jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feifei Zhang | Kai He | Zheng Li | Bo Huang
Source: Metals (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

Strain-Rate Effect on Anisotropic Deformation Characterization and Material Modeling of High-Strength Aluminum Alloy Sheet Aluminum alloy sheets are widely applied as structure components in automotive, aircraft and other industries to realize lightweight. Nowadays, many high strain rate forming techniques have been developed to improve their formability and widen their application. To ensure the reliability of the aluminum alloy structure components under high strain rate conditions, it is imperative to develop a thorough understanding of the alloy’s mechanical properties. In this paper, taking high-strength 6XXX aluminum alloy sheet as an example, the anisotropic deformation characterization and corresponding material models at various strain-rate conditions are investigated systematically. The material hardening curves and anisotropic plastic yielding stresses were achieved based on the quasi-static uniaxial tensile test and the split Hopkinson tensile bar tests. In this study, the Johnson–Cook hardening model and two anisotropic yield functions are applied to well describe the strain-rate-dependent anisotropic plastic deformation behavior. In addition, the fractographic characterization of the fractured samples at various strain-rate conditions are measured and compared. The study systematically investigates the influence of strain rate on the anisotropic deformation behavior of the high-strength aluminum alloy sheets and gives the basic experimental data for their application in engineering fields in the future. Materials science ; Formability ; Anisotropy ; Strain rate ; Alloy ; Deformation (meteorology) ; Strain hardening exponent ; Split-Hopkinson pressure bar ; Ultimate tensile strength ; Sheet metal ; Composite material ; Tensile testing ; Hardening (computing) ; Characterization (materials science) ; Aluminium ; Superplasticity ; Metallurgy High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; Microstructure and mechanical properties
Authors: Noratikah Zawani Mahabob | Zakiah Mohd Yusoff | Aqib Fawwaz Mohd Amidon | Nurlaila Ismail | Mohd Nasir Taib
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2022-09-21)
Field: Chemistry — Organic Chemistry

A novel application of artificial neural network for classifying agarwood essential oil quality &lt;p&gt;&lt;span lang="EN-US"&gt;This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.&lt;/span&gt;&lt;/p&gt; Agarwood ; Artificial neural network ; Mathematics ; Artificial intelligence ; Stepwise regression ; MATLAB ; Mean squared error ; Computer science ; Pattern recognition (psychology) ; Statistics ; Medicine Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Industrial Vision Systems and Defect Detection
Authors: Antonino Imburgia | Giuseppe Rizzo | Pietro Romano | Guido Ala | R. Candela
Source: Energies (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Time Evolution of Partial Discharges in a Dielectric Subjected to the DC Periodic Voltage Partial discharge (PD) detection can be considered one of the most useful tools for assessing the insulation conditions of the power apparatus in high-voltage systems. Under AC conditions, this analysis is widely employed in online and offline tests, such as type tests or commissioning, and can be carried out by applying the phase-resolved PD (PRPD) method, since the patterns can give information about the defect classification. Under DC voltages, the classic pattern recognition method cannot be performed, and the measurements show complexities related to the nature of the phenomena. For this reason, to date, a standard for PD measurements under DC does not exist. In previous papers, a new method for PD detection under DC stress voltages has been proposed by the authors. It is based on the application of a direct current periodic (DCP) waveform useful in obtaining PRPD patterns. The dependence of partial discharge inception voltage (PDIV) and PD repetition rate (PDRR) on the δ shape parameter of the DCP for different materials, as well as the capability to recognize different discharge phenomena, provided valid indications on the behavior of PD in the transition from AC to DC. The aim of this paper is to evaluate the time dependence of PD occurring in a dielectric by applying the DCP waveform. In our previous studies, the investigations were focused on the PD behavior under different values of the DC voltage periodic part. In another work, the DCP waveform with both positive and negative polarity was applied to several dielectric materials. In the proposed work, instead, the DCP waveform is applied for a long time in order to observe its effect on the PD behavior for 72 h. In this way, due to the space charge accumulation phenomenon, the aging effect, also due to the space charge accumulation phenomenon, is evaluated. The methodological approach was to acquire PRPD patterns over time and evaluate their trends in comparison with the sinusoidal case. The experimental results show that, with a DCP waveform having δ = 0.6, the aging effect similar to that provided by pure DC stress is observed, while the acquired PRPD patterns are easily interpretated, as in the AC case. Partial discharge ; Waveform ; Voltage ; Dielectric ; Power (physics) ; Polarity (international relations) ; Work (physics) ; Materials science ; Direct current ; High voltage ; Stress (linguistics) ; Computer science ; Electrical engineering ; Electronic engineering ; Acoustics ; Physics ; Optoelectronics ; Chemistry ; Engineering ; Thermodynamics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Alessandra Biancolillo | Samantha Reale | Martina Foschi | Emanuele Bertini | Lavinia Antonelli | Angelo Antonio D’Archivio
Source: Molecules (article, 2022-10-31)
Field: Chemistry — Analytical Chemistry

Characterization and Authentication of “Ricotta” Whey Cheeses through GC-FID Analysis of Fatty Acid Profile and Chemometrics The fatty acid (FA) profiles of 240 samples of ricotta whey cheese made from sheep, goat, cow, or water buffalo milk were analyzed by gas-chromatography (GC). Then, sequential preprocessing through orthogonalization (SPORT) was used in order to classify samples according to the nature of the milk they were made from. This strategy achieved excellent results, correctly classifying 77 (out of 80) validation samples. Eventually, since 36 (over 114) sheep ricotta whey cheeses were PDO products, a second classification problem, finalizing the discrimination of PDO and Non-PDO dairies, was faced. In this case, two classifiers were used, SPORT and soft independent modelling by class analogy (SIMCA). Both approaches provided more than satisfying results; in fact, SPORT properly assigned 63 (of 65) test samples, whereas the SIMCA model accepted 14 PDO individuals over 15 (93.3% sensitivity) and correctly rejected all the other samples (100.0% specificity). In conclusion, all the tested approaches resulted as suitable for the two fixed purposes. Eventually, variable importance in projection (VIP) analysis was used to understand which FAs characterize the different categories of ricotta. Among the 22 analyzed compounds, about 10 are considered the most relevant for the solution of the investigated problems. Chemometrics ; Mathematics ; Gas chromatography ; Food science ; Chemistry ; Raw milk ; Chromatography ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Yiming Liu | Donghang Li | Huaiming Li | Xiaoping Ren | Yan Zhu | Weixing Cao | Jun Ni
Source: Frontiers in Plant Science (article, 2022-05-06)
Field: Chemistry — Analytical Chemistry

Design of a Phenotypic Sensor About Protein and Moisture in Wheat Grain A near-infrared (NIR) spectrometer can perceive the change in characteristics of the grain reflectance spectrum quickly and nondestructively, which can be used to determine grain quality information. The full-band spectral information of samples of multiple physical states can be measured using existing instruments, yet it is difficult for the full-band instrument to be widely used in grain quality detection due to its high price, large size, non-portability, and inability to directly output the grain quality information. Because of the above problems, a phenotypic sensor about grain quality was developed for wheat, and four wavelengths were chosen. The interference of noise signals such as ambient light was eliminated by the phenotypic sensor using the modulated light signal and closed sample pool, the shape and size of the incident light spot of the light source were determined according to the requirement for collecting the reflectance spectrum of the grain, and the luminous units of the light source with stable light intensity and balanced luminescence were developed. Moreover, the sensor extracted the reflectance spectrum information using a weak optical signal conditioning circuit, which improved the resolution of the reflectance signal. A grain quality prediction model was created based on the actual moisture and protein content of grain obtained through Physico-chemical analyses. The calibration test showed that the R<sup>2</sup> of the relative diffuse reflectance (RDR) of all four wavelengths of the phenotypic sensor and the reflectance of the diffusion fabrics were higher than 0.99. In the noise level and repeatability tests, the standard deviations of the RDR of two types of wheat measured by the sensor were much lower than 1.0%, indicating that the sensor could accurately collect the RDR of wheat. In the calibration test, the root mean square errors (RMSE) of protein and moisture content of wheat in the Test set were 0.4866 and 0.2161%, the mean absolute errors (MAEs) were 0.6515 and 0.3078%, respectively. The results showed that the NIR phenotypic sensor about grain quality developed in this study could be used to collect the diffuse reflectance of grains and the moisture and protein content in real-time. Grain size ; Materials science ; Optics ; Wavelength ; Remote sensing ; Moisture ; Spectrometer ; Environmental science ; Optoelectronics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Kosala Gunawardane | Nisitha Padmawansa | Nihal Kularatna | Kasun Subasinghage | Tek Tjing Lie
Source: Applied Sciences (article, 2022-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Current Context and Research Trends in Linear DC–DC Converters With the introduction of switch-mode power supplies (SMPS) in the mid-1970s, the efficiency of DC–DC conversion rose from 60 to 80% and SMPS became a popular power supply solution. However, linear regulators have not become obsolete. The modern power management system in portable devices supports a complex mix of DC–DC converters, combining switch-mode power supplies (SMPS), switched capacitor converters (SCCs), and linear regulators in the form of low-dropout regulators (LDOs). LDOs are used to supply low-voltage DC power rails with very low noise and high current slew rate capability, which are usually fed by the output rail of SMPS. This paper provides a comprehensive review of the evolution of the application scope of linear-type DC–DC converters in the power supply context and the present research trends. First, we review the context of linear DC–DC converters in detail, particularly in portable device power supplies. Then, the details of LDO regulators and their recent industry development and research trends are discussed. Then, the discussion focuses on supercapacitor-assisted low-dropout (SCALDO) regulator design and its scope in the portable device power management together with SCALDO-based dual output and reduced switch designs, and finally, the conclusions follow. Converters ; Linear regulator ; Switched-mode power supply ; Electrical engineering ; Electronic engineering ; Low-dropout regulator ; Power (physics) ; Computer science ; Switched-mode power supply applications ; Power management ; Context (archaeology) ; Voltage ; Engineering ; Voltage regulator ; Regulator ; Physics ; Dropout voltage ; Constant power circuit Supercapacitor Materials and Fabrication ; Advanced DC-DC Converters ; Advanced Battery Technologies Research
Authors: Tara K. Bartolec | Xabier Vázquez-Campos | Alexander Norman | Clement Luong | Richard J. Payne | Marc R. Wilkins | Joel P. Mackay | Jason K. K. Low
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-11-16)
Field: Chemistry — Spectroscopy

Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome ABSTRACT Significant recent advances in structural biology, particularly in the field of cryo-electron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability or – in the case of complexes – simply not having yet been analysed. Here, we demonstrate the power of combining cross-linking mass spectrometry (XL-MS) with artificial intelligence-based structure prediction to discover and experimentally substantiate models for protein and protein complex structures at proteome scale. We present the deepest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein-protein interactions. We show that integrative models of complexes driven by AlphaFold Multimer and inspired and corroborated by the XL-MS data offer new opportunities to deeply mine the structural proteome and interactome and reveal new mechanisms underlying protein structure and function. Proteome ; Interactome ; Computational biology ; Mass spectrometry ; Structural bioinformatics ; Proteomics ; Protein function ; Structural biology ; Human proteome project ; Protein structure ; Chemistry ; Function (biology) ; Biology ; Biophysics ; Biochemistry ; Cell biology ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function
Authors: Long-Yi Ho | Chi Wai Kan
Source: Coatings (article, 2022-11-21)
Field: Materials Science — Polymers and Plastics

Effect of Resin Finishing on Some Properties of 100% Cotton Light Weight Woven Fabric In this study, experimental investigation was conducted to assess the wrinkle-resistance performance of resin-treated 100% cotton light-weight fabric. Resin (Dimethyloldihydroethylene urea, DMDHEU) was used for treating 100% cotton light-weight fabric with different treatment parameters (resin concentration: 30 g/L, 45 g/L, 60 g/L; pick-up: 60%, 70%, 80%; drying temperature: 110 °C, 120 °C; and curing time: 2 min, 2.5 min, 3 min). After resin treatment, wrinkle properties, as well as the tearing strength and dimensional stability, of the resin treated fabrics were evaluated. Experimental results revealed that the resin concentration, pick-up, drying temperature, and curing time are inter-related, which could affect the final performance of the 100% cotton light-weight woven fabric. Thus, the effects of different parameters on the performance of the 100% cotton light-weight woven fabric were compared and discussed. Generally speaking, the resin finishing improved the wrinkle properties and dimensional stability but reduced the tearing strength of 100% cotton light-weight woven fabric. Tearing ; Curing (chemistry) ; Composite material ; Wrinkle ; Materials science ; Woven fabric ; Tear resistance ; Natural rubber Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Natural Fiber Reinforced Composites
Authors: Leonid Stupishin | Владимир Мондрус
Source: Buildings (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

Critical Energy Properties Study for Unsymmetrical Deformable Structures There are difficulties in the formulation and solution of problems for follower loading, temperature actions, and whether the Lagrange principle is used. By dividing the external loads and internal deformation fields that exist according to their own laws, we focused on the advantages in mechanics of deformable solids. This paper develops an approach to the study of the internal strain energy of deformed systems, based on the criterion of the critical levels of the internal strain energy. According to the criterion, the achievement of the limiting values of the internal strain energy by the system with varying internal parameters of the structure is possible for certain types of “self-stress” (“self-balance”) for deformable bodies. The latter corresponds to the levels of the critical energy of the body determined by the eigenvalues of the internal strain energy. New problems, namely the “weak link” and “progressive limiting state of the system”, are formulated and demonstrated in the examples of the study of asymmetric rod systems. The methodology used here is based on matrix methods of the structural mechanics and a mathematical apparatus for eigenvalue problems. Eigenvalues and eigenvectors ; Internal energy ; Internal pressure ; Strain energy ; Limiting ; Internal forces ; Energy balance ; Energy (signal processing) ; Mechanical energy ; Stress (linguistics) ; Deformation (meteorology) ; Matrix (chemical analysis) ; Mechanics ; Classical mechanics ; Mathematics ; Physics ; Structural engineering ; Materials science ; Mechanical engineering ; Engineering ; Finite element method ; Thermodynamics Structural mechanics and materials ; Elasticity and Wave Propagation ; Composite Structure Analysis and Optimization
Authors: Siva Dasetty | Igor Coropceanu | Joshua Portner | Jiyuan Li | Juan Pablo | Dmitri V. Talapin | Andrew L. Ferguson
Source: Molecular Systems Design & Engineering (article, 2022-01-01)
Field: Materials Science — Biomaterials

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices Coarse-grained molecular modeling and active learning enable the design of polarizable nanoparticles capable of triggered assembly and disassembly. Nanoparticle ; Nanotechnology ; Materials science ; Polarizability ; Superlattice ; Phase diagram ; Phase (matter) ; Chemistry ; Optoelectronics ; Organic chemistry Supramolecular Self-Assembly in Materials ; Quantum Dots Synthesis And Properties ; Pickering emulsions and particle stabilization
Authors: Zhibin Qiu | Zijian Wu | Louxing Zhang | Yu Song | Jianben Liu
Source: IET Generation Transmission & Distribution (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations Abstract It is a long‐term goal in external insulation studies to determine the discharge voltages of complicated engineering gaps by simulation methods. Based on the one‐to‐one correspondence between air gap structure and the static electric field (EF) distribution, this paper characterizes the transmission tower gap configuration by spatial EF features, which were used for machine learning to achieve switching impulse discharge voltage prediction. An interelectrode path and a conical zone between the energized sub‐conductor and the crossarm or the tower window were considered as EF regions strongly associated with gap breakdown, where 73 parameters were extracted to construct a feature set. Taking 15 extra‐high voltage (EHV) transmission tower gaps as training samples, with different gap distances and tower configurations, their EF features were input to a support vector machine (SVM) for model training to establish the relationships with discharge voltages. The trained SVM model was used to predict the impulse discharge voltages of 20 EHV and ultra‐high voltage (UHV) transmission tower gaps. The prediction results with different feature dimensions and various sizes of conical zones were compared to the experimental values, which demonstrate similar variation trends and acceptable errors. This study contributes to realize insulation strength calculation of engineering gaps. Air gap (plumbing) ; Computer science ; Machine learning ; Dielectric ; Dielectric strength ; Artificial intelligence ; Industrial engineering ; Engineering ; Materials science ; Electrical engineering ; Composite material High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Dielectric materials and actuators
Authors: Nikolai Neshev | Ignat Ignatov | Reneta Toshkova | Cristos Drossinakis | Georgi Gluhchev
Source: Libri Oncologici Croatian Journal of Oncology (article, 2022-12-22)
Field: Chemistry — Physical and Theoretical Chemistry

Hydrogen bonds energy distribution and information-theoretic analysis of blood serum from hamsters with experimental Graffi tumor The aim of the present study was to estimate the feasibility of blood serum hydrogen bonds energy distribution, and information-theoretic characteristics, as diagnostic markers for tumor disease in Graffi tumor bearing hamsters. Golden Syrian hamsters were randomly divided into two groups: a healthy control and an experimental one, challenged with Graffi tumor cells. The distributions of hydrogen bonds energies (-E) in the blood serum samples were investigated with a nonequilibrium process of droplets evaporation and sequential measurements of their wetting angle (Non-equilibrium Energy Spectrum (NES)). The measurements show significantly lower average hydrogen bond energy of the experimental samples than the controls. Shifts in NES toward depletion of (-E) at energy levels 0.0937, 0.1187, 0.1212, 0.1337, and 0.1387 eV, and an additional population at 0.0987, 0.1012, 0.1137, and 0.1237 eV energy levels were observed in the experimental samples compared to the controls. Such a pattern points to the possibility of utilizing the f(E) shifts at these energy levels as diagnostic markers of Graffi tumor in hamsters. Information-theoretic measures of Shannon entropy, Variation of information and Transformational information entropy were used to investigate possible differences between the experimental and control spectrum. Although they differ significantly in definitions, a comparison with the three information-theoretic measures did not reveal a statistically significant difference. Hydrogen bond ; Chemistry ; Population ; Thermodynamics ; Materials science ; Physics ; Medicine ; Molecule thermodynamics and calorimetric analyses ; Chemical Reactions and Isotopes ; Biofield Effects and Biophysics