jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Nawiko | Rosehan | M. Sobron Yamin Lubis
Source: Jurnal Syntax Transformation (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Pengaruh Variasi Kuat Arus Pengelasan SMAW Terhadap Uji Tarik Sambungan Bahan ASTM A36 Pengelasan Dalam industri manufaktur yang banyak menyangkut penggunaan baja dan logam, secara khusus dalam bidang pembangunan dengan memanfaatkan proses pengelasan maka akan sangant dibutuhkan berbagai penelitian dengan tujuan agar mendapatkan sambungan las yang memiliki mutu tinggi. Penelitian ini bertujuan untuk mengetahui dan melihat hasil dari kekuatan sambungan pengelasan pada material ASTM A36 dengan cara memberikan beberapa variasi kuat arus 80 A, 100 A dan 120 A dengan menggunakan metode pengujian yaitu pengujian tensile test. Berdasarkan hasil pengujian tarik yang telah dilakukan dengan menggunakan pengelasan SMAW memiliki kekuatan teganagan tarik yang baik pada kuat arus 120 A dengan nilai tegangan tarik sebesar 472,33 Pa. kekuatan sambungan las juga akan sangat dipengaruhi oleh waktu pengelasan dan panas yang diterima oleh benda atau bahan uji Physics ; Materials science ; Tensile testing ; Ultimate tensile strength ; Composite material ; Metallurgy Material Selection and Properties ; Aluminum Alloys Composites Properties
Authors: Yang Lu | Stephen Thomas | Tianjie Zhang
Source: CivilEng (review, 2022-11-15)
Field: Materials Science — Materials Chemistry

Concurrent AtC Multiscale Modeling of Material Coupled Thermo-Mechanical Behaviors: A Review Advances in the field of processing and characterization of material behaviors are driving innovations in materials design at a nanoscale. Thus, it is demanding to develop physics-based computational methods that can advance the understanding of material Multiphysics behaviors from a bottom-up manner at a higher level of precision. Traditional computational modeling techniques such as finite element analysis (FE) and molecular dynamics (MD) fail to fully explain experimental observations at the nanoscale because of the inherent nature of each method. Concurrently coupled atomic to the continuum (AtC) multi-scale material models have the potential to meet the needs of nano-scale engineering. With the goal of representing atomistic details without explicitly treating every atom, the AtC coupling provides a framework to ensure that full atomistic detail is retained in regions of the problem while continuum assumptions reduce the computational demand. This review is intended to provide an on-demand review of the AtC methods for simulating thermo-mechanical behavior. Emphasis is given to the fundamental concepts necessary to understand several coupling methods that have been developed. Three methods that couple mechanical behavior, three methods that couple thermal behavior, and three methods that couple thermo-mechanical behavior is reviewed to provide an evolutionary perspective of the thermo-mechanical coupling methods. Multiphysics ; Multiscale modeling ; Coupling (piping) ; Finite element method ; Computer science ; Computational model ; Field (mathematics) ; Scale (ratio) ; Mechanical engineering ; Nanotechnology ; Statistical physics ; Systems engineering ; Simulation ; Materials science ; Engineering ; Physics ; Structural engineering ; Chemistry ; Mathematics Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Paul Rohrbach | Hideki Kobayashi | Robert Scheichl | Nigel B. Wilding | Robert L. Jack
Source: The Journal of Chemical Physics (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

Multilevel simulation of hard-sphere mixtures We present a multilevel Monte Carlo simulation method for analyzing multi-scale physical systems via a hierarchy of coarse-grained representations, to obtain numerically exact results, at the most detailed level. We apply the method to a mixture of size-asymmetric hard spheres, in the grand canonical ensemble. A three-level version of the method is compared with a previously studied two-level version. The extra level interpolates between the full mixture and a coarse-grained description where only the large particles are present-this is achieved by restricting the small particles to regions close to the large ones. The three-level method improves the performance of the estimator, at fixed computational cost. We analyze the asymptotic variance of the estimator and discuss the mechanisms for the improved performance. Monte Carlo method ; Estimator ; Hierarchy ; Variance (accounting) ; Hard spheres ; Statistical physics ; Scale (ratio) ; Computer science ; Applied mathematics ; Mathematics ; Algorithm ; Statistics ; Physics ; Thermodynamics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: R. Rohit | Syed Ahsan Haider | Abhishek Raj
Source: Research Square (preprint, 2022-12-16)
Field: Materials Science — Surfaces, Coatings and Films

ANN-aided Stiffness Characterization of Thin Membranes using Droplet Motion Characterization (materials science) ; Stiffness ; Membrane ; Motion (physics) ; Materials science ; Nanotechnology ; Computer science ; Composite material ; Artificial intelligence ; Chemistry Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Advanced Sensor and Energy Harvesting Materials
Authors: Jun Luo | Boxun Liao
Source: International Transactions on Electrical Energy Systems (article, 2022-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Fault-Tolerant Structure for Nano-Power Communication Based on the Multidimensional Crossbar Switch Network In order to realize fault tolerance and further reduce the transmission delay, a fault-tolerant structure design method for nano-power communication based on a multidimensional crossbar switch network is proposed. The TSV router is designed as a double crossbar structure, namely the master crossbar (MasterCrossbar) and the slave crossbar (SlaveCrossbar). Each input port of the TSV router is divided into two subports. The port connected to the master crossbar has no input buffer, and the port connected to the slave crossbar has an input buffer. Master crossbar is the first choice for data transmission, and slave crossbar is selected when it is busy to reduce the transmission delay of data packets and reduce power consumption. Dual crossbar switches can also realize the fault tolerance of crossbar switches. The experimental results show that the author's fault-tolerant scheme, without incorporating the double crossbar switch, still has a much smaller transmission delay than the reference because the author realizes fault tolerance for defective TSVs by adding bidirectional TSVs to replace faulty TSVs. Therefore, when there is a TSV failure, the reference transmission delay increases with the number of failures, but the author's design allows packets to be transmitted in the network without being affected; the author's bidirectional TSV fault-tolerant design is combined with the double crossbar design. After that, the transmission delay is smaller than the original, and the maximum transmission delay is about 40% faster than the reference. The authors' design is superior and improves the reliability of the 3DNoC system. Crossbar switch ; Router ; Computer science ; Network packet ; Fault tolerance ; Port (circuit theory) ; Reliability (semiconductor) ; Fault (geology) ; Transmission (telecommunications) ; Power (physics) ; Computer network ; Engineering ; Electronic engineering ; Distributed computing ; Telecommunications ; Physics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Advanced Data and IoT Technologies
Authors: Hamad S. Rashed | Andrew J. Parrott | Alison Nordon | Matthew J. Baker | David Scott Palmer
Source: Vibrational Spectroscopy (article, 2022-11-01)
Field: Chemistry — Analytical Chemistry

Pocket-size near-IR spectrometers for rapid examination of contaminated textile fibres at the crime-scene In a typical forensic investigation, fabric analysis plays a vital role in solving different crimes. Several types of textile fibre materials (cotton, polyester, denim, polypropylene, polycotton, and viscose) were analysed in the presence of common contaminants (blood, rainwater, seawater, sand and gunshot-residue) to evaluate the performance of two NIR spectrometers for in situ analysis of different crime scene conditions. The spectrometers used were SCIO® by Consumer Physics and NIRscan Nano by Texas Instruments. The SCIO instrument covers the third overtone region (740–1070 nm) and NIRscan Nano instrument covers the first and second overtone regions (900–1700 nm). Spectra from both instruments were pre-processed using the PRFFECTv2 software to eliminate noise and smooth the data for classification model construction. The models showed high accuracy, sensitivity and specificity with a range of 69–100% for binary classification (one class versus others) and range of 76–100% for multi-class classification of fibre material. This study shows for the first time the capability of pocket-size spectrometers coupled with random forest models for classification of fibre material in the presence of common contaminants in a rapid and non-destructive manner, and so is suitable for investigation of crime scenes. Spectrometer ; Textile ; Contamination ; Environmental science ; Materials science ; Polypropylene ; Computer science ; Pulp and paper industry ; Composite material ; Optics ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cultural Heritage Materials Analysis
Authors: Travis Kessler | Amina SubLaban | J. Hunter Mack
Source:  (article, 2022-11-10)
Field: Chemistry — Organic Chemistry

Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds. Cetane number ; Artificial neural network ; Outlier ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Mathematics ; Statistics ; Chemistry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Simion Kreimer | Ali Haghani | Aleksandra Binek | Alisse Hauspurg | Saeed Seyedmohammad | Alejandro Rivas | Amanda Momenzadeh | Jesse G. Meyer | Koen Raedschelders | Jennifer E. Van Eyk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-02)
Field: Chemistry — Spectroscopy

Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis Abstract Proteomic analysis on the scale that captures population and biological heterogeneity over hundreds to thousands of samples requires rapid mass spectrometry methods which maximize instrument utilization (IU) and proteome coverage while maintaining precise and reproducible quantification. To achieve this, a short liquid chromatography gradient paired to rapid mass spectrometry data acquisition can be used to reproducibly profile a moderate set of analytes. High throughput profiling at a limited depth is becoming an increasingly utilized strategy for tackling large sample sets but the time spent on loading the sample, flushing the column(s), and re-equilibrating the system reduces the ratio of meaningful data acquired to total operation time and IU. The dual-trap single-column configuration presented here maximizes IU in rapid analysis (15 min per sample) of blood and cell lysates by parallelizing trap column cleaning and sample loading and desalting with analysis of the previous sample. We achieved 90% IU in low micro-flow (9.5 µL/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6,000 precursor ions. The same IU was achieved for cell lysates, in which over 4,000 proteins (3,000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 minutes/sample. Thus, deployment of this dual-trap single column configuration enables high throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening. Mass spectrometry ; Analyte ; Chromatography ; Proteome ; Sample preparation ; Sample (material) ; Chemistry ; Trap (plumbing) ; Population ; Throughput ; Single-cell analysis ; Analytical Chemistry (journal) ; Computer science ; Physics ; Cell Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Naseeb Singh | V.K. Tewari | Prabir Kumar Biswas | L. K. Dhruw
Source: Research Square (preprint, 2022-10-27)
Field: Materials Science — Polymers and Plastics

Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls Convolutional neural network ; Segmentation ; Field (mathematics) ; Computer science ; Artificial intelligence ; Mathematics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Research in Cotton Cultivation
Authors: Alexandre Fenneteau | David Helbert | Pascal Bourdon | Imane M'Rabet | Christine Fernández-Maloigne | Rémy Guillevin
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A size-adaptative segmentation method for better detection of multiple sclerosis lesions The automatic segmentation of multiple sclerosis lesions in Magnetic Resonance Images is an open research area aiming to bring more reproducibility in the radiological visual assessment of the disease while reducing the burden of this time-consuming task. The development of artificial intelligence has led to significant improvements in computer aided diagnosis tools for radiology. It exists several efficient approaches for the voxel-wise segmentation of multiple sclerosis lesions using artificial neural networks and convolutional neural networks in particular. However, thesmall lesions are frequently neglected by those algorithms despite their radiological importance.We propose here an adaptable method to improve the detection of small lesions. The problem of small lesions detection mainly comes from the under-representation of those lesions at a voxel level and the segmentation loss function. The presented method consists in weighting the lesion importance during the training of a convolutional neural network depending on lesion size to correct the impact of voxel lesion imbalances. The designed weighting function is configurable and can be extended to other segmentation problems.With our method, the lesion segmentation computed with the Dice score is only slightly improved but the detection sensitivity is significantly improved at the cost of a limited augmentation of lesion false positive rate. The F1 score has been substantially improved with the correct set ofparameters. The improved prediction quality of segmentation maps has been confirmed visually with the help of a radiologist on a dataset acquired in our institution.The described method improves the lesion detection by giving more importance to small lesions during the multiple sclerosis lesions segmentation learning, bringing a more accurate help for the radiologist towards a better impact for the patient care. Segmentation ; Multiple sclerosis ; Artificial intelligence ; Computer science ; Medicine ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Image Processing Techniques and Applications
Authors: Z. Faidon Brotzakis
Source: FEBS Open Bio (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Guide for determination of protein structural ensembles by combining cryo‐<scp>EM</scp> data with metadynamics Metadynamics electron microscopy metaInference (MEMMI) is an integrative structural biology method that enables a rapid and accurate characterization of protein structural dynamics at the atomic level and the error in the cryo-EM experimental data, even in cases where conformations are separated by high energy barriers. It achieves this by incorporating (a) cryo-electron microscopy electron density maps with (b) metadynamic-enhanced-sampling molecular dynamics. Here, I showcase the setup and analysis protocol of MEMMI, used to discover the atomistic structural ensemble and error in the cryo-EM electron density map of the fuzzy coat of IAPP, a fibril implicated in type II diabetes. Metadynamics ; Cryo-electron microscopy ; Structural biology ; Molecular dynamics ; Electron microscope ; Biological system ; Computer science ; Physics ; Chemistry ; Crystallography ; Materials science ; Statistical physics ; Biology ; Computational chemistry ; Biochemistry ; Nuclear magnetic resonance Enzyme Structure and Function ; Protein Structure and Dynamics ; Photosynthetic Processes and Mechanisms
Authors: Devianti Devianti | Sufardi Sufardi | Mustaqimah Mustaqimah | Agus Arip Munawar
Source: Transdisciplinary Journal of Engineering & Science (article, 2022-02-17)
Field: Chemistry — Analytical Chemistry

Near Infrared Technology in Agricultural Sustainability: Rapid Prediction of Nitrogen Content from Organic Fertilizer Presented study employs the near infrared reflectance (NIRS) as a rapid and effective sensing technology in detecting and determining quality parameter of organic fertilizer in form of nitrogen content (N). a total of 10 organic fertilizers were used as samples made from agricultural waste. Near infrared spectra data were acquired and measured as absorbance for all samples in wavenumbers range 5,000 – 11,000 cm-1. On the other hand, actual N content was measured by means of standard laboratory procedures. Spectra data were corrected using de-trending second order (DT-2), standard normal variate (SNV) and combination of them (SNV+DT). Moreover, prediction models for N content determination were developed using principal component regression (PCR) followed by leverage cross validation. The results showed that N content can be predicted rapidly without involving chemical materials with maximum coefficient of determination are 0.98 for calibration and 0.95 for validation phase respectively. It may conclude that sensing technology based on NIRS can be applied as a rapid and effective method for N determination of organic fertilizers. Absorbance ; Calibration ; Fertilizer ; Near-infrared spectroscopy ; Leverage (statistics) ; Environmental science ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Statistics ; Environmental chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Muktar Bedaso | Million Meshesha | Chala Diriba
Source: Research Square (preprint, 2022-08-19)
Field: Chemistry — Analytical Chemistry

Comparing performance of classification algorithms to use for grading coffee raw quality by using image processing techniques. Abstract This study tries to apply digital image processing techniques towards sample Coffee raw quality value grading. More specifically, this study emphases on comparing performance of classification algorithms to use for grading Coffee raw quality by using image processing methods. To ease experimentation image processing phases are followed, including image acquisition, image preprocessing (image filtering and attribute selection), image analysis (segmentation, feature extraction and classification), and image understanding for raw quality image grading. Artificial Neural Network, support vector machine and K-Nearest neighbor classifiers on each classification parameter of morphology, color and the mixture of the two has been made. Experimental outcomes confirm that Artificial Neural Network classifier generated the highest performance of 89.45% accuracy as compared to support vector machine (with 83.75%) and K-Nearest neighbor classifier (with 77.85%). Thus, suitable selection of image processing and classification techniques paves the way for higher accuracy in the higher-level process for decision making. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Support vector machine ; Preprocessor ; Image processing ; Artificial neural network ; Feature selection ; Feature extraction ; k-nearest neighbors algorithm ; Grading (engineering) ; Digital image processing ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jérôme Sol | David R. Smith | Philipp del Hougne
Source: Nature Communications (preprint, 2022-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Meta-programmable analog differentiator We present wave-based signal differentiation with unprecedented fidelity and flexibility by purposefully perturbing overmoded random scattering systems such that zeros of their scattering matrices lie exactly at the desired locations on the real frequency axis. Our technique overcomes limitations of hitherto existing approaches based on few-mode systems, both regarding their extreme vulnerability to fabrication inaccuracies or environmental perturbations and their inability to maintain high fidelity under in-situ adaptability. We demonstrate our technique experimentally by placing a programmable metasurface with hundreds of degrees of freedom inside a 3D disordered metallic box. Regarding the integrability of wave processors, such repurposing of existing enclosures is an enticing alternative to fabricating miniaturized devices. Our over-the-air differentiator can process in parallel multiple signals on distinct carriers and maintains high fidelity when reprogrammed to different carriers. We also perform programmable higher-order differentiation. Conceivable applications include segmentation or compression of communication or radar signals and machine vision. Differentiator ; Computer science ; SIGNAL (programming language) ; Flexibility (engineering) ; Signal processing ; Filter (signal processing) ; Analog signal processing ; Envelope (radar) ; Electronic engineering ; Acoustics ; Radar ; Telecommunications ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Optical Imaging Technologies