jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying‐Fang Zou | Aolin Zhang | Xiaobin Wang | Yang Lei | Meng Ding
Source: Journal of Forensic Sciences (article, 2024-01-30)
Field: Chemistry — Analytical Chemistry

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination. Extreme learning machine ; Artificial intelligence ; Feature selection ; Raman spectroscopy ; Spectroscopy ; Population ; Chemometrics ; Computer science ; Pattern recognition (psychology) ; Fusion ; Materials science ; Biological system ; Optics ; Machine learning ; Artificial neural network ; Physics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Currency Recognition and Detection
Authors: Yabing Liu | Ke Pan | Zhongyin Liu | Yuqiao Dai | Xueyi Duan | Min Wang | Qiang Shen
Source: Sensors (article, 2024-05-24)
Field: Chemistry — Analytical Chemistry

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea. Gallate ; Black tea ; Extreme learning machine ; Epigallocatechin gallate ; Wavelength ; Near-infrared spectroscopy ; Feature selection ; Partial least squares regression ; Biological system ; Selection (genetic algorithm) ; Artificial intelligence ; Black box ; Reduction (mathematics) ; Computer science ; Epicatechin gallate ; Feature (linguistics) ; Spectroscopy ; Pattern recognition (psychology) ; Catechin ; Chemistry ; Mathematics ; Machine learning ; Physics ; Artificial neural network ; Optics ; Polyphenol Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ju Wang | Haiyan Tan | Yingxue Fu | Ashutosh Mishra | Huan Sun | Zhen Wang | Zhiping Wu | Xusheng Wang | Geidy E. Serrano | Thomas G. Beach | Junmin Peng | Anthony A. High
Source: Journal of the American Society for Mass Spectrometry (article, 2024-05-16)
Field: Chemistry — Spectroscopy

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 <i>m</i>/<i>z</i>), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% <i>n</i>-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts. Chemistry ; Identification (biology) ; Chromatography ; Computational biology ; Botany Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Xianxian Wang | Yanchao Cai | Xiucheng Liu | Cunfu He
Source: Sensors (article, 2024-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction. Barkhausen effect ; Materials science ; Feature (linguistics) ; Feature extraction ; Noise (video) ; Artificial neural network ; Extraction (chemistry) ; Barkhausen stability criterion ; Hysteresis ; Magnetic hysteresis ; Acoustics ; Computer science ; Artificial intelligence ; Magnetic field ; Magnetization ; Physics ; Condensed matter physics ; Chemistry Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Giorgia Stocco | Laura G. Gómez‐Mascaraque | Gaurav Kr Deshwal | Jordi Cruz | Arnaud Molle | Valentina Pizzamiglio | Paolo Berzaghi | Georgi Gergov | Claudio Cipolat‐Gotet
Source: Frontiers in Nutrition (article, 2024-02-06)
Field: Chemistry — Analytical Chemistry

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950-1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses. Raman spectroscopy ; Analytical Chemistry (journal) ; Linear discriminant analysis ; Partial least squares regression ; Chemometrics ; Food science ; Near-infrared spectroscopy ; Texture (cosmology) ; Chemistry ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Chromatography ; Statistics ; Computer science ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Ida Ekmark | M. Hoppe | Tünde Fülöp | Patrik Jansson | Liam Antonsson | O. Vallhagen | István Pusztai
Source: Journal of Plasma Physics (preprint, 2024-05-21)
Field: Materials Science — Materials Chemistry

Fluid and kinetic studies of tokamak disruptions using Bayesian optimization When simulating runaway electron dynamics in tokamak disruptions, fluid models with lower numerical cost are often preferred to more accurate kinetic models. The aim of this work is to compare fluid and kinetic simulations of a large variety of different disruption scenarios in ITER. We consider both non-activated and activated scenarios; for the latter, we derive and implement kinetic sources for the Compton scattering and tritium beta decay runaway electron generation mechanisms in our simulation tool Dream (Hoppe et al. , Comput. Phys. Commun. , vol. 268, 2021, 108098). To achieve a diverse set of disruption scenarios, Bayesian optimization is used to explore a range of massive material injection densities for deuterium and neon. The cost function is designed to distinguish between successful and unsuccessful disruption mitigation based on the runaway current, current quench time and transported fraction of the heat loss. In the non-activated scenarios, we find that fluid and kinetic disruption simulations can have significantly different runaway electron dynamics, due to an overestimation of the runaway seed by the fluid model. The primary cause of this is that the fluid hot-tail generation model neglects superthermal electron transport losses during the thermal quench. In the activated scenarios, the fluid and kinetic models give similar predictions, which can be explained by the significant influence of the activated sources on the runaway dynamics and the seed. Tokamak ; Kinetic energy ; Bayesian optimization ; Bayesian probability ; Computer science ; Physics ; Plasma ; Statistical physics ; Nuclear physics ; Artificial intelligence ; Classical mechanics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Magnetic confinement fusion research
Authors: Qin Fu | Manasa Vegesna | Niveda Sundararaman | Eugen Damoc | Tabiwang N. Arrey | Anna Pashkova | Emebet Mengesha | Philip Debbas | Sandy Joung | Dalin Li | Susan Cheng | Jonathan Braun | Dermot McGovern | Christopher I. Murray | Yue Xuan | Jennifer E. Van Eyk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-22)
Field: Chemistry — Spectroscopy

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies. Biomarker discovery ; Biomarker ; Computational biology ; Computer science ; Bioinformatics ; Medicine ; Oncology ; Data mining ; Proteomics ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: F. Cordella | Mauro Cappelli | Marco Ciotti | G. Claps | V. De Leo | C. Mazzotta | D. Pacella | A. Tamburrino | Fabio Panza
Source: The European Physical Journal Plus (article, 2024-02-10)
Field: Materials Science — Materials Chemistry

Genetic algorithm for multilayer shield optimization with a custom parallel computing architecture Abstract This paper introduces a novel architecture for optimizing radiation shielding using a genetic algorithm with dynamic penalties and a custom parallel computing architecture. A practical example focuses on minimizing the Total Ionizing Dose for a silicon slab, considering only the layer number and the total thickness (additional constraints, e.g., cost and density, can be easily added). Genetic algorithm coupled with Geant4 simulations in a custom parallel computing architecture demonstrates convergence for the Total Ionizing Dose values. To address genetic algorithm issues (premature convergence, not perfectly fitted search parameters), a Total Ionizing Dose Database Vault object was introduced to enhance search speed (data persistence) and to preserve all solutions’ details independently. The Total Ionizing Dose Database Vault analysis highlights boron carbide as the best material for the first layer for neutron shielding and high-Z material (e.g., Tungsten) for the last layers to stop secondary gammas. A validation point between Geant4 and MCNP was conducted for specific simulation conditions. The advantages of the custom parallel computing architecture introduced here, are discussed in terms of resilience, scalability, autonomy, flexibility, and efficiency, with the benefit of saving computational time. The proposed genetic algorithm-based approach optimizes radiation shielding materials and configurations efficiently benefiting space exploration, medical devices, nuclear facilities, radioactive sources, and radiogenic devices. Architecture ; Genetic algorithm ; Computer science ; Parallel computing ; Optimization algorithm ; Shield ; Computer architecture ; Algorithm ; Computational science ; Mathematical optimization ; Mathematics ; Geology ; History ; Machine learning Graphite, nuclear technology, radiation studies ; Welding Techniques and Residual Stresses ; High-Velocity Impact and Material Behavior
Authors: Hyosoo Moon | Seyedali Mirmotalebi | Youjin Jang | Yonghan Ahn | Nahyun Kwon
Source: Buildings (article, 2024-05-25)
Field: Materials Science — Materials Chemistry

Risk Evaluation of Radioactive Concrete Structure Decommissioning in Nuclear Power Plants Using Fuzzy-AHP Over 50% of nuclear power plants (NPPs) worldwide have operated for over three decades, leading to a surge in decommissioning projects. This study addresses the gap in current guidelines by analyzing risks in nuclear decommissioning. Using the fuzzy-AHP technique, tasks within dismantling radioactive concrete structures are prioritized. Findings reveal structural and human-related risks across five main cutting tasks. Collision emerges as a significant concern, particularly during wire saw installation and concrete block hoisting hole creation. Subcategory risk priorities highlight variations in risk across tasks, with jamming, falling, and falling objects identified as top concerns during wire saw transportation. This study emphasizes the importance of comprehensive risk assessment in enhancing safety during decommissioning. It underscores the need to consider both physical risks and risks to personnel throughout the process. By prioritizing safety, stakeholders can ensure worker safety and operational efficiency while minimizing hazards. This research contributes to standardized safety protocols for nuclear decommissioning worldwide, aligning with sustainable energy practices. The outcomes offer practical insights for safety manual development and decision-making processes. This study represents progress in ensuring safety during nuclear decommissioning, paving the way for further refinement of safety protocols and guidelines tailored to decommissioning sites. Nuclear decommissioning ; Nuclear power ; Nuclear power plant ; Nuclear engineering ; Radioactive waste ; Fuzzy logic ; Analytic hierarchy process ; Engineering ; Environmental science ; Construction engineering ; Civil engineering ; Waste management ; Reliability engineering ; Forensic engineering ; Computer science ; Nuclear physics ; Operations research ; Physics ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Luke Gent | Maria Elena Chiappetta | Stuart J. Hesketh | Paweł Palmowski | Andrew C.G. Porter | Andrea Bonicelli | Ed C. Schwalbe | Noemi Procopio
Source: Journal of Proteome Research (article, 2024-04-15)
Field: Chemistry — Spectroscopy

Bone Proteomics Method Optimization for Forensic Investigations The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications. Workflow ; Proteomics ; Proteome ; Computer science ; Lysis ; Identification (biology) ; Protocol (science) ; Computational biology ; Chromatography ; Chemistry ; Bioinformatics ; Biology ; Database ; Medicine ; Biochemistry Advanced Proteomics Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Metabolomics and Mass Spectrometry Studies