jablonkagroup/rise_ai_scientists huggingface dataset:
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, 2021-08-16)
Field: Chemistry — Organic Chemistry

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification &lt;span&gt;Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most &lt;span&gt;accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a&lt;/span&gt; benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.&lt;/span&gt; Agarwood ; Multilayer perceptron ; Backpropagation ; Stepwise regression ; Linear regression ; Mathematics ; Computer science ; Artificial intelligence ; Grading (engineering) ; Artificial neural network ; Statistics ; Engineering ; Medicine Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Industrial Vision Systems and Defect Detection
Authors: Mohd Azrizal Fauzi | Mohd Fadzil Arshad | Noorsuhada Md Nor
Source: Civil Engineering and Architecture (article, 2021-09-01)
Field: Materials Science — General Materials Science

The Need of Statistical Approach for Optimising Mixture Design of Controlled Low-Strength Materials The statistical phase method was introduced to achieve optimal mixing controlled low-strength material (CLSM) proportions, utilising statistical studies. There is no well-known explicit formulation for predicting hardened properties (in terms of unconfined compressive strength (UCS)) of CLSM. The proposed approach to optimising CLSM mix design is demonstrated in the most common case where experimental mixing was considered in compliance with the full factorial experimental design involving three variables with two levels (2<sup>3</sup>) and the Box-Wilson central composite design (CCD) method. Twenty CLSMs with six replicates (one hundred and twenty specimens) were considered in changing the levels of the main factors that affect CLSM compression strength, the water/cementitious ratio (2.53-2.73) and the wastepaper ash (WSA) percentage (50-100%) and cementitious materials content (160-200 kg/m<sup>3</sup>). The experimental results were used to analyse variance and design a UCS polynomial regression equation for design factors considered in this research. In order to emphasise how to optimise CLSM mixtures with different options, a statistical model was developed. Reliability engineering ; Structural engineering ; Engineering ; Statistics ; Mathematics Material Properties and Applications
Authors: Sebastiaan P. Huber | Emanuele Bosoni | Marnik Bercx | J. D. Broder | Augustin Degomme | Vladimir Dikan | Kristjan Eimre | Espen Flage−Larsen | Alberto Garcı́a | Luigi Genovese | Dominik Gresch | Conrad Johnston | Guido Petretto | Samuel Poncé | Gian‐Marco Rignanese | Christopher J. Sewell | Berend Smit | Vasily Tseplyaev | Martin Uhrin | Daniel Wortmann | Aliaksandr V. Yakutovich | Austin Zadoks | Pezhman Zarabadi–Poor | Bonan Zhu | Nicola Marzari | Giovanni Pizzi
Source: npj Computational Materials (preprint, 2021-08-19)
Field: Materials Science — Materials Chemistry

Common workflows for computing material properties using different quantum engines Abstract The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. Workflow ; Computer science ; Interoperability ; Robustness (evolution) ; Task (project management) ; Software engineering ; Distributed computing ; Theoretical computer science ; Database ; Systems engineering ; World Wide Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Data Storage Technologies
Authors: V. M. Kolokoltsev | E. V. Petrochenko | O. S. Molochkova
Source: CIS Iron and Steel Review (article, 2021-12-29)
Field: Materials Science — Materials Chemistry

Influence of aluminium and niobium alloying on phase composition, structure and properties of heat- and wear-resistant cast iron of Cr-Mn-Ni-Ti system The paper presents the data on phase composition and structure forming for the alloys and oxide layers, on distribution of elements among the alloy structural components and oxidation surface through the depth of oxide and sub-oxide layers, on variation of wear resistance, scale resistance, growing stability and mechanical properties of cast iron of Cr-Mn-Ni-Ti-Al-Nb system depending on different aluminium and niobium content and thermal accumulating capacity of a casting mould. Complex carbides (Nb, Ti)C are forming in white cast iron during niobium alloying. Quantitative metallographic analysis of (Nb, Ti)C carbides and (Cr, Fe, Mn)7C3 complex carbides was carried out on the samples with examined composition. The tests for scale resistance were conducted, structure and properties of cast iron were investigated. It was determined that chemical composition and structure of oxide layers depend on distribution of alloying elements among the alloy structural components. It was established that the areas of oxide film surface layer, which were formed on eutectics, contain mainly manganese; its concentration is more than 65 %, while Al is 4 % and Cr is 1 %. Manganese leads to increase of defects amount, such as pores, micro-cracks and vacancies, in oxide film during high-temperature gas oxidation; penetration ability of this film also increases, what has a negative effect on metal resistance to further destruction caused by oxidation. The film becomes porous, its thickness enlarges. Aluminium provides favourable influence on forming of the thin protective spinel-type films (dense substance with good metal adhesion) with minimal amount of defects; diffusion through such oxide film is very difficult. The areas of oxide layer, which were formed on austenite dendrites, contain mainly aluminium; its concentration is more than 24 %, while Mn is 16 % and Cr is 12 %. High aluminium content provides small film thickness. Joint alloying by aluminium and niobium leads to simultaneous increase of heat resistance and wear resistance. Wear resistance increased as a result of enlargement of the part of primary carbides (Nb, Ti)C with high hardness in the structure of cast iron. Composition of oxide films includes aluminium which strengthens their protective properties and rises of the alloy scale resistance. Alloying by niobium leads to secondary hardening during cooling in a casting mould. Dispersion particles of М7С3 carbides are forming in solid state, thereby no structure degradation occurs during testing at increased temperatures, and growing stability rises. Niobium ; Metallurgy ; Materials science ; Aluminium ; Phase (matter) ; Composition (language) ; Chemistry Metal Alloys Wear and Properties ; Materials Engineering and Processing ; Material Properties and Applications
Authors: Ying Tong | Jiang Zhao | Guo-zheng Quan
Source: High Temperature Materials and Processes (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Visualization of the damage evolution for Ti–3Al–2Mo–2Zr alloy during a uniaxial tensile process using a microvoids proliferation damage model Abstract Understanding the damage evolution of alloys during a plastic deformation process is significant to the structural design of components and accident prevention. In order to visualize the damage evolution in the plastic deformation of Ti–3Al–2Mo–2Zr alloy, a series of uniaxial tensile experiments for this alloy were carried out under the strain rates of 0.1–10 s −1 at room temperature, and the stress–strain curves were achieved. On the other hand, the finite element (FE) models of these uniaxial tensile processes were established. A microvoids proliferation model, Gurson–Tvergaard–Needleman (GTN) damage model, was implanted into the uniaxial tensile models, and the simulated stress–strain curves corresponding to different GTN parameter combinations were obtained. Based on the simulated and experimental stress–strain curves, the GTN parameters of this alloy were solved by response surface methodology (RSM). The solved GTN parameters suggest that higher strain rate can enhance the proliferation and coalescence of microvoids. Furthermore, the uniaxial tensile tests over different strain rates were simulated using the solved GTN parameters. Then, the damage processes were visualized and evaluated. The result shows that the degradation speed of this alloy is slow at the initial stage of the tensile deformation and then accelerates once the voids volume fraction reaches a critical value. Materials science ; Ultimate tensile strength ; Alloy ; Coalescence (physics) ; Composite material ; Volume fraction ; Deformation (meteorology) ; Strain rate ; Plasticity High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Juliana Verdan Silva Camilo | Anna Áurea Gomes Ferreira | Josane Assis Costa | Cláudia R. E. Mansur | Fernando Gomes de Souza
Source: Brazilian Journal of Experimental Design Data Analysis and Inferential Statistics (article, 2021-12-01)
Field: Chemistry — Organic Chemistry

Surface modeling as a tool for constructing pseudo ternary diagrams Microemulsions have been developed and evaluated for application in various fields. For their development, ternary phase diagrams are initially constructed, obtained by investigating mixtures of the aqueous and oil phases and a surfactant, to determine the regions composed of liquid microemulsions. However, the construction of these diagrams requires many steps, so simplification of this method can be useful for the formulation and application of these microemulsified systems. Therefore, this article proposes a method to obtain these diagrams employing mathematical modeling, by testing systems composed of kerosene (as oil phase), a nonionic surfactant based on ethoxylated nonylphenol (NP80), and salt water. The study aimed to ascertain which models (linear, quadratic or special cubic) were able to describe the empirical observations, i.e., the ternary phase diagram containing 99 points. The results obtained showed that a quadratic model with a smaller sample (31 points) was able to represent these systems with 61% variability of the observed data and a confidence level higher than 99.9%. The smaller sample (31 points) was defined by means extrapolation lines, as described in the standard ISO11358, permitting identifying the liquid microemulsion regions of the system composed of kerosene, NP80 and salt water. This result is very promising since it permits determining the conditions of interest with significantly less experimental work, and commensurate reduction of the final cost of the experiments. Microemulsion ; Kerosene ; Ternary operation ; Extrapolation ; Phase diagram ; Pulmonary surfactant ; Ternary numeral system ; Quadratic equation ; Materials science ; Thermodynamics ; Phase (matter) ; Biological system ; Computer science ; Process engineering ; Mathematics ; Chemistry ; Statistics ; Physics ; Organic chemistry ; Geometry ; Engineering Surfactants and Colloidal Systems ; Adsorption, diffusion, and thermodynamic properties of materials ; Surface Roughness and Optical Measurements
Authors: Ali Kafash Hoshiar | Shahriar Dadras Javan | Tuan‐Anh Le | Mohammad Reza Hairi Yazdi | Jungwon Yoon
Source: Nanomaterials (article, 2021-10-17)
Field: Materials Science — Biomaterials

Studies on Aggregated Nanoparticles Steering during Deep Brain Membrane Crossing Many central nervous system (CNS) diseases, such as Alzheimer's disease (AD), affect the deep brain region, which hinders their effective treatment. The hippocampus, a deep brain area critical for learning and memory, is especially vulnerable to damage during early stages of AD. Magnetic drug targeting has shown high potential in delivering drugs to a targeted disease site effectively by applying a strong electromagnetic force. This study illustrates a nanotechnology-based scheme for delivering magnetic nanoparticles (MNP) to the deep brain region. First, we developed a mathematical model and a molecular dynamic simulation to analyze membrane crossing, and to study the effects of particle size, aggregation, and crossing velocities. Then, using in vitro experiments, we studied effective parameters in aggregation. We have also studied the process and environmental parameters. We have demonstrated that aggregation size can be controlled when particles are subjected to external electromagnetic fields. Our simulations and experimental studies can be used for capturing MNPs in brain, the transport of particles across the intact BBB and deep region targeting. These results are in line with previous in vivo studies and establish an effective strategy for deep brain region targeting with drug loaded MNPs through the application of an external electromagnetic field. Nanoparticle ; Membrane ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Biochemistry Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Lipid Membrane Structure and Behavior
Authors: Anwar Hasmy | Simona Ispas | Bernard Hehlen
Source: Research Square (preprint, 2021-04-19)
Field: Materials Science — Materials Chemistry

Percolation transitions in compressed SiO2 glasses Abstract Amorphous-amorphous transformations under pressure are generally explained by changes in the local structure from low to higher fold coordinated polyhedra [1-4]. However, as the notion of scale invariance at the critical thresholds has not been addressed, it is still unclear whether these transformations could be associated to true phase transitions. Here we report ab initio based calculations of compressed silica (SiO 2 ) glasses showing that the structural changes from low- to high-density amorphous structures occur through a sequence of percolation transitions. When the pressure is increased up to 82 GPa, a series of long range ('infinite') percolating clusters built up by corner- or edge-shared tetrahedra, pentahedra, and eventually octahedra, emerge at some critical pressures and replace the previous phase of lower fold coordinated polyhedra and lower connectivity. This mechanism provides a natural explanation for the well-known mechanical anomaly around 3 GPa as well as for the structural irreversibility beyond 10 GPa, among others. Some of the amorphous structures that have been discovered mimic those of coesite IV and V crystals reported recently [5,6], highlighting the major role of SiO 5 pentahedra-based polyamorphs in the densification process of vitreous silica. Our observations demonstrate that the percolation theory provides a robust framework to understand the nature and the pathway of the amorphous-amorphous transformations, and open a new avenue to predict unraveled amorphous solid phases and related liquids [7,8]. Percolation (cognitive psychology) ; Materials science ; Statistical physics ; Physics ; Psychology Material Dynamics and Properties ; Glass properties and applications ; Mineralogy and Gemology Studies
Authors: Zhi‐Chun Gu | Shou-Rui Huang | Dong Li | Qin Zhou | Jing Wang | Bo Fu | Jin Chen
Source: Research Square (preprint, 2021-02-23)
Field: Chemistry — Analytical Chemistry

An adapted neural-fuzzy inference system model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in patients after heart valve replacement Abstract Background Tailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and huge inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). Methods This retrospective study enrolled patients who underwent HVR between June 1, 2012 and June 1, 2016 from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose, and the models’ predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general liner model and created two ANFIS models using imbalanced and balanced training sets. We finally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Stratified analyses were conducted across warfarin doses (low, medium, and high doses). Results A total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2,820 in the balanced training set; 1,511 in the internal validation set; and 1,511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (&lt; 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage: 74.39–78.16%, MAE: 0.37 mg/daily, MSE: 0.39 mg/daily) and the balanced model (ideal prediction percentage: 73.46–75.31%, MAE: 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a significantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P &lt; 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort confirmed this finding. Conclusions The ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low- and high-dose warfarin groups. Dosing ; Warfarin ; Adaptive neuro fuzzy inference system ; Computer science ; Fuzzy logic ; Artificial intelligence ; Data mining ; Medicine ; Internal medicine ; Fuzzy control system ; Atrial fibrillation Analytical Methods in Pharmaceuticals
Authors: Yannick Weesepoel | Freek Daniels | Martin Alewijn | Mireille Baart | Judith Müller-Maatsch | Görkem Şimşek-Şenel | H. Rijgersberg | J.L. Top | Edith Feskens
Source:  (article, 2021-03-17)
Field: Chemistry — Analytical Chemistry

Towards the universal assessment of dietary intake using spectral imaging solutions Personal(ised) sensors or ”wearables” could, in the future, be applied to provide personalized nutritional advice. A challenge here is to assess dietary intake. To date, this has mainly been done with questionnaires or interviews, for example, food frequency questionnaires or 24-hour recalls. However, these methods are prone to bias due to conscious or unconscious misreports, and more objective measurement methods are desirable. By applying and combining spectral imaging techniques like hyperspectral imaging (HSI) and RGB-depth (RGBD) imaging, information on the macro-composition, identity and quantity of food consumed can be obtained. In this work, we demonstrate that HSI was effective for estimation of the fat content and layer thickness of butter on slices of bread with root mean squared errors of predictions of 4.6 (fat w/w %) and 0.056 mm respectively. Identification and volume estimation of vegetables and preparation methods were successful with RGBD imaging. Using Convolutional Neural Networks, all samples were correctly identified. For volume estimations of vegetables, R-square scores between 0.80 – 0.96 were achieved. Computer science ; Environmental science Spectroscopy and Chemometric Analyses ; Nutritional Studies and Diet