jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Hakola | A. Keitaanranta | H. Kumpulainen | A. Lahtinen | J. Likonen | M. Balden | M. Cavedon | K. Krieger | Markus Airila | M. Groth
Source: Nuclear Materials and Energy (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

ERO modelling of net and gross erosion of marker samples exposed to L-mode plasmas on ASDEX Upgrade In this paper, we report experimental and numerical investigations of gross and net erosion of gold (Au) and molybdenum (Mo), proxies for the common plasma-facing material tungsten (W), during L-mode plasma discharges in deuterium (D) in the outer strike-point region of the ASDEX Upgrade tokamak. To this end, erosion profiles of different marker spots (for Au, dimensions 1 × 1 and 5 × 5 mm2) and marker coatings (for Mo) have been determined and modelled using the ERO code. The smaller marker spots were designed to quantify the gross-erosion rate while on the bigger markers local prompt re-deposition of Au allowed obtaining data on net erosion. The experimental results indicate relatively uniform erosion profiles across the marker spots or coatings, very little re-deposition elsewhere, and the largest erosion taking place close to the strike point. Compared to W, the markers show up to 15 times higher net erosion but no major differences in the poloidal migration lengths of Au and W can be seen. Gold thus appears to be a proper choice for studying migration of W in the divertor region. The ERO simulations with different background plasmas are able to reproduce the main features of the experimental net erosion profile of Au. Of the studied parameters, electron temperature has the strongest impact on erosion: doubling the temperature enhances erosion by a factor of 2.5–3. In contrast, for Mo, the simulated net erosion is ~ 3 times smaller than what experimental data indicate. The discrepancies can be attributed to the deviations of the background plasma profiles from the measured ones as well as to the applied models or approximations for the ion temperature, plasma potential, and sheath characteristics in ERO. In addition, the surrounding areas of the marker samples being covered with impurities and W from previous experiments may have considerably reduced the actual re-deposition of Mo. All the simulations predict a toroidal tail of re-deposited particles, downstream of the markers, but the particle density seems to be below the experimental detection threshold. The comparison between the 1 × 1 mm2 and 5 × 5 mm2 marker spots further reveal that re-deposition drops from >50% to <40% when decreasing the marker size. This indicates that small enough marker samples can be used for accurately determining gross erosion in ASDEX Upgrade. ASDEX Upgrade ; Divertor ; Erosion ; Tungsten ; Tokamak ; Plasma ; Deposition (geology) ; Materials science ; Molybdenum ; Atomic physics ; Nuclear physics ; Physics ; Metallurgy ; Geology Fusion materials and technologies ; Nuclear Materials and Properties ; Magnetic confinement fusion research
Authors: Kamil Halbiniak | Tomasz Olas | Łukasz Szustak | Adam Kulawik | Marco Lapegna
Source: Concurrency and Computation Practice and Experience (article, 2020-07-04)
Field: Materials Science — Materials Chemistry

Dynamic workload prediction and distribution in numerical modeling of solidification on multi‐/manycore architectures Summary This work is a part of the global tendency to use modern computing systems for modeling the phase‐field phenomena. The main goal of this article is to improve the performance of a parallel application for the solidification modeling, assuming the dynamic intensity of computations in successive time steps when calculations are performed using a carefully selected group of nodes in the grid. A two‐step method is proposed to optimize the application for multi‐/manycore architectures. In the first step, the loop fusion is used to execute all kernels in a single nested loop and reduce the number of conditional operators. These modifications are vital to implementing the second step, which includes an algorithm for the dynamic workload prediction and load balancing across cores of a computing platform. Two versions of the algorithm are proposed—with the 1D and 2D maps used for predicting the computational domain within the grid. The proposed optimizations allow increasing the application performance significantly for all tested configurations of computing resources. The highest performance gain is achieved for two Intel Xeon Platinum 8180 CPUs, where the new code based on the 2D map yields the speedup of up to 2.74 times, while the usage of the proposed method with the 2D map for a single KNL accelerator permits reducing the execution time up to 1.91 times. Computer science ; Xeon Phi ; Parallel computing ; Speedup ; Xeon ; Workload ; Grid ; Computation ; Domain (mathematical analysis) ; Supercomputer ; Field (mathematics) ; Computational science ; Distributed computing ; Algorithm ; Operating system Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Uemura Takero | Katsuhiko Yamaguchi
Source: Journal of Advanced Simulation in Science and Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Estimation of radiation source distribution using machine learning with γ ray energy spectra A method is proposed for estimating the original radiation source distribution by machine learning using the dose and energy spectrum of γ rays emitted from radiation sources placed at various positions. This method does not require complicated parameter settings and can be also applied when there is a shield liked Pb between the radiation source and the measurement point. The estimation results displayed the original radiation source distribution with high accuracy. It is expected to be used for decontamination and decommissioning by developing this method. Radiation ; Nuclear decommissioning ; Point source ; Spectral power distribution ; Computer science ; Energy (signal processing) ; Point (geometry) ; Spectral energy distribution ; Light source ; Spectral line ; Optics ; Physics ; Mathematics ; Statistics ; Nuclear physics Graphite, nuclear technology, radiation studies ; Radiation Detection and Scintillator Technologies ; Nuclear Physics and Applications
Authors: E. A. Oyedoh | G.A. Erumi | C.E. Akhabue
Source: Journal of applied science and environmental management (article, 2020-04-20)
Field: Chemistry — Analytical Chemistry

Comparative Study of Response Surface Methodology and Artificial Neural Network for Modeling and Optimization of Extraction Process Parameters on &lt;I&gt;Tetrapleura Tetraptera&lt;/I&gt; Bioactive compounds in the fruits of Tetrapleura tetraptera is widely used in food as a flavouring agent and for spices. In this study, bioactive compounds were extracted by solid-liquid extraction process and the yield was optimized by response surface methodology (RSM) and artificial neural network (ANN). The process parameters optimized were the extraction temperature, particle size and extraction time. Box-Behnken Design was used to study the effect of the process parameters on the extract yield. A quadratic model was obtained by RSM which was used topredict the extract yield. While for ANN, Bayesian Regularization learning algorithm with hyperbolic function (Tanh) for both hidden and output layers was the best model for predicting the extract yield. The performance of both models was established based on their R2 and RMSE values. (R2 and RMSE values were 0.9391 and 3.10 for RSM and 0.9637 and 0.8193 for ANN respectively). ANN gave the maximum extract yield of 29.15 % higher than that of RSM which evaluated a yield of 27.70 % with optimum conditions at extraction temperature of 90℃, particle size of 3.26 mm and extraction time of 50 mins. It was therefore concluded that ANN is better than RSM in the modeling and optimization of the extraction process parameters.&#x0D; Keywords: Tetrapleura tetraptera, bioactive compounds, process parameters, optimization&#x0D; Response surface methodology ; Extraction (chemistry) ; Artificial neural network ; Yield (engineering) ; Quadratic model ; Box–Behnken design ; Particle size ; Mathematics ; Biological system ; Machine learning ; Chromatography ; Materials science ; Computer science ; Chemistry ; Statistics ; Engineering ; Chemical engineering ; Biology Spectroscopy and Chemometric Analyses
Authors: Cristian Monea | Gabriel Iana | Silviu Ioniță | Mihai Oproescu
Source:  (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Multi-criteria decision algorithm for NQR signal detection Nuclear quadrupole resonance is a spectroscopy technique used for chemical analysis, temperature measurement and detection of prohibited substances. It offers advantages like highly specific and non-invasive detection, but has the disadvantages of low signal-to-noise ratio and temperature dependence of the response signal, which are generally compensated by special hardware and signal processing solutions. This paper proposes a data processing algorithm based on multiple criteria designed to ensure nuclear quadrupole resonance detection in specific scenarios. In contrast with current algorithms, this solution does not require statistical estimation of the response's parameters. It is evaluated using a real data set composed of 8000 acquisitions and is shown to reach a detection probability of 94% that outperforms existing solutions for a specific detection threshold. Nuclear quadrupole resonance ; Detection theory ; SIGNAL (programming language) ; Algorithm ; Computer science ; Signal processing ; Signal-to-noise ratio (imaging) ; Noise (video) ; Set (abstract data type) ; Artificial intelligence ; Detector ; Nuclear magnetic resonance ; Physics ; Digital signal processing ; Telecommunications Solid-state spectroscopy and crystallography ; Fault Detection and Control Systems ; Pesticide Residue Analysis and Safety
Authors: Eunjung Ju | Myung Geol Choi
Source: Journal of the Korea Computer Graphics Society (article, 2020-12-01)
Field: Materials Science — Polymers and Plastics

Estimating Simulation Parameters for Kint Fabrics from Static Drapes We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick’s drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a signiﬁcant inﬂuence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we ﬁrst collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is ﬁtted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes. Computer science ; Environmental science ; Mathematics Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Optical measurement and interference techniques
Authors: Aytül Gökçe | Stephen Coombes | Daniele Avitabile
Source: Physical Review Research (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

Quasicrystal patterns in a neural field model The authors show that a modulation of this connectivity structure can lead to the formation of quasicrystal patterns, thus expanding the repertoire of drug induced geometric hallucinations that can be explained by a Turing mechanism. Quasicrystal ; Turing ; Field (mathematics) ; Mechanism (biology) ; Repertoire ; Modulation (music) ; Physics ; Computer science ; Theoretical physics ; Mathematics ; Condensed matter physics ; Pure mathematics ; Quantum mechanics Quasicrystal Structures and Properties ; Mathematical Dynamics and Fractals
Authors: Benjamin D. Knapp | Lillian Zhu | Kerwyn Casey Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-29)
Field: Chemistry — Physical and Theoretical Chemistry

SiCTeC: an inexpensive, easily assembled Peltier device for rapid temperature shifting during single-cell imaging Abstract Single-cell imaging, combined with recent advances in image analysis and microfluidic technologies, have enabled fundamental discoveries of cellular responses to chemical perturbations that are often obscured by traditional liquid-culture experiments. Temperature is an environmental variable well known to impact growth and to elicit specific stress responses at extreme values; it is often used as a genetic tool to interrogate essential genes. However, the dynamic effects of temperature shifts have remained mostly unstudied at the single-cell level, due largely to engineering challenges related to sample stability, heatsink considerations, and temperature measurement and feedback. Additionally, the few commercially available temperature-control platforms are costly. Here, we report an inexpensive (&lt;$110) and modular Si ngle- C ell Te mperature C ontroller (SiCTeC) device for microbial imaging, based on straightforward modifications of the typical slide-sample-coverslip approach to microbial imaging, that controls temperature using a ring-shaped Peltier module and microcontroller feedback. Through stable and precise (±0.15 °C) temperature control, SiCTeC achieves reproducible and fast (1-2 min) temperature transitions with programmable waveforms between room temperature and 45 °C with an air objective. At the device’s maximum temperature of 89 °C, SiCTeC revealed that Escherichia coli cells progressively shrink and lose cellular contents. During oscillations between 30 °C and 37 °C, cells rapidly adapted their response to temperature upshifts. Furthermore, SiCTeC enabled the discovery of rapid morphological changes and enhanced sensitivity to substrate stiffness during upshifts to nonpermissive temperatures in temperature-sensitive mutants of cell-wall synthesis enzymes. Overall, the simplicity and affordability of SiCTeC empowers future studies of the temperature dependence of single-cell physiology. Temperature control ; Microfluidics ; Modular design ; Materials science ; Nanotechnology ; Thermoelectric effect ; Substrate (aquarium) ; Thermoelectric cooling ; Heat sink ; Optoelectronics ; Computer science ; Mechanical engineering ; Biology ; Engineering thermodynamics and calorimetric analyses ; Cell Image Analysis Techniques ; 3D Printing in Biomedical Research
Authors: Mir Saeid Hesarian | Jafar Tavoosi | Seyyed Hossein Hosseini
Source: International Journal of Engineering (article, 2020-12-01)
Field: Materials Science — Polymers and Plastics

Neuro-fuzzy Modelling and Experimental Study of the Physiological Comfort of Green Cotton Fabric Based on Yarn Properties In textile and garment industry, the physiological comfort of fabric as one of the important parameters, can be improved by the fabric finishing treatment. Nevertheless, the toxic chemicals produced in this process leads to the pollution of the environment. Therefore, this study aims to improve the physiological comfort of the cotton fabric without applying the finishing process as green technology. Accordingly, air permeability and moisture transfer as two important parameters of the fabric physiological comfort are evaluated with the structural parameters of the cotton yarn using experimental and theoretical procedures. For theoretical evaluations, a novel neuro-fuzzy network (ANFIS) is proposed and used for modelling and estimation. The structural parameters of yarn are the yarn linear density, yarn twist and fineness of fibers, which are defined as inputs and air permeability and moisture transfer of the cotton samples are considered as the outputs of developed ANFIS model. According to the experimental and modeling results, the fiber fineness, yarn linear density (Ne) and yarn twist have the same effect on the output parameters. It is also found that both parameters of the physiological comfort sensory can be improved effectively without finishing process. Simulation results show the novel proposed ANFIS that has high learning capability, fast convergence and accuracy greater than 99% and negligible error value smaller than of 1% can be reasonably used in textile industry. In addition, for the winter garments, the optimum points of turns per meter (T.P.M) coefficient, English count of yarn, and fibers fineness are 4.5, 25 and 3, respectively. Yarn ; Fineness ; Air permeability specific surface ; Linear density ; Adaptive neuro fuzzy inference system ; Textile ; Composite material ; Process engineering ; Materials science ; Fuzzy logic ; Computer science ; Engineering ; Fuzzy control system ; Artificial intelligence Textile materials and evaluations
Authors: Mohamed Ben Hassen | MOHAMED TAHER HALIM | Emad H. Abualsauod | Asem Majed Othman
Source: Industria Textila (article, 2020-10-28)
Field: Materials Science — Polymers and Plastics

Quality yarn index using AHP and Fuzzy method The yarn quality depends on many parameters: characteristics parameters, mechanical and physical properties. Making the hypothesis that the quality of the yarn is a multicriteria problem, in this paper, we propose a new method to determine the Quality Yarn Index QYI based on Analytic Hierarchy Process AHP and Fuzzy theory. A questionnaire was designed for experts of each field (weaving and knitting) to evaluate the relative importance for each property to determine coefficients of the AHP matrix. Results revealed that matrix coefficients changed with yarn application (weft or warp weaving and knitting) The QYI can be used in any application, where a rapid decision is needed, to evaluate the global quality of yarn and to make a comparison between different yarn qualities Yarn ; Weaving ; Analytic hierarchy process ; Quality (philosophy) ; Fuzzy logic ; Matrix (chemical analysis) ; Index (typography) ; Computer science ; Hierarchy ; Process (computing) ; Engineering ; Mathematics ; Engineering drawing ; Artificial intelligence ; Mechanical engineering ; Operations research ; Materials science ; Composite material Textile materials and evaluations
Authors: Chen Yee Being | Norhashila Hashim | Bernard Maringgal | Hafizz Wondi
Source: Journal of Agricultural and Food Engineering (review, 2020-03-12)
Field: Chemistry — Analytical Chemistry

A review of non-destructive techniques applied for measuring quality of oil palm fresh fruit bunches The quality of oil palm fruits is evaluated through several characteristics such as the ripeness level, oil content, and free fatty acid. Besides human visual assessment and destructive techniques, another alternative method that provides quality assessment on oil palm fresh fruits bunches (FFB) is through the application of non-destructive techniques. A few of the non-destructive techniques are covered in this review such as machine vision system, visible or near infrared spectroscopy, image processing using relative entropy, fluorescence technique, Kinect camera, and optical sensor system. The main quality parameter that is being evaluated is the ripeness level of the FFB because the maturity of FFB has a direct impact on the quality of the extracted oil that will eventually affect the economic value of palm oil. Ripeness ; Palm oil ; Bunches ; Palm ; Environmental science ; Computer science ; Process engineering ; Pulp and paper industry ; Engineering ; Food science ; Chemistry ; Optics ; Physics ; Agroforestry ; Ripening Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Date Palm Research Studies
Authors: Zhen Zhong | Minjuan Wang | Wanlin Gao
Source: KSII Transactions on Internet and Information Systems (article, 2020-11-30)
Field: Chemistry — Analytical Chemistry

A multisource image fusion method for multimodal pig-body feature detection The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency. Computer science ; Artificial intelligence ; Image fusion ; Feature (linguistics) ; Computer vision ; Image (mathematics) ; Fusion ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Image Fusion Techniques ; Infrared Thermography in Medicine
Authors: Noratikah Zawani Noratikah Zawani Mahabob
Source: International Journal of Emerging Trends in Engineering Research (article, 2020-07-25)
Field: Chemistry — Organic Chemistry

Linear Regression of Gaharu Oil Significant Compounds for Oil Quality Differentiation This study presented the Linear Regression model that is trained in Feed Forward Neural Network (FFNN). The model is trained using Matlab version R2017a. The sample of dataset of Gaharu oil that used in this research study was obtained from Forest Research Institute Malaysia (FRIM), Selangor Malaysia, and BioAromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP), Malaysia. As Feed-Forward Neural Network consists of input layer, hidden layer and output layer, this related to situation that is used in the research. In this experiment, the seven significant compounds of gaharu oil that consists of 96 data samples from high and low quality are representing the input layer. The hidden layer is varying from 1 to 10 hidden neurons to evaluate each of the performances. The output layer is presenting the quality of gaharu oil within the range of 0 and 1 which is low and high, respectively. The experiment involved the data pre-processing consists of data normalization, randomization and division. The parameter concerned is on the value of correlation coefficient, R and the mean squared error (MSE) for each of the hidden neurons. The training algorithm is involving Levenberg Marquadt as a default algorithm in FFNN besides has a good stability and fast in convergence. Based on the results of the study, hidden neurons number 2 outperforms others due to successfully show the best performance towards regression value and MSE. Linear regression ; Regression analysis ; Quality (philosophy) ; Regression ; Statistics ; Environmental science ; Mathematics Wood and Agarwood Research ; Food Quality and Safety Studies ; Biological Stains and Phytochemicals