jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Camila Proni | Eduardo Hideki Oshiro | Edenir Rodrigues Pereira‐Filho | Érica Regina Filletti
Source: C Q D - Revista Eletrônica Paulista de Matemática (article, 2023-07-31)
Field: Chemistry — Analytical Chemistry

Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models In this work, some characteristics of the black and whiteinks that are part of the graphic printing processes,calledrotogravure, were evaluated. In this process, the ink playsa decisive role in the quality of the material produced,therefore, its properties must be evaluated and guaranteedin order to obtain a product that works properly in theprinting process.Thus, an analytical method was devel-oped that combines infrared spectroscopy with ArtificialNeural Networks (ANN) to estimate the viscosity, densityand solids content of inks, having the advantage of provid-ing highly accurate results very quickly and with littlecomputational effort. The best models were those devel-oped for density, with average percentage errors of: 1% intraining and validation, and 2% in testof the black andwhite inks together; 1% in training and validation, and0.7% in test of theblack ink; 0.2% in training, 0.8% invalidation and 0.7% in test of white ink. The method de-veloped has the potential to be applied in printing indus-tries as an improvement for the production of high qualityrotogravure printed material Process engineering ; Inkwell ; Viscosity ; 3D printing ; Artificial neural network ; Computer science ; Process (computing) ; Materials science ; Artificial intelligence ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Color Science and Applications ; Dye analysis and toxicity
Authors: Saima Afzal | Muhammad Iqbal | Ayesha Afzal | Hassan S. Bakouch | Sadiah M. A. Aljeddani
Source: Mathematical Problems in Engineering (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Novel Approaches to Identify Clusters Using Independent Components Analysis with Application As a statistical and computational technique, independent component analysis (ICA) is employed to separate the source variables into statistically independent components. ICA methods have received growing attention as effective data mining tools. In this paper, two novel ICA‐based approaches are proposed to identify the clusters of variables. The identified clusters reduce the dimensionality of the data in a natural way. The first approach, namely “Estimated Mixing Coefficients,” is based on the sum of squares of mixing coefficients, and the second approach, namely “Ranked ,” uses the ranking pattern of of the original and reconstructed series at predefined threshold levels. The proposed techniques are applied to financial time series data to validate their effectiveness. The main focus of the study is on the clustering of multivariate time series datasets using two new proposed approaches based on independent component analysis. The internal and external structures of clusters are also explored using different metrics. Both proposed techniques are compared with some existing clustering techniques. The experimental evaluation results show that the performance of the proposed techniques is better than the existing techniques. Computer science ; Computational biology ; Data mining ; Biology Spectroscopy and Chemometric Analyses ; Blind Source Separation Techniques ; Advanced Chemical Sensor Technologies
Authors: Juan Huo | Huaiqi Li | Changtong Lu
Source: MethodsX (article, 2023-11-19)
Field: Chemistry — Analytical Chemistry

An improved sample container cup and its effect on NIRS of tobacco smoke for quality stability estimation This paper describes a design of an improved self-made Bruker NIR cup and analyzes the effect of the equipment modification to fit the Cambridge filter pad, which enhances experimental efficiency and reduces operational complexity. A self-made NIR cup based on the classical NIR cup is designed to speed up the operation process and reduce the experiment's time cost. To estimate the effect of this equipment modification, the NIR spectra from the classical sample cup and the new self-made cup are compared and analyzed. Furthermore, the quality evaluation results from NIR data of the two cups are also compared according to a distance metric chemometrics method, which shows quality analytical values between these two cups are approaching each other while the experiment efficiency is improved.•This paper introduces a newdesign of a self-made container cup improved from the Bruker's traditional sample container cup to better fit the filter pad and improve the experiment efficiency and convenience.•This paper also analyzes the effect of this container cup change by comparing the NIR spectra before and after modification. Container (type theory) ; Chemometrics ; Sample (material) ; Computer science ; Filter (signal processing) ; Process engineering ; Design of experiments ; Materials science ; Mathematics ; Engineering ; Chemistry ; Chromatography ; Composite material ; Statistics ; Computer vision ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Matheus Costa Pereira | Gustavo Henrique Judice | Pedro José Papandréa
Source: Revista Eletrônica de Engenharia Elétrica e Engenharia Mecânica (article, 2023-12-26)
Field: Materials Science — Polymers and Plastics

Especificação de um sistema de ar condicionado: cálculo de cargas térmicas e seleção de equipamentos Avaliação analítica de um sistema de ar-condicionado, com estudo de caso baseado em um evento localizado em Pouso Alegre/MG. Através da definição e do estudo arquitetônico do espaço, realizado por meio de um croqui do ambiente, foi possível elaborar um memorial de cálculos com o propósito de avaliar uma série de dados. Essa análise precisa permitiu a escolha dos melhores equipamentos, garantindo uma refrigeração ideal, além de proporcionar uma boa limpeza e um ambiente com cheiro agradável, que seja totalmente saudável e adequado. Vale ressaltar que o diagnóstico e o estudo foram desenvolvidos com a assistência de profissionais especializados no ramo, que mesclaram seu conhecimento empírico na análise de viabilidade. Humanities ; Art Textile materials and evaluations ; Material Selection and Properties ; Polymer Nanocomposites and Properties
Authors: Hui Zhong | Yuheng Zuo | Runze Wang | Li Zhang | Xiaoshan Ouyang
Source: Symmetry (article, 2023-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate Electromagnetic Force Analysis of Offshore Wind Power Transformer Windings Based on Fractional Order Lumped Parameter Model Accurate calculation of the electromagnetic force distribution of transformer windings under different loads and fault conditions is of great significance for transformer maintenance, condition evaluation and life prediction. Due to the influence of offshore wind power systems, offshore wind power transformers have high harmonic content and large changes in load rates, which can easily cause the coil destabilization, winding deformation or even damage because of the uneven distribution of the electromagnetic force. To improve the accuracy of electromagnetic force calculation, this paper proposes a fractional order numerical method. First, a three-dimensional axisymmetric transformer model and a symmetrical lumped parameter equivalent circuit model are established, respectively, based on field-circuit coupling. Second, the fractional order approximation of circuit components is realized by using the improved Oustaloup filter. In the fractional order model, the transformer is replaced by the lumped parameter equivalent circuit model. Third, as in the calculation process for integer order electromagnetic force, the integer order current has a large error, and the current waveform does not match the actual power frequency. The fractional order current and electromagnetic force at the 0.9 order are closer to the rated value. Finally, the effects of different load rates, three-phase short circuits and harmonic conditions are studied with the fractional order model. Compared with the traditional integer order finite element electromagnetic model, the fractional order equivalent circuit model established in this paper is more accurate and suitable for electromagnetic force calculation. The proposed method is significant for the structural design and state detection of transformers and also could be applied in the analysis of other dry-type transformers. Electromagnetic coil ; Transformer ; Equivalent circuit ; Electromagnetic field ; Offshore wind power ; Distribution transformer ; Electrical engineering ; Physics ; Engineering ; Wind power ; Voltage Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Power Transformer Diagnostics and Insulation
Authors: José M. Mazón | J. Toledo
Source: Analysis and Applications (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

Cahn–Hilliard equations on random walk spaces In this paper, we study a nonlocal Cahn–Hilliard equation (CHE) in the framework of random walk spaces, which includes as particular cases, the CHE on locally finite weighted connected graphs, the CHE determined by finite Markov chains or the Cahn–Hilliard Equations driven by convolution integrable kernels. We consider different transitions for the phase and the chemical potential, and a large class of potentials including obstacle ones. We prove existence and uniqueness of solutions in [Formula: see text] of the Cahn–Hilliard Equation. We also show that the Cahn–Hilliard equation is the gradient flow of the Ginzburg–Landau free energy functional on an appropriate Hilbert space. We finally study the asymptotic behavior of the solutions. Cahn–Hilliard equation ; Mathematics ; Uniqueness ; Integrable system ; Space (punctuation) ; Random walk ; Hilbert space ; Markov chain ; Balanced flow ; Mathematical analysis ; Convolution (computer science) ; Partial differential equation Solidification and crystal growth phenomena ; Nonlinear Partial Differential Equations ; Stochastic processes and statistical mechanics
Authors: Xumiao Li | Yiming Huang | Kuo Zheng | Guanyu Yu | Qinqin Wang | Lei Gu | Jingquan Li | Hui Wang | Wei Zhang | Yidi Sun | Chen Li
Source: Biophysics Reports (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs <i>in vivo</i> and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, <i>in vivo</i> drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation. Phosphoproteomics ; Regorafenib ; Proteomics ; Druggability ; Computational biology ; Cancer ; Medicine ; Colorectal cancer ; Drug discovery ; Bioinformatics ; Cancer research ; Kinase ; Biology ; Protein phosphorylation ; Protein kinase A ; Internal medicine Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Emmanouil Mavrogeorgis | Tianlin He | Harald Mischak | Agnieszka Latosińska | Antonia Vlahou | Joost P. Schanstra | Lorenzo Catanese | Kerstin Amann | Tobias B. Huber | Joachim Beige | Harald Rupprecht | Justyna Siwy
Source: medRxiv (preprint, 2023-05-26)
Field: Chemistry — Spectroscopy

Uniform Manifold Approximation and Projection-based Assessment of Chronic Kidney Disease Aetiologies based on Urinary Peptidomics Abstract Background The impact of artificial intelligence combined with advanced techniques is ever-increasing in the biomedical field appearing promising, among others, in chronic kidney disease (CKD) diagnosis. However, existing models are often single-aetiology specific. Proposed here is a pipeline for the development of single models able to distinguish and spatially visualize multiple CKD aetiologies. Methods Acquired were from the Human Urinary Proteome Database the urinary peptide data of 1850 healthy control (HC) and CKD (diabetic kidney disease-DKD, IgA nephropathy-IgAN, vasculitis) participants. The uniform manifold approximation and projection (UMAP) method was coupled to a support vector machine (SVM) algorithm. Binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications were performed, including or skipping the UMAP step. Last, the pipeline was compared to the current state-of-the-art single-aetiology CKD urinary models. Findings In an independent test set, the developed models (including the UMAP step) achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications (96.14% and 85.06%, skipping the UMAP step). Overall, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D-space based on their disease state. Interpretation Urinary peptide data appear to potentially be an effective basis for CKD aetiology differentiation. The UMAP step may provide a unique visualization advantage capturing the relevant molecular (patho)physiology. Further studies are warranted to validate the pipeline’s clinical potential in the presented as well as other CKD aetiologies or even other diseases. Kidney disease ; Urinary system ; Medicine ; Pipeline (software) ; Projection (relational algebra) ; Computer science ; Artificial intelligence ; Support vector machine ; Machine learning ; Internal medicine ; Computational biology ; Bioinformatics ; Algorithm ; Biology Advanced Proteomics Techniques and Applications ; Chronic Kidney Disease and Diabetes ; Single-cell and spatial transcriptomics
Authors: Joanna Kirkpatrick | Paul M. Stemmer | Brian C. Searle | Laura E. Herring | LeRoy Martin | Mukul K. Midha | Brett S. Phinney | Baozhen Shan | Magnus Palmblad | Yan Wang | Pratik Jagtap | Benjamin A. Neely
Source: Journal of Biomolecular Techniques JBT (article, 2023-06-02)
Field: Chemistry — Spectroscopy

2019 Association of Biomolecular Resource FacilitiesMulti-Laboratory Data-Independent Acquisition ProteomicsStudy Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels. Computer science ; Resource (disambiguation) ; Sample (material) ; Software ; Set (abstract data type) ; Data science ; Data acquisition ; Data mining ; Data set ; Proteomics ; Instrumentation (computer programming) ; Artificial intelligence ; Biology ; Operating system ; Chemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: J. Navaneetha Krishnan | Uttam Kumar | Satinder Kaur | Parveen Chhuneja
Source: Indian Journal of Genetics and Plant Breeding (The) (article, 2023-12-29)
Field: Chemistry — Analytical Chemistry

Development of a novel image-based grain counting setup for thousand-grain weight estimation in wheat Thousand-grain weight (TGW) is one of the major yield-contributing traits routinely used as a selection criterion by plant breeders. Itis also an important grain quality trait that determines milling yield. Accurate phenotyping of TGW is imperative to dissect its geneticsfor yield improvement. The traditional approach to TGW estimation involves manual grain counting and weighing, which is laborious,tedious and less accurate for large sample sizes. As an alternative, we propose a customized grain counting setup for accurate estimationof TGW in wheat by assembling a photo lighting tent and a smartphone for image acquisition of grain samples. A popular open-sourcesoftware, ‘imageJ’ was used to process the images to estimate the grain count. The counted grain samples were weighed to calculatethe TGW. The TGW estimate derived from the proposed grain counting setup displayed a high degree of correlation with the manuallyestimated TGW data (r = 0.99, p &lt;0.05). It took significantly less time to count the grain samples using the proposed setup comparedto manual counting with better accuracy and minimal labor. The error rate in grain counting using the imaging-based setup was verylow (&lt;1%) and 30 to 40 grain samples can be imaged per hour. This setup can be extended to estimate the TGW of different crops,excluding those having spherical seeds. Veterinary medicine ; Biology ; Wheat grain ; Mathematics ; Agricultural engineering ; Agronomy ; Engineering ; Medicine Spectroscopy and Chemometric Analyses ; Genetic Mapping and Diversity in Plants and Animals ; Crop Yield and Soil Fertility
Authors: Jiaxin Feng | R. H. Li | Jiale Wang | Wu Chen
Source: Highlights in Science Engineering and Technology (article, 2023-11-06)
Field: Materials Science — Polymers and Plastics

Study on Intercalated Melt-blown Nonwovens Based on Product Performance Control Mechanism There are many factors affecting the preparation of intercalation melt-blown nonwovens, and scientists are committed to studying the conditions required for optimal product performance. Therefore, in order to study the optimal conditions, this paper first uses multiple linear regression to explore the correlation between intercalation rate and the change rule. It is found that intercalation rate is related to the change rule of thickness and filtration resistance, but has nothing to do with other indicators. In order to explore the relationship between process parameters and structure variables, a multi-layer perceptron (BP neural network) model with process parameters as input and structure variables as output is established. The relationship between input and output is obtained by using the trained network and the functional relationship is calculated. By analyzing the function, it is found that the correlation between the receiving distance and thickness and porosity is stronger than the positive correlation between the hot air speed and the compression resilience is stronger than the negative correlation between the hot air speed. The correlation between hot air velocity and thickness and porosity is weaker than that of receiving distance, and the correlation between hot air velocity and compressive resilience is weaker than that of receiving distance. Resilience (materials science) ; Porosity ; Materials science ; Filtration (mathematics) ; Artificial neural network ; Linear regression ; Composite material ; Biological system ; Mechanics ; Mathematics ; Control theory (sociology) ; Statistics ; Computer science ; Artificial intelligence ; Physics ; Control (management) Textile materials and evaluations ; Color Science and Applications
Authors: Yul Antonisfia | Roza Susanti | Efendi Efendi | Sir Anderson | Fitri Anisa
Source: International Journal of Advanced Science Computing and Engineering (article, 2023-12-08)
Field: Chemistry — Inorganic Chemistry

Design and Development of a Coffe Blending Device with Carbon Monoxide (CO) Level Identification Based on Artificial Neural Networks Coffee is categorized into three types Robusta, Arabica, and Liberica. The roasting process is the most crucial step in developing the aroma, flavor, and underlying color that determine coffee quality. The coffee roasting process produces complex aroma compounds that impart the desired taste and aroma characteristics of coffee. This research aims to design a coffee content detection tool by determining the carbon monoxide (CO) level in Arabica, Robusta, and Liberica coffee. The research uses the Backpropagation artificial neural network method with 2 hidden layers, including 4 input layers and 3 output layers, to identify the tested coffee varieties. The highest carbon monoxide (CO) levels were found in Arabica Special coffee, with an ADC level of 662 carbon monoxide gas. The lowest carbon monoxide (CO) levels were detected in Liberica coffee, with an ADC level of 105 carbon monoxide gas. Coffee identification was carried out using an artificial neural network method with a success rate of 98% for Liberica coffee, 100% for Arabica coffee, and 98% for Robusta coffee. Aroma ; Roasting ; Carbon monoxide ; Flavor ; Food science ; Chemistry ; Materials science ; Metallurgy ; Organic chemistry Coconut Research and Applications ; Coffee research and impacts