jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Karl Svozil
Source: arXiv (Cornell University) (preprint, 2024-01-28)
Field: Materials Science — Biomaterials

Anti-Gravity from Vacancies in Fractal Space-Time: The Case of a Menger Sponge We explore the idea that anti-gravity, interpreted as matter-matter repulsion, may emerge as an effective description of spacetime with a reduced local ``substratum density,'' modeled heuristically by vacancies in a fractal lattice. Using the Menger Sponge as a prototypical vacancy-dominated fractal, we motivate a phenomenological identification of a net vacancy parameter with the mass parameter in the Schwarzschild solution. This yields an effective negative-mass Schwarzschild-like metric for embedded observers when vacancies dominate. We analyze curvature, geodesic motion, and energy-condition issues, and we emphasize that our construction is not a microscopic derivation from a specific stress-energy tensor but a re-interpretation of the negative-mass Schwarzschild geometry in terms of fractal vacancies. We discuss conceptual implications, stability challenges, and possible observational signatures. Sponge ; Geology ; Geodesy ; Computer science ; Paleontology Diatoms and Algae Research
Authors: Yongxian Qian | Ying‐Chia Lin | Xingye Chen | Yulin Ge | Yvonne W. Lui | Fernando E. Boada
Source: arXiv (Cornell University) (preprint, 2024-07-13)
Field: Chemistry — Spectroscopy

Multi-TE Single-Quantum Sodium (23Na) MRI: A Clinically Translatable Technique for Separation of Mono- and Bi-T2 Sodium Signals Sodium magnetic resonance imaging (MRI) is sensitive and specific to ionic balance of cells owing to 10 fold difference in sodium concentration across membrane actively maintained by sodium potassium (Na+ K+) pump. Disruption of the pump and membrane integrity, as seen in neurological disorders such as epilepsy, multiple sclerosis, bipolar disease, and mild traumatic brain injury, leads to a large increase in intracellular sodium. Such a cellular level alteration is however overshadowed by large signal from extracellular sodium, leaving behind a long standing pursuit to separate signals from sodium exhibiting mono vs biexponential transverse (T2) decay under the inherent constraint of low signal to noise ratio even at advanced clinical field of 3 Tesla. Here we propose a novel technique that exploits intrinsic difference in their T2 decays by simply acquiring single quantum images at multiple echo times (TEs) and performing accurate matrix inversion at voxel. This approach was then investigated using numerical models, agar phantoms and human subjects, showing high accuracy of the separation in phantoms (95.8 percent for monoT2 and 72.5 to 80.4 percent for biT2) and clinical feasibility in humans. Thus, sodium MRI at 3T can now facilitate detection of neurological disorders early at cellular level and response to treatment as well. Keywords. sodium MRI, single quantum MRI, triple quantum MRI, neuroimaging, neurodegeneration Sodium ; Exponential function ; Quantum ; Physics ; Exponential growth ; Nuclear magnetic resonance ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Chromatography ; Quantum mechanics ; Mathematical analysis ; Organic chemistry Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Advanced MRI Techniques and Applications
Authors: R.W. McKee | K.I. Johnson | H.D. Huber | M.C. Bierschbach
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Effects of Spent Fuel Aging on Repository Disposal Requirements A study has been carried out to analyze the effects of extended spent fuel aging on spent fuel disposal requirements. The analysis considers additional spent fuel aging up to a maximum of 50 years relative to the currently planned 2010 repository startup. As part of the analysis, an equal energy deposition (EED) methodology was developed for determining allowable waste emplacement densities in a tuff geologic repository as a function of spent fuel age and exposure. The analysis also demonstrated the feasibility of delivering an energy levelized mix of spent fuel to the repository each year either directly from the reactors or from a Monitored Retrievable Storage (MRS) facility and the feasibility of representing the complex spent fuel mixture received by the repository by simply the average age and exposure. Results of this analysis indicate that substantial benefits of spent fuel aging will already have been achieved by a repository startup in 2010 (when the average spent fuel age will be 28 years). Even so, further significant aging benefits in terms of reduced emplacement areas and mining requirements and reduced number of waste containers, will continue to accrue if the repository startup is delayed for at least another 50 years, when the average spent fuel age would be 78 years. Spent nuclear fuel ; Waste management ; High-level waste ; Radioactive waste ; Waste disposal ; Environmental science ; Engineering Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Nuclear Materials and Properties
Authors: Swati Soni | K. Kabra | Jyoti Sahu | Divya Prakash Dubey | B. Dalela | P. A. Alvi | Shalendra Kumar | Mukul Gupta | S. Dalela
Source: Journal of Magnetism and Magnetic Materials (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Correlation of electronic structure and magnetic properties of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si19.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">e</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">S</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> nanostructure using synchrotron soft XAS Correlation ; Computer science ; Scalable Vector Graphics ; Materials science ; Mathematics ; World Wide Web ZnO doping and properties ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Jiří Maxa | Vilém Neděla | Pavla Šabacká | Tomáš Binar
Source: Sensors (article, 2024-05-20)
Field: Materials Science — Surfaces, Coatings and Films

Mathematical Physics Analysis of Nozzle Shaping at the Gas Outlet from the Aperture to the Differentially Pumped Chamber in Environmental Scanning Electron Microscopy (ESEM) A combination of experimental measurement preparations using pressure and temperature sensors in conjunction with the theory of one-dimensional isentropic flow and mathematical physics analyses is presented as a tool for analysis in this paper. Furthermore, the subsequent development of a nozzle for use in environmental electron microscopy between the specimen chamber and the differentially pumped chamber is described. Based on experimental measurements, an analysis of the impact of the nozzle shaping located behind the aperture on the character of the supersonic flow and the resulting dispersion of the electron beam passing through the differential pumped chamber is carried out on the determined pressure ratio using a combination of theory and mathematical physics analyses. The results show that nozzle shapes causing under-expanded gas outflow from the aperture to the nozzle have a worse impact on the dispersion of the primary electron beam. This is due to the flow velocity control. The controlled reduction in the static pressure curve on the primary electron beam path thus causes a significantly higher course of electron dispersion values than variants with shapes causing over-expanded gas outflow. Nozzle ; Aperture (computer memory) ; Optics ; Beam (structure) ; Electron ; Chamber pressure ; Mechanics ; Physics ; Cathode ray ; Outflow ; Materials science ; Dispersion (optics) ; Environmental scanning electron microscope ; Supersonic speed ; Scanning electron microscope ; Nuclear physics ; Acoustics ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Field-Flow Fractionation Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Giovanni Alessandrini | Antonino Morassi | E. Rosset | Eva Sincich | Sergio Vessella
Source: arXiv (Cornell University) (preprint, 2024-08-24)
Field: Materials Science — Materials Chemistry

Stable determination of the Winkler subgrade coefficient in a nanoplate We study the inverse problem of determining the Winkler coefficient in a nanoplate resting on an elastic foundation and clamped at the boundary. The nanoplate is described within a simplified strain gradient elasticity theory for isotropic materials, under the Kirchhoff-Love kinematic assumptions in infinitesimal deformation. We prove a global Hölder stability estimate of the subgrade coefficient by performing a single interior measurement of the transverse deflection of the nanoplate induced by a load concentrated at one point. Subgrade ; Environmental science ; Geotechnical engineering ; Geology Nonlocal and gradient elasticity in micro/nano structures ; Thermoelastic and Magnetoelastic Phenomena ; Numerical methods in engineering
Authors: Ángel T. Alvarado | Josefina Zakzuk | Mora Salamanca AF | J LOPEZ | Salcedo Mejía F | Alvis Zakzuk NJ | A. Moreno-Calderón
Source: Value in Health (article, 2024-06-01)
Field: Chemistry — Analytical Chemistry

MSR85 Efficacy of Modern Oral Antidiabetic Drugs: A Systematic Literature Review &amp; Network Meta-Analysis Meta-analysis ; Medicine ; Pharmacology ; Internal medicine Analytical Methods in Pharmaceuticals ; Diabetes Treatment and Management
Authors: Rahul Senjam | Devi Singh | Aakanksha Tiwari
Source: International Journal of Advanced Biochemistry Research (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Evaluation of different lotus varieties in agroclimatic condition of Prayagraj, Uttar Pradesh An experiment entitled “Evaluation of different Lotus varieties in agroclimatic condition of Prayagraj, Uttar Pradesh” was conducted in Horticulture Research Field, Faculty of Agriculture, Department of Horticulture, Naini Agricultural Institute, SHUATS, Prayagraj during April 2023 to September 2023. The experiment was conducted in Randomized Block Design (RBD), with 5 varieties which have been replicated for 4 times. The varieties are: 108, Akhila, Fong Hu, Sai Thong Shaui, Super Lotus. The varieties showed significant variations in all the parameters studied. The variety Akhila reported performed better in terms of plant height (30.00 cm), leaf length (24.09cm), leaf width (25.00 cm), flower diameter (11.88cm), total number of flowers per plant (10.75), total number of flowers per hectare (408500). The variety Sai Thong Shaui reported more numbers of leaves (11.50). The variety Super Lotus 14 reported flower bud takes more time to open than other varieties (26.88). The longer duration of flowering reported in Akhila (9.26) followed by 108 (7.38) in days. The longest vase life was observed in the variety Akhila (6.13) followed by Sai Thong Shaui (5.88) in days. The maximum gross return (₹2859500/ha), net return (₹1960100/ha) and benefit cost ratio (3.17) were observed with Akhila. On average, the Akhila, Fong Hu, Sai Thong Shaui performed the best in Prayagraj agro climatic conditions. Therefore, it recommends cultivate these varieties for Prayagraj agro climatic conditions, Uttar Pradesh. Uttar pradesh ; Lotus ; Geography ; Biology ; Socioeconomics ; Botany ; Sociology Chromatography in Natural Products
Authors: Lili Wang | Lei Tian | Pengcheng Gao | Yong Liu | Yongqian Wang
Source:  (article, 2024-02-19)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy based on transfer learning, detection of tobacco chemical composition The use of near-infrared spectroscopy enables rapid and efficient detection of tobacco leaf chemical components. However, changes in environmental and climatic factors can affect the accuracy of the originally established prediction model. To reduce the workload involved in re-sampling and modeling, a more stable prediction model was established using transfer learning, transferring valuable information from fragmented leaf spectra to enhance the stability of the whole-leaf spectral measurement model. New prediction models were developed for total sugars, reducing sugars, total alkaloids, potassium, and chlorine contents. Experimental results show that the mathematical model established through transfer learning provides highly consistent predictions with results obtained through continuous flow methods. It is more stable than existing models and can be directly applied for model transfer without the need for re-sampling. It can be used for the analysis of tobacco leaf chemical components in the tobacco production process, meeting the requirements for rapid detection of routine chemical components in tobacco leaves. The model exhibits high accuracy in predicting new samples and can meet the demands of tobacco production companies. Biological system ; Tobacco leaf ; Computer science ; Near-infrared spectroscopy ; Process engineering ; Process (computing) ; Artificial intelligence ; Biochemical engineering ; Agricultural engineering ; Engineering ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaojian Liu | Hang Li | Zhifeng Li | Shaoheng Song | Weihua Liu | Yixin Bai | Yaqin Song
Source:  (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Transfer Learning for Evaluating Internal Defect Sizes in Insulating Materials Using Simulated Ultrasonic Measurements Data-driven deep learning methods are widely used in ultrasonic testing for internal defects in electrical insulation components, but their effectiveness is constrained by the scarcity of real detection signals. Many studies focus on using the finite element method to generate lots of ultrasonic signals with diverse defect types. However, the differences between simulation and real detection signals limit the performance of machine learning. In view of this, a transfer learning-based internal defect sizes evaluation strategy is proposed in this paper. FEM simulations were performed to generate void defect ultrasonic detection signals for 45 uniformly distributed diameters from 0.4 to 2.6 mm, while 5 actual void defects within the same diameter range were fabricated to obtain experimental signals. A pre-trained CNN model was initially trained on simulation data, and then the fine-tuning method was applied using experimental data, ultimately achieving a void size evaluation error of less than 6%. Ultrasonic sensor ; Materials science ; Ultrasonic testing ; Acoustics ; Composite material ; Computer science ; Physics High voltage insulation and dielectric phenomena ; Non-Destructive Testing Techniques ; Water Systems and Optimization
Authors: Jin‐Hoon Yang | Ji‐Young Lee | Hajin Kwon | Eun‐Ho Sohn | Hyunju Chang | Seunghun Jang
Source: Macromolecular Rapid Communications (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data Citation ; Front cover ; Cover (algebra) ; Glass transition ; Computer science ; Art history ; Information retrieval ; Library science ; Polymer science ; Chemistry ; Polymer ; Art ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Jianmin Wang
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 3 approved, 2 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Advanced Physical and Chemical Molecular Interactions
Authors: Qifeng Bai
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 2 approved, 3 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning ; Space (punctuation) Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Ka‐Chun Wong
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 1 approved, 4 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Junwu Chen | Xu Huang | Hua Cheng | Yulian He | Philippe Schwaller
Source: ChemRxiv (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

AdsMT: A multi-modal transformer for predicting global minimum adsorption energy The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the GMAE through density functional theory (DFT). Thus, we designed a novel multi-modal transformer called AdsMT to rapidly predict the GMAE based on surface graphs and adsorbate feature vectors without any site-binding information. The AdsMT model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were constructed, opening new avenues for further research on the challenging GMAE prediction task. Our AdsMT framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 eV, respectively. Beyond GMAE prediction, AdsMT's cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions. While primarily focused on heterogeneous catalyst screening, our multi-modal approach has potential applications across materials science and chemistry. Adsorption ; Modal ; Computer science ; Benchmark (surveying) ; Encoder ; Density functional theory ; Transformer ; Catalysis ; Materials science ; Algorithm ; Chemistry ; Computational chemistry ; Voltage ; Physics ; Physical chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Matthew Anderson | Mengyuan Wu | Jeremy Sharapov
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Post Irradiation Examination Dislocation Defect Detection Software This software provides dislocation-type defect identification and segmentation using a standard open source computer vision model, YOLOv8, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of expert annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on multiple alloys. It includes multiple layers of frozen layers used for transfer learning from multidisciplinary data and is extensible to alloys that are not included in the training dataset. Dislocation ; Irradiation ; Materials science ; Radiochemistry ; Crystallography ; Chemistry ; Composite material ; Nuclear physics ; Physics Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Georgi Tancev
Source:  (preprint, 2024-02-14)
Field: Materials Science — Materials Chemistry

Crystallization Process Design by Model-Free Deep Reinforcement Learning Chemical process design is the search for an optimal manufacturing protocol to perform chemical operations. For transient processes such as crystallization, the optimal conditions can change over time, requiring a dynamic strategy. Model-free deep reinforcement learning is an approach that can be used to identify the best sequence of states with respect to a predefined reward function. In this work, proximal policy optimization is applied in a simulated environment to identify operational strategies that are optimal with respect to the desired particle properties in unseeded batch cooling crystallization processes of paracetamol in ethanol. For this purpose, the corresponding Markov decision process is formulated, and it is shown that the method is promising for the development of novel routes that allow the tuning of particle size (623 μm) and provide high yields (96%) within a defined period of time (12 h). Reinforcement learning ; Process (computing) ; Computer science ; Artificial intelligence ; Crystallization ; Engineering ; Chemical engineering ; Programming language Crystallization and Solubility Studies
Authors: Georgi Tancev
Source:  (preprint, 2024-05-02)
Field: Materials Science — Materials Chemistry

Crystallization Process Design by Proximal Policy Optimization Chemical process design is the search for an optimal manufacturing protocol to perform chemical operations. For transient processes such as crystallization, the optimal conditions can change over time, requiring a dynamic strategy. Model-free deep reinforcement learning is an approach that can be used to identify the best sequence of states with respect to a predefined reward function. In this work, proximal policy optimization is applied in a simulated environment to identify operational strategies that are optimal with respect to the desired particle properties in unseeded batch cooling crystallization processes of paracetamol in ethanol. For this purpose, the corresponding Markov decision process is formulated, and it is shown that the method is promising for the development of novel routes that allow the tuning of particle size (623 μm) and provide high yields (96%) within a defined period of time (12 h). Crystallization ; Process (computing) ; Process optimization ; Computer science ; Process engineering ; Chemical engineering ; Engineering Crystallization and Solubility Studies
Authors: José Rodrigues Torraca Neto | Bruno Didier Olivier Capron | Argimiro R. Secchi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Robust Deep Reinforcement Learning Approach for the Control of Crystallization Processes Reinforcement learning ; Crystallization ; Control (management) ; Reinforcement ; Computer science ; Artificial intelligence ; Control theory (sociology) ; Materials science ; Engineering ; Chemical engineering ; Composite material Crystallization and Solubility Studies ; Fault Detection and Control Systems