jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Balachandar Guduri | R.C. Batra
Source: SN Applied Sciences (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Robust model reference adaptive controller for atmospheric plasma spray process Abstract We add the σ -modification and the low-frequency learning to the model reference adaptive controller (MRAC) (Guduri et al. in SN Appl Sci 3:1–21, 2021) to make it robust in the presence of two simultaneous bounded disturbances and maintain consistent mean particles’ temperature and velocity collectively called mean particles’ states (MPSs) when they impact the substrate to be coated. The MPSs affect the coating quality. Even though results are applicable to several coating processes, we consider an atmospheric plasma spray process (APSP). It is shown that the proposed controller can quickly adopt to disturbances in the average injection velocity of powder particles and in the arc voltage to change the input current, and the argon and the hydrogen flow rates to maintain constant values of the MPSs. The effects of the parameter values in the MRAC, the MRAC with $$\sigma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> -modification (R-MRAC), and the R-MRAC with low-frequency learning (MR-MRAC) schemes on tracking error convergence, steady-state tracking error, disturbance rejection and the presence of overshoot have been studied. The numerical experiments suggest that $$2 \le \gamma \le 20,$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>γ</mml:mi> <mml:mo>≤</mml:mo> <mml:mn>20</mml:mn> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> $$10 \le \sigma \le 100,$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>σ</mml:mi> <mml:mo>≤</mml:mo> <mml:mn>100</mml:mn> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> and $$20 \le \lambda \le 80$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>20</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>≤</mml:mo> <mml:mn>80</mml:mn> </mml:mrow> </mml:math> for the MR-MRAC provide fast adaptation, no overshoot, and low tracking error in the controlled response. The parameter $$\lambda &gt; 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>λ</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> suppresses high-frequency oscillations in the closed-loop control system, and $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> serves to tune the controller gains. The control scheme has been tested using the software, LAVA-P, that simulates well an APSP. Algorithm ; Analytical Chemistry (journal) ; Controller (irrigation) ; Substrate (aquarium) ; Materials science ; Computer science ; Physics ; Chemistry ; Chromatography Advancements in Solid Oxide Fuel Cells ; Plasma and Flow Control in Aerodynamics ; Catalytic Processes in Materials Science
Authors: Tianyu Xiang | Tao Lei | Ting Chen | Zhaoyang Shen | Jing Zhang
Source: Materials (article, 2022-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-Loss Dual-Band Transparency Metamaterial with Toroidal Dipole In this paper, a low-loss toroidal dipole metamaterial composed of four metal split ring resonators is proposed and verified at microwave range. Dual-band Fano resonances could be excited by normal incident electromagnetic waves at 6 GHz and 7.23 GHz. Analysis of the current distribution at the resonance frequency and the scattered power of multipoles shows that both Fano resonances derive from the predominant novel toroidal dipole. The simulation results exhibit that the sensitivity to refractive index of the analyte is 1.56 GHz/RIU and 1.8 GHz/RIU. Meanwhile, the group delay at two Fano peaks can reach to 11.38 ns and 12.85 ns, which means the presented toroidal metamaterial has significant slow light effects. The proposed dual-band toroidal dipole metamaterial may offer a new path for designing ultra-sensitive sensors, filters, modulators, slow light devices, and so on. Metamaterial ; Dipole ; Fano resonance ; Resonator ; Slow light ; Physics ; Split-ring resonator ; Electromagnetically induced transparency ; Optics ; Toroid ; Microwave ; Resonance (particle physics) ; Excited state ; Optoelectronics ; Plasmon ; Atomic physics ; Photonic crystal Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Andy Lin | Temana Short | William Stafford Noble | Uri Keich
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-05-11)
Field: Chemistry — Spectroscopy

Improving peptide-level mass spectrometry analysis via double competition Abstract The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum’s best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here we first highlight and empirically augment a little-known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method (“PSM-only”) offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level (“PSM-and-peptide”), is the most powerful method, yielding an average increase of 17% more discovered peptides at a 1% FDR threshold relative to the PSM-only method. Decoy ; Peptide ; False discovery rate ; Computer science ; Shotgun proteomics ; Competition (biology) ; Mascot ; Data mining ; Proteomics ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Peng Yu | Siliu Yu | Sheng He | Xinheng Huang | Weijing Yun
Source: Shock and Vibration (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

Inversion for Constitutive Model Parameters of Bird in Case of Bird Striking The strike between flying birds and airplanes results in the unacceptable losses of aircraft structures. The normal approach to evaluate the bird impact resistance is the combined full-scale experimental-numerical study. However, the simulation results from the current available bird constitutive models are usually not in good agreement with the experimental data. Establishing a reasonable bird constitutive model is difficult and significant to the simulation of the bird striking process. In this paper, based on the displacement measurements of an aluminum plate subjected to soft body impact, an inversion of the bird constitutive model is conducted by using backpropagation (BP) neural network. A comparative evaluation of this inversion model and other constitutive models is carried out, indicating that the proposed inversion model is more reasonable. Constitutive equation ; Inversion (geology) ; Artificial neural network ; Backpropagation ; Engineering ; Structural engineering ; Marine engineering ; Computer science ; Geology ; Finite element method ; Artificial intelligence ; Seismology High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Soil Mechanics and Vehicle Dynamics
Authors: Isara Sornsen | Chatchai Suppitaksakul | Pollakrit Toonkum
Source: ECTI Transactions on Electrical Engineering Electronics and Communications (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Mother Wavelet Performance Evaluation for Noise Removal in Partial Discharge Signals This article aims to study the pattern of partial discharge (PD) signals occurring on the insulators of high-voltage systems. A mother wavelet comparison and wavelet decomposition are presented to detect and locate PD signals by dividing them into three processes: 1) Signal test generation, employing RC and RLC impedance circuits whereby the output voltage pulses in the RC impedance circuit are expressed as damped exponential pulses (DEPs) and those of the RLC as damped oscillatory pulses (DOPs). White Gaussian noise (AWGN) is then added, which mimics the effect of many random processes in measurement systems. The concept involves applying noise to the original signal and removing it with wavelet transform using various wavelet templates such as Daubechies, Coiflet, Symlet, and biorthogonal to separate the signal components. The experiment results are then compared, and a performance evaluation performed using mean square error (MSE) as in the first two signals. 2) DEPs and DOPs are added with a sine wave to simulate a virtual measurement from a measuring instrument according to the superposition principle using a band-pass filter with the frequency range specified by the two elements to determine the frequency of the resulting PD. 3). The results of the PD signal experiments developed in the laboratory are also evaluated for efficiency by subjective measurement by a PD signal specialist. Therefore, partial discharge denoizing is evaluated using the mother wavelet as a preprocessing step for feature extraction and classification. Wavelet ; RLC circuit ; Partial discharge ; SIGNAL (programming language) ; Wavelet packet decomposition ; White noise ; Mathematics ; Acoustics ; Noise (video) ; Electronic engineering ; Wavelet transform ; Computer science ; Artificial intelligence ; Engineering ; Voltage ; Physics ; Statistics ; Electrical engineering ; Capacitor High voltage insulation and dielectric phenomena ; Sensor Technology and Measurement Systems ; Power Transformer Diagnostics and Insulation
Authors: NAYAB KHAN | Michal Vik | FARIDA IRSHAD | ASSAD FAROOQ | Muhammad Azeem Ashraf
Source: TURKISH JOURNAL OF AGRICULTURE AND FORESTRY (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Development of a novel method for trash segmentation of cotton fibre color measurement The representation of cotton color is a key element in deciding the cotton grade. The color of cotton fibre is measured with instruments as well as visual assessment. But, these two methods always represent disagreement in the measurements. The main reason for disagreement is the trash particles on the cotton surface. The threshold segregation technique is used for the segmentation of trash and cotton fibres from the image of cotton sample. It is used in this research for the trash segmentation and precise color measurement. The reduction of disagreement between visual and instrument measurements is observed and the color of cotton is presented in more precise manner by using different correlation methods. In the presence of trash particles on the surface, it is not possible to measure the accurate color of cotton sample and the trash particles have to be removed. But, in this research image analysis method is used and the final color value will not include the trash color as it will be segmented from the image. Artificial intelligence ; Segmentation ; Sample (material) ; Color measurement ; Computer vision ; Visual inspection ; Computer science ; Mathematics ; Lightness ; Chemistry ; Chromatography Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Belkebir Amel | Yacine Bourek | Hani Benguesmia
Source: Diagnostyka (article, 2022-09-22)
Field: Materials Science — Materials Chemistry

Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators. Particle swarm optimization ; Insulator (electricity) ; Artificial neural network ; Arc flash ; Voltage ; Particle (ecology) ; Materials science ; Environmental science ; Biological system ; Computer science ; Engineering ; Electrical engineering ; Composite material ; Artificial intelligence ; Algorithm ; Biology ; Ecology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Gülşah Pamuk
Source: TEKSTİL VE KONFEKSİYON (article, 2022-09-13)
Field: Materials Science — Polymers and Plastics

Maximization of Sewing Strength and Minimization of Seam Pucker for Denim Fabrics Using Taguchi Method Seams provide the shape of the garments and support the fabric pieces together. The seam quality is important for the serviceability of any garment. In general, the seam quality depends on various factors such as fabric weight, sewing thread type, and stitches per unit area. In this research, three different levels of fabric types with the same construction and different weights, which are mostly used in the production of denim products, were examined. For this purpose, each fabric was sewn in the warp direction with three different levels of sewing threads and three different levels of stitch densities. Then, the values of seam strength, and the seam pucker values were obtained with three replications for each combination of levels. In this study, it is aimed to obtain the optimum levels of fabric weight, sewing thread type, and stitch density of denim fabrics by using the Taguchi method. Custom Taguchi Design is employed to observe the efficiency of factor levels on seam strength and seam pucker, and existence of interactions among fabric weight, sewing thread type, and stitch density. Denim ; Taguchi methods ; Thread (computing) ; Breaking strength ; Engineering ; Serviceability (structure) ; Maximization ; Structural engineering ; Clothing ; Mathematics ; Mechanical engineering ; Composite material ; Materials science Textile materials and evaluations ; Ergonomics and Musculoskeletal Disorders ; Dyeing and Modifying Textile Fibers
Authors: Aziza Ramadan Mohammad | Seshagiri Nageneini
Source: International Journal of Emerging Research in Engineering and Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Temporal Waste Heat Index (TWHI) for Process Efficiency By measuring the hidden "waste heat" of time spent by inefficiencies, delay &amp; also interruptions, the Temporal Waste Heat Index (TWHI) is a novel, quantitative tool meant to evaluate time-output efficiency across more numerous locations. TWHI is a diagnostic tool to find regions where time is consumed without producing quantifiable outcomes in a situation where operational time is sometimes distributed over by more numerous sites &amp; also activities. This metric comes from a basic but powerful idea: more complex processes create temporal waste when operations are postponed or stopped, just as machines spew waste heat during ineffective operation. TWHI seeks to provide more executives with a better view of inefficiencies that could otherwise go unnoticed by tying standard productivity measures with actual time operational responsiveness. Three key domains Time-Based Analysis, which gauges the relationship between time spent &amp; also value generated; Latency Mapping, which identifies delays across process nodes; and Disruption Indexing, which tracks the frequency &amp; degree of their operational interruptions are investigated in our work. To create TWHI as a consistent &amp; more flexible index relevant across many other industries, particularly in manufacturing, logistics &amp; remote digital teams, the study uses a hybrid approach combining data modelling, time-motion analysis &amp; also simulation. Results show that companies using TWHI might find 25–40% of unnecessary time, therefore enabling more strategic process optimization. TWHI provides a framework for more predictive efficiency planning, therefore helping teams to forecast the possible effects of future system modifications or delays on operations. TWHI offers a progressive approach for management as employment is more dispersed and time becomes more limited not just tracking hours but also understanding their relevance. The index opens fresh prospects for real-time dashboards, AI-driven process changes, cross-site coordination strategies meant to save waste and improve major production Index (typography) ; Environmental science ; Process (computing) ; Waste management ; Computer science ; Engineering ; World Wide Web Thermal and Kinetic Analysis ; Iron and Steelmaking Processes ; Process Optimization and Integration
Authors: Francisco Bulnes
Source: IntechOpen eBooks (review, 2022-07-12)
Field: Chemistry — Analytical Chemistry

A Modern Review of Wavelet Transform in Its Spectral Analysis The spectral analysis, in much aspects as are the wavelet transform in its numerous versions and its relation with other transforms and special functions requires a special review, since the exploration in the frequency domain to the wavelet transform is more detailed and majorly more specific in different applications. For example, the wavelet transform of special function can be very useful to create and design special signal filters or, for example, to the interphase between reception-emission devices with sensorial parts of the human body. Also the quantum wavelet transform is very useful in the spectral study of traces of particles. Likewise, in this chapter, these aspects are considered as an inherent property of the wavelet transform in the spectral exploration of some phenomena. Finally, general results to the discrete case are given, which is analyzed to the wavelet transform and its spectra. Discrete wavelet transform ; Harmonic wavelet transform ; Wavelet ; Wavelet transform ; Second-generation wavelet transform ; Wavelet packet decomposition ; Stationary wavelet transform ; Lifting scheme ; Constant Q transform ; Computer science ; Mathematics ; Fast wavelet transform ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods ; Blind Source Separation Techniques
Authors: Mathias Uhlén | María Bueno Álvez | Fredrik Edfors | Kalle von Feilitzen | Martin Zwahlen | Adil Mardinoğlu | Per‐Henrik Edqvist | Tobias Sjöblom | Emma Lundin | Natallia Rameika | Tomas Axelsson | Mikael Åberg | Jessica Nordlund | Wen Zhong | Max Karlsson | Ulf Gyllensten | Fredrik Pontén | Linn Fagerberg
Source: Research Square (preprint, 2022-11-01)
Field: Chemistry — Spectroscopy

Next generation pan-cancer blood proteome profiling using proximity extension assay Profiling (computer programming) ; Computational biology ; Proteome ; Extension (predicate logic) ; Computer science ; Biology ; Bioinformatics Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Ji-Young Choi | Jeong‐Seok Cho | Kee Jai Park | Sang Seop Kim | Jeong‐Ho Lim
Source: Korean Journal of Food Preservation (article, 2022-10-01)
Field: Chemistry — Analytical Chemistry

Grading the pungency of red pepper powder using hyperspectral imaging coupled with multivariate analysis In this study, the spectral image of red pepper powder, which had been prepared in accordance with the standard particle size distribution ratio, was acquired in the short-wave infrared region using a hyperspectral camera. Spectral information was analyzed using multivariate statistical analyses including principal component analysis (PCA) and least partial squares (PLS) analysis. PCA revealed that powders were grouped according to their pungency level, regardless of their particle size distribution (PC1=97%, PC2=2%). The regression coefficient derived in PLS discriminant analysis indicated that 1,201-1,226 nm, 1,387-1,411 nm, and 1,508-1,529 nm are key wavelengths that are affected by the vibration of C-H, O-H, and N-H bonds present in capsaicinoid molecules. Pungency grade was successfully determined, and capsaicinoid content was predicted with high accuracy using PLS analysis of raw data at key wavelength (Rc2=0.9389, Rp2= 0.9261). It was possible to reduce the time required for data calculation and analysis by reducing the amount of spectral data utilized to predict spiciness from 256 to 21 bands. Finally, the distribution of capsaicinoids was mapped visually according to particle size. In conclusion, hyperspectral imaging is a suitable technology for real time, non-destructive monitoring of red pepper powder quality relative to the standard method used during the manufacturing process. Hyperspectral imaging ; Partial least squares regression ; Principal component analysis ; Pungency ; Pepper ; Linear discriminant analysis ; Multivariate statistics ; Chemometrics ; Particle-size distribution ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Particle size ; Materials science ; Pattern recognition (psychology) ; Mathematics ; Biological system ; Artificial intelligence ; Chemistry ; Statistics ; Chromatography ; Computer science ; Optics ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Advanced Chemical Sensor Technologies
Authors: Yi Luo | Saientan Bag | Orysia Zaremba | Adrian Cierpka | Jacopo Andreo | Stefan Wuttke | Pascal Friederich | Manuel Tsotsalas
Source: Angewandte Chemie (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen** Abstract Trotz großer Fortschritte auf dem Gebiet der metallorganischen Gerüststrukturen (MOF) ist das volle Potential des Maschinellen Lernens (ML) für die Vorhersage von MOF‐Syntheseparametern bisher noch nicht erschlossen. In diesem Beitrag wird dargestellt, wie Methoden des ML für die Rationalisierung und Beschleunigung von MOF‐Entwicklungsverfahren eingesetzt werden können, indem die Synthesebedingungen der MOFs direkt anhand ihrer Kristallstruktur vorhergesagt werden. Unser Ansatz stützt sich auf: i) die Erstellung der ersten MOF‐Synthese‐Datenbank durch automatische Extraktion der Syntheseparameter aus der Fachliteratur, ii) das Trainieren und die Optimierung von ML‐Modellen mit Daten der MOF‐Datenbank und iii) die ML basierte Vorhersage der Synthesebedingungen neuer MOF‐Strukturen. Schon jetzt übertreffen die Ergebnisse der Vorhersagemodelle die Vorhersagen menschlicher ExpertInnen, welche in einer Befragung ermittelt wurden. Die automatisierte Synthesevorhersage ist über ein Web‐Tool unter https://mof‐synthesis.aimat.science verfügbar. Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials