jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elmer A. G. Peñaloza | Vilma A. Oliveira | Paulo E. Cruvinel
Source: Sensors (article, 2021-02-10)
Field: Chemistry — Analytical Chemistry

Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k-NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making. Sprayer ; Soft sensor ; Principal component analysis ; Quality (philosophy) ; Component (thermodynamics) ; Agricultural engineering ; Scale (ratio) ; Engineering ; Production (economics) ; Computer science ; Artificial intelligence ; Process (computing) ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Plant Surface Properties and Treatments
Authors: Yasmine Grooten | Quinten Marcelis | Debby Mangelings | Yvan Vander Heyden
Source: Journal of Chromatography A (article, 2021-12-16)
Field: Chemistry — Spectroscopy

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules Chemistry ; Chromatography ; Permeation ; Micellar liquid chromatography ; Permeability (electromagnetism) ; Particle size ; Volumetric flow rate ; Monolithic HPLC column ; Linear regression ; Analytical Chemistry (journal) ; High-performance liquid chromatography ; Micelle ; Membrane ; Organic chemistry ; Aqueous solution Analytical Chemistry and Chromatography ; Advancements in Transdermal Drug Delivery ; Analytical Methods in Pharmaceuticals
Authors: Ehtasham Mustafa | Regina Mária Németh | Ramy S. A. Afia | Zoltán Ádám Tamus
Source: Periodica Polytechnica Electrical Engineering and Computer Science (article, 2021-04-16)
Field: Materials Science — Materials Chemistry

Parameterization of Debye Model for Dielectrics Using Voltage Response Measurements and a Benchmark Problem The Voltage Response measurement since its introduction in the 1960s has been used successfully for the diagnostics of electrical insulation. The method is based on two quantities of decay and return voltage slopes and can be used to study the conduction and polarization processes inside the insulation. Extended Voltage Response method, being an advanced version of the Voltage Response measurement helps in further studying the polarization process by using a large polarization spectrum and hence dielectric relaxation processes. These dielectric relaxation processes can be modeled by the Debye model. Since as most of the techniques used for diagnostic purpose does not give the information about the conduction and polarization processes separately, it is difficult to determine the R-C parameters of the Debye model. The Voltage Response technique is very useful in this regard because of the two voltage slopes. The paper shows a novel experimental benchmark for testing the function fitting methodologies of the Voltage Response methodologies, which helps in determining the R-C parameters. Moreover, the problem can be used for testing the novel genetic, evolutionary algorithms, where benchmarking is an actual challenge. The proposed nonlinear function fitting method uses the genetic algorithms via the Ārtap framework, which lets it possible to select the most accurate optimization algorithm from the provided list of the algorithms and achieve better fitting precision, faster calculation time or more powerful processing ability. Debye ; Voltage ; Benchmark (surveying) ; Polarization (electrochemistry) ; Dielectric ; Computer science ; Nonlinear system ; Genetic algorithm ; Relaxation (psychology) ; Debye function ; Algorithm ; Electronic engineering ; Physics ; Debye length ; Engineering ; Electrical engineering ; Machine learning ; Chemistry ; Optoelectronics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical and Bioimpedance Tomography
Authors: Dionisio Badagliacco | A. Valenza
Source: Processes (article, 2021-10-14)
Field: Materials Science — Polymers and Plastics

Viscoelastic Behavior of an Epoxy Resin Modified with Recycled Waste Particles Analyzed through a Fractional Model It is well-known that the addition of randomly dispersed particles in polymers influences their linear viscoelastic behavior and dynamic mechanical properties. The aim of this study was to describe the viscoelastic behavior of an epoxy resin modified by waste glass and rubber particles using the linear fractional spring-pot model. Unlike complex classical exponential models, fractional models, being only two-parameter dependent, make it easier to characterize the viscoelastic behavior of materials. Isothermal relaxation and single frequency sweep temperature dynamic tests were carried out in a dynamic mechanical analyzer DMA150 by varying the content of the particles from 0 to 20% by weight. Overall, the results of this study evidence that using waste materials as additives for polymer compounds is a practical and sustainable possibility when it comes to modifying their viscoelastic properties. Viscoelasticity ; Epoxy ; Materials science ; Composite material ; Polymer ; Relaxation (psychology) ; Isothermal process ; Natural rubber ; Fractional calculus ; Dynamic mechanical analysis ; Exponential function ; Thermodynamics ; Mathematics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Asphalt Pavement Performance Evaluation
Authors: Vasileios Rantos | Kai Karius | Jan Kosiński
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-06)
Field: Materials Science — Materials Chemistry

Integrative structural modelling of macromolecular complexes using Assembline Abstract Integrative modelling enables structure determination of macromolecular complexes by combining data from multiple experimental sources such as X-ray crystallography, electron microscopy (EM), or crosslinking mass spectrometry (XL-MS). It is particularly useful for complexes not amenable to high-resolution EM—complexes that are flexible, heterogenous, or imaged in cells with cryo-electron tomography. We have recently developed an integrative modelling protocol that allowed us to model multi-megadalton complexes as large as the nuclear pore complex. Here, we describe the Assembline software package, which combines multiple programs and libraries with our own algorithms in a streamlined modelling pipeline. Assembline builds ensembles of models satisfying data from atomic structures or homology models, EM maps and other experimental data, and provides tools for their analysis. Comparing to other methods, Assembline enables efficient sampling of conformational space through a multi-step procedure, provides new modeling restraints, and includes a unique configuration system for setting up the modelling project. Our protocol achieves exhaustive sampling in less than 100 – 1,000 CPU-hours even for complexes in the megadalton range. For larger complexes, resources available in institutional or public computer clusters are needed and sufficient to run the protocol. We also provide step-by-step instructions for preparing the input, running the core modelling steps, and assessing modelling performance at any stage. Computer science ; Pipeline (software) ; Protocol (science) ; Software ; Range (aeronautics) ; Resolution (logic) ; Data mining ; Computational science ; Biological system ; Artificial intelligence ; Materials science ; Programming language Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; RNA modifications and cancer
Authors: Haoyun Li | Qiang Sun | Shunan Liu | Li Liu | Yinggang Shi
Source: Tehnicki vjesnik - Technical Gazette (article, 2021-08-17)
Field: Chemistry — Analytical Chemistry

A Novel Tomato Volume Measurement Method based on Machine Vision Density is one of the auxiliary indicators for judging the internal quality of tomatoes.However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately.To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision.The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model.When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%.Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant.Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement.In the end, the final experimental results showed that the proposed method achieved better measurement results.However, the volume measured by the two models is larger than the real volume of tomatoes.For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%. Volume (thermodynamics) ; Computer vision ; Machine vision ; Artificial intelligence ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yanni Tai | Haibin Qu | Xingchu Gong
Source: Separations (article, 2021-05-24)
Field: Chemistry — Analytical Chemistry

Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production can be regarded as noise parameters. In this study, based on the QbD concept, the ethanol precipitation process of Carthami Flos extract was optimized, considering a noise parameter. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as critical process parameters (CPPs), using a definitive screening design. The mathematical models among CPPs and evaluation indicators were established. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, refrigeration temperature was treated as a noise parameter. A calculation method for the design space in the presence of the noise parameter was proposed. The design space was calculated according to the probability of reaching the standards of evaluation indicators. Controlling parameters within the design space was expected to reduce the influence of noise parameter fluctuations on the quality of the ethanol precipitation supernatant. With more data obtained, the design space was updated. In industry, it is also recommended to adopt a similar idea: that is, continuing to collect industrial data and regularly updating mathematical models, which can further update the design space and make it more stable and reliable. Noise (video) ; Flos ; Quality by Design ; Refrigeration ; Process engineering ; Process variable ; Process (computing) ; Computer science ; Control theory (sociology) ; Engineering ; Mechanical engineering ; Chemistry ; Control (management) ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Process Optimization and Integration ; Fault Detection and Control Systems
Authors: Ophelia Frotscher | Roland Herzog | Markus Richter
Source: International Journal of Thermophysics (article, 2021-05-05)
Field: Chemistry — Organic Chemistry

Planning of Measurement Series for Thermodynamic Properties Based on Optimal Experimental Design Abstract Decreasing the time required for accurate thermodynamic property measurements is extremely desirable for model development that can respond to the needs of science and industry within a short time frame. Here, we demonstrate the application of optimal experimental design to measurements of thermodynamic properties. The technique is explored using the fitting of a Schilling-type equation from published $$(p, \rho , T)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>p</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ρ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -measurements of ethylene glycol and propylene glycol. The analysis shows that a fixed-exponent fit using ( p , T )-measurements along the five most informative isotherms produces models of relative density errors comparable to those obtained using the data along all investigated isotherms, i.e., eight or nine. It is also argued that a calculation of optimal isotherms prior to the measurement series can further increase the precision at no additional experimental effort. Series (stratigraphy) ; Experimental data ; Observational error ; Optimal design ; Time series ; Design of experiments ; Property (philosophy) ; Material properties Chemical Thermodynamics and Molecular Structure ; thermodynamics and calorimetric analyses ; Phase Equilibria and Thermodynamics
Authors: Ryota Jono | Syogo Tejima | Jun‐ichi Fujita
Source: Scientific Reports (article, 2021-12-17)
Field: Materials Science — Materials Chemistry

Microstructure of the fluid particles around the rigid body at the shear-thickening state toward understanding of the fluid mechanics We studied the shear-thickening behavior of systems containing rigid spherical bodies immersed in smaller particles using non-equilibrium molecular dynamics simulations. We generated shear-thickening states through particle mass modulation of the systems. From the microstructures, i.e., two-dimensional pair distribution functions, we found anisotropic structures resulting from shear thickening, that are explained by the difference between the velocities of rigid bodies and fluid particles. The increasing viscosity in our system originated from collisions between fluid particles and rigid bodies. The lubrication forces defined in macroscale physics are then briefly discussed. Dilatant ; Shear (geology) ; Mechanics ; Microstructure ; Thickening ; Lubrication ; Anisotropy ; Classical mechanics ; Rheology ; Particle (ecology) ; Viscosity ; Physics ; Materials science ; Composite material ; Geology Material Dynamics and Properties ; Granular flow and fluidized beds ; Rheology and Fluid Dynamics Studies
Authors: Mohamad Ahmad | Raffaele Vitale | Carolina S. Silva | Cyril Ruckebusch | Marina Cocchi
Source: Analytica Chimica Acta (article, 2021-11-15)
Field: Chemistry — Analytical Chemistry

A novel proposal to investigate the interplay between the spatial and spectral domains in near-infrared spectral imaging data by means of Image Decomposition, Encoding and Localization (IDEL) Spectral imaging ; Chemistry ; Infrared ; Encoding (memory) ; Pattern recognition (psychology) ; Spatial analysis ; Imaging spectroscopy ; Artificial intelligence ; Spectral signature ; Chemical imaging ; Spectral bands ; Wavelet ; Biological system ; Remote sensing ; Computer vision ; Hyperspectral imaging ; Computer science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: David Bombara | Calum Williams | Stephen Borg | Hyun Jung Kim
Source: Optical Engineering (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

Automated real-time spectral characterization of phase-change tunable filters using a linear variable filter and IR camera for wide-field MWIR imaging Actively tunable optical filters based on chalcogenide phase-change materials (PCMs) are an emerging technology with applications across chemical spectroscopy and thermal imaging. The refractive index of an embedded PCM thin film is modulated through an amorphous-to-crystalline phase transition induced through thermal stimulus. Performance metrics include transmittance, passband center wavelength (CWL), and bandwidth; ideally monitored during operation (in situ) or after a set number of tuning cycles to validate real-time operation. Measuring these aforementioned metrics in real-time is challenging. Fourier-transform infrared (IR) spectroscopy provides the gold-standard for performance characterization yet is expensive and inflexible—incorporating the PCM tuning mechanism is not straightforward, hence in situ electro-optical measurements are challenging. In this work, we implement an open-source MATLAB®-controlled real-time performance characterization system consisting of an inexpensive linear variable filter (LVF) and mid-wave IR camera, capable of switching the PCM-based filters while simultaneously recording in situ filter performance metrics and spectral filtering profile. These metrics are calculated through pixel intensity measurements and displayed on a custom-developed graphical user interface in real-time. The CWL is determined through spatial position of intensity maxima along the LVF’s longitudinal axis. Furthermore, plans are detailed for a future experimental system that further reduces cost, is compact, and utilizes a near-IR camera. Optics ; Computer science ; Transmittance ; Materials science ; Optical filter ; Passband ; Optoelectronics ; Band-pass filter Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Thermography and Photoacoustic Techniques
Authors: Yannick G. Spill | Yasaman Karami | Pierre Maisonneuve | Nicolas Wolff | Michaël Nilges
Source: Frontiers in Molecular Biosciences (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

Automatic Bayesian Weighting for SAXS Data Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state. Small-angle X-ray scattering ; Computer science ; Biological system ; Scattering ; Physics ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms