jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeynab Samandari | Seyyedeh Fatemeh Molaeezadeh
Source: arXiv (Cornell University) (preprint, 2023-12-28)
Field: Chemistry — Analytical Chemistry

Towards Bloodless Potassium Measurement from ECG using Neuro-Fuzzy Systems Potassium disorders are generally asymptomatic, potentially lethal, and common in patients with renal or cardiac disease. The morphology of the electrocardiogram (ECG) signal is very sensitive to the changes in potassium ions, so ECG has a high potential for detecting dyskalemias before laboratory results. In this regard, this paper introduces a new system for ECG-based potassium measurement. The proposed system consists of three main steps. First, cohort selection &amp; data labeling were carried out by using a 5- minute interval between ECGs and potassium measurements and defining three labels: hypokalemia, normal, and hyperkalemia. After that, feature extraction &amp; selection were performed. The extracted features are RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, T axis, and ACCI. Kruskal-Wallis technique was also used to assess the importance of the features and to select discriminative ones. Finally, an ANFIS model based on FCM clustering (FCM-ANFIS) was designed based on the selected features. The used database is ECG-ViEW II. Results showed that T axis compared with other features has a significant relationship with potassium levels (P&lt;0.01, r=0.62). The absolute error of FCM-ANFIS is 0.4+-0.3 mM, its mean absolute percentage error (MAPE) is 9.99%, and its r-squared value is 0.74. Its classification accuracy is 85.71%. In detecting hypokalemia and hyperkalemia, the sensitivities are 60% and 80%, respectively, and the specificities are 100% and 97.3%, respectively. This research has shed light on the design of noninvasive instruments to measure potassium concentration and to detect dyskalemias, thereby reducing cardiac events. Hypokalemia ; Hyperkalemia ; QRS complex ; Cardiology ; Pattern recognition (psychology) ; Internal medicine ; QT interval ; Electrocardiography ; Medicine ; Interval (graph theory) ; Mathematics ; Statistics ; Artificial intelligence ; Computer science Analytical Methods in Pharmaceuticals ; Fuel Cells and Related Materials
Authors: Alfonsus Julanto Endharta | Jongwoon Kim | Youngseoung Kim
Source: Journal of Engineering and Management in Industrial System (article, 2023-05-23)
Field: Materials Science — Materials Chemistry

ONLINE PARTIAL DISCHARGE MEASUREMENT FOR CONDITION-BASED MAINTENANCE OF HV POWER CABLES IN RAILWAY INFRASTRUCTURE Partial discharge (PD) measurement as one of well-known method to evaluate the condition of high voltage (HV) power cables has been studied over many decades. Cable insulation failure could result in a power outage, which could then cause a loss of service in the transportation system and even dangerous events like fire accidents. It is of a great interest to railway infrastructure operators to monitor and identify the cable faults before any possible accident occurs. The paper focuses on the diagnostic problem to detect the HV cable fault based on the Phase Resolved Partial Discharge (PRPD) patterns. Classification models, such as Random Forest and Convolutional Neural Network, are considered to classify the pattern of PRPD based on the mostly occurring PD types in HV cables, such as corona, surface, and void patterns. Experiments are performed and the PRPD data from the experiments are collected. The optimal model is applied in the online monitoring program which will be used continuously to evaluate the cable condition and arrange the optimal schedule for maintenance. According to the analysis, both algorithm perform well in the PRPD pattern categorization, with accuracy up to 83.45%. This indicates that due to the more effective behavior, PD assessment with PD sensors is preferable. Partial discharge ; High voltage ; Schedule ; Reliability engineering ; Computer science ; Fault (geology) ; Power cable ; Condition monitoring ; Voltage ; Engineering ; Electrical engineering ; Materials science High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: H Loos | S Lotter | V Jamali | A Radenovic | G See Marega | A Ratwatte | S Balasubramaniam | V Ravichandran | Y Huang | T Ross | J Shaska | A Sangwan | J Jornet | D Sarkar | M Schafer | S See Lotter | R Schober | F Sebastiano | X See Xue | M Gursoy | Y Cheng | A Krieger | M Thairu | O Venturelli | J Boedicker | J Handelsman | Mitra | U | B Sheu | S Kao | Y Chou | Y See Chen | M Sie | Y Chang | R Sliz | M Lodi | S Somathilaka | S Sun | H Tai | C Lai | M Tripathi | K Tseng | Y Tseng | P Wang | Y Tsou | G Jian | Decloakface | X Jiang
Source: IEEE Nanotechnology Magazine (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

2023 Index IEEE Nanotechnology Magazine Vol. 17 Index (typography) ; Nanotechnology ; Materials science ; Computer science ; World Wide Web MXene and MAX Phase Materials
Authors: Binbin Lu | Xiaofei Zang | Bowen Sun | Yiwen Zhou | Haoxiang Chi | Yiming Zhu | Songlin Zhuang
Source: Research Square (preprint, 2023-06-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Multi-Foci Metalens for Generating Polarization-Dependent Images with Uniform Intensity Distributions Inverse ; Polarization (electrochemistry) ; Intensity (physics) ; Optics ; Physics ; Mathematics ; Chemistry ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Photonic Crystals and Applications
Authors: Nick Ruben Neuling | Robin D. Allert | Dominik B. Bucher
Source: arXiv (Cornell University) (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Prospects of Single-Cell NMR Spectroscopy with Quantum Sensors Single-cell analysis can unravel functional heterogeneity within cell populations otherwise obscured by ensemble measurements. However, non-invasive techniques that probe chemical entities and their dynamics are still lacking. This challenge could be overcome by novel sensors based on nitrogen-vacancy (NV) centers in diamond, which enable nuclear magnetic resonance (NMR) spectroscopy on unprecedented sample volumes. In this perspective, we briefly introduce NV-based quantum sensing and review the progress made in microscale NV-NMR spectroscopy. Lastly, we discuss approaches to enhance the sensitivity of NV ensemble magnetometers to detect biologically relevant concentrations and provide a roadmap towards their application in single-cell analysis. Microscale chemistry ; Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Quantum sensor ; Nanotechnology ; Vacancy defect ; Nuclear magnetic resonance ; Materials science ; Chemical physics ; Physics ; Quantum ; Quantum computer ; Quantum network ; Quantum mechanics Diamond and Carbon-based Materials Research ; Advanced Memory and Neural Computing ; Magnetic properties of thin films
Authors: Khairul Khaizi Mohd Shariff | Megat Syahirul Amin Megat Ali | Ahmad Ihsan Mohd Yassin | Noor Ezan Abdullah | Ali Abd Almisreb | A. H. Jahidin
Source: TEM Journal (article, 2023-05-29)
Field: Materials Science — Surfaces, Coatings and Films

Small but Diverse SEM Image Dataset: Impact of Image Augmentation on the Performance of AlexNet To this date, scanning electron microscope has produced among the most complex and diverse images at nanoscale resolution. The highly magnified images of backscattered electrons reflected from the surface of samples are non-uniformed, even for the same class of images. The study investigates the impact of having a small but diverse dataset on the performance of AlexNet. A total of 160 samples from EUDAT Collaborative Database Infrastructure is used for the study. Compared to the use of new non-augmented samples to increase the size of dataset, image augmentation has been significantly improved classification performance and generalization ability of the AlexNet. Computer science ; Scanning electron microscope ; Resolution (logic) ; Generalization ; Image (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) ; Image resolution ; Computer vision ; Materials science ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advancements in Photolithography Techniques
Authors: Oliver Hoidn | Aashwin Mishra | Apurva Mehta
Source: arXiv (Cornell University) (preprint, 2023-08-24)
Field: Chemistry — Analytical Chemistry

Probabilistic Mixture Model-Based Spectral Unmixing Identifying pure components in mixtures is a common yet challenging problem. The associated unmixing process requires the pure components, also known as endmembers, to be sufficiently spectrally distinct. Even with this requirement met, extracting the endmembers from a single mixture is impossible; an ensemble of mixtures with sufficient diversity is needed. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most of them assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference approach that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and presence of non-isotropic Gaussian noise. Access to reliable uncertainties on the unmixing solutions would enable robust solutions as well as informed decision making. Endmember ; Mixture model ; Hyperspectral imaging ; Inference ; Computer science ; Probabilistic logic ; Bayesian inference ; Bayesian probability ; Mixing (physics) ; Noise (video) ; Artificial intelligence ; Set (abstract data type) ; Pattern recognition (psychology) ; Mathematics ; Image (mathematics) ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Yigitcan Comlek | Liwei Wang | Wei Chen
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP) with Sobol' analysis to develop the first metamodel-based mixed-variable GSA method. Through numerical case studies, we validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the proposed GSA method is general enough to benefit various engineering design applications, we integrate it with multi-objective Bayesian optimization (BO) to create a sensitivity-aware design framework in accelerating the Pareto front design exploration for metal-organic framework (MOF) materials with many-level combinatorial design spaces. Although MOFs are constructed only from qualitative variables that are notoriously difficult to design, our method can utilize sensitivity analysis to navigate the optimization in the many-level large combinatorial design space, greatly expediting the exploration of novel MOF candidates. Expediting ; Sobol sequence ; Computer science ; Sensitivity (control systems) ; Engineering design process ; Categorical variable ; Variable (mathematics) ; Context (archaeology) ; Design of experiments ; Probabilistic design ; Surrogate model ; Mathematical optimization ; Machine learning ; Systems engineering ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Xin Wang | Tao Jiang | Chong Ji | Changxiao Zhao | Yuting Wang | Zhenru Gao
Source: Research Square (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Mechanical properties and constitutive modeling of 6063-T5 aluminum alloy over a wide range of strain rates and temperatures Abstract In the present study, the stress-strain relationship of 6063-T5 aluminum alloy over wide ranges of temperatures and strain rates was investigated. Firstly, the quasi-static tensile tests and high temperature split Hopkinson pressure bar (SHPB) tests were carried out for 6063-T5 aluminum alloy. In the tests, the experimental temperature ranged from room temperature to 350℃, and the dynamic strain rate was 500/s-6000/s. Secondly, the effects of temperature and strain rate on mechanical properties of materials were discussed. The results showed that 6063-T5 aluminum alloy had obvious temperature softening effect and strain rate hardening effect, but strain rate softening effect may also occur under certain temperature conditions. Thirdly, the fracture morphology of tensile specimen and the metallographic structure of dynamic compression specimen under different temperature and strain rates were analyzed. Finally, by improving the fitting method and constitutive model, the modified Johnson-Cook constitutive model for 6063-T5 aluminum alloy was obtained. Compared with the traditional one, the modified Johnson-Cook constitutive model can predict the mechanical behavior of 6063-T5 aluminum alloy more accurately under high temperature and dynamic conditions. Materials science ; Strain rate ; 6063 aluminium alloy ; Alloy ; Split-Hopkinson pressure bar ; Constitutive equation ; Ultimate tensile strength ; Softening ; Hardening (computing) ; Strain hardening exponent ; Metallurgy ; Composite material ; Aluminium ; Atmospheric temperature range ; Structural engineering ; Finite element method ; Thermodynamics High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Energetic Materials and Combustion
Authors: Johannes Popp | Lukas Seitner | Felix Naunheimer | Georg Janowski | Michael Haider | Christian Jirauschek
Source:  (preprint, 2023-11-27)
Field: Chemistry — Spectroscopy

Multi-Domain Modeling of Free-Running Harmonic Frequency Comb Formation in Terahertz Quantum Cascade Lasers &lt;p&gt;In our manuscript, we present a detailed multi-domain modeling approach for the numerical study of free-running harmonic frequency comb (HFC) formation in terahertz quantum cascade lasers (QCLs). We investigate the influence of the chosen eigenstate basis on the gain spectrum and present self-consistent simulation results of stable HFC operation in a double metal terahertz QCL.&lt;br&gt; In our simulations, the studied QCL gain medium shows self-starting harmonic mode-locking for different bias and waveguide configurations, resulting in a mode spacing of up to eight times the cavity round trip frequency. Furthermore, we characterize the spectral time evolution of the coherent HFC formation process, yielding the spontaneous build-up of a dense multimode state which is gradually transferred into a broad and clear HFC state.&lt;/p&gt; Terahertz radiation ; Cascade ; Laser ; Harmonic ; Multi-mode optical fiber ; Physics ; Quantum ; Quantum cascade laser ; Frequency domain ; Photomixing ; Frequency comb ; Optics ; Optoelectronics ; Far-infrared laser ; Quantum mechanics ; Optical fiber ; Chemistry ; Terahertz metamaterials ; Mathematics Spectroscopy and Laser Applications ; Laser Design and Applications ; Advanced Fiber Laser Technologies
Authors: Y Zhang | Jie Wang | Xiuhong Wu | Hongyu LUO | Juan YANG | Yingfu Zhong | Z. Chen | Quan WU | Ze Xu
Source: Food Science and Technology (article, 2023-06-13)
Field: Chemistry — Analytical Chemistry

Fast and nondestructive discrimination of quality level of Yongchuan Xiuya tea based on near infrared spectroscopy combined with artificial neural network algorithm Near infrared spectroscopy (NIRS) was used to discriminate the quality level of Yongchuan Xiuya tea quickly and nondestructively. Three quality levels of Yongchuan Xiuya tea were collected, then scanning NIRS, pretreating spectral noise information, screening characteristic spectral intervals by backward interval partial least squares, proceeding principal component analysis. Last, the jump connection nets artificial neural network (J-BP-ANN) with three kinds of transfer functions was applied to establish models. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, which accounting for 27.23% spectral data. The cumulative contribution rate of the first three principal components of the selected characteristic spectra was 97.85%. When the J-BP-ANN calibration set model was established with the tanh function, NIRS model had the best results, whose root mean square error and determination coefficient of the cross validation were 0.953 and 0.031, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.942 and 0.041, respectively. The absolute deviation values of prediction set samples were &lt;0.08. The results showed NIRS can predict the quality levels of Yongchuan Xiuya tea quickly and accurately. Mathematics ; Principal component analysis ; Mean squared error ; Second derivative ; Artificial neural network ; Statistics ; Calibration ; Coefficient of determination ; Near-infrared spectroscopy ; Root mean square ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematical analysis ; Computer science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Dong‐Hyun Seo | Yang Hoon Huh | Hee-Jin Yoo | Kun Cho | Hae‐Kap Cheong | Eun‐Hee Kim | Joonhyeok Choi | Hak Nam Kim | Jeong Kyu Bang | Jong-Soo Lim | Min Jung Lee | Kyoung‐Seok Ryu
Source: Research Square (preprint, 2023-05-30)
Field: Chemistry — Spectroscopy

Investigating the hidden mechanism underlying the tau interaction by methylene blue Abstract Methylene blue (MB) has recently completed another Phase-3 clinical trial as the leuco-methylthioninium (LMT) bis(hydromethanesulfonate) (LMTM) for the treatment of Alzheimer's disease (AD). We aimed to elucidate the detailed mechanisms of MB as a tau aggregation inhibitor, focusing on tau monomers and fibrils. Methylthioninium (MT + ) oxidized the Cys residues of tau proteins (2N4R, K18, and dGAE) to disulfide bonds by utilizing O 2 molecule, independent of the MB redox-cycle between MT + and LMT. The previous MB-mediated 1 H- 15 N heteronuclear single quantum coherence (HSQC) peak-broadenings in a large area (&gt; 60 residues) of 2N4R likely resort to disulfide-centered hydrogen bonds, specific long-range interactions with the NH, and the basic side-chains in solution. In addition, at least two different MB binding sites were identified for dGAE fibrils, and the different K i values of MT + and LMT were measured against thioflavin-T (ThT), which could serve as a molecular basis to support the disaggregation activity of MB. Methylene blue ; Mechanism (biology) ; Computer science ; Psychology ; Chemistry ; Epistemology ; Philosophy ; Biochemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research
Authors: Sean M. McCarty
Source: Deep Blue (University of Michigan) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

3D Islet Assay Development and SAR of Chromenones for Beta-Cell Replenishment Diabetes poses a global health crisis affecting individuals across age groups and backgrounds, with a prevalence estimate of 700 million people worldwide by 2045. Current therapeutic strategies primarily rely on insulin therapy or hypoglycemic agents, which fail to address the root cause of the disease - the loss of pancreatic insulin-producing beta-cells. Therefore, bioassays that recapitulate intact islets are needed to enable drug discovery for beta-cell replenishment, protection from beta-cell loss, and islet-cell interactions. Standard cancer insulinoma beta-cell lines MIN6 and INS-1 have been used to interrogate beta-cell metabolic pathways and function but are not suitable for studying proliferative effects. Screening using primary human/rodent intact islets offers a higher level of physiological relevance to enhance diabetes drug discovery and development. However, the three-dimensionality of intact islets has presented challenges in developing robust, high-throughput assays to detect beta-cell proliferative effects. Established methods rely on either dissociated islet cells plated in 2D monolayer cultures for imaging or reconstituted pseudo-islets formed in round bottom plates to achieve homogeneity. These approaches have significant limitations due to the islet cell dispersion process. To address these limitations, we have developed a robust, intact pancreatic islet imaging platform in 384-well format that is capable of detecting diabetes-relevant endpoints including beta-cell proliferation, chemoprotection, and islet spatial morphometrics. We were then able to apply our method to a preliminary in vivo study of a new scaffold for beta-cell modulation. Islets were isolated from the pancreata of mice after a week of exposure to our compound, GNF-9228. This method proved to be a drastic improvement over classical histological analysis for in vivo beta-cell proliferation and encouraged us to pursue a structure-activity relationship campaign to improve the physical properties of our lead compound. Subsequent work led to the evaluation of over seventy compounds in bio various assays. Additionally, we were able to leverage existing scRNA seq data from Type 2 diabetes samples to evaluate our compound’s effects on the human kinome. Islet ; Beta cell ; BETA (programming language) ; Computer science ; Biology ; Endocrinology ; Diabetes mellitus ; Programming language Photochromic and Fluorescence Chemistry ; Synthesis and Characterization of Heterocyclic Compounds ; RNA Interference and Gene Delivery