jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kunihiko Yamauchi | Thi Phuong Thao Nguyen | Tamio Oguchi
Source: arXiv (Cornell University) (preprint, 2024-08-09)
Field: Materials Science — Materials Chemistry

Dislocation-induced flexoelectricity in SrTiO$_3$ nanostructure from first principles Flexoelectricity refers to a linear coupling between the electric polarization and the strain gradient, such as bending or asymmetric compression. This effect is enhanced in nano-scale structures, where grain boundaries or dislocation cores induce the strain gradient. In this study, we theoretically investigate the flexoelectric polarization induced by misfit dislocations in a thin film. A nano-scale dislocation structure is modeled in a periodic SrTiO$_3$ supercell, and then the structure is optimized by using neural-network-potential and first-principles approaches. We point out that a pyramidal TiO$_5$ coordination forms near the dislocation cores, which in turn dominantly causes the sizable flexoelectric polarization. Flexoelectricity ; Nanostructure ; Dislocation ; Materials science ; Condensed matter physics ; Nanotechnology ; Physics ; Composite material ; Piezoelectricity Nonlocal and gradient elasticity in micro/nano structures
Authors: A.N. Ermolenko | K. V. Koneva
Source: Vestnik UGATU (article, 2024-01-01)
Field: Materials Science — General Materials Science

Comparative analysis of material models during the inducing of residual stresses in GTE parts by shot peening Shot peening ; Residual stress ; Peening ; Materials science ; Shot (pellet) ; Residual ; Structural engineering ; Composite material ; Metallurgy ; Computer science ; Engineering Material Properties and Applications ; Surface Treatment and Residual Stress ; Engineering Diagnostics and Reliability
Authors: Taehun Cha | Dong‐Hoon Lee
Source: arXiv (Cornell University) (preprint, 2024-12-15)
Field: Materials Science — Materials Chemistry

ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria \citep{cohn1994active}. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold. Causal inference ; Bayesian probability ; Inference ; Bayesian inference ; Econometrics ; Randomized experiment ; Randomized controlled trial ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Statistics ; Medicine ; Internal medicine Machine Learning in Materials Science
Authors: Peter Sydney Aprioku | Godsday Idanegbe Usiabulu | Ifeanyi Eddy Okoh | Idorenyin Isim Uboh | Prisca Kanebi
Source: Global Journal of Researches in Engineering (article, 2024-11-26)
Field: Chemistry — Analytical Chemistry

Modeling the Constant Composition Expansion Test of Black Oil using Pressure, Volume, and Temperature (PVT) Calculations The black oil pressure, volume, and temperature (PVT) properties of well X were measured in the laboratory in a PT cell with subsurface and surface recombination samples. Four sets of black oil samples were collected for the analysis. The black oil standard test is a constant composition expansion test (CCE) separator flash test for volatile oils and rich oil gas condensate and a constant volume depletion test (CVD). The PVT analysis was carried out at Reservoir Fluid Laboratory, Port Harcourt. Oil samples were collected from the Q oil field. The PVT analysis results were correlated to validate the bubble point pressure (Pb), oil isothermal combustibility, (Co), oil formation volume factor (Bo), and the oil viscosity (μo). The PVT report gives Po= 2000 psi while the standing correlation gives Pb= 1934.271 psi a difference of 65.7 psi, i.e. 3.3% and solution gas/oil ratio 647.3 SCF/STB while the standing correlation gives 671.03 SCF/STB a difference of 3.5%,oil formation volume factor (Bo) of 1.456 res. Bbl/STB while standing correlations give (Bo) of 1.0675 res bbl/STB a difference of 3.6%. The isothermal compressibility of the oil ranges from 10.12 x 10-6psi-1at P&lt; Pb(a t 4500 psi) to 4.1309 x 1018cp at 15 psi. The conclusion is that Gas began evolving at 2000 psig and increased as the pressure decreased. Also, it was noticed that at high pressure of 4500 psig the black oil viscosity was as low as 0.54 cp while at a lower pressure of 15 psiag the viscosity recorded was 1.38 cp. The crude is of high viscosity, with an average absolute error = 3.5% (0.035). The reservoir contains heavy crude oil with an API rating of 30. Volume (thermodynamics) ; Bubble point ; Chemistry ; Analytical Chemistry (journal) ; Separator (oil production) ; Isothermal process ; Mineralogy ; Thermodynamics ; Bubble ; Chromatography ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Reservoir Engineering and Simulation Methods
Authors: Yiming Che | Fazle Rafsani | Jay Shah | Md Mahfuzur Rahman Siddiquee | Teresa Wu
Source: arXiv (Cornell University) (preprint, 2024-04-24)
Field: Materials Science — Materials Chemistry

AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRI Weakly-supervised diffusion models (DMs) in anomaly segmentation, leveraging image-level labels, have attracted significant attention for their superior performance compared to unsupervised methods. It eliminates the need for pixel-level labels in training, offering a more cost-effective alternative to supervised methods. However, existing methods are not fully weakly-supervised because they heavily rely on costly pixel-level labels for hyperparameter tuning in inference. To tackle this challenge, we introduce Anomaly Segmentation with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without the need of pixel-level labels. Leveraging the unguided forward process as a reference for the guided forward process, we select hyperparameters such as the noise scale, the threshold for segmentation and the guidance strength. We aggregate anomaly maps from guided forward process, enhancing the signal strength of anomalous regions. Remarkably, our proposed method outperforms recent state-of-the-art weakly-supervised approaches, even without utilizing pixel-level labels. Diffusion MRI ; Segmentation ; Process (computing) ; Anomaly (physics) ; Diffusion ; Computer science ; Diffusion process ; Artificial intelligence ; Medicine ; Magnetic resonance imaging ; Physics ; Innovation diffusion ; Radiology ; Knowledge management ; Condensed matter physics Machine Learning in Materials Science
Authors: Željka Nikitović | Zoran Raspopović
Source: Science of Sintering (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Comparison between transport properties for H2+ in H2 and H2+ in Ar gas In this work we present a cross-sections dataset and transport properties of H2+ in H2 gas and H2+ ions in Ar gas. The ionic charge transfer reactions with molecules are indispensable elementary processes in the modeling of kinetics in terrestrial, industrial, and astrophysical plasma in the detection of dark matter. A Monte Carlo simulation method is applied to accurately calculate transport parameters for the hydrodynamic regime. We discuss new data for H2+ in H2 gas and H2+ ions in Ar gas where the drift velocity, characteristic energy, flux and bulk values of reduced mobility and diffusion coefficients are given as a function of low and moderate reduced electric fields, E/N (E-electric field strength, N-gas density). Hydrogen ; Materials science ; Environmental science ; Chemical physics ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Gas Dynamics and Kinetic Theory
Authors: Miguel Ferreira | Ernst Niederleithinger | Fahim Al-Neshawy | Vera Lay | Sylvia Keßler | Salvador Villalobos | Javier Sánchez-Montero
Source: Journal of Advanced Concrete Technology (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Non-Destructive Evaluation of Concrete in Nuclear Applications This special issue of the Journal of Advanced Concrete Technology features invited papers that originated from the International Conference on Non-destructive Evaluation of Concrete in Nuclear Applications, held in Espoo, Finland, from 25 - 27 January 2023. These papers were selected by the Scientific Committee after a careful review process, and they were chosen based on the recommendations of the session chairpersons, from a total of 48 accepted contributions. The event centered on the non-destructive evaluation (NDE) of concrete in nuclear applications, addressing both the safety of nuclear power plants and the management of radioactive waste. The primary aim was to share NDE solutions that tackle real-world challenges faced at nuclear power plants and waste management facilities. NDE techniques are crucial in the nuclear industry as they allow for the inspection of concrete structures without causing damage, ensuring the integrity and safety of these facilities. A significant emphasis was placed on interdisciplinary approaches, bringing together experts from different fields to foster collaboration and innovation. This interdisciplinary focus is essential for developing new NDE techniques and improving existing ones, ensuring they can effectively address the unique challenges posed by nuclear applications. Additionally, the event highlighted novel research areas that promise innovative NDE applications. Advancements in digital imaging and machine learning, for instance, are opening new possibilities for more accurate and efficient evaluation of concrete structures. These technologies enhance the ability to detect and analyze defects, leading to better maintenance and safety practices in the nuclear industry. This is noticeable through the selected journal papers highlighted in this issue. Materials science ; Forensic engineering ; Engineering Graphite, nuclear technology, radiation studies ; Non-Destructive Testing Techniques ; Geophysical Methods and Applications
Authors: A. Lozovskyi | A. Yu. Lyashkov | А. С. Тонкошкур
Source: Journal of Physics and Electronics (article, 2024-12-30)
Field: Chemistry — Organic Chemistry

Analysis of kinetic dependencies of the response of a gas sensor based on a model of a stretched exponential function with the use of computer technologies The results of improving the software of an information measuring system for studying relaxation phenomena in various objects based on a model of a stretched exponential function by supplementing it with intelligent components that eliminate operator participation are present.A variant of the implementation of such computer processing of experimental relaxation dependencies is presented, which makes it possible to implement the choice of- the most informative time interval from the entire measurement time range for the processed kinetic dependencies in order to most accurately estimate all model parameters;- optimal analytical expressions for representing trends in the influence of one or another specified external factor on the parameters of the specified function.The proposed algorithm and its software implementation are used to process research data on gas-sensitive sensor materials. The results of the testing of the developed application showed satisfactory correspondence of the obtained results to the existing physical concepts and allowed us to conclude that this approach is promising for automating the processing and analysis of experimental relaxation dependencies. Exponential function ; Function (biology) ; Kinetic energy ; Double exponential function ; Biological system ; Exponential integral ; Computer science ; Exponential decay ; Statistical physics ; Mathematics ; Physics ; Mathematical analysis ; Nuclear physics ; Classical mechanics Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Sensor Technologies ; thermodynamics and calorimetric analyses
Authors: Voblikova | Likhanova | Manurikov
Source: Agrarian Bulletin of the (article, 2024-12-28)
Field: Chemistry — Analytical Chemistry

Determination of wave numbers characterizing the lipid profile of milk using Fourier transform infrared spectroscopy title Fourier transform ; Fourier transform infrared spectroscopy ; Fourier transform spectroscopy ; Infrared ; Spectroscopy ; Wavenumber ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Physics ; Mathematics ; Optics ; Chromatography ; Mathematical analysis Spectroscopy and Chemometric Analyses
Authors: Lukyan Anatychuk | V.V. Lysko | I.A. Konstantynovych | M.V. Havryliuk
Source: Journal of Thermoelectricity (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Universal thermoelectric generator with heat removal by water tanks The work describes the design of a universal thermoelectric generator that can operate from any heat source - heated flat surfaces of stoves, gas or multi-fuel tourist primus, open flame, etc. The electrical energy received from the generator with a power 20-40 W provides autonomous power supply for various low-power radio electronic equipment, portable radio stations, mobile communication systems, lighting, as well as charging universal mobile batteries - power banks. To remove heat from the cold side of thermoelectric modules, tanks with water are used, which can be used for domestic purposes. The results of bench and field experimental studies of the developed generator are presented. Bibl. 30, Figs. 10, Table. 1. Computer science Advanced Thermoelectric Materials and Devices ; Advanced Materials and Semiconductor Technologies ; Integrated Energy Systems Optimization
Authors: Kymani T. K. Armstrong-Williams | Edward Hirst | Blake Jackson | Kyu Hwan Lee
Source: arXiv (Cornell University) (preprint, 2024-11-06)
Field: Materials Science — Materials Chemistry

Machine Learning Mutation-Acyclicity of Quivers Machine learning (ML) has emerged as a powerful tool in mathematical research in recent years. This paper applies ML techniques to the study of quivers -- a type of directed multigraph with significant relevance in algebra, combinatorics, computer science, and mathematical physics. Specifically, we focus on the challenging problem of determining the mutation-acyclicity of a quiver on 4 vertices, a property that is pivotal since mutation-acyclicity is often a necessary condition for theorems involving path algebras and cluster algebras. Although this classification is known for quivers with at most 3 vertices, little is known about quivers on more than 3 vertices. We give a computer-assisted proof of a theorem to prove that mutation-acyclicity is decidable for quivers on 4 vertices with edge weight at most 2. By leveraging neural networks (NNs) and support vector machines (SVMs), we then accurately classify more general 4-vertex quivers as mutation-acyclic or non-mutation-acyclic. Our results demonstrate that ML models can efficiently detect mutation-acyclicity, providing a promising computational approach to this combinatorial problem, from which the trained SVM equation provides a starting point to guide future theoretical development. Mutation ; Computer science ; Artificial intelligence ; Theoretical computer science ; Computational biology ; Biology ; Genetics ; Gene Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Oriol Corcoll Andreu | Athanasios Vlontzos | Michael O’Riordan | Ciaran M. Gilligan-Lee
Source: arXiv (Cornell University) (preprint, 2024-11-28)
Field: Chemistry — Organic Chemistry

Contrastive representations of high-dimensional, structured treatments Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects, such as text, video, or audio. This provides a challenge to traditional causal effect estimation. While leveraging the shared structure across different treatments can help generalize to unseen treatments at test time, we show in this paper that using such structure blindly can lead to biased causal effect estimation. We address this challenge by devising a novel contrastive approach to learn a representation of the high-dimensional treatments, and prove that it identifies underlying causal factors and discards non-causally relevant factors. We prove that this treatment representation leads to unbiased estimates of the causal effect, and empirically validate and benchmark our results on synthetic and real-world datasets. Linguistics ; Computer science ; Psychology ; Natural language processing ; Philosophy Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Melanie Nijs | Etienne Waelkens | Bart De Moor
Source: Journal of the American Society for Mass Spectrometry (article, 2024-10-09)
Field: Chemistry — Analytical Chemistry

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization One of the main challenges in mass spectrometry imaging data analysis remains the analysis of <i>m</i>/<i>z</i>-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance. In this work, we present a recursive rank-2 non-negative matrix factorization (rr2-NMF) algorithm that automatically returns spectral and spatial visualization of colocalized molecules, both highly and lowly abundant. Using this hierarchical decomposition, our method finds spatial and spectral correlations on different levels of abundances. The quality of the analysis is evaluated on MALDI-TOF data of healthy mouse pancreatic tissue for the annotation of molecules of interest in the lower abundances. The results show interesting findings regarding the functioning and colocalization of certain molecules. Chemistry ; Mass spectrometry imaging ; Non-negative matrix factorization ; Biclustering ; Mass spectrometry ; Matrix decomposition ; Rank (graph theory) ; Matrix (chemical analysis) ; Chromatography ; Artificial intelligence ; Cluster analysis ; Computer science ; Combinatorics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Gene expression and cancer classification
Authors: Sascha Thieltges | Sargon Youssef | Uwe Hartmann
Source: Journal of Nondestructive Evaluation (article, 2024-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconstruction of the Ferromagnetic Hysteresis in the Rayleigh Regime by Means of Impedance Analysis of the Excitation Coil Abstract Ferromagnetic hysteresis measurements can be used to determine both magnetic and mechanical state variables that correlate with each other. Therefore, hysteresis measurements are suitable for mechanical material characterization. The application of standardized hysteresis measurement methods is complex and can only be used to a limited extent in an industrial environment. In this work, a BH (ferromagnetic hysteresis) curve is to be reconstructed on 22NiMoCr3-7 samples that are in different mechanically deformed states (different histories). The reconstruction of the hysteresis is performed by an impedance analysis of the H-field generating coil. The applied method demonstrates that the ferromagnetic hysteresis can be reconstructed by means of an impedance analysis and is sensitive to different states of plastical deformation. Excitation ; Electromagnetic coil ; Electrical impedance ; Hysteresis ; Materials science ; Ferromagnetism ; Solid mechanics ; Rayleigh scattering ; Acoustics ; Nuclear magnetic resonance ; Condensed matter physics ; Optics ; Electrical engineering ; Physics ; Composite material ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Jun‐Young Park | Hyun Yu | Ki Mun Bang | Woochul Kim | Hyungyu Jin
Source:  (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Additive-manufactured topology-optimized heat sinks for enhancing thermoelectric generator conversion efficiency Thermoelectric generators (TEGs) are gaining great attention as a promising technology for waste heat recovery due to their ability to directly convert heat into electricity. However, their relatively low conversion efficiency limits widespread adoption. While efficient thermal management of TEGs is crucial to achieve high conversion efficiencies, less attention has been paid to this aspect thus far compared to materials development. Efficient TEGs require efficient heat dissipation on the cold side of TEGs, demanding careful heat sink design. In this study, a topology optimization model was developed to design air-cooled heat sinks specifically for TEG applications, incorporating both thermoelectric properties and geometric structure of TEGs. The optimized heat sink was fabricated using additive manufacturing, and its performance was experimentally validated. Compared to a conventional rectangular fin heat sink, the topology optimized (TO) heat sink significantly reduced thermal resistance across a wide range of pressure drops (6 Pa to 30 Pa), achieving a 33.5% reduction at a 12 Pa pressure drop. This led to a 44.7% increase in the output power of a commercial TEG. Despite a ~40% increase in power consumption, the TO heat sink resulted in a 21.3% improvement in conversion efficiency. This improvement is equivalent to a 27% increase in the device thermoelectric figure of merit (ZT). Numerical simulations revealed that the TO heat sink can enhance temperature uniformity on the cold side of the TEG, further contributing to performance gains. These findings highlight the effectiveness of heat sink topology optimization in enhancing conversion efficiency of TEGs. Heat sink ; Materials science ; Thermoelectric generator ; Topology optimization ; Pressure drop ; Thermoelectric effect ; Thermal resistance ; Topology (electrical circuits) ; Energy conversion efficiency ; Waste heat ; Waste heat recovery unit ; Mechanical engineering ; Thermal ; Automotive engineering ; Optoelectronics ; Thermodynamics ; Electrical engineering ; Heat exchanger ; Finite element method ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies