jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aya Bendada | D. Boutchicha | Samir Khatir | Erica Magagnini | Roberto Capozucca | Mohamad Abdel Wahab
Source: Ghent University Academic Bibliography (Ghent University) (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Mechanical characterization of an epoxy panel reinforced by date palm petiole particle The past years were marked by an increase in the use of wood waste in civil and mechanical constructions. Date palm waste remains also one of the most solicited renewable and recyclable natural resources in the composition of composite materials. In Algeria, a great amount of this type of plant wastes accumulates every year. In order to make use of this waste, a new wood-epoxy composite material based on date palm petiole particleboard is developed. It makes use of date palm petiole particleboard as reinforcement and epoxy resin as matrix. The size of the particles reinforcement are between 1 similar to 3 mm and proportion of reinforcement used is 37%. In this work, experimental and numerical studies are conducted in order to characterize the wood fibre-epoxy plates. Firstly, experimental modal analysis test was carried out to determine Young's modulus of the elaborated material. Then, in order to validate the results, compression test was conducted. Furthermore, additional information about the shear modulus of this material is obtained by performing an experimental modal analysis to extract the first torsional mode. Moreover, a finite element model is developed using ANSYS software to simulate the vibration behaviour of the plates. The results show a good agreement with the experimental modal analysis, which confirms the values of Young's modulus and shear modulus. Epoxy ; Materials science ; Composite material ; Modal analysis ; Structural engineering ; Modulus ; Reinforcement ; Finite element method ; Engineering Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Tree Root and Stability Studies
Authors: Hikmet Yazıcı
Source: BioResources (article, 2020-09-10)
Field: Materials Science — Polymers and Plastics

Feasibility of using waste sweet bay wood (Laurus nobilis L.) in particleboard production Turkey holds a 95% market share of global sweet bay (Laurus nobilis L.) leaf trade, and it has 25 leaf processing and manufacturing facilities with different capacities. In this study, the usability of waste sweet bay wood (BW) that was removed from bay leaf processing plants was studied. For this purpose, three-layer particleboards were produced by mixing industrial chips (IC) and waste sweet bay wood chips (BWC) at a mixture rate of 0%, 25%, 50%, 75%, and 100%. For panel production, urea formaldehyde adhesive (UF) was used in 10% of the surface layers and in 8% of the middle layer based on dry chip weight. Some mechanical properties, such as bending strength (BS), modulus of elasticity in bending (MOE), internal bond strength (IB) of the test panels, thickness swelling (TS), and water absorption (WA) amounts, were determined. The results showed that all panel groups except group C (25% BWC + 75% IC) met the general purpose panel class (P1) requirements for use in dry conditions according to TS EN 312 (2012). In addition, group A panels (100% BWC) met the requirements of P2 class for the MOE and BS, and group E panels (75% BWC + 25% IC) met the P3 standards. The results showed that BWC could be used to produce particleboard for general purposes, including furniture. Laurus nobilis ; Adhesive ; Flexural strength ; Materials science ; Pulp and paper industry ; Bay ; Urea-formaldehyde ; Raw material ; Absorption of water ; Composite material ; Layer (electronics) ; Engineering ; Food science ; Chemistry Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Tree Root and Stability Studies
Authors: A. Anvari
Source: Journal of Engineering (article, 2020-06-29)
Field: Materials Science — Materials Chemistry

Thermomechanical Fatigue of Unidirectional Carbon Fiber/Epoxy Composite in Space Thermomechanical fatigue is one of the challenges for spacecrafts during space missions. As a result of the extreme temperature variation in space, thermal cycles are created, and due to the imposed mechanical loads to spacecrafts such as engine loads while they turn on and off, mechanical cycles are created. The worst structural fatigue for spacecrafts occurs when both thermal and mechanical cycles happen, simultaneously. The reason is that both thermal and mechanical cycles could cause stress concentration in the spacecraft structure. Therefore, the probability of crack initiation or propagation in the spacecraft structure increases. In this study, for the first time, novel thermomechanical fatigue relations are introduced to evaluate the safety of unidirectional carbon fiber/epoxy composite in the space environment. This composite material has been used in the spacecraft structure due to its lightweight and high strength. Furthermore, with applying thermal fatigue relations, thermally-safe planets, moons, and asteroids in the solar system for unidirectional carbon fiber/epoxy composite are identified. Spacecraft ; Epoxy ; Materials science ; Composite material ; Composite number ; Thermal ; Carbon fiber composite ; Stress (linguistics) ; Structural engineering ; Aerospace engineering ; Engineering ; Physics Carbon Nanotubes in Composites ; Cellular and Composite Structures ; Rocket and propulsion systems research
Authors: Hugo Krynauw | Rodaina Omar | Josepha Koehne | Georges Limbert | Neil H. Davies | Deon Bezuidenhout | Thomas Franz
Source: SN Applied Sciences (article, 2020-04-24)
Field: Materials Science — Biomaterials

Electrospun polyester-urethane scaffold preserves mechanical properties and exhibits strain stiffening during in situ tissue ingrowth and degradation Scaffold ; Polyester ; Materials science ; Biomedical engineering ; Electrospinning ; Tissue engineering ; Hydrolytic degradation ; In vivo ; Regeneration (biology) ; Elastic modulus ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Bone Tissue Engineering Materials
Authors: Liang Xue | Yueyue Zhu | Chuankai Yang | Sisil Kumarawadu
Source: Scientific Reports (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

Online quantitative partial discharge monitor based on interferometry Interferometry-based online partial discharge (PD) monitor presented in this paper can detect the occurrence of PD sensitively, evaluate the peak value of the discharge inception voltage with random waveform and the damage extent relatively cost effectively. The interferograms affected by the PD are collected online. By extracting the phase information of the interference fringes quantitatively, the peak value of the discharge inception voltage with random waveform can be retrieved real-time. Merits of the proposed method as an online quantitative PD monitor are validated via theoretical analysis as well as experimentations by the use of an artificially localized PD source. Furthermore, the proposed method can capture the light signal emitted by the discharge. Quite in contrast to many commonly used sensor-based methods, our approach avoids the need of amplifying the light signal strength making its practical implantation much convenient. The proposed method promises strong potential for field application. Waveform ; Partial discharge ; Interferometry ; Interference (communication) ; Computer science ; SIGNAL (programming language) ; Voltage ; Signal strength ; Acoustics ; Materials science ; Optics ; Physics ; Telecommunications ; Electrical engineering ; Channel (broadcasting) ; Engineering High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Magneto-Optical Properties and Applications
Authors: Song Zheng | Kafei Tang | Shuai Chen | Xiaoqing Zheng
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Operation Cost Optimization on an Ultralow Emission System Based on Improved Collaborative Optimization Due to the lack of an effective overall coordinated treatment method, it is difficult to achieve low cost and efficient removal of pollutants from coal-fired flue gas. This paper establishes a collaborative optimization model for ultralow emission systems, including a system level model of operation cost and three discipline level models for denitration, desulfurization, and dust removal. An improved collaborative optimization method with a dynamic penalty function is proposed to optimize an ultralow emission system. Simulation results show that the improved method achieves better global optimization and effectively reduces the operation cost of the system under ultralow emission constraints. Computer science ; Flue-gas desulfurization ; System optimization ; Optimization problem ; Flue gas ; Process engineering ; Mathematical optimization ; Engineering ; Waste management Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Air Quality Monitoring and Forecasting
Authors: J. Piątkowski | Bożena Gajdzik | A. Mesjasz
Source: Energies (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

Assessment of Material Durability of Steam Pipelines Based on Statistical Analysis of Strength Properties—Selected Models The paper presents a research method concerning the application of statistical prognostic models for assessment of material durability and operational reliability of steel for steam pipelines, whose operation has exceeded the working time of 100,000 h. Decisions on the admission of long-lived materials to work for power industry results from extensive diagnostic examinations are based on the results of tests of mechanical properties, microstructure degradation, and corrosion processes. Considering the economic reasons and available data published in diagnostic reports, the determination of failure-free operating time of steam pipelines is based on the results of static tensile tests—tensile strength (Rm); conventional yield point (Rp); elongation (A) and Vickers hardness (V), correlated with the operating time and the media type (fresh steam and secondarily super-heated steam) for the most sensitive element of a pipeline, namely the elbow. The results of changes in strength properties during operation are presented in the form of graphs of the analyzed material feature vs. operating time in the range from zero hours (for a new material) to 300,000 h, taking into account the impact of random and systematic disturbances within the adopted tolerance limits. It has been found that because of the R2 factor and significance level in the t-Student test for regression and correlation coefficients, exponential, hyperbolic and quadratic models are best fitted to empirical points. Based on the tensile strength results (Rm), it has been found that the forecast time of the steam pipeline ranges from 193,400 to 258,300 h. Taking the yield strength (Rp) into account, it has been ascertained that the time ranges from 225,000 to 293,000 h, and for the working time forecast of steam pipelines based on Vickers hardness results, it ranges from 192,100 to 246,800 h. Ultimate tensile strength ; Pipeline transport ; Durability ; Pipeline (software) ; Structural engineering ; Materials science ; Forensic engineering ; Engineering ; Composite material ; Mechanical engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Environmental and Industrial Safety
Authors: Hugo Krynauw | Jannik Buescher | Josepha Koehne | Loes Verrijt | Georges Limbert | Neil H. Davies | Deon Bezuidenhout | Thomas Franz
Source: Cardiovascular Engineering and Technology (article, 2020-07-01)
Field: Materials Science — Biomaterials

Tissue Ingrowth Markedly Reduces Mechanical Anisotropy and Stiffness in Fibre Direction of Highly Aligned Electrospun Polyurethane Scaffolds Scaffold ; Materials science ; Biomedical engineering ; Electrospinning ; Tissue engineering ; Stiffness ; Composite material ; Polymer ; Medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Conducting polymers and applications
Authors: Jian Shi | Jingcheng Xiao | Jiapeng Li | Xinwen Wang | Lucy Her | Matthew J. Sorensen | Hao‐Jie Zhu
Source: PROTEOMICS (article, 2020-10-21)
Field: Chemistry — Spectroscopy

FRACPRED‐2D‐PRM: A Fraction Prediction Algorithm‐Assisted 2D Liquid Chromatography‐Based Parallel Reaction Monitoring‐Mass Spectrometry Approach for Measuring Low‐Abundance Proteins in Human Plasma Multidimensional fractionation-based enrichment methods improve the sensitivity of proteomic analysis for low-abundance proteins. However, a major limitation of conventional multidimensional proteomics is the extensive labor and instrument time required for analyzing many fractions obtained from the first dimension separation. Here, a fraction prediction algorithm-assisted 2D LC-based parallel reaction monitoring-mass spectrometry (FRACPRED-2D-PRM) approach for measuring low-abundance proteins in human plasma is presented. Plasma digests are separated by the first dimension high-pH RP-LC with data-dependent acquisition (DDA). The FRACPRED algorithm is then usedto predict the retention times of undetectable target peptides according to those of other abundant plasma peptides during the first dimension separation. Fractions predicted to contain target peptides are analyzed by the second dimension low-pH nano RP-LC PRM. The accuracy and robustness of fraction prediction with the FRACPRED algorithm are demonstrated by measuring two low-abundance proteins, aldolase B and carboxylesterase 1, in human plasma. The FRACPRED-2D-PRM proteomics approach demonstrates markedly improved efficiency and sensitivity over conventional 2D-LC proteomics assays. It is expected that this approach will be widely used in the study of low-abundance proteins in plasma and other complex biological samples. Chromatography ; Proteomics ; Mass spectrometry ; Chemistry ; Proteome ; Fractionation ; Human plasma ; Analytical Chemistry (journal) ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Shubhangi Neware
Source: International Journal of Advanced Trends in Computer Science and Engineering (article, 2020-02-15)
Field: Chemistry — Analytical Chemistry

Fruit Grading System using k means clustering and Artificial Neural Network Automation is in great demand in the field of agriculture science, to check quality of fruits, vegetables and crops. Automation helps to increase quality of fruits, vegetables and crops results in economic growth and productivity of the country. Classifying fruits or vegetables into different grades helps to evaluate cost based on grading. Automatic quality evaluation is in great demand in the export market. Cost of fruits, the consumer's preference and choice are greatly depend on appearance of fruits and vegetables. In India mostly the sorting and grading is being done manually by human being but it is not consistent. It is also very time consuming process. In addition to this it is expensive and grading decision may vary by surrounding influence. Therefore an automated fruit grading system is required to grade the fruits. This paper proposing fruit grading system using fruit image processing and classification. Using fruit classification grades of fruits can be determined easily. Fruit grading is based on attribute of fruits such as size, color, texture, shape and defects or disease of fruit. Artificial neural network ; Cluster analysis ; Computer science ; Grading (engineering) ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Jia Jia Leam | Cheng Seong Khor | Sarat C. Dass
Source: Journal of Petroleum Exploration and Production Technology (article, 2020-11-12)
Field: Chemistry — Analytical Chemistry

Saybolt color prediction for condensates and light crude oils Abstract Saybolt color determination is one of the techniques used to evaluate the quality of petroleum products as an indicator of the degree of refinement. As color is a property readily observed by operators, conventional procedures require operators to determine Saybolt color either through direct visual observation or through Saybolt chromometers. These methods are subjective due to the variability in perception of colors across different observers and may be influenced by external factors such as the level of illuminance. Digital oil color analyzers, on the other hand, cost almost four times as much as Saybolt chromometers. An alternative approach to color measurement is to develop a correlation model between Saybolt color with the physical and chemical properties of condensates and light crude oils from Malaysian oil and gas fields. This work applies several multiple linear regression techniques (such as stepwise regression) performed both manually and using the R software (version 3.6.1) to obtain statistically significant results. The step, regsubsets and glmulti functions from R are explored to develop the correlation model which predicts Saybolt color using only identified key properties, overcoming the possible drawbacks associated with conventional laboratory analysis. The models developed through these different techniques are analyzed and compared based on criteria indicated through the coefficient of multiple determination, R 2 and F- tests to infer on suitable regression approaches. Results obtained from these regression methods for models with and without interaction terms report deviations of less than 5% for 75% of the samples used for validation. Linear regression ; Correlation coefficient ; Property (philosophy) ; Artificial intelligence ; Stepwise regression ; Mathematics ; Statistics ; Computer science ; Regression ; Biological system ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification
Authors: Hsin‐Yung Yen | Idlir Liko | Joseph Gault | Di Wu | Weston B. Struwe | Carol V. Robinson
Source: Angewandte Chemie (article, 2020-07-07)
Field: Chemistry — Spectroscopy

Correlating Glycoforms of DC‐SIGN with Stability Using a Combination of Enzymatic Digestion and Ion Mobility Mass Spectrometry Abstract The immune scavenger protein DC‐SIGN interacts with glycosylated proteins and has a putative role in facilitating viral infection. How these recognition events take place with different viruses is not clear and the effects of glycosylation on the folding and stability of DC‐SIGN have not been reported. Herein, we report the development and application of a mass‐spectrometry‐based approach to both uncover and characterise the effects of O‐glycans on the stability of DC‐SIGN. We first quantify the Core 1 and 2 O‐glycan structures on the carbohydrate recognition and extracellular domains of the protein using sequential exoglycosidase sequencing. Using ion mobility mass spectrometry, we show how specific O‐glycans, and/or single monosaccharide substitutions, alter both the overall collision cross section and the gas‐phase stability of the DC‐SIGN isoforms. We find that rather than the mass or length of glycoprotein modifications, the stability of DC‐SIGN is better correlated with the number of glycosylation sites. Exoglycosidase ; Chemistry ; Glycan ; Glycosylation ; DC-SIGN ; Mass spectrometry ; Glycoprotein ; Ion-mobility spectrometry ; N-linked glycosylation ; Monosaccharide ; Biochemistry ; Biophysics ; Chromatography ; Immune system ; Biology Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Jonas Pfab | Dong Si
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-02-13)
Field: Materials Science — Materials Chemistry

DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Density Maps of Protein Complexes Abstract Motivation Accurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm. Results We trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours. Availability The repository of this project will be published. Contact dongsi@uw.edu Supplementary information Supplementary data will be available at Bioinformatics online. Cryo-electron microscopy ; Resolution (logic) ; Amino acid ; Field (mathematics) ; Computer science ; Protein structure ; Protein structure prediction ; Set (abstract data type) ; Biological system ; Algorithm ; Crystallography ; Artificial intelligence ; Chemistry ; Biology ; Mathematics ; Biochemistry Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: M. Mouelhi | Arnaud Cuisset | Francis Hindle | C. Jellali | S. Galalou | H. Aroui | R. Bocquet | G. Mouret | F. Rohart
Source: Journal of Quantitative Spectroscopy and Radiative Transfer (article, 2020-03-10)
Field: Chemistry — Spectroscopy

Self and N2 broadening coefficients of H2S probed by submillimeter spectroscopy: Comparison with IR measurements and semi-classical calculations Rotational–vibrational spectroscopy ; Spectral line ; Atomic physics ; Physics ; Voigt profile ; Spectroscopy ; Spectrometer ; Materials science ; Nuclear magnetic resonance ; Optics ; Excited state ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies