jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingfan Chen | Muzhaozi Yuan | Caitlin A. Madison | Shoshana Eitan | Ya Wang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-24)
Field: Materials Science — Biomaterials

Blood-brain Barrier Crossing using Magnetic Stimulated Nanoparticles ABSTRACT Due to the low permeability and high selectivity of the blood-brain barrier (BBB), existing brain therapeutic technologies are limited by the inefficient BBB crossing of conventional drugs. Magnetic nanoparticles (MNPs) have shown great potential as nano-carriers for efficient BBB crossing under the external static magnetic field (SMF). To quantify the impact of SMF on MNPs’ in vivo dynamics towards BBB crossing, we developed a physiologically based pharmacokinetic (PBPK) model for intraperitoneal (IP) injected superparamagnetic iron oxide nanoparticles coated by gold and conjugated with poly(ethylene glycol) (PEG) (SPIO-Au-PEG NPs) in mice. Unlike most reported PBPK models that ignore brain permeability, we first obtained the brain permeabilities with and without SMF by determining the concentration of SPIO-Au-PEG NPs in the cerebral blood and brain tissue. This concentration in the brain was simulated by the advection-diffusion equations and was numerically solved in COMSOL Multiphysics. The results from the PBPK model after incorporating the brain permeability showed a good agreement (regression coefficient R 2 = 0.825) with the in vivo results, verifying the capability of using the proposed PBPK model to predict the in vivo biodistribution of SPIO-Au-PEG NPs under the exposure to SMF. Furthermore, the in vivo results revealed that the brain bioavailability under the exposure to SMF (4.01%) is slightly better than the control group (3.68%). In addition, the modification of SPIO-Au-PEG NPs with insulin (SPIO-Au-PEG-insulin) showed an improvement of the brain bioavailability by 24.47 % in comparison to the non-insulin group. With the SMF stimulation, the brain bioavailability of SPIO-Au-PEG-insulin was further improved by 3.91 % compared to the group without SMF. In vivo ; Biodistribution ; Bioavailability ; Blood–brain barrier ; Pharmacokinetics ; Chemistry ; PEG ratio ; Physiologically based pharmacokinetic modelling ; Nanoparticle ; Permeability (electromagnetism) ; Nanomedicine ; Biophysics ; Pharmacology ; Materials science ; Biomedical engineering ; Nanotechnology ; In vitro ; Membrane ; Biochemistry ; Internal medicine ; Medicine Nanoparticle-Based Drug Delivery ; Advanced Neuroimaging Techniques and Applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Jingjing Si | G. Li | Yinbo Cheng | Rui Zhang | Godwin Enemali | Chang Liu
Source: ResearchOnline (Glasgow Caledonian University) (preprint, 2021-06-05)
Field: Chemistry — Spectroscopy

Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions. Region of interest ; Convolutional neural network ; Artificial intelligence ; Image resolution ; Computer science ; Iterative reconstruction ; Tomographic reconstruction ; Tomography ; Projection (relational algebra) ; Computer vision ; Temporal resolution ; Materials science ; Algorithm ; Optics ; Physics Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Photoacoustic and Ultrasonic Imaging
Authors: Bin Xu | Chuanli Wang | Liping Wang | Yingsong Cheng | Xiumiao Yang
Source: AIP Advances (article, 2021-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design, modeling, and analysis of giant magnetostrictive injector In view of the shortcomings of the traditional high-pressure common rail electronically controlled injector in needle valve driving mode and driving components, a direct-drive type giant magnetostrictive injector (GMI) is designed. Based on the working principle of the GMI, the magnetic field model, hysteresis nonlinear model, and magnetostrictive model are established. The magnetic field model and hysteresis nonlinear model are simulated and analyzed, and the GMA magnetostrictive model is solved numerically and verified experimentally. Finally, COMSOL Multiphysics software is used to conduct electro-magnetic-mechanical coupling simulation analysis on the whole GMI output model, and the relationship between the needle valve lift of the injector and the output displacement of the actuator was obtained. The research results show that the displacement curve calculated using the GMA magnetostrictive model is basically consistent with the displacement curve measured by experiment, which proves that the established model can reflect the actual situation and verify the correctness of the model. The magnitude and direction of the GMI needle valve lift are determined by the magnitude and direction of GMA output displacement. The two directions are opposite, and the size ratio is 4, so when the current is 6 A, the GMA output displacement is 0.12 mm, and the needle valve lift is 0.48 mm, which meets the requirements of the injector needle valve lift, verifies the feasibility of GMI structure design, and has good theoretical and practical guiding values. Magnetostriction ; Injector ; Multiphysics ; Nonlinear system ; Displacement (psychology) ; Lift (data mining) ; Magnetic field ; Mechanics ; Hysteresis ; Materials science ; Actuator ; Acoustics ; Mechanical engineering ; Engineering ; Finite element method ; Structural engineering ; Physics ; Computer science ; Electrical engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Bearings and Levitation Dynamics
Authors: Sümeyye Üstüntağ
Source: TEKSTİL VE KONFEKSİYON (article, 2021-08-17)
Field: Materials Science — Polymers and Plastics

Optimization of Draft and Twist Values for Improvement of Breaking Strength and Elongation Properties of Dual-Core Yarns Dual-core yarns, which are developed to have high elasticity and strength properties at the same time in the yarn structure, have been the focus of interest in yarn sector. Considering the purpose of developing dual-core yarns, it is obvious that the breaking strength and elongation performances expected from these yarns are very important. In this paper, the twist coefficient and PET (Polyethylene terephthalate)/Elastane draft ratios of dual-core yarns were optimized by Taguchi Method for improved of breaking strength and elongation performance. The signal to noise ratio (SNR) was calculated for each output and optimum levels were determined over these ratios. As a result of the study, it was determined that PET draft was the most effective parameter for both outputs. Considering the individual optimum levels determined for each output, common factor-level combinations covering both outputs were determined. Consequently, optimal common combination for the multi-performances was found as 4.2 twist coefficient, 1.15 PET draft ratio and 3.8 elastane ratio. Breaking strength ; Elongation ; Yarn ; Taguchi methods ; Twist ; Materials science ; Polyethylene terephthalate ; Structural engineering ; Core (optical fiber) ; Hull ; Composite material ; Engineering ; Mathematics ; Ultimate tensile strength ; Geometry Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Mechanical Behavior of Composites
Authors: Sven Degroeve | Ralf Gabriels | Kevin Velghe | Robbin Bouwmeester | Natalia Tichshenko | Lennart Martens
Source: Research Square (preprint, 2021-08-19)
Field: Chemistry — Spectroscopy

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification Abstract Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification. Computer science ; Merge (version control) ; Artificial intelligence ; Fragmentation (computing) ; Identification (biology) ; Machine learning ; Peptide ; Data mining ; Chemistry ; Information retrieval Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: G. Udhaya Sankar | C. Ganesa Moorthy | C. T. RAMASAMY | Raj Kumar G.
Source: International Journal of Energy Applications and Technologies (review, 2021-06-30)
Field: Materials Science — Materials Chemistry

A review on recent opportunities in MATLAB software based modelling for thermoelectric applications TThe thermoelectric application is one of most popular energy harvesting application from waste heat. The thermoelectric generators, thermoelectric coolers, and thermoelectric modular devices criterion comes in both sustainable energy as well as renewability of electrical energy from waste heat sinks. The recovery operations are optimized with the help of modeling using MATLAB software. The material science based thermoelectric applications can be modeled with the help of MATLAB simulink modeling. The numerical and algorithmic method of MATLAB modeling is for the development of hybrid thermoelectric coolers and generators (solar thermoelectric generators, radiative cooler, heat sinks). Later, the use of MATLAB software gives opportunities to develop the cost effective and high power thermoelectric generators. The emerging commercial device making is also discussed for thermoelectric generator using MATLAB optimization. MATLAB ; Thermoelectric generator ; Thermoelectric effect ; Thermoelectric cooling ; Software ; Heat sink ; Modular design ; Computer science ; Thermoelectric materials ; Waste heat ; Mechanical engineering ; Process engineering ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Md. Najmol Hoque | M. D. Fuad Mondal | G. H. M. Sagor | Md. Mehedi Hasan | Abdul Hannan
Source: Journal of Bio-Science (review, 2021-08-04)
Field: Chemistry — Organic Chemistry

Inclusive Scenario of Natural Products Obtained From Agar Plant (Aquilaria SP.) In Bangladesh- A Review Agarwood is the resinous heartwood obtained from the injured parts of trees under Thymelaeaceae family particularly from Aquilaria species. Agarwood is considered as the most prized non-timber forest product (NTFP) used in attars as well as medicines. Quality of agarwood is the determiner for defining its commercial value. Different countries use different grading system to explain the quality of agarwood. In Bangladesh, more than 45 compounds have been identified so far, mostly sesquiterpenoids, alkanes, fatty acid and other volatile aromatic compounds that are responsible for its fragrance and high prices. Ether extract, total phenolic contents, flavonoid, antioxidant and microbiological tests have shown tremendous positive results. Chemical profiling of agar products is the best identified using GC-MS technique by the various authors. Present review discusses the full scenario of Aquilaria production, inoculation, extraction methods, determination of agarwood quality and chemical constituents of agar oils and possibilities and barriers of this industry in Bangladesh. J. Bio-Sci. 29(2): 151-173, 2021 (December) Agarwood ; Chemical constituents ; Food science ; Agar ; Traditional medicine ; Biology ; Petroleum ether ; Chemistry ; Horticulture ; Botany ; Extraction (chemistry) ; Organic chemistry ; Chromatography ; Medicine ; Bacteria Wood and Agarwood Research ; Identification and Quantification in Food ; Advanced Image and Video Retrieval Techniques
Authors: Mohsen Zandi | Ali Ganjloo | Mandana Bimakr
Source: Food Science and Technology (article, 2021-12-01)
Field: Chemistry — Analytical Chemistry

Ohmic assisted hydrodistillation extraction of musk willow (Salix aegyptiaca L.) essential oil and artificial neural network modeling of extraction kinetic Ohmic assisted hydrodistillation extraction of musk willow (Salix aegyptiaca L.) essential oil and artificial neural network modeling of extraction kinetic Willow ; Extraction (chemistry) ; Botany ; Chemistry ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity
Authors: Rodolfo Ramos Castelo Branco | Ketinlly Yasmyne Nascimento Martins | Anna Kellssya Leite Filgueira | Eduardo Jorge Oliveira Valadares | Kátia Elizabete Galdino | Misael Elias de Morais | Maria das Graças Ouriques Ramos | Núbia do Nascimento Martins | Khelvyn Yhasley Nascimento Martins | Jonh Kennedy Guedes Rodrigues
Source: Research Society and Development (article, 2021-07-15)
Field: Materials Science — Biomaterials

Caracterização da performance do material Poliácido Lático (PLA) manufaturado pela tecnologia de Modelagem de Fusão e Deposição (FDM) O objetivo deste estudo foi caracterizar a performance do material poli ácido lático (PLA) quando manufaturado pela tecnologia modelagem por fusão e deposição (FDM). Para a elaboração e organização do procedimento experimental, este estudo foi construído de acordo com a norma ASTM D3039/3039-95A, utilizando um total de 36 corpos de prova, modelados através do software CAD Autodesk Inventor e, posteriormente, exportados para o software CAM Makerware, onde definiu-se os parâmetros e processos para a impressão 3D. Os ensaios de tração foram realizados na máquina EMIC DL 3000, mantendo uma condição padrão com o laboratório a uma temperatura em torno de 20°C. Os valores definidos como significativos para este estudo foram: Tensão Máxima; Tensão de Ruptura e Módulo de Elasticidade. Os corpos de prova manufaturados apresentaram comportamento anisotrópico. Nos testes de tensão-deformação, as rupturas ocorreram em perfeito rompimento de perfil. As curvas tensão-deformação apresentaram-se praticamente sem área de escoamento. As taxas de deposição de 100% para o eixo X demonstraram maiores índices para tensão de ruptura em contraposto as taxas de deposição de 20% do eixo Y. No que se refere ao alongamento, as peças com 20% de taxa de deposição no eixo X apresentaram maiores índices. As propriedades mecânicas, resistência à tração e resistência à flexão, aumentaram conforme o preenchimento. O PLA, manufaturado pelo processo de FDM, demonstrou melhor performance mecânica quando produzido em eixo X com taxas de deposição de 100% para o preenchimento interno estrutural e temperatura de extrusão de 200°C. Caracteriza-se como típico frágil polimérico. Physics ; Materials science biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution
Authors: K. Supriya
Source: International Journal of Current Microbiology and Applied Sciences (article, 2021-01-10)
Field: Chemistry — Analytical Chemistry

A Study on the Performance of the ARIMAX-ANN Hybrid Forecasting Model over the other Time Series Forecasting Models ARIMAX and ANN in Forecasting the Rice Yield Rice being the staple food for more than 50% of the world population, it is very essential to forecast the production of rice so as to meet the need of the rapidly growing population. Forecasting is also essential for better planning and decision making. Many forecasting techniques have evolved and it is the matter of prediction accuracy. In this study, the performance of ARIMAX-ANN hybrid forecasting model is compared with two other time series forecasting models, Autoregressive Integrated moving average with exogenous variables (ARIMAX) and Artificial Neural Networks (ANN) in forecasting the rice yield during both kharif and rabi seasons of Telangana state. The exogenous variables used in the study are percentage of dead hearts and percentage of white ears which are the damage symptoms of rice yield due to yellow stem borer (<em>Scirpophaga incertulas</em>). To compare the effectiveness of these three models 27 years rice yield data of both kharif and rabi seasons pertaining telangana state was used i.e., from 1990-2016 (both years inclusive). The results showed that ARIMAX-ANN hybrid model performed reasonably well compared to the other models i.e., Autoregressive integrated moving average model (ARIMA) and Autoregressive integrated moving average model with exogenous variables (ARIMAX). Yield (engineering) ; Series (stratigraphy) ; Time series ; Econometrics ; Computer science ; Mathematics ; Statistics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Agricultural Economics and Practices
Authors: И. В. Богачев
Source: Applied Sciences (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Determination of Prestress in Circular Inhomogeneous Solid and Annular Plates in the Framework of the Timoshenko Hypotheses Determination of prestress fields in structures is of the utmost importance, since they have a significant impact on operational characteristics, and their level and distribution must be strictly controlled. In this paper, we present modeling of bending vibrations of solid and annular round inhomogeneous prestressed plates within the framework of the Timoshenko hypotheses. New inverse problems of prestress identification in plates are studied on the basis of the acoustic response subjected to some probing load. To solve direct problems on calculating oscillations and amplitude-frequency characteristics, a computational Galerkin-method-based scheme has been developed. In order to treat the inverse problems, we use a special projection approach based on the constructed weak problems statements, which makes it possible to determine the desired characteristics in the given classes of functions. The developed techniques for solving direct problems are implemented in the form of software packages realized via Maple. For both solid and annular plates, we estimate the sensitivity of the amplitude-frequency characteristics the values of which are used as the additional data in the inverse problems to a change in the prestress level; we conclude that the most favorable frequency range should be selected in the resonance vicinity. We have conducted a series of computational tests on reconstructing the plate’s prestresses of various levels and distribution patterns (decreasing, increasing, sign-changing laws). The results of computational tests revealed that the technique developed allows for the determination of the prestresses with a low error for two cases: when the cause of prestress formation and its type are known and when arbitrary prestress changing laws are considered. Galerkin method ; Inverse ; Inverse problem ; Maple ; Vibration ; Range (aeronautics) ; Computer science ; Amplitude ; Bending ; Applied mathematics ; Structural engineering ; Algorithm ; Finite element method ; Mathematics ; Mathematical analysis ; Acoustics ; Materials science ; Geometry ; Physics ; Engineering Material Properties and Failure Mechanisms ; Structural mechanics and materials ; Engineering Diagnostics and Reliability
Authors: Bruno Albuquerque de Castro | Guilherme Beraldi Lucas | Gabriel Scota Fernandes | José Renato Castro Pompéia Fraga | Rudolf Ribeiro Riehl | André Luiz Andreoli
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Assessment of Partial Discharges Evolution in Bushing by Infrared Analysis The quality of power systems is related to their capability to predict failures, avoid stoppages, and increase the lifetime of their components. Therefore, science has been developing monitoring systems to identify failures in induction motors, transformers, and transmission lines. In this context, one of the most crucial components of the electrical systems is the insulation devices such as bushings, which are constantly subjected to dust, thermal stresses, moisture, etc. These conditions promote insulation deterioration, leading to the occurrence of partial discharges. Partial discharges are localized dielectric breakdown that emits ultra-violet radiation, heat, electromagnet, and acoustics waves. The most traditional techniques to identify these flaws on bushings are based on the current, ultra high frequency, and acoustic emission analysis. However, thermal analysis stands out as a noise-resistant technique to monitor several components in the power systems. Although the thermal method is applied to detect different types of faults, such as bad contacts, overloads, etc, this technique has not been previously applied to perform partial discharge detection and evaluate its evolution on bushings. Based on this issue, this article proposes two new indexes to characterize the discharge evolution based on the infrared thermal analysis: the area ratio coefficient and the Red, Green, and Blue (RGB) ratio coefficient. Seven discharge levels were induced in a contaminated bushing, and an infrared thermal camera captured 20 images per condition, totalizing 140 images. New coefficients were used to perform the identification of discharge evolution. Results indicated that values of the new indexes increase with the partial discharge activity. Thus, the new imaging processing approach can be a promising contribution to literature, improving the reliability and maintenance planning for power transmission systems. Bushing ; Partial discharge ; Infrared ; Acoustics ; Materials science ; Transformer ; Thermal ; Electrical engineering ; Insulation system ; Voltage ; Nuclear engineering ; Mechanical engineering ; Computer science ; Engineering ; Composite material ; Physics ; Optics High voltage insulation and dielectric phenomena ; Thermography and Photoacoustic Techniques ; Power Transformer Diagnostics and Insulation