M. Waliullah | Yutaka Takahashi | Kenji Kikushima | Tomohito Sato | Kei Koizumi | Hiroyuki Ogura | Tomoaki Kahyo | Satoshi Baba | Norihiko Shiiya | Haruhiko Sugimura | Katsumasa Nakamura | Mitsutoshi Setou
Source: PLoS ONE (article, 2023-05-10)
Field: Chemistry — Spectroscopy

Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study Cancer tissues reflect a greater number of pathological characteristics of cancer compared to cancer cells, so the evaluation of cancer tissues can be effective in determining cancer treatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues and even identify molecules while preserving spatial information. Cluster analysis of cancer tissues' MSI data is currently used to evaluate the phenotype heterogeneity of the tissues. Interestingly, it has been reported that phenotype heterogeneity does not always coincide with genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few gene mutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminal breast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides (TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly the proportion of PC and TG correlated with the proportion of cancer and stroma on HE images. Furthermore, the number of carbons in these lipid class varied from cluster to cluster. This was consistent with the fact that enzymes that synthesize long-chain fatty acids are increased through cancer metabolism. It was then thought that clusters containing PCs with high carbon counts might reflect high malignancy. These results indicate that lipidomics-based phenotype heterogeneity could potentially be used to classify cancer for which genetic analysis alone is insufficient for classification. Lipidomics ; Mass spectrometry imaging ; Breast cancer ; Mass spectrometry ; Cancer ; Mammography ; Medicine ; Pathology ; Computational biology ; Biology ; Chemistry ; Bioinformatics ; Chromatography ; Internal medicine Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Daniel J. Savage | Luca Lutterotti | Christopher M. Biwer | Michael McKerns | C. A. Bolme | Marko Knežević | Sven C. Vogel
Source: Journal of Applied Crystallography (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

<i>MILK</i>: a Python scripting interface to <i>MAUD</i> for automation of Rietveld analysis Modern diffraction experiments (<i>e.g. in situ</i> parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (<i>i.e.</i> the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software <i>Material Analysis Using Diffraction</i> (<i>MAUD</i>), the <i>MAUD Interface Language Kit</i> (<i>MILK</i>) is developed along with an updated text batch interface for <i>MAUD</i>. The open-source software <i>MILK</i> is computer-platform independent and is packaged as a Python library that interfaces with <i>MAUD</i>. Using <i>MILK</i>, model selection (<i>e.g.</i> various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with <i>MAUD</i>. Through modification to the <i>MAUD</i> batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with <i>MILK</i> are compatible with <i>Cinema: Debye-Scherrer</i>, a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of <i>MILK</i> is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer. Python (programming language) ; Scripting language ; Rietveld refinement ; Computer graphics (images) ; Computer science ; Programming language ; Crystallography ; Chemistry X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Yanxiang Wang | Minfeng Xing | Hongguo Zhang | Binbin He | Yi Zhang
Source: Remote Sensing (article, 2023-06-06)
Field: Chemistry — Analytical Chemistry

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles. Hyperspectral imaging ; Remote sensing ; Feature selection ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Environmental science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Mycotoxins in Agriculture and Food
Authors: Z. He | Amaia Zuazua-Ros | César Martín-Gómez
Source: Journal of Building Engineering (review, 2023-08-25)
Field: Materials Science — Materials Chemistry

Thermoelectric system applications in buildings: A review of key factors and control methods Key (lock) ; Control (management) ; Control system ; Computer science ; Temperature control ; Thermoelectric effect ; Process (computing) ; Thermoelectric cooling ; Work (physics) ; Reliability engineering ; Systems engineering ; Engineering ; Control engineering ; Mechanical engineering ; Electrical engineering ; Artificial intelligence ; Computer security Advanced Thermoelectric Materials and Devices ; Building Energy and Comfort Optimization ; Thermal Radiation and Cooling Technologies
Authors: Zhongdong Wu | Zhuo Zhang | Li Zheng | Tianfeng Yan | Chunyang Tang
Source: Sensors (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features. Partial discharge ; Noise reduction ; Thresholding ; Wavelet ; Waveform ; Algorithm ; Computer science ; Adaptive filter ; Artificial intelligence ; Pattern recognition (psychology) ; Engineering ; Voltage ; Telecommunications High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image and Signal Denoising Methods
Authors: Arnaud Taute | Sadek Al-jibouri | Capucine Laprais | S. Monfray | Julien Lumeau | Antonin Moreau | Xavier Letartre | Nicolas Baboux | Guillaume Saint‐Girons | Lotfi Berguiga | Sébastien Cueff
Source: Optical Materials Express (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Programming multilevel crystallization states in phase-change-material thin films We propose and demonstrate a simple method to accurately monitor and program arbitrary states of partial crystallization in phase-change materials (PCMs). The method relies both on the optical absorption in PCMs as well as on the physics of crystallization kinetics. Instead of raising temperature incrementally to increase the fraction of crystallized material, we leverage the time evolution of crystallization at constant temperatures and couple this to a real-time optical monitoring to precisely control the change of phase. We experimentally demonstrate this scheme by encoding a dozen of distinct states of crystallization in two different PCMs: GST and Sb 2 S 3 . We further exploit this ’time-crystallization’ for the in-situ analysis of phase change mechanisms and demonstrate that the physics of crystallization in Sb 2 S 3 is fully described by the so-called Johnson-Mehl-Avrami-Kolmogorov formalism. The presented method not only paves the way towards real-time and model-free programming of non-volatile reconfigurable photonic integrated devices, but also provides crucial insights into the physics of crystallization in PCMs. Crystallization ; Materials science ; Leverage (statistics) ; Photonics ; Computer science ; Optoelectronics ; Thermodynamics ; Physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Neurobiology and Insect Physiology Research
Authors: Weiqing Zhang | Mei Lin | Hong-Ju He | Yuling Wang | Jingru Wang | Hongjie Liu
Source: Molecules (article, 2023-02-09)
Field: Chemistry — Analytical Chemistry

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912-1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (<i>r</i><sub>P</sub>) of 0.949 and root mean square error (RMSE<sub>P</sub>) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R<sup>2</sup> = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm. Partial least squares regression ; Mean squared error ; Normalization (sociology) ; Linear regression ; Vitamin ; Chemistry ; Mathematics ; Regression analysis ; Analytical Chemistry (journal) ; Biological system ; Chromatography ; Statistics ; Biochemistry ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Aqeel Hussain Naqvi | Duc Anh Pham | Syed Imran Hussain Shah | Sungjoon Lim
Source: Micromachines (article, 2023-06-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

1-Bit Transmission-Type Digital Programmable Coding Metasurface with Multi-Functional Beam-Shaping Capability for Ka-Band Applications Digital programmable coding metasurfaces (DPCMs) have recently attracted enormous attention and have been broadly applied, owing to their ability to manipulate electromagnetic (EM) wave behaviours and programmable multi-functionality. Recent DPCM works are divided into reflection and transmission types (R-DPCM and T-DPCM, respectively); however, there are only a few reported T-DPCM works in the millimetre-wave spectrum, owing to the difficulty of realising the large-phase controllable range while maintaining low transmission losses with electronic control components. Consequently, most millimetre-wave T-DPCMs are demonstrated only with limited functions in a single design. Additionally, all these designs use high-cost substrate materials that constrain practical applicability, owing to cost-ineffectiveness. Herein, we propose a 1-bit T-DPCM that simultaneously performs three dynamic beam-shaping functions with a single structure for millimetre-wave applications. The proposed structure is completely constructed using low-cost FR-4 materials, and operation of each meta-cell is manipulated using PIN-diodes, thus driving the achievement of multiple effective dynamic functionalities including dual-beam scanning, multi-beam shaping, and orbital-angular-momentum-mode generation. It should be noted that there are no reported millimetre-wave T-DPCMs demonstrating multi-function design, thus showing a gap in the recent literature of millimetre-wave T-DPCMs. Moreover, cost-effectiveness can be significantly enhanced, owing to the construction of the proposed T-DPCM using only low-cost material. Computer science ; Millimetre wave ; Electronic engineering ; Transmission (telecommunications) ; Computer hardware ; Optics ; Physics ; Engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ye Sun | Xiaochan Wang | Leiqing Pan | Yonghong Hu
Source: Current Research in Food Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Influence of maturity on bruise detection of peach by structured multispectral imaging Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels. Postharvest ; Bruise ; Multispectral image ; Browning ; Maturity (psychological) ; Horticulture ; Hue ; Biology ; Chemistry ; Computer science ; Artificial intelligence ; Medicine ; Surgery Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Kyungjoo Cho | Dong-Woo Cho | Bo-Kyoung Koo | Yo-sun Yun
Source: Buildings (article, 2023-11-24)
Field: Chemistry — Spectroscopy

Thermal Performance Analysis of Windows, Based on Argon Gas Percentages between Window Glasses Windows in new buildings in Korea are equipped with double or triple glazing, a low-E coating, and gas injection between the panes of glass, in accordance with the regulations for the reinforced insulation of windows. However, these windows have certain issues, such as thermal transmittance variations and injected gas leakage. In this study, we investigated the current status of double-glazed windows filled with argon gas and analyzed the actual deterioration degree in terms of insulation performance via field experiments and simulations. Accordingly, we manufactured a 26-millimeter low-E double-glazed window test specimen and conducted tests in accordance with the KS F 2278 standard. The test results indicated that the thermal performance decreased by 10.9% when the argon gas filling rate was reduced from 95% to 0%. The simulation results showed that the thermal performance of windows that were insulated using only glazing decreased by 22.6% with the decrease in the argon gas filling rate; the thermal performance of the double-glazed windows also decreased by 13.6%. A comprehensive analysis of the field surveys and simulations predicted a 92% probability that the argon gas filling rate of double-glazed windows would be below 65% by two years after completion, in addition to a ~4.3% decrease in thermal performance. Glazing ; Argon ; Argon gas ; Thermal transmittance ; Materials science ; Thermal ; Composite material ; Window (computing) ; Thermal resistance ; Computer science ; Chemistry Aerogels and thermal insulation ; Energy and Environmental Systems ; Engineering Applied Research
Authors: Magdalena Szota | Paweł Wolski | Cristina Carucci | Flaminia Cesare Marincola | Jacek Gurgul | Tomasz Pańczyk | Andrea Salis | Barbara Jachimska
Source: International Journal of Molecular Sciences (article, 2023-01-03)
Field: Materials Science — Polymers and Plastics

Effect of Ionization Degree of Poly(amidoamine) Dendrimer and 5-Fluorouracil on the Efficiency of Complex Formation—A Theoretical and Experimental Approach Due to their unique structure, poly(amidoamine) (PAMAM) dendrimers can bind active ingredients in two ways: inside the structure or on their surface. The location of drug molecules significantly impacts the kinetics of active substance release and the mechanism of internalization into the cell. This study focuses on the effect of the protonation degree of the G4PAMAM dendrimer and the anticancer drug 5-fluorouracil (5FU) on the efficiency of complex formation. The most favorable conditions for constructing the G4PAMAM-5FU complex are a low degree of protonation of the dendrimer molecule with the drug simultaneously present in a deprotonated form. The fluorine components in the XPS spectra confirm the formation of the stable complex. Through SAXS and DLS methods, a decrease in the dendrimer's molecular size resulting from protonation changes at alkaline conditions was demonstrated. The gradual closure of the dendrimer structure observed at high pH values makes it difficult for the 5FU molecules to migrate to the interior of the support structure, thereby promoting drug immobilization on the surface. The <sup>1</sup>H NMR and DOSY spectra indicate that electrostatic interactions determine the complex formation process. Through MD simulations, the localization profile and the number of 5FU molecules forming the complex were visualized on an atomic scale. Dendrimer ; Amidoamine ; Poly(amidoamine) ; Protonation ; Chemistry ; Molecule ; Small-angle X-ray scattering ; Combinatorial chemistry ; Polymer chemistry ; Organic chemistry ; Ion Dendrimers and Hyperbranched Polymers ; Chemical Synthesis and Analysis ; Advanced Polymer Synthesis and Characterization