jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jay Matlock | Stephen Warwick | Philipp Sharikov | Jenner Richards | Afzal Suleman
Source: Transactions of the Canadian Society for Mechanical Engineering (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Evaluation of energy efficient propulsion technologies for unmanned aerial vehicles The transition to cleaner, more efficient and longer-endurance aircraft is at the forefront of research and development in air vehicles. The focus of this research is to experimentally evaluate hybrid propulsion and energy harvesting systems in unmanned aerial vehicles (UAVs). Hybrid systems offer benefits over conventional gasoline and electric systems including lower environmental impacts, reduced fuel consumption, redundancy, and distributed propulsion. Additional energy efficiency can be achieved by harvesting some of the thermal energy of the exhaust gases. The development and experimental evaluation of a hybrid propulsion UAV was carried out at the University of Victoria Center for Aerospace Research (UVIC-CfAR) in the framework of the Green Aviation Research &amp; Development Network (GARDN) grant. The work involved the development of a framework to evaluate UAV hybrid propulsion efficiency, and to predict the amount of power harvestable from thermoelectric generators (TEGs). The objective was to combine all of the components into a modular test bench that will allow the performance of the parallel hybrid system to be characterized and compared with theoretical results. Several experiments were performed to collect performance data of various components including a triple-TEG system connected to an engine, and system variables were modified to simulate flight profiles. Propulsion ; Automotive engineering ; Hybrid vehicle ; Fuel efficiency ; In-space propulsion technologies ; Test bench ; Modular design ; Hybrid system ; Aerospace ; Efficient energy use ; Energy consumption ; Aerospace engineering ; Electrically powered spacecraft propulsion ; Computer science ; Engineering ; Power (physics) ; Mechanical engineering ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Advanced Battery Technologies Research ; Advanced Aircraft Design and Technologies
Authors: Ivana Šestak | Lea Mihaljevski Boltek | Milan Mesić | Željka Zgorelec | Aleksandra Perčin
Source: Journal of Central European Agriculture (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral sensing of soil pH, total carbon and total nitrogen content based on linear and non-linear calibration methods Soil properties can be estimated non-destructively by visible and near infrared (VNIR) reflectance spectroscopy. However, results of calibration models differ in dependence of measurement precision, spectral range, variability of soil properties and calibration methods used for prediction. The objective of research was to estimate the ability of hyperspectral VNIR sensing for field-scale prediction of soil pH, total carbon (TC %) and total nitrogen (TN %) content in arable Stagnosols. Total of 200 soil samples taken from field experiment (soil depth: 30 cm; sampling grid: 15x15 m; 2016) were scanned in laboratory using portable spectroradiometer (FieldSpec®3, 350-1,050 nm). Partial least squares regression (PLSR) and artificial neural networks (ANN) were used to build prediction models of selected soil properties based on VNIR spectra. Very strong to complete correlation and low root mean squared error were obtained between predicted and measured values for the calibration and validation dataset, and both prediction methods. ANN models were more efficient in capturing the complex link between selected soil properties and soil reflectance spectra than PLSR. Calibrations defined in this research should help to support site-specific soil survey as addition to standard laboratory analysis, and represent valuable input for spectral database that should be built for Croatian soils. VNIR ; Partial least squares regression ; Hyperspectral imaging ; Calibration ; Remote sensing ; Spectroradiometer ; Environmental science ; Soil test ; Soil science ; Mean squared error ; Linear regression ; Soil water ; Sampling (signal processing) ; Mathematics ; Reflectivity ; Statistics ; Computer science ; Geology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Smart Agriculture and AI
Authors: Xuan Hao | Jinfeng Liu | Hongyuan Luo | Yanqiang Han | Wenxin Hu | Jinyun Liu | Jinjin Li | Xiao He
Source: Crystals (article, 2019-05-17)
Field: Chemistry — Physical and Theoretical Chemistry

Crystal Structure Optimization and Gibbs Free Energy Comparison of Five Sulfathiazole Polymorphs by the Embedded Fragment QM Method at the DFT Level Molecular crystal plays an important role in many fields of science and technology, but it often crystallizes in different polymorphs with different physical properties. To guide the experimental synthesis of candidate materials, the atomic-scale model is frequently used to predict the most stable polymorph and its structural properties. Here, we show how an ab initio method can be used to achieve a rapid and accurate prediction of sulfathiazole crystal polymorphs (an antibiotic drug), based on the Gibbs free energy calculation and Raman spectra analysis. At the atmospheric pressure and the temperature of 300 K, we demonstrate that form III (FIII) is the most stable structure of sulfathiazole. The agreement between the predicted and experimental crystal structures corresponds to the order of stability for five sulfathiazole polymorphs as FI &lt; FV &lt; FIV &lt; FII &lt; FIII, which is achieved by employing the density functional theory (DFT) calculations. Sulfathiazole ; Gibbs free energy ; Crystal structure prediction ; Raman spectroscopy ; Ab initio ; Crystal structure ; Crystal (programming language) ; Density functional theory ; Crystallography ; Chemistry ; Polymorphism (computer science) ; Materials science ; Computational chemistry ; Thermodynamics ; Physics Crystallography and molecular interactions ; Phenothiazines and Benzothiazines Synthesis and Activities ; Advanced Chemical Physics Studies
Authors: Dedes Amertaning | Zaenal Bachrudin | Jamhari | Koo Bok Chin | Yuny Erwanto
Source: Pakistan Journal of Nutrition (article, 2019-04-15)
Field: Materials Science — Biomaterials

Characteristics of Gelatin Extracted from Indonesian Local Cattle Hides Using Acid and Base Curing Background and Objective: Gelatin quality is affected by factors including the animal source materials, the age of the animal and curing and extraction processes. However, the effects of the cattle breed on gelatin quality have not been reported. This study aimed to determine the characteristics of gelatin from Indonesian local cattle [Bali, Madura and Ongole Crossbred (OC)] hides with acid and base curing. Materials and Methods: The hides were treated with 0.25 M hydrochloric acid and 0.25 M sodium hydroxide. The data were analyzed using a completely randomized design with a nested pattern and three replicates. The mean differences in the data were analyzed using Duncans multiple range test. Results: The highest gelatin yield was 11.04%, the pH was 9.91, the protein content was 83.45%, the soluble protein content was 12.82 mg mLG 1 , the viscosity was 8 cP and the gel strength was 166 Bloom. The A w , moisture content, fat content and color values of the gelatins did not differ significantly (p>0.05) among the cattle breeds or between the curing treatments. The molecular weight determinations of the HCl-derived gelatin showed clearer bands than those from NaOH, including showing a band for low-molecular weight proteins (size range between 10 and 25 kDa). Fourier transform infrared (FTIR) spectroscopy showed that while the absorbance intensities of the O-H, C-H, C / C, C / N, C = C, C-C and C-O functional groups from the gelatin samples varied, the peaks were still indicative of the functional groups typically present in gelatin. The most abundant amino acid was glycine at 48.73 g/100 g, followed by glutamic acid at 18.69 g/100 g and arginine at 14.77 g/100 g and histidine was not detected. Conclusion: The use of 0.25 M HCl was more effective and efficient than using 0.25 M NaOH. The amino acid content from the OC hide treated with NaOH was higher than those obtained with the other treatments. Indonesian ; Curing (chemistry) ; Gelatin ; Chemistry ; Food science ; Base (topology) ; Pulp and paper industry ; Biochemistry ; Mathematics ; Polymer chemistry ; Engineering Collagen: Extraction and Characterization
Authors: Bin Zhang | Jin Wang | Yang Wang | Yu Wang | Ziran Li
Source: Materials (article, 2019-02-22)
Field: Materials Science — Materials Chemistry

Strain-Rate-Dependent Tensile Response of Ti–5Al–2.5Sn Alloy This study is an experimental investigation on the tensile responses of Ti⁻5Al⁻2.5Sn alloy over a wide range of strain rates. Uniaxial tension tests within the rate range of 10<sup>-3</sup>⁻10¹ s<sup>-1</sup> are performed using a hydraulic driven MTS810 machine and a moderate strain-rate testing system. The high-rate uniaxial tension and tension recovery tests are conducted using a split-Hopkinson tension bar to obtain the adiabatic and isothermal stress⁻strain responses of the alloy under dynamic loading conditions. The experimental results show that the value of the initial yield stress increases with the increasing strain rate, while the strain rate sensitivity is greater at high strain rates. The isothermal strain-hardening behavior changes little with the strain rate, and the adiabatic temperature rise is the main reason for the reduction of the strain-hardening rate during high strain-rate tension. The electron backscatter diffraction (EBSD) analysis of the post-deformed samples indicates that there are deformation twins under quasi-static and high-rate tensile loadings. Scanning electron microscope (SEM) micrographs of the fracture surfaces of the post-deformed samples show dimple-like features. The Zerilli⁻Armstrong model is modified to incorporate the thermal-softening effect of the adiabatic temperature rise at high strain rates and describe the tension responses of Ti⁻5Al⁻2.5Sn alloy over strain rates from quasi-static to 1050 s<sup>-1</sup>. Materials science ; Strain rate ; Ultimate tensile strength ; Alloy ; Strain (injury) ; Composite material ; Tensile strain ; Titanium alloy ; Metallurgy ; Medicine Titanium Alloys Microstructure and Properties ; High-Velocity Impact and Material Behavior ; Intermetallics and Advanced Alloy Properties
Authors: Mareike Dorozynski | D. Clermont | Franz Rottensteiner
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (article, 2019-08-21)
Field: Materials Science — Biomaterials

MULTI-TASK DEEP LEARNING WITH INCOMPLETE TRAINING SAMPLES FOR THEIMAGE-BASED PREDICTION OF VARIABLES DESCRIBING SILK FABRICS Abstract. This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW (http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data and investigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for the feature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The training procedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fully labeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to singletask learning based on two different class structures. We achieve overall accuracies of 92–95 % and average F1-scores of 88–90 % in our best experiments. Artificial intelligence ; Computer science ; Task (project management) ; Context (archaeology) ; Convolutional neural network ; Machine learning ; Artificial neural network ; Pattern recognition (psychology) ; SILK ; Feature extraction ; Feature (linguistics) ; Deep learning ; Class (philosophy) ; Contextual image classification ; Image (mathematics) ; Engineering Silk-based biomaterials and applications
Authors: Ombretta Paladino | Francesca Fissore | Matteo Neviani
Source: Journal of Sensor and Actuator Networks (article, 2019-10-20)
Field: Chemistry — Analytical Chemistry

A Low-Cost Monitoring System and Operating Database for Quality Control in Small Food Processing Industry The use of completely automated systems for collecting sensor data with the aim of monitoring and controlling the quality of small-scale food processes is not widespread. Small and micro-enterprises usually do not carry out their own precompetitive research or prototype development as regards to automation technologies. This study proposes a web-based, low-cost monitoring and supervisory control and data acquisition (SCADA) system whose kernel is available for free, as a possible solution that could be adopted by these food producers. It is mainly based on open SW/HW so as its configuration is adaptable to the application and type of plant. It presents a modular architecture and its main functionalities encompass the acquisition, management, aggregation and visualization of process data, providing an operating database. It also provides food tracking and process quality control: The time series are browsable due to QR-Code generation and different early warning detection strategies are implemented. A tool for solving migration problems based on Fick’s equation is offered as a packaging decision support system. Computer science ; Modular design ; SCADA ; Automation ; Database ; Scalability ; Process (computing) ; Embedded system ; Process management ; Operating system Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Control Systems Optimization
Authors: Ohkyung Kwon | Hyung Gu Lee | Sang-Yun Yang | Hyunbin Kim | Se‐Yeong Park | In-Gyu Choi | Hwanmyeong Yeo
Source: Journal of the Korean Wood Science and Technology (article, 2019-05-01)
Field: Chemistry — Organic Chemistry

Performance Enhancement of Automatic Wood Classification of Korean Softwood by Ensembles of Convolutional Neural Networks In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of 128 × 128 × 3 pixels via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3. Softwood ; Transverse plane ; Convolutional neural network ; Larch ; Artificial intelligence ; Ensemble learning ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Surface (topology) ; Mathematics ; Engineering ; Structural engineering ; Pulp and paper industry ; Botany ; Geometry Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Nguyễn Thị Quỳnh Trang | Nguyen Van Hop | Nguyễn Đăng Giáng Châu | Tran Thuc Binh
Source: Advances in Materials Science and Engineering (article, 2019-04-01)
Field: Chemistry — Analytical Chemistry

Simultaneous Determination of Amlodipine, Hydrochlorothiazide, and Valsartan in Pharmaceutical Products by a Combination of Full Spectrum Measurement and Kalman Filter Algorithm In this work, a new solution has been found for selecting the approximate initial value of concentration (by means of the classical least squares) and variance (calculated by the Horwitz equation) for the Kalman filter algorithm. With this solution, the Kalman method is less error-prone and has a better repeatability than the least squares method when using the full spectrum. A protocol for simultaneous determination of amlodipine (AML), hydrochlorothiazide (HYD), and valsartan (VAL) in pharmaceutical products was developed based on the spectrophotometry-chemometric method using full spectrum measurement in combination with the Kalman filter algorithm written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method was validated on the Exforge HCT tablets with good repeatability (RSD) (varied from 2.2% to 2.3% ( n = 3) for all the three studied compounds) and good recovery (90.0%–94.0% for AML, 90.3%–94.5% for HYD, and 98.5%–103.1% for VAL ( n = 3)). The results were in good agreement with the measurements achieved from the high-performance liquid chromatography (HPLC) method. Repeatability ; Hydrochlorothiazide ; Kalman filter ; Algorithm ; Valsartan ; Chromatography ; Mathematics ; Materials science ; Computer science ; Chemistry ; Statistics ; Medicine Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Scientific Measurement and Uncertainty Evaluation
Authors: María-José García-Bonete | Gergely Katona
Source: Acta Crystallographica Section A Foundations and Advances (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Bayesian machine learning improves single-wavelength anomalous diffraction phasing Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I (+) and I (−) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods. Phaser ; Bayesian probability ; Anomalous scattering ; Diffraction ; Multivariate statistics ; Physics ; Monoclinic crystal system ; Correlation ; Optics ; Mathematics ; Crystallography ; Statistics ; Chemistry ; Crystal structure ; Geometry Enzyme Structure and Function ; Protein Structure and Dynamics ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Eva Cristina Correa Hernando | Jean‐Michel Roger | L. Lleó | Natalia Hernández | Pilar Barreiro Elorza | Belén Diezma Iglesias
Source: Scientific Reports (article, 2019-09-27)
Field: Chemistry — Analytical Chemistry

Optimal management of oil content variability in olive mill batches by NIR spectroscopy Total oil content (OC) is one of the main parameters used to characterize the whole of olives entering a commercial mill, quantified by the total fresh weight of the lot and the oil concentration (%) assessed in a representative sample on olive paste, by means of chemical extraction. Nuclear magnetic resonance (NMR) and NIR spectroscopy are alternative methods even at individual olives. This work evaluates several strategies to calibrate precise NIR models for the estimation of the total OC. To this end, 278 olives were analysed covering whole season variability in terms of olive fresh-weight and the corresponding OC by chemical extraction in 31 batches. The average spectra from hyperspectral NIR images (1003-2208 nm) were computed for each fruit and the actual OC (g) of those olives determined by NMR (0.09 to 1.29 g with a precision of 0.017 g). According to the results, current batch based assessment of the OC (Soxhlet, %) in mills only reproduces 44% of the underlying heterogeneity, despite being the factory standard. The incorporation of individual NIR spectra (278) to the 31 Soxhlet values of the batches allows a 67% explanation of the OC (%) of olives. When estimating OC (g) gathering individual fresh weight and the estimation of oil concentration in olives, a standard error of prediction of 0.061 g is reached (r<sup>2</sup> = 0.93), a precision value that approaches the potential limit according to the NMR reference (0.017 g). Extraction (chemistry) ; Olive oil ; Chemistry ; Near-infrared spectroscopy ; Spectroscopy ; Analytical Chemistry (journal) ; Environmental science ; Chromatography ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research