jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahsa Nakhostinrouhi | Mohammadmehdi Saberioon | Mohsen Makki | Kolja Thestorf | Saeid Homayouni | Majid Kiavarz | Seyed Kazem Alavipanah
Source:  (preprint, 2023-02-26)
Field: Chemistry — Analytical Chemistry

Prediction of Pb and Zn in urban soil using VIS-NIR-SWIR spectroscopy Heavy metals serve as a subset of chemical elements with higher density than iron. Besides, these environmental pollutants are constant and nonbiodegradable elements that can cause toxicity and genetic mutations to the live cells. Depending on the study area, an increase in soil heavy metals from a specific level often created by human activities can lead to many adverse effects on individuals, soil, and plants. In case of their existence in the food chain or transfer to groundwater resources, human health is seriously threatened. Over numerous years, being affected by a colossal number of pollutant resources such as world war and household waste, industry, transportation systems, and urbanization has changed Berlin to a city at risk of soil pollution by heavy metals. That is why carrying out a study on heavy metals in this city is of great significance. Chemical analysis is the first and most traditional ways to measure soil heavy metals. Despite high precision, this method is complicated, time-consuming, costly, and ineffective on a large scale. However, the spectral data facilitates the rapid and cost-effective assessment of these elements. Therefore, in this study, the ability of spectral data to predict heavy metals in Berlin&amp;#8217;s soil is examined.When it comes to the data required, there are two categories: 1) heavy metals (Pb and Zn) related to more than 600 soil samples collected from 2016 to 2018 and measured in the laboratory, and 2) the spectral data measured for each sample in the range between 350 to 2500nm in a spectrometry lab. All data is divided into training (80%) and testing (20%) to reach this aim. Next, the first group is used to train the machine learning algorithms, including partial least square regression (PLSR), support vector regression (SVR), and random forest (RF). Moreover, the second group is used to test the models. Finally, the accuracy of models is evaluated by correlation of determination (R2), and Root mean square error (MSE). As a part of the results, R2 and MSE were achieved 0.25, and 4394.45 for Pb, and 0.18 and 6558.49 for Zn. Environmental science ; Pollutant ; Heavy metals ; Environmental chemistry ; Pollution ; Soil Pollutants ; Soil test ; Human health ; Soil contamination ; Soil water ; Chemistry ; Soil science ; Ecology Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping
Authors: Stanislav I. Pekov | Denis S. Zavorotnyuk | Anatoly Sorokin | Denis S. Bormotov | Mariya M. Derkach | Dmitrii Starkov | Igor Popov
Source: F1000Research (preprint, 2023-07-20)
Field: Chemistry — Spectroscopy

All ions must serve: The role of various regimes of data acquisition in joint classifier for intraoperative mass spectrometry-based glial tumour identification <ns4:p> <ns4:bold>Background:</ns4:bold> Ambient ionisation mass spectrometry, in combination with machine learning techniques, provides a promising tool for rapid intraoperative tumour tissue identification. However, deficiency of non-tumour control samples leads to the classifiers overfitting, especially in neurosurgical applications. Ensemble learning approaches based on the analysis of multimodal mass spectrometry data are able to overcome the overfitting problem through the extended time of data acquisition. In this work, the contribution of each regime of the data acquisition and the requirements for the metrics for further mass spectrometry set-up optimisation are evaluated. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Two independent datasets of the multimodal molecular profiles, a total of 81 glial tumour and non-tumour pathological tissues, were analysed in a cross-validation set-up. The XGboost algorithm was used to build classifiers, and their performance was evaluated using different testing and validating sets. The individual classifiers for each mass spectrometry regime were aggregated into joint classifiers. The impact of each regime was evaluated by the exclusion of specific regimes from the aggregation. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> The aggregated classifiers with excluded regimes show lower accuracy for most, but not all, excluded regimes. False positive rates have been found to be increased in most cases proving the strong effect of the ensemble learning approach on the overcoming of the “small sample size” problem. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> The impact of each group of regimes – with different ion polarity, resolution or mass range of spectra was found to be non-linear. It might be attributed to biochemical reasons as well as to the physical limitation of mass analysers. The required metrics for the evaluation of each regime contribution to the classification efficiency should be a numerical estimation of how the classifier depends on any given regime and could not be estimated only by excluding any group of regimes at all. </ns4:p> Overfitting ; Mass spectrometry ; Artificial intelligence ; Classifier (UML) ; Computer science ; Pattern recognition (psychology) ; Biology ; Chemistry ; Artificial neural network Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Siwoo Lee | Stefan Heinen | Danish Khan | O. Anatole von Lilienfeld
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials Supplementary Information for "Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials." grouped_dataset_acetonitrile_neutral.csv contains the SMILES, the reported oxidation potentials for samples (column labelled as "Oxidation potential SCE"), the publication years of the corresponding papers and reference numbers in the Supplementary Information, compound names, and the mean oxidation potentials for samples with multiple reported values in the literature (column labelled as "Oxidation potential SCE cleaned"). The folder "Extracted Data Set XTB" contains folders labelled by the sample names, which each contain XTB-optimized geometries ("xtbopt.xyz") and the output from the XTB-optimizations which contain calculated values (in "SUMMARY.txt"). The same content is contained in the folder "QM9 XTB" for molecules in the QM9 data set. Computer science ; Data extraction ; Extraction (chemistry) ; Artificial intelligence ; Machine learning ; Peer review ; Chemistry ; MEDLINE ; Chromatography Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ling Ma | Jianshe Li | Jing Wang | Xingwu Tian | Yiyang Zhang | Yan Ma | Siyan Ma | Xiaomin Wang | Longguo Wu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Detection Study of Fv/Fm in Melon Leaves Based on Fluorescence Hyperspectral Imaging Technology Hyperspectral imaging ; Remote sensing ; Fluorescence ; Melon ; Artificial intelligence ; Computer science ; Geography ; Biology ; Horticulture ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Abdelmoumin Oulad Brahim | Roberto Capozucca | Erica Magagnini | Abdelwahhab Khatir | Samir Khatir | Idir Belaidi
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Metals and Alloys

Damage Size Prediction for Api X 70 Steel Pipelines Based on Abaqus, Fe-Safe and Ann-Pso Materials science ; Structural engineering ; Metallurgy ; Computer science ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques
Authors: Shiwen Li | Tian Li | Yaoyi Cai | Zekai Yao | Miaolei He
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Quantitative Analysis of Rongalite Adulteration in Rice Flour Using Autoencoder and Residual-Based Model Associated with Portable Raman Spectroscopy Autoencoder ; Residual ; Rice flour ; Raman spectroscopy ; Food science ; Biological system ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Algorithm ; Physics ; Artificial neural network ; Biology ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Source: On-Demand Webinars (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Electronics in Harsh Environments Conference 2023 Select On-Demand Presentations ABSTRACT Couldn't make it in person to the 2023 Electronics in Harsh Environments Conference in Amsterdam? The next best option is to access the select presentation recordings that were captured live, listed below: Keynote: The Remarkable Return of Post-Reflow Cleaning as a Mainstream Process to Improve Reliability Mike Konrad, Aqueous Technologies Electronics in Harsh Environments Session: Risk Prediction of Electrochemical Migration on Electronic Control Units - A Statistical Approach Lothar Henneken, Ph.D., Robert Bosch GmbH A Mechanistic Insight into the Correlation of Water Layer Build-up on a PCBA Surface with Electro-Chemical Migration and Subsequent Dendrite Formation A.R.Lakkaraju, Technical University of Denmark Partial Discharge Measurements and Electrical Fatigue in Insulation Materials Martin Wickham, Ph.D., National Physical Laboratory UK Soldering Materials for Ruggedized Electronics Session: Optimized Materials to Deliver Ruggedized Electronics Lenora Clark, MacDermid Alpha Electronics Solutions Ensuring High Quality by Accerlerating the Reliability Testing of Home Appliance Boards for Simulating Harsh Environments Önder Sunetci, Arcelik Managing Thermochemical Behavior in Automotive Electronics Maurice Dore, Valeo Recent Developments in Low Temperature Solders and the Challenge of Harsh Environments Ron Lasky, Ph.D., P.E., Indium Corporation Acceptable Levels of Flux and Process Residues Session: Cleaning Process Simulation by Glass Test Boards Vladimir Sitko, PBT-Works s.r.o Solder Joint Reliability in Harsh Environments Session: High-Reliability Lead-Free Solder for Electronics in Automotive Applications Pritha Choudhury, Ph.D., MacDermid Alpha Electronics Solutions Predicting PCBA Failures Under Humidity Conditions Session: Machine Learning for Predicting PCBA Failures under Humidity Conditions Sajjad Bahrebar, Technical University of Denmark Optical Analysis of Dew Formation on PCB by CNN for the Validation of Electrical Signals Quang-Duc Pham, Robert Bosch GmbH Design for Cleaning Session: Accelerated Electrical Test Process Control Method Tom Forsythe, Magnalytix, LLC This popular annual conference tackles the challenges and best practices for building reliable electronic devices that will perform to design standards when used in harsh environments. Specific topics include building reliable high density assemblies, power electronics, electric hybrids, product assembly challenges, cleaning, coating, process control, and monitoring and tracking production hardware. Challenging areas such as high temperature soldering, solder material advances, and new standards are presented. On Demand Webinar Electronics ; Reliability (semiconductor) ; Soldering ; Process (computing) ; Electronic equipment ; Automotive electronics ; Surface-mount technology High voltage insulation and dielectric phenomena ; Experience-Based Knowledge Management ; Recycling and Waste Management Techniques
Authors: Andres Barrio-Zhang | Arezoo M. Ardekani
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Sub-Micron Weak Phase Particle Characterization Using the Reconstructed Volume Intensities from In-Line Digital Holography Microscopy Characterization (materials science) ; Digital holographic microscopy ; Holography ; Microscopy ; Digital holography ; Particle (ecology) ; Optics ; Phase (matter) ; Volume (thermodynamics) ; Line (geometry) ; Materials science ; Physics ; Geology ; Geometry ; Mathematics Electrostatics and Colloid Interactions ; Characterization and Applications of Magnetic Nanoparticles ; Digital Holography and Microscopy
Authors: Tareq Nafea Alharby | Muteb Alanazi
Source: Arabian Journal of Chemistry (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

Development of advanced computational simulation of two-dimensional plate-like crystals: A comparison with population balance model Solid can be divided into three main categories such as bulk, particles and molecules. However, the particle productions, as well as distribution of size, are very crucial to achieve particular properties for any specific industrial uses. This research aim is to develop empirical relations using an artificial neural network (ANN) for the sonofragmentation experimental results in terms of number fractions for different lengths and widths of particles corresponding to ultrasonic amplitude equal to 10%. For the ANN model, three and four hidden layers are chosen for lengths and widths models, respectively. Then it empirically calculated the number of fractions for different lengths and widths of a particle was compared with the population balance equation (PBE). To further strengthen the empirical expression, the ANN result was also compared with the population balance model and found to be agreed well with the PBE simulations. The model was trained and validated using the measured (experimental) data of the number fraction. The model was found to be valid and reliable, with R2 equal to or greater than 0.95 in both training and validation. Chemistry ; Population ; Particle (ecology) ; Amplitude ; Artificial neural network ; Fraction (chemistry) ; Balance (ability) ; Statistical physics ; Statistics ; Artificial intelligence ; Mathematics ; Optics ; Chromatography ; Physics ; Computer science ; Psychology Ultrasound and Cavitation Phenomena ; Coagulation and Flocculation Studies ; Cyclone Separators and Fluid Dynamics
Authors: Lei Zhang | Gábor Cśanyi | E. van der Giessen | Francesco Maresca
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Efficient, Accurate, and Transferable Machine Learning Potentials: Application to Dislocations and Cracks in Iron Density functional theory ; Dislocation ; Materials science ; Mean squared error ; Atom (system on chip) ; Computer science ; Computational chemistry ; Mathematics ; Chemistry ; Composite material ; Statistics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Benoit Lehallier | Chun‐Miao Feng | Tibor Nánási | Ricardo Gonzalo | Carla Minguet | Ana María Ortiz | Isabel Bravo | Scott Lohr | Merçé Boada | Oscar L. López | Antonio Páez | Cesar Cerezo | Montserrat Costa | Steven P. Braithwaite
Source: Alzheimer s & Dementia (article, 2023-12-01)
Field: Chemistry — Spectroscopy

Nowhere to run, nowhere to hide ‐ targeting the immune system in AD with multimodal plasma therapeutics Abstract Background Age‐related diseases, especially Alzheimer’s Disease (AD), have multifaceted nature because of the different factors and pathways contributing to these diseases. Multi‐modal plasma therapies have the potential to impact multiple pathways and modulate different mechanisms of diseases in a safe and versatile way. Here we discuss insights from deep plasma proteomics profiling in patients from the GRF6019 and AMBAR clinical trials in which multi‐modal plasma therapeutics were used and limited cognitive decline was observed. Method The GRF6019 phase 2 trials were based on the infusion of a complex plasma fraction while the AMBAR phase 2b/3 trial used plasma exchange and albumin replacement (PE‐Alb). Patients in these trials were mild‐to‐moderate AD and 1143 plasma samples were analyzed using the SomaScan platform. The trajectories of 7k proteins were analyzed using classic and new machine learning techniques. Result GRF6019 infusion resulted in an acute and broad impact on the plasma proteome but the detectable long‐term effects on molecular level were minimal. Pathways analysis of protein trajectories pointed out to the immune system, ECM remodeling but also brain specific biological processes linked to neurons and axon guidance, suggesting that various biological pathways were significantly modulated (q&lt;0.05). PE‐Alb treatment also induced an acute effect on several hundreds of proteins (461 proteins with log2FC←1 &amp; q&lt;0.05) but some proteomics changes lasted for weeks after the end of the intensive period (25 proteins with q&lt;0.05). A significant part of these changes (19% of acute and 56% of lasting response proteins) were related to immune and inflammatory‐related pathways. In addition, several individual proteins from the immune pathway were associated with improvement of CDR‐SB or ADCS‐CGIC at the end of the AMBAR trial suggesting that multiple parts of the immune system might need to be targeted to slow‐down AD progression. Conclusion AD has multifaceted nature and plasma therapeutics have proven safety, efficacy, and versatility. The proteomics results from these trials shed the light on the underlying mode of action of multi‐modal plasma‐derived therapies and demonstrate that they can modulate multiple pathways relevant to AD, with a strong effect on the immune and inflammatory responses which may contribute to slowing down AD progression. Proteome ; Immune system ; Proteomics ; Blood proteins ; Albumin ; Computational biology ; Medicine ; Bioinformatics ; Pharmacology ; Biology ; Immunology ; Internal medicine ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Alzheimer's disease research and treatments
Authors: Anna Hartmann | Marion Schulz | Sijie Qiu | Qiang Fang | Yu Jiang | Wojciech Danowski | Ben L. Feringa | Lars Heinke | Max Hohenschutz | Pierre Bauduin | Carlos G. Lopez | B Fçrster | Walter Richtering | S Rçsch | C. W. Oates | Angela Goodfellow | Michæl Bühl | Michael Clarke | W.-W Zhang | Qingyuan Wang | Su Zhang | Chunxia Zheng | Simin You | Aled Clayton | Edward O. Pyzer‐Knapp | Mark Purdie | Michael Jones | Alexandre Barthelme | John Pavey | Nikil Kapur | Thomas W. Chamberlain | A. John Blacker | Richard A. Bourne | Xingtao Zhou | Jinxin Xu | Jian Zhang | Na Wang | Guo Zhang | J. F. Hu | Shiyuan Liu | S Liu | Y Ma | X Jiang | J Yin | C Weder | J Berrocal | * Shi ; Dnyaneshwar | V Veer | K Chandrakant Wakchaure | R Asokan | T Dixit | R Goswami | R Saha | H Gonnade | J Zorn | M Stach | M Crump | M Hayes | P Der Kamp | C Race | Willis | H Mechanochemistry | * Kobayashi | T Suzuki | M Sagawa | A Saito | J Fukuoka* E ; Li-S Batteries | J Zhu | G Cao | J Cai | W Zhang | S Zhang | J Xie | H Wang | J Jin | X Xu | S Kong | * Jin | * Li | ; C.-Y Ji | D.-D Shi | B.-S He | Q Wang | H Zhang | D.-H Tian | ; Qu | Y Suzuki | M Kamei | Y Yamashita | Y Seino | T Yamaguchi | M Yoshino | * Kojima | ; Matsunaga | I Trifonova | W Leach | R De Haas | M Havenith | J Tromp | Klein
Source: Angewandte Chemie International Edition (article, 2023-01-09)
Field: Chemistry — Inorganic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 3/2023 Photochromic MaterialsIn their Communication (e202214202), Lars Heinke et al. report nanoporous films with oriented arrays of molecular motors for light-controlled adsorption and diffusion. Photochromism ; Nanoporous ; Adsorption ; INT ; Molecular motor ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Computer science ; Physical chemistry ; Engineering ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry ; Axial and Atropisomeric Chirality Synthesis
Authors: Jiaqi Lv | Qizhen Hong | Xiaoyong Wang | Zhiping Mao | Quanhua Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deepstsnet: Reconstructing the Quantum State-Resolved Thermochemical Nonequilibrium Flowfield Using Deep Neural Operator Learning With Scarce Data Non-equilibrium thermodynamics ; Quantum ; Artificial neural network ; Operator (biology) ; Computer science ; State (computer science) ; Deep learning ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Algorithm ; Chemistry Machine Learning in Materials Science
Authors: Himanshu Sharma | Jim Gaffney | Dimitrios Tsapetis | Michael D. Shields
Source: arXiv (Cornell University) (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

Learning thermodynamically constrained equations of state with uncertainty Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon, using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty reduces by considering the thermodynamic constraints. Uncertainty quantification ; Parametric statistics ; Consistency (knowledge bases) ; Computer science ; Sensitivity analysis ; Statistical physics ; Gaussian process ; Uncertainty analysis ; Propagation of uncertainty ; Kriging ; Equation of state ; Calibration ; Stability (learning theory) ; Applied mathematics ; Gaussian ; Mathematics ; Algorithm ; Physics ; Thermodynamics ; Machine learning ; Artificial intelligence ; Simulation Machine Learning in Materials Science ; High-pressure geophysics and materials ; Nuclear Materials and Properties