jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moharam Habibnejad Korayem | Ali Asghar Farid | Rouzbeh Nouhi Hefzabad
Source: Beilstein Journal of Nanotechnology (article, 2020-01-13)
Field: Materials Science — Materials Chemistry

Nonclassical dynamic modeling of nano/microparticles during nanomanipulation processes Since the manipulation of particles using atomic force microscopy is not observable in real-time, modeling the manipulation process is of notable importance, enabling us to investigate the dynamical behavior of nanoparticles. To model this process, previous studies employed classical continuum mechanics and molecular dynamics simulations which had certain limitations; the former does not consider size effects at the nanoscale while the latter is time consuming and faces computational restrictions. To optimize accuracy and computational costs, a new nonclassical modeling of the nanomanipulation process based on the modified couple stress theory is proposed that includes the size effects. To this end, after simulating the critical times and forces that are required for the onset of nanoparticle motion on the substrate, along with the dominant motion mode, the nonclassical theory of continuum mechanics and a developed von Mises yield criterion are employed to investigate the dynamical behavior of a cylindrical gold nanoparticle during manipulation. Timoshenko and Euler-Bernoulli beam theories based on the modified couple stress theory are used to model the dynamics of cylindrical gold nanoparticles while the finite element method is utilized to solve the governing equations of motion. The results show a difference of 90% between the classical and nonclassical models in predicting the maximum deflection before the beginning of the dominant mode and a difference of more than 25% in the dynamic modeling of a 200 nm manipulation of a gold nanoparticle with a length of 25 µm and aspect ratio of 30. This difference increases with each increment of the aspect ratio and reduction of manipulation distance. Furthermore, by applying an extended von Mises criterion on the modified couple stress theory, it is found that the failure aspect ratio of a cylindrical gold nanoparticle based on nonclassical models is 212% more than that of the classical model. In the end, the results are compared with those of the classical method on polystyrene nanorods. The results for cylindrical gold nanoparticles indicate that the material length scale has a major effect on the exact positioning of cylindrical nanoparticles. Continuum mechanics ; Timoshenko beam theory ; von Mises yield criterion ; Finite element method ; Nanoparticle ; Mechanics ; Equations of motion ; Classical mechanics ; Materials science ; Nanotechnology ; Physics ; Statistical physics ; Thermodynamics Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Cellular Mechanics and Interactions
Authors: Olivier Paquet‐Durand | Viktoria Zettel | Abdolrahim Yousefi‐Darani | Bernd Hitzmann
Source: Processes (article, 2020-12-17)
Field: Chemistry — Analytical Chemistry

The Supervision of Dough Fermentation Using Image Analysis Complemented by a Continuous Discrete Extended Kalman Filter Dough fermentation is an important step during the preparation of fermented baking goods. For the supervision of dough fermentation, a continuous-discrete extended Kalman filter was applied, which uses an image analysis system as the measurement. By estimation a fixed number of gas bubbles inside the dough, the radius of an average bubble was determined. A mathematical dough model was used by the extended Kalman filter to estimate the radius of the average bubble, the CO2 concentration of the non-gas dough phase and the number of CO2 molecules in the average bubble. During a fermentation of 50 min, the extended Kalman filter estimated that the average radius increased from 50 µm to 127 µm, the CO2 concentration in the non-gas dough increased to 23 mol/m³, and the CO2 amount in the bubble increased from 0.1 × 10−10 to 4 × 10−10 mol. Also, the specific CO2 production rate was estimated to be in the range from 1.5 × 10−3 to more than 4 × 10−3 mol·m³/kg/s. The advantages of an extended Kalman filter for the supervision of the dough fermentation process are discussed. Kalman filter ; Fermentation ; Bubble ; RADIUS ; Mathematics ; Filter (signal processing) ; Materials science ; Biological system ; Analytical Chemistry (journal) ; Food science ; Chemistry ; Chromatography ; Mechanics ; Statistics ; Physics ; Computer science ; Biology ; Computer vision Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: M. M. M. Elshamy | A. N. Tiraturyan
Source: Russian Journal of Building Construction and Architecture (article, 2020-10-30)
Field: Materials Science — Materials Chemistry

USING APPLICATION OF AN ARTIFICIAL NEURAL NETWORK SYSTEM TO BACKCALCULATE PAVEMENT ELASTIC MODULUS Statement of the problem. The article is devoted to the use of artificial neural networks in solving the problems of processing the results of instrumental recording of bowls of flexible pavement deflections using FWD shock loading settings. Results. The analysis was carried out, the shortcomings of the existing processing methods were noted, in particular the “backcalculation” method, which consists of a long calculation time, and the instability of the results obtained. The structure of the artificial neural network was built to determine the elastic moduli of the pavement layers. Training of an artificial neural network was carried out using the method of back propagation of error. Conclusions. The developed neural network has shown good results in training on the test data set, as well as high accuracy of prediction of the elastic moduli of the pavement. Artificial neural network ; Computer science ; Set (abstract data type) ; Elastic modulus ; Backpropagation ; Structural engineering ; Moduli ; Artificial intelligence ; Algorithm ; Engineering ; Materials science ; Composite material Structural mechanics and materials ; Geodetic Measurements and Engineering Structures ; Material Properties and Failure Mechanisms
Authors: Alessandro Massaro
Source:  (preprint, 2020-09-03)
Field: Chemistry — Analytical Chemistry

Blockchain and Technologies Matching with the Case of Study of Vegetables Production &lt;div&gt;Alessandro Massaro: Professor Alessandro Massaro (ING/INF/01, FIS/01,FIS/03) carried out scientific research &lt;/div&gt;&lt;div&gt;at the Polytechnic University of Marche, at CNR, and at Italian Institute of Technology (IIT) as Team Leader by activating laboratories for nanocomposite sensors for &lt;/div&gt;&lt;div&gt;industrial robotics. He is in MIUR register as scientific expert in competitive Industrial Research and social &lt;/div&gt;&lt;div&gt;development, and he is currently head of the Research and Development section and scientific director of MIUR Research Institute Dyrecta Lab Srl. Member of the International Scientific Committee of Measurers IMEKO and IEEE Senior member, recently received an award from the National Council of Engineers as Best Engineer of Italy 2018 (Top Young Engineer 2018). &lt;/div&gt; Management ; Engineering ; Engineering management ; Political science ; Economics Spectroscopy and Chemometric Analyses ; Economic and Technological Innovation ; Blockchain Technology Applications and Security
Authors: Manuel Eugenio Morocho-Cayamcela | Wansu Lim
Source: Electronics Letters (article, 2020-09-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lateral confinement of high‐impedance surface‐waves through reinforcement learning The authors present a model‐free policy‐based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward when the patch sizes allow the transmission along a constrained path and penalties when the patch sizes make the surface wave radiate to the sides of the metamaterial. After convergence, the proposed model learns an optimal patch pattern that achieves lateral confinement along the metasurface. Simulation results show that the proposed learned‐pattern can effectively guide the electromagnetic wave through a metasurface, maintaining its instantaneous eigenstate when the homogeneity is perturbed. Moreover, the pattern learned to prevent reflections by changing the patch sizes adiabatically. The reflection coefficient shows that most of the power gets transferred from the source to the destination with the proposed design. Metamaterial ; Reinforcement learning ; Electrical impedance ; Computer science ; Convergence (economics) ; Reflection coefficient ; Acoustics ; Path (computing) ; Reflection (computer programming) ; Transmission (telecommunications) ; Eigenvalues and eigenvectors ; Transmission coefficient ; Surface (topology) ; Physics ; Topology (electrical circuits) ; Optics ; Mathematics ; Geometry ; Engineering ; Telecommunications ; Artificial intelligence ; Electrical engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Desalegn Atalie | Rotich K. Gideon
Source: Tekstilec (article, 2020-03-05)
Field: Materials Science — Polymers and Plastics

Prediction of Psychological Comfort Properties of 100% Cotton Plain Woven Fabrics made from Yarns with Different Parameters The psychological satisfaction of the textile customer is the fi rst criteria used to evaluate clothing and a lack of aesthetics, while fashionability and physical appearance contribute to the psychological discomfort of users. Either inherently or due to processing, cotton cloths demonstrate diff erent psychological comfort behaviours. Manufacturers must therefore produce fabrics with optimum psychological comfort parameters. The objective of this research was to study the eff ect of cotton yarn parameters on the psychological comfort properties of woven fabrics. Four woven fabrics were produced from cotton yarns with diff erent yarn twists, yarn counts, strengths and yarn elongations. Psychological comfort parameters such as wrinkle, drape, crease, bending modules and fl exural rigidity were measured and analysed in accordance with the ES ISO 9867, ISO 9073-9, ES ISO 2313 and ASTM D1388-18 standards, respectively. Multiple regression equations were developed to predict the comfort properties of clothing in relation to yarn parameters. A statistical analysis showed that the wrinkle recovery and drapeability of fabrics were signifi cantly aff ected by yarn twist, count and tenacity, and the elongation of yarns. However, yarn twist, count, tenacity and elongation had an insignifi cant eff ect on the crease recovery of woven samples at an F-value of 3.546 and a P-value of 0.069. The stiff ness properties of the fabrics such as fl exural rigidity and bending modules also showed insignificant diff erence between samples at F = 38487.969, P = 0.057 and F = 25.506, P = 0.055 respectively. A multiple regression analysis showed a positive correlation between yarn parameters (factors) and response, with Adj. R 2 of 0.0998, Adj. R 2 of 0.975 and Adj. R 2 of 1 for crease recovery, wrinkle recovery and drape coeffi cient, respectively. The equations developed are helpful to fabric manufacturers in sourcing yarns with specifi c parameters to produce the desired comfort level in a fabric. Woven fabric ; Composite material ; Materials science Textile materials and evaluations ; Color perception and design ; Ergonomics and Musculoskeletal Disorders
Authors: Xijun Liang | Zhonghang Xia | Ling Jian | Yongxiang Wang | Xinnan Niu | Andrew J. Link
Source: BMC Genomics (article, 2020-04-25)
Field: Chemistry — Spectroscopy

A cost-sensitive online learning method for peptide identification The new model can reduce its false discovery rate on datasets with a distribution of unbalanced PSMs. Experimental studies show that OLCS-Ranker outperforms other methods in terms of accuracy and stability, especially on datasets with a distribution of unbalanced PSMs. Furthermore, OLCS-Ranker is 15-85 times faster than CRanker. Overfitting ; Computer science ; Machine learning ; Identification (biology) ; Artificial intelligence ; False discovery rate ; Stability (learning theory) ; Kernel (algebra) ; Data mining ; Mathematics ; Biology ; Artificial neural network Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: Qianning Liu | Qingsong Shan
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A Stochastic Analysis of the One Compartment Pharmacokinetic Model Considering Optimal Controls First-order 1-compartment pharmacokinetic model for extravascular administered drugs can be used to derive many useful quantities by comparing the predicted values with actual data. However, less research has been done in actually formulating them as optimal control problems. Moreover, real pharmacological processes are always exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the estimation of PK/PD models' (pharmacokinetic/pharmacodynamic models') parameters and the derived conclusions.Therefore there is an increasing need to extend the deterministic models to models including a stochastic component. In our study, we modify the 1-compartment pharmacokinetic model to a stochastic differential equation model based on an optimal control problem. A schedule of optimal dosing and timing has been given from our proposed model. Compartment (ship) ; Computer science ; Schedule ; Stochastic modelling ; Optimal control ; Pharmacokinetics ; Mathematical optimization ; Component (thermodynamics) ; Stochastic differential equation ; Applied mathematics ; Mathematics ; Statistics ; Medicine ; Pharmacology Lanthanide and Transition Metal Complexes ; Statistical Methods in Clinical Trials ; Electrolyte and hormonal disorders
Authors: Bo Zhao | Ye Wang | Jun Fu | Rongqiang Zhao | Yashuo Li | Xin Dong | Chengxu Lv | Hanlu Jiang
Source: Journal of Sensors (article, 2020-01-13)
Field: Chemistry — Analytical Chemistry

Online Measuring and Size Sorting for Perillae Based on Machine Vision Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach. Sorting ; Computer science ; Matching (statistics) ; Machine vision ; Artificial intelligence ; Point (geometry) ; Variance (accounting) ; Statistics ; Computer vision ; Algorithm ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hyunseok Oh | Jiwon Yun | M. H. Abobeih | Kyung-Hoon Jung | Kiho Kim | T. H. Taminiau | Dohun Kim
Source: Scientific Reports (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Algorithmic decomposition for efficient multiple nuclear spin detection in diamond Efficiently detecting and characterizing individual spins in solid-state hosts is an essential step to expand the fields of quantum sensing and quantum information processing. While selective detection and control of a few <sup>13</sup>C nuclear spins in diamond have been demonstrated using the electron spin of nitrogen-vacancy (NV) centers, a reliable, efficient, and automatic characterization method is desired. Here, we develop an automated algorithmic method for decomposing spectral data to identify and characterize multiple nuclear spins in diamond. We demonstrate efficient nuclear spin identification and accurate reproduction of hyperfine interaction components for both virtual and experimental nuclear spectroscopy data. We conduct a systematic analysis of this methodology and discuss the range of hyperfine interaction components of each nuclear spin that the method can efficiently detect. The result demonstrates a systematic approach that automatically detects nuclear spins with the aid of computational methods, facilitating the future scalability of devices. Spins ; Hyperfine structure ; Diamond ; Spin (aerodynamics) ; Quantum ; Scalability ; Spin engineering Diamond and Carbon-based Materials Research ; Chemical and Physical Properties of Materials ; Quantum and electron transport phenomena
Authors: Ye Fei | Yunpeng Liu | Jiangbo Chen | Lin Cheng | Mengqi Li | Yi Liu | Minfeng Shao
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The Attenuation and Propagation Law of Ultrasonic Wave in UHV Gas Insulated Line Gas insulated transmission line (GIL) is widely used for power transmission in special geographical environment such as river crossing, mountain crossing and city tunnel crossing scenarios. Because of its totally enclosed structure, it is difficult to locate the fault position when the internal breakdown discharge occurs. Fault location by using the ultrasonic wave aroused by discharge is an efficient method for GIL maintenance. This paper reports the experimental and theoretical results of attenuation and propagation law of ultrasonic signal in GIL. An ultra-high voltage (UHV) GIL model was set up to simulate the geometry and material conditions of real GIL. The common lead breaking signal was adopted to generate ultrasonic source, and the propagation velocity and attenuation coefficient of ultrasonic wave were obtained. The experimental results show that the attenuation coefficient of ultrasonic wave on GIL linear unit is about 0.234 dB/m, and its propagation velocity is about 3.3 km/s. But the attenuation level of ultrasonic wave is much higher when it passes through the epoxy insulators or expansion joints. The validity of the method is verified by the creeping discharge test on the GIL model. In order to analyze the propagation path of ultrasonic wave, the relationship between the path and time is calculated and the shortest propagation time is obtained. The results of this research provide support for the fault location of GIL by ultrasonic wave. Attenuation ; Ultrasonic sensor ; Acoustics ; Fault (geology) ; Wave propagation ; Electric power transmission ; Transmission line ; Attenuation coefficient ; Electrical engineering ; Physics ; Materials science ; Optics ; Engineering ; Geology ; Seismology High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Aerosol Filtration and Electrostatic Precipitation
Authors: Zhenpeng Yao | Benjamín Sánchez-Lengeling | N. Scott Bobbitt | Benjamin J. Bucior | Sai Govind Hari Kumar | Sean P. Collins | Thomas Burns | Tom K. Woo | Omar K. Farha | Randall Q. Snurr | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-08-25)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks (i.e., nodes and linkers) in different topologies. Many of them have high internal surface areas and other desirable properties for gas storage, separation, and other applications. The notable variety of the possible building blocks and the diverse ways they can be assembled endow reticular frameworks with a near-infinite combinatorial design space, making reticular chemistry both promising and challenging for prospective materials design. Here, we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder (SmVAE) for the generative design of reticular materials with desired functions. We demonstrate the automated design process with a class of metal-organic framework (MOF) structures and the goal of separating CO2 from natural gas or flue gas. Our model exhibits high fidelity in capturing structural features and reconstructing MOF structures. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported. This platform lays the groundwork for the design of reticular frameworks for desired applications. Autoencoder ; Computer science ; Reticular connective tissue ; Nanoporous ; Nanotechnology ; Materials science ; Artificial intelligence ; Deep learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Makoto Fukuda | Hiroki Yoshinoto | Hitoshi Saomoto | Kiyotaka Sakai
Source: Preprints.org (preprint, 2020-08-06)
Field: Materials Science — Biomaterials

Validity of Three-Dimensional Tortuous Pore Structure and Fouling of Hemoconcentration Capillary Membrane Using the Tortuous Pore Diffusion Model and Scanning Probe Microscopy Hemoconcentration membranes used in cardiopulmonary bypass require a pore structure design with high pure water permeability, and which does not allow protein adsorption and useful protein loss. However, studies on hemoconcentration membranes have not been conducted yet. The purpose of this study was to analyze three-dimensional pore structures and protein fouling before and after blood contact with capillary membranes using the tortuous pore diffusion model and a scanning probe microscope system. We examined two commercially available capillary membranes of similar polymer composition that are successfully used in hemoconcentration clinically. Assuming the conditions of actual use in cardiopulmonary bypass, we perfused these membranes with bovine blood. Pure water permeability before and after bovine blood perfusion was measured using the dead-end filtration. The scanning probe microscopy system was used for analysis. High-resolution three-dimensional pore structures on the inner surface of the membranes were observed before blood contact. On the other hand, pore structures after blood contact could not be observed due to protein fouling. The pore diameters calculated by the tortuous pore diffusion model and scanning probe microscopy were mostly similar and could be validated reciprocally. Achievable pure water permeabilities showed no difference despite protein fouling, leading to low values of albumin SC. This is due to the mechanism that protein fouling occurs on the membrane surface, while there is little internal pore blocking. Therefore, controlling the fouling is essential for membranes in medical use. These characteristics of the hemoconcentration membranes examined in this study are suitable for clinical use. Hemoconcentration ; Membrane ; Fouling ; Chemistry ; Capillary action ; Chromatography ; Contact angle ; Membrane fouling ; Biofouling ; Protein adsorption ; Permeability (electromagnetism) ; Diffusion ; Chemical engineering ; Filtration (mathematics) ; Membrane permeability ; Adsorption ; Analytical Chemistry (journal) ; Materials science ; Composite material ; Biochemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Tissue Engineering and Regenerative Medicine