jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. A. Shmalko | И.А. Никитин | D. A. Velina | Lilya Ponomareva | S. E. Terentev
Source: Proceedings of the Voronezh State University of Engineering Technologies (article, 2023-03-30)
Field: Chemistry — Analytical Chemistry

Testing the hypothesis of the normality of the falling number of oatmeal in small samples Verification of the hypothesis of the normality of small samples is required to establish whether the empirical distribution obtained belongs to the theoretical distribution. The condition for testing the hypothesis of normality for a set of small independent samples is the presence of a sufficient number of them with the same volume. In this case, it is possible to test the hypothesis of the normality of the general aggregates from which the studied samples were taken, assuming that the parameters of these aggregates have different values. When testing the hypothesis of normality for a large number of small samples, only one value of the first, second, etc. measurements is randomly selected from each sample, thereby allowing simplification and random selection of data. The object of this study is small samples of the falling number of oat flour used in bakery production in the development of bakery products. The purpose of this work was to test the hypothesis of normality for small samples of the experiment using the nonparametric criterion of agreement ω2 of the smallest of each of the four definitions of the incidence number, since rounding the values of direct measurements excludes the random nature of the quantity or its normal distribution in favor of a uniform one. It was found that at a significance level of p = 0?05, the table value (nω2)1-p is greater than the calculated value of nω2 for all four definitions, hence the hypothesis of the normal distribution of small samples for all four definitions (as random variables) of the falling number of oatmeal does not deviate. The results obtained in this work are consistent with the generally accepted classical concepts of testing the statistical hypothesis of the normal distribution of samples. The statistical method provides sufficient accuracy of the studied indicator in technical systems and does not require the synthesis of a statistical criterion to test the hypothesis of the normality of small sample. Normality ; Mathematics ; Statistics ; Statistical hypothesis testing ; Nonparametric statistics ; p-value ; Normality test ; Sample size determination ; Normal distribution ; Percentile ; Distribution (mathematics) ; Econometrics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology
Authors: Melih Savran
Source: Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi (article, 2023-01-01)
Field: Materials Science — General Materials Science

Modeling and optimization of dynamic-mechanical properties of hybrid polymer composites by multiple nonlinear neuro-regression method The purpose of this research is to improve the dynamic-mechanical properties of the polypro-pylene filled by artichoke stem (AS) particles and wollastonite (W) in different weight frac-tions. The effect of weight ratios of fillers in polypropylene was mathematically modeled using the data obtained as a result of the experimental work. In the modeling phase, multiple nonlin-ear neuro-regression analysis was used. In this context, proposed linear and nonlinear models have been examined by performing R2training, R2adjusted, R2testing, and boundedness check. The models that satisfy these four criteria were selected as the objective functions for the optimiza-tion phase. Finally, Modified Differential Evolution Algorithm was used to obtain maximum storage modulus and loss modulus by adjusting weight percent ratio of artichoke stem particle and wollastonite. The experimental results and the modeling optimization results showed that when the polypropylene-artichoke stem particle-wollastonite hybrid polymer composite was used instead of other non-hybrid polymer composite, the storage modulus and the loss mod-ulus improved by approximately 40%. Nonlinear system ; Composite material ; Nonlinear regression ; Materials science ; Polymer ; Regression analysis ; Computer science ; Machine learning ; Physics Material Properties and Applications
Authors: Adri Gabriel Sooai | Sisilia Daeng Bakka Mau | Yovinia Carmeneja Hoar Siki | Donatus Joseph Manehat | Shine Crossifixio Sianturi | Alicia Herlin Mondolang
Source: Jurnal Ilmiah KURSOR (article, 2023-12-10)
Field: Chemistry — Analytical Chemistry

OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy. Computer science ; Classifier (UML) ; Artificial intelligence ; Lantana ; Pattern recognition (psychology) ; Feature extraction ; Decision tree ; Machine learning ; Linear discriminant analysis ; Cross-validation ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Christian Carbogno | Luca M. Ghiringhelli | S. Lübeck | Matthias Scheffler | Claudia Draxl | Daniel T. Speckhard
Source: Research Square (preprint, 2023-06-01)
Field: Materials Science — Materials Chemistry

Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models Extrapolation ; Quantile ; Limit (mathematics) ; Basis (linear algebra) ; Econometrics ; Statistical physics ; Mathematics ; Set (abstract data type) ; Statistics ; Applied mathematics ; Computer science ; Physics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Murat Peksen | Benjamin Steinrücken | H. Spliethoff
Source: ECS Transactions (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Recent Developments of the International Future Lab of Hydrogen Economy: Using Artificial Intelligence (AI) Based Machine Learning (ML) in r-SOC Development Current r-SOC research places special emphasis on the integrated approach of traditional numerical methods like CFD and accelerated AI methods. Using experimentally validated numerical models, the complex thermochemistry of auxiliary components containing syngas was evaluated. This method is used to generate data for the successful development and training of AI-based machine-learning models. Analyses shed light on the interactions between process variables in order to improve and prepare SOC-ready fuel, which is crucial for operation success. A recently developed ML model is utilised effectively to forecast and optimise reforming processes with various fuel constellations, including syngas compositions containing oxygen. Consequently, the results contribute to a greater understanding and qualitative benefits of preparing high-quality, pure syngas, improved fuel utilisation advancing sustainable research, and safe, consistent r-SOC operation. Consequently, early availability of valuable information is achieved. In addition, the strategy reduced prohibitive experiments, which contributed to the sustainable utilisation of resources. Syngas ; Computer science ; Process (computing) ; Process engineering ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Manufacturing engineering ; Engineering ; Hydrogen ; Chemistry Catalytic Processes in Materials Science ; Iron and Steelmaking Processes ; Catalysis and Oxidation Reactions
Authors: Oluwamayowa Joshua Ogun | Georg Thaller | Doreen Becker
Source: Pathogens (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Molecular Structural Analysis of Porcine CMAH–Native Ligand Complex and High Throughput Virtual Screening to Identify Novel Inhibitors Porcine meat is the most consumed red meat worldwide. Pigs are also vital tools in biological and medical research. However, xenoreactivity between porcine's N-glycolylneuraminic acid (Neu5Gc) and human anti-Neu5Gc antibodies poses a significant challenge. On the one hand, dietary Neu5Gc intake has been connected to particular human disorders. On the other hand, some pathogens connected to pig diseases have a preference for Neu5Gc. The Cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyses the conversion of N-acetylneuraminic acid (Neu5Ac) to Neu5Gc. In this study, we predicted the tertiary structure of CMAH, performed molecular docking, and analysed the protein-native ligand complex. We performed a virtual screening from a drug library of 5M compounds and selected the two top inhibitors with Vina scores of -9.9 kcal/mol for inhibitor 1 and -9.4 kcal/mol for inhibitor 2. We further analysed their pharmacokinetic and pharmacophoric properties. We conducted stability analyses of the complexes with molecular dynamic simulations of 200 ns and binding free energy calculations. The overall analyses revealed the inhibitors' stable binding, which was further validated by the MMGBSA studies. In conclusion, this result may pave the way for future studies to determine how to inhibit CMAH activities. Further in vitro studies can provide in-depth insight into these compounds' therapeutic potential. Virtual screening ; Ligand (biochemistry) ; Chemistry ; High-throughput screening ; Docking (animal) ; Cytidine ; Drug ; Biochemistry ; Computational biology ; Drug discovery ; Stereochemistry ; Enzyme ; Biology ; Pharmacology ; Medicine ; Receptor Enzyme Structure and Function ; Protein purification and stability ; Monoclonal and Polyclonal Antibodies Research
Authors: Sergio Pérez-Conesa | Lucie Delemotte
Source:  (preprint, 2023-07-04)
Field: Chemistry — Spectroscopy

Free energy landscapes of KcsA inactivation Abstract The bacterial ion channel KcsA has become a useful model of complex K+-ion channels thanks to its single pore domain structure whose sequence shares many similarities with eukaryotic channels. Like many physiologically-relevant ion channels, KcsA inactivates after prolonged exposure to stimuli (in this case, a lowered pH). The inactivation mechanism has been heavily investigated, using structural, functional and simulations methods, but the molecular basis underlying the energetics of the process remains actively debated. In this work, we use the “string method with swarms of trajectories” enhanced sampling technique to characterize the free energy landscape lining the KcsA inactivation process. After channel opening following a pH drop, KcsA presents metastable open states leading to an inactivated state. The final inactivation step consists of a constriction of the selectivty filter and entry of three water molecules into binding sites behind each selectivity filter subunit. Based our simulations, we propose a key role for residue L81 in opening a gateway for water molecules to enter their buried sites, rather than for Y82 which has previously been suggested to act as a lid. In addition, since we found the energetically favored inactivation mechanism to be dependent on the force field, our results also address the importance of parameter choice for this type of mechanism. In particular, inactivation involves passing through the fully-open state only when using the AMBER force field. In contrast, using CHARMM, selectivity filter constriction proceeds directly from the partially open state. Finally, our simulations suggest that removing the co-purifying lipids stabilizes the partially open states, rationalizing their importance for the proper inactivation of the channel. KcsA potassium channel ; Energy (signal processing) ; Chemistry ; Biophysics ; Biological system ; Mathematics ; Biology ; Biochemistry ; Statistics ; Ion channel Mass Spectrometry Techniques and Applications ; Cardiac electrophysiology and arrhythmias ; Ion channel regulation and function
Authors: Ayşin BİLGİLİ
Source: DÜMF Mühendislik Dergisi (article, 2023-10-24)
Field: Chemistry — Analytical Chemistry

Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants Plant diseases can lead to significant yield losses and economic damages, but these losses can be mitigated through early disease diagnosis. In recent times, remote sensing techniques have been widely used for early disease detection even before visible symptoms appear. This study focused on the potential of early detection of Fusarium Root and Crown Rot in Tomato Plants, which causes substantial yield losses in tomato plants, under controlled conditions using thermal images. In this research, thermal images were obtained from both disease-inoculated and disease-free control plants throughout the plant growth period under controlled conditions. These images underwent preprocessing in a computer environment, and various feature parameters related to temperature changes in both groups (such as minimum, maximum, standard deviation, and skewness) were extracted. These extracted features were then used as inputs for different machine learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Naive Bayes (NB), to classify healthy and diseased plants. Overall, the disease-inoculated plants exhibited higher average temperatures compared to the healthy control plants. The performance of the compared machine learning techniques in distinguishing between healthy and diseased plants was found to be in the order of KNN, NB, and LR, with success rates of 72%, 68%, and 60%, respectively. This study demonstrated the potential of using combined thermal images with different machine learning techniques for early diagnosis of Fusarium Root and Crown Rot in Tomato Plants. The results show promising prospects for utilizing thermal imaging in the early detection of plant diseases, leading to better management and reduction of yield losses and economic impacts. Fusarium ; Crown (dentistry) ; Root rot ; Plant disease ; Naive Bayes classifier ; Artificial intelligence ; Horticulture ; Machine learning ; Biology ; Mathematics ; Computer science ; Biotechnology ; Support vector machine ; Medicine Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Essential Oils and Antimicrobial Activity
Authors: Andreas N. Rasmussen | Benjamin L. Thomsen | Jesper B. Christensen | Jan C. Petersen | Mikael Lassen
Source: Preprints.org (preprint, 2023-07-10)
Field: Chemistry — Spectroscopy

Quartz-Enhanced Photoacoustic Spectroscopy Assisted by Partial Least-Squares Regression for Multi-Gas Measurements We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photo-acoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3) and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a MIR (mid-infrared) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra is a common challenge for gas spectroscopy. To mitigate this, we use a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here in this proof-of-concept experiment we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3 and CH4 and with an absolute sensitivity of approximately 300(±50) ppm/V, 50(±5) ppm/V and 5(±2) ppm/V for water, ammonia and methane, respectively. Thus, demonstrating that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications. Photoacoustic spectroscopy ; Partial least squares regression ; HITRAN ; Optical parametric oscillator ; Analytical Chemistry (journal) ; Spectral line ; Spectroscopy ; Methane ; Absorption spectroscopy ; Chemistry ; Infrared spectroscopy ; Absorption (acoustics) ; Materials science ; Wavelength ; Optics ; Optoelectronics ; Environmental chemistry ; Physics ; Mathematics ; Statistics Spectroscopy and Laser Applications ; Photoacoustic and Ultrasonic Imaging ; Atmospheric and Environmental Gas Dynamics
Authors: Anna Fabijańska | Gabriel D. Cahalan
Source: Scientific Reports (article, 2023-05-02)
Field: Chemistry — Organic Chemistry

Automatic resin duct detection and measurement from wood core images using convolutional neural networks The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively. Convolutional neural network ; Computer science ; Artificial intelligence ; Core (optical fiber) ; Pattern recognition (psychology) ; Telecommunications Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Antonio Sclocchi | Mario Geiger | Matthieu Wyart
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can operate in distinct training regimes. Here we study how the magnitude of this noise $T$ affects performance as the size of the training set $P$ and the scale of initialization $α$ are varied. For gradient descent, $α$ is a key parameter that controls if the network is `lazy'($α\gg1$) or instead learns features ($α\ll1$). For classification of MNIST and CIFAR10 images, our central results are: (i) obtaining phase diagrams for performance in the $(α,T)$ plane. They show that SGD noise can be detrimental or instead useful depending on the training regime. Moreover, although increasing $T$ or decreasing $α$ both allow the net to escape the lazy regime, these changes can have opposite effects on performance. (ii) Most importantly, we find that the characteristic temperature $T_c$ where the noise of SGD starts affecting the trained model (and eventually performance) is a power law of $P$. We relate this finding with the observation that key dynamical quantities, such as the total variation of weights during training, depend on both $T$ and $P$ as power laws. These results indicate that a key effect of SGD noise occurs late in training by affecting the stopping process whereby all data are fitted. Indeed, we argue that due to SGD noise, nets must develop a stronger `signal', i.e. larger informative weights, to fit the data, leading to a longer training time. A stronger signal and a longer training time are also required when the size of the training set $P$ increases. We confirm these views in the perceptron model, where signal and noise can be precisely measured. Interestingly, exponents characterizing the effect of SGD depend on the density of data near the decision boundary, as we explain. Stochastic gradient descent ; Noise (video) ; Computer science ; Key (lock) ; Artificial intelligence ; Initialization ; Gradient descent ; Artificial neural network ; Generalization ; MNIST database ; Set (abstract data type) ; Machine learning ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Neural Networks and Applications ; Generative Adversarial Networks and Image Synthesis
Authors: K. Kuczyński | Maciej Lisicki | Piotr Bilski | J. Szymański | Adrian Bilski
Source: Microsystem Technologies (article, 2023-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetoelectric ring sensor—modelling and experimentation Abstract The paper presents the theoretical model and implementation of the magnetoelectric ring sensor. The designed device is capable of measuring the constant magnetic field of low amplitudes (even several dozen nT). To determine its capabilities and resolution, the hysteresis characteristics were evaluated and measured. Besides the theoretical description of the sensor, two heuristic approaches were used to approximate the internal characteristics (including the hysteresis loop), solving the regression task: a multilayered perceptron and support vector machine. Experiments show that the former has minimally Mean Square Error, which suggests its better applicability for heuristic modeling of the real-world device. Heuristic ; Hysteresis ; Perceptron ; Computer science ; Square (algebra) ; Constant (computer programming) ; Amplitude ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Artificial neural network ; Optics ; Geometry Magnetic Properties and Applications ; Multiferroics and related materials ; Non-Destructive Testing Techniques