jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marco De Lucia | Michael Kühn | Alexander Lindemann | Max Lübke | Bettina Schnor
Source:  (preprint, 2021-07-14)
Field: Chemistry — Inorganic Chemistry

<tt>POET</tt> (v0.1): Speedup of Many-Cores Parallel Reactive Transport Simulations with Fast DHT-Lookups Abstract. Coupled reactive transport simulations are extremely demanding in terms of required computational power, which hampers their application and leads to coarsened and oversimplified domains. The chemical sub-process represents the major bottleneck: its acceleration is an urgent challenge which gathers increasing interdisciplinary interest along with pressing requirements for subsurface utilization such as spent nuclear fuel storage, geothermal energy and CO2 storage. In this context we 5 developed POET (POtsdam rEactive Transport), a research parallel reactive transport simulator integrating algorithmic improvements which decisively speedup coupled simulations. In particular, POET is designed with a master/worker architecture, which ensures computational efficiency on both multicore and cluster compute environments. POET does not rely on contiguous grid partitions for the parallelization of chemistry, but forms work packages composed of grid cells distant from each other. Such scattering prevents particularly expensive geochemical simulations, usually concentrated in the vicinity of a reactive front, from generating load imbalance between the available CPUs, as it is often the case with classical partitions. Furthermore, POET leverages an original implementation of Distributed Hash Table (DHT) mechanism to cache the results of geochemical simulations for further reuse in subsequent time-steps during the coupled simulation. The caching is hence particularly advantageous for initially chemically homogeneous simulations and for smooth reaction fronts. We tune the rounding employed in the DHT on a 2D benchmark to validate the caching approach, and we evaluate the performance gain of POET's master/worker architecture and the DHT speedup on a 3D benchmark comprising around 650 k grid elements. The runtime for 200 coupling iterations, corresponding to 960 simulation days, reduced from about 24 h on 11 workers to 29 minutes on 719 workers. Activating the DHT reduces the runtime further to 2 h and 8 minutes respectively. Only with this kind of reduced hardware requirements and computational costs it is possible to realistically perform the large scale, long-term complex reactive transport simulations, as well as performing the uncertainty analyses required by pressing societal challenges connected with subsurface utilization. Speedup ; Computer science ; Benchmark (surveying) ; Parallel computing ; Bottleneck ; Context (archaeology) ; Grid ; Embedded system Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; CO2 Sequestration and Geologic Interactions
Authors: Leon Amadeus Varga | Jan Makowski | Andreas Zell
Source: arXiv (Cornell University) (preprint, 2021-04-20)
Field: Chemistry — Analytical Chemistry

Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep\n Learning We present a system to measure the ripeness of fruit with a hyperspectral\ncamera and a suitable deep neural network architecture. This architecture did\noutperform competitive baseline models on the prediction of the ripeness state\nof fruit. For this, we recorded a data set of ripening avocados and kiwis,\nwhich we make public. We also describe the process of data collection in a\nmanner that the adaption for other fruit is easy. The trained network is\nvalidated empirically, and we investigate the trained features. Furthermore, a\ntechnique is introduced to visualize the ripening process.\n Ripeness ; Hyperspectral imaging ; Computer science ; Ripening ; Artificial intelligence ; Deep learning ; Process (computing) ; Measure (data warehouse) ; Artificial neural network ; Pattern recognition (psychology) ; Data mining ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Horticultural and Viticultural Research
Authors: Константин В. Саватеев | Victor V. Fedotov | В. Л. Русинов | S. K. Kotovskaya | А. А. Спасов | A. F. Kucheryavenko | Pavel M. Vasiliev | В. А. Косолапов | В. С. Сиротенко | К. А. Гайдукова | Georgiy M. Uskov
Source: Preprints.org (preprint, 2021-12-06)
Field: Chemistry — Organic Chemistry

Azolo[1,5-a]pyrimidines and Their Condensed Analogs with Anticoagulant Activity Hypercytokinemia, or cytokine storm, is one of the severe complications of viral and bacterial infections, involving the release of abnormal amounts of cytokines, resulting in a massive inflammatory response. Cytokine storm is associated with COVID-19 and sepsis high mortality rate by developing epithelial dysfunction and coagulopathy, leading to thromboembolism and multiple organ dysfunction syndrome. The anticoagulant therapy is an important tactic to prevent thrombosis in sepsis and COVID-19, but recent data show the incompatibility of modern direct oral anticoagulants and antiviral agents. It seems relevant to develop dual-action drugs with antiviral and anticoagulant properties. At the same time it was shown that azolo[1,5-a]pyrimidines are heterocycles with a broad spectrum of antiviral activity. We have synthesized a new family of azolo[1,5-a]pyrimidines and their condensed polycyclic analogs by cyclocondensation reactions and direct CH-functionalization and studied their anticoagulant properties. Five compounds among 1,2,4-triazolo[1,5-a]pyrimidin-7-ones and 5-alkyl-1,3,4-thiadiazolo[3,2-a]purin-8-ones demonstrated higher anticoagulant activity than the reference drug, dabigatran etexilate. Antithrombin activity of lead compounds was confirmed using lipopolysaccharide (LPS) treated blood to mimic conditions of cytokine release syndrome. The studied compounds affected only the thrombin time value, reliably increasing it 6.5&amp;ndash;15.2 times as compared to LPS-treated blood. Cytokine storm ; Sepsis ; Antithrombin ; Anticoagulant ; Coagulopathy ; Cytokine ; Lipopolysaccharide ; Pharmacology ; Medicine ; Chemistry ; Immunology ; Internal medicine ; Coronavirus disease 2019 (COVID-19) ; Heparin Quinazolinone synthesis and applications ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Benjamin Schneiderman | Andrew Chihpin Chuang | Péter Kenesei | Zhenzhen Yu
Source: Research Square (preprint, 2021-01-20)
Field: Materials Science — Materials Chemistry

In-Situ Synchrotron Diffraction and Modeling of Non-Equilibrium Solidification of a MnFeCoNiCu Alloy Abstract The solidification mechanism and segregation behavior of laser-melted Mn 35 Fe 5 Co 20 Ni 20 Cu 20 was firstly investigated via in-situ synchrotron x-ray diffraction at millisecond temporal resolution. The transient composition evolution of the random solid solution during sequential solidification of dendritic and interdendritic regions complicates the analysis of synchrotron diffraction data via any single conventional tool, such as Rietveld refinement. Therefore, a novel approach combining a hard-sphere approximation model, thermodynamic simulation, thermal expansion measurement and microstructural characterization was developed to assist in a fundamental understanding of the evolution of local composition, lattice parameter, and dendrite volume fraction corresponding to the diffraction data. This methodology yields self-consistent results across different methods. Via this approach, four distinct stages were identified, including: (I) FCC dendrite solidification, (II) solidification of FCC interdendritic region, (III) solid-state interdiffusion and (IV) final cooling with marginal diffusion. It was found out that in Stage I, Cu and Mn were rejected into liquid as Mn 35 Fe 5 Co 20 Ni 20 Cu 20 solidified dendritically. During Stage II, the lattice parameter disparity between dendrite and interdendritic region escalated as Cu and Mn continued segregating into the interdendritic region. After complete solidification, during Stage III, the lattice parameter disparity gradually decreases, demonstrating a degree of composition homogenization. The volume fraction of dendrites slightly grew from 58.3–65.5%, based on the evolving composition profile across a dendrite/interdendritic interface in diffusion calculations. Postmortem metallography further confirmed that dendrites have a volume fraction of 64.7% ± 5.3% in the final microstructure. Materials science ; Diffraction ; Volume fraction ; Dendrite (mathematics) ; Synchrotron ; Alloy ; Lattice constant ; Lattice (music) ; Thermodynamics ; Metallography ; Microstructure ; Crystallography ; Metallurgy ; Chemistry ; Composite material ; Optics ; Geometry Solidification and crystal growth phenomena ; Metallic Glasses and Amorphous Alloys ; High Entropy Alloys Studies
Authors: David Hicks | Cormac Toher | Denise C. Ford | Frisco Rose | Carlo De Santo | Ohad Levy | Michael J. Mehl | Stefano Curtarolo
Source: npj Computational Materials (preprint, 2021-02-11)
Field: Materials Science — Materials Chemistry

AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes Abstract The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the internal degrees of freedom consistent with the International Tables for Crystallography. To ensure uniqueness, structures are analyzed and compared via symmetry, local atomic geometries, and crystal mapping techniques, simultaneously grouping them by similarity. The software (i) distinguishes distinct crystal prototypes and atom decorations, (ii) determines equivalent spin configurations, (iii) reveals compounds with similar properties, and (iv) guides the discovery of unexplored materials. The operations are accessible through a Python module ready for workflows, and through command line syntax. All the 4+ million compounds in the AFLOW.org repositories are mapped to their ideal prototype, allowing users to search database entries via symbolic structure-type. Furthermore, 15,000 unique structures — sorted by prevalence — are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia. Python (programming language) ; Computer science ; Workflow ; Encyclopedia ; Software ; Syntax ; Information retrieval ; Theoretical computer science ; Database ; Programming language ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Carlos Flores-Garrigós | Juan Vicent-Camisón | Juan José Garcés-Iniesta | Emilio Soria‐Olivas | Juan Gómez‐Sanchís | Fernando Mateo
Source: Applied Sciences (article, 2021-12-10)
Field: Chemistry — Spectroscopy

Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms In ultra-high vacuum systems, obtaining the composition of a mass spectrum is often a challenging task due to the highly overlapping nature of the individual profiles of the gas species that contribute to that spectrum, as well as the high differences in terms of degree of contribution (several orders of magnitude). This problem is even more complex when not only the presence but also a quantitative estimation of the contribution (partial pressure) of each species is required. This paper aims at estimating the relative contribution of each species in a target mass spectrum by combining a state-of-the-art machine learning method (multilabel classifier) to obtain a pool of candidate species based on a threshold applied to the probability scores given by the classifier with a genetic algorithm that aims at finding the partial pressure at which each one of the species contributes to the target mass spectrum. For this purpose, we use a dataset of synthetically generated samples. We explore different acceptance thresholds for the generation of initial populations, and we establish comparative metrics against the most novel method to date for automatically obtaining partial pressure contributions. Our results show a clear advantage in terms of the integral error metric (up to 112 times lower for simpler spectra) and computational times (up to 4 times lower for complex spectra) in favor of the proposed method, which is considered a substantial improvement for this task. Mass spectrum ; Classifier (UML) ; Algorithm ; Metric (unit) ; Computer science ; Genetic algorithm ; Task (project management) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Mass spectrometry ; Machine learning ; Physics ; Engineering Spectroscopy and Laser Applications ; Scientific Measurement and Uncertainty Evaluation ; Mass Spectrometry Techniques and Applications
Authors: Serkan Öğüt | Hasan Kaya | Aykut Kentli | Mehmet Uçar
Source: Research Square (preprint, 2021-04-07)
Field: Materials Science — Materials Chemistry

Applying Hybrid Equal Channel Angular Pressing (HECAP) to pure copper using optimized Exp.-ECAP die Abstract Equal channel angular pressing (ECAP), expansion equal channel angular pressing (Exp.-ECAP) and hybrid equal channel angular pressing (HECAP) processes were applied to pure copper specimens within this study. Before the ECAP and HECAP processes, an Exp.-ECAP mold with optimum geometric parameters was produced to be used in these processes. The samples, on which ECAP, Exp.-ECAP and HECAP processes were applied, were subjected to microstructure analysis and mechanical tests, and the effects of these processes were examined. The results obtained showed that the Exp.-ECAP process gave better results in grain refinement and mechanical properties, and the Exp.-ECAP passes applied after the ECAP process within the scope of the HECAP process provided a more homogeneous distribution for grain size and hardness. Pressing ; Materials science ; Die (integrated circuit) ; Metallurgy ; Copper ; Microstructure ; Grain size ; Mold ; Composite material Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Copper Interconnects and Reliability
Authors: Peter Blaško | Jozef Petrík | Pavol Palfy | Alena Pribulová | Peter Futáš | Andrea Blašková
Source: Metal ... (article, 2021-01-01)
Field: Materials Science — General Materials Science

CALCULATION OF MECHANICAL AND FOUNDRY CHARACTERISTICS OF Al-Si ALLOYS USING REGRESSION ANALYSIS Al-Si alloys alloyed with Mn and Mg are widely used in industry, especially as cast alloys. The presence of Mg allows subsequent heat treatment, which will improve mechanical properties. Al-Si alloys generally contain Fe; its content between 0.3 and 0.5 wt% increases strength and fluidity. But the content higher than 1 wt % causes the formation long, hard, and brittle needles of phase (FeSiAl5) which act as notches. Mn improves strength and mechanical properties at high temperature. Excessive Fe segregate with Mn in coarse "chinese script" particles (Fe,Mn)3Si2Al15 which are less harmful than phase. Alloys containing 3.8-11.20 wt% Si, 0.12-1.76 wt% Fe, 0-2.25 wt% Mn and 0-0.73 wt% Mg, modified with 0.02 wt% Sr were analysed. The samples were cast in the form of a "harp" for calculating the fluidity by the vertical method and in samples for tensile testsultimate tensile strength (Rm), total elongation (A5), the reduction of the area (contraction Z), and hardness HV10 were determined. In addition, the microstructure was evaluated. From these sources, the equations that define the relationship between the composition of the alloy and its properties were determined by linear regression analysis. There is a strong correlation (r 2 = 0.73-0.81) between the properties calculated with obtained equations and the actual values. The content of Mn (0.3 wt%) and Mg (0.25 wt%) was determined by optimization using DoE (design of experiments), which will ensure optimal values of the analysed properties. Foundry ; Materials science ; Regression analysis ; Metallurgy ; Computer science ; Machine learning Material Properties and Applications
Authors: Ugochukwu Ejike Akpudo | Jang-Wook Hur
Source: Electronics (article, 2021-11-25)
Field: Chemistry — Analytical Chemistry

A Wavelet-Based Diagnostic Framework for CRD Engine Injection Systems under Emulsified Fuel Conditions The impact of the constituent oxides of nitrogen, carbon, sulphur, and other particulate matter which make up the gas emissions from diesel engines has motivated several control techniques for these pollutants. Water-in-diesel emulsions provide a reliable solution, but the wear effects on the fuel injection system (FIS) still pose remarkable concerns. Because pressure signals from the common rail (CR) reflect the dynamics associated with varying emulsion compositions and at varying engine RPMs, an investigative (and diagnostic) study was conducted on a KIA Sorento 2004 four-cylinder line engine at various water-in-diesel emulsion compositions and engine speeds. Alongside visual/microscopic inspections and spectral analyses, the diagnostic framework proposed herein functions on the use of standardized first-order differentials of the CR pressure signals to generate reliable continuous wavelet coefficients (CWCs) which capture discriminative spectral and transient information for accurate diagnosis. The results show that by extracting the CWCs from the first-order CR pressure differentials up to the 512th scale on a Mexican hat wavelet, adequate fault parameters can be extracted for use by a deep neural network (DNN) whose hyperparameters were globally optimized following a grid search. With a test accuracy of 92.3% against other widely-used ML-based diagnostic tools, the proposed DNN-based diagnostics tool was empirically assessed using several performance evaluation metrics. Wavelet ; Diesel fuel ; Hyperparameter optimization ; Diesel engine ; Hyperparameter ; Automotive engineering ; Discriminative model ; Artificial neural network ; Computer science ; Wavelet transform ; Artificial intelligence ; Engineering ; Support vector machine Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Lubricants and Their Additives
Authors: Arba Karcini | Iulia M. Lazar
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-25)
Field: Chemistry — Spectroscopy

The SKBR3 Cell-Membrane Proteome: Role in Aberrant Cancer Cell Proliferation and Resource for Precision Medicine Applications Abstract The plasma membrane proteome resides at the interface between the extra- and intra-cellular environment and through its various roles in signal transduction, immune recognition, nutrient transport, and cell-cell and cell-matrix interactions plays an absolutely critical role in determining the fate of a cell. Our work was aimed at exploring the landscape of the cancer cell-membrane proteome responsible for sustaining uncontrolled cell proliferation, and its intrinsic resources for enabling detection and therapeutic interventions. SKBR3/HER2+ breast cancer cells were used as a model system and mass spectrometry for characterizing the proteome. The number of identified cell-membrane proteins exceeded 2,000, with ~1,300 being matched by two or more unique peptides. Classification in four major categories, i.e., proteins with receptor or enzymatic activity, CD antigens, transporters, and cell adhesion proteins, uncovered overlapping roles in biological processes that drive cell growth, apoptosis, differentiation, immune response, adhesion and migration, as well as capabilities for signaling crosstalk and alternate pathways for proliferation. The large number of tumor markers (&gt;50) and putative drug targets (&gt;100) exposed a vast potential for yet unexplored detection and targeting opportunities, whereas the presence of 15 antigen immunological markers enabled an assessment of epithelial, mesenchymal or stemness characteristics. Serum-starved cells displayed altered processes related to mitochondrial OXPHOS/ATP synthesis, protein folding and localization, while serum-treated cells exhibited attributes that support tissue invasion and metastasis. Altogether, our findings advance the understanding of the biological triggers that sustain aberrant cancer cell proliferation, survival and development of resistance to therapeutic drugs, and reveal the vast innate opportunities for guiding immunological profiling and precision medicine applications aimed at target selection or drug discovery. Biology ; Proteome ; Cell growth ; Cell biology ; Cell ; Cell adhesion ; Cancer cell ; SKBR3 ; Signal transduction ; Cancer ; Bioinformatics ; Biochemistry ; Genetics Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Mass Spectrometry Techniques and Applications
Authors: Çağdaş YILMAZ | Didem Demir | Nimet Bölgen | Ali Akdağlı
Source: International Advanced Researches and Engineering Journal (article, 2021-08-15)
Field: Materials Science — Biomaterials

A novel formula derived by using ABC algorithm for calculation of the average fiber diameter of electrospun poly (Ԑ-caprolactone) scaffolds The characteristics of a scaffold that is the basic component of tissue engineering are considerably influenced by the fiber diameter of the fibrous scaffolds. Since the significant effect of the fiber diameter on the scaffold properties, many researchers have focused on estimating the fiber diameter based on the electrospinning parameters. With similar motivation, in this paper, a new and simple closed-form expression, which can help researchers in fabricating the electrospun poly (ԑ-caprolactone) (PCL) scaffold with desired fiber diameter, is presented. In order to construct the expression proposed, an experimental study has been performed to obtain the data set, in which 25 experimental data including average fiber diameter (AFD) values dependent on different combinations of parameters such as voltage, solution concentration, tip to collector (TTC) distance, and flow rate. Then, an expression has been constructed that is used to estimate the AFD of the electrospun PCL, and the coefficients of the expression were determined by using the artificial bee colony (ABC) algorithm. In order to validate the estimation ability of the expression, the metrics such as mean absolute error (MAE) and mean absolute percentage error (MAPE) have been used, and the optimization and test errors were respectively obtained as 3.30% and 1.27% in terms of MAPE. In addition, the results obtained were compared with those reported in the literature. Results show that our new expression can be successfully used to estimate the AFD of electrospun PCL prior to the electrospinning process. Thus, the number of test repetitions could be reduced by using the expression proposed, and time, cost, and labor could be saved in this way. This study contributes to the literature because there have been only a limited number of studies that focus on estimating the AFD of PCL nanofiber despite many studies about various polymers. Electrospinning ; Mean absolute percentage error ; Fiber ; Materials science ; Algorithm ; Scaffold ; Biomedical engineering ; Caprolactone ; Biological system ; Composite material ; Mathematics ; Mean squared error ; Statistics ; Polymer ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications