jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joanna M. Macnar | Dariusz Brzeziński | M. Chruszcz | Dominik Gront
Source: Protein Science (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Analysis of protein structures containing <scp>HEPES</scp> and <scp>MES</scp> molecules Abstract X‐ray crystallography is the main experimental method behind ligand–macromolecule complexes found in the Protein Data Bank (PDB). Applying bioinformatics methods to such structural data can fuel drug discovery, albeit under the condition that the information is correct. Regrettably, a small number of structures in the PDB are of suboptimal quality due to incorrectly identified and modeled ligands in protein–ligand complexes. In this paper, we combine a theoretical‐graph approach, nuclear density estimates, bioinformatics methods, and prior chemical knowledge to analyze two non‐physiological ligands, HEPES and MES, that are frequent components of crystallization and purifications buffers. Our analysis includes quantum mechanics calculations and Cambridge Structure Database (CSD) queries to define the ideal conformation of these ligands, geometry analysis of PDB deposits regarding several quality factors, and a search for homologous structures to identify other small molecules that could bind in place of the parasitic ligand. Our results highlight the need for careful refinement of macromolecule–ligand complexes and better validation tools that integrate results from all relevant resources. Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Ligand (biochemistry) ; Small molecule ; Macromolecule ; Chemistry ; Molecule ; Computational biology ; Protein structure ; Crystallization ; Computer science ; Data mining ; Bioinformatics ; Biochemistry ; Biology ; Receptor Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: А.Р. Кенжегалиева | Д.Б. Абдихан | Е.К. Онгарбаев
Source: Горение и Плазмохимия (article, 2022-03-16)
Field: Materials Science — Polymers and Plastics

Модификация нефтяного битума механохимически активированным шунгитом месторождения коксу В работе приведены результаты экспериментов по использованию шунгитовых пород месторождения Коксу после механохимической активации в качестве модифицирующей добавки для улучшения физико-механических характеристик нефтяного дорожного битума. Использованы образцы шунгита марки «Таурит» сланцевого и карбонатного происхождения с размерами частиц 1 мм и 20 мкм. Измельченные образцы шунгита размерами частиц 5 мкм получены путем механохимической активации. Модификация битума образцами шунгита привела к снижению глубины проникания иглы и растяжимости, повышению температуры размягчения и адгезионной способности битума. Положительное влияние механохимически активированного шунгита на физико-механические свойства битума объясняется изменением структуры шунгита в результате измельчения. Оптимальным модификатором оказался образец шунгита карбонатного происхождения в количестве 0,5 мас.% после механохимической активации. Благоприятное воздействие на характеристики битума позволяет использовать природное сырье – шунгит после механохимической активации в качестве наполнителя битумных вяжущих и асфальтобетонных смесей на их основе. Computer science Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Jonathan He | Olivia Liu | Xuan Guo
Source: Journal of Student Research (article, 2022-08-31)
Field: Chemistry — Spectroscopy

Deep Learning for MS2 Feature Detection in Liquid Chromatography Mass Spectrometry Accuracy of peptide identification is crucial for LC-MS analysis to reveal information regarding many different aspects of proteins that aid in the discovery of biomarkers and profiling of complex proteomes. Preprocessing steps such as feature detection are crucial yet challenging; current feature detection tools are not robust enough to detect low-abundance, low-peak fragments of peptides found in MS2 data from tandem mass spectrometry. In this study, we developed a deep learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct accurate feature detection on MS2 data. Experimental results show that our model is able to produce more accurate values and identifications than existing feature detection tools. Therefore, we believe that our model can realize the full potential of neural networks in the field of bioinformatics and yields long-term benefits in the advancement of proteomic inquiry. Preprocessor ; Computer science ; Profiling (computer programming) ; Artificial intelligence ; Feature (linguistics) ; Data pre-processing ; Mass spectrometry ; Pattern recognition (psychology) ; High resolution ; Data mining ; Chromatography ; Chemistry ; Remote sensing Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Hui Chen | Junjia Wang | Hejun Hu | Xiaofeng Li | Yiyun Huang
Source: Energies (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

Aging Detection of 110 kV XLPE Cable for a CFETR Power Supply System Based on Deep Neural Network To detect the aging of power cables in the TOKAMAK power supply systems, this paper proposed a deep neural network diagnosis model and algorithm for power cable aging, based on logistic regression according to the characteristics of different high-order harmonics generated by different aging parts of the power cable. The experimental results showed that the model has high diagnostic accuracy, and the average error is only 2.35%. The method proposed in this paper has certain application potential in the CFETR power cable auxiliary monitoring system. Artificial neural network ; Harmonics ; Power (physics) ; Power cable ; Tokamak ; Engineering ; Computer science ; Electrical engineering ; Automotive engineering ; Reliability engineering ; Electronic engineering ; Voltage ; Artificial intelligence ; Materials science ; Physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Machine Fault Diagnosis Techniques
Authors: Jovana Kovačević | Aleksandar Kovačević | Tijana Miletić | Jelena Đuriš | Svetlana Ibrić
Source: Arhiv za farmaciju (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Data mining techniques applied in the analysis of historical data Understanding the effect of the characteristics of formulation and process parameters on the physicochemical properties of a pharmaceutical product is very significant for the development of solid dosage forms, as the knowledge gained on small scale batches in the early phase of development is used in the later phases of product lifecycle or in the development of other products. One of the approaches for gaining a better understanding of the effects of the formulation and production process on the quality of the finished product is to apply a systematical approach which concerns performing experiments according to a predefined factorial or fractional factorial experimental plan. However, often it is the case that there are available data gathered in a non-systematic way, because experiments were not performed according to a predetermined experimental plan. In such a case, data mining techniques could be used to extract useful data from the historical data set. In this research, the possibility of using several data mining techniques to build models that describe the effect of formulation characteristics on acid resistance and dissolution profile of a model drug from gastro-resistant pellets. The model drug used in the research is duloxetine hydrochloride from the group of antidepressants. It belongs to the BCS 2 class of active pharmaceutical ingredients, and it is therefore necessary for the release profile of duloxetine to be characterized by a higher number of tested time points. The developed models can be used for planning future laboratory trials, or in the development of other products. Computer science ; Data mining ; New product development ; Process (computing) ; Biochemical engineering ; Experimental data ; Management science ; Mathematics ; Engineering ; Statistics ; Business Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Analytical Methods in Pharmaceuticals
Authors: Sanita Vucāne | Ingmārs Cinkmanis | Lauris Leitāns | State Plant Protection Service, Latvia | Martins Sabovics
Source: Research for Rural Development/Research for Rural Development (Online) (article, 2022-11-15)
Field: Chemistry — Organic Chemistry

The authenticity of vegetable oil using smartphone-based image Chlorophylls, tocopherols, and phenolic compounds are fluorescent substances found in vegetable oils. As a result, fluorescence analysis can be used to authenticate vegetable oils in a simple, effective, and non-destructive manner. The goal of the study was to determine the authenticity of vegetable oil using smartphone-based image analysis in fluorescence light. The image was taken with a Huawei P30 Lite and saved as an 8-bit JPG file. Mactronic PHH0062 390 nm-LED flashlight was employed as a light source. Due to the Hue spectral histograms, the image was analyzed in the HSV (Hue, Saturation, Value) color model. Eleven vegetable oils were chosen for testing: sea buckthorn, sunflower, rice, macadamia nut, hemp, corn, grape, linseed, rapeseed, olive, and milk thistle. Because vegetable oils have a diverse Hue spectrum, fluorescence examination can be used to verify their authenticity. Hue ; Rapeseed ; Vegetable oil ; Food science ; Sunflower oil ; Mathematics ; Horticulture ; Environmental science ; Artificial intelligence ; Computer science ; Chemistry ; Biology Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Omár Péter Hamadi | Tamás Varga
Source: Periodica Polytechnica Chemical Engineering (article, 2022-02-10)
Field: Chemistry — Spectroscopy

Semi-supervised Clustering Algorithm for Retention Time Alignment of Gas Chromatographic Data Gas chromatography (GC) is an effective tool for the analysis of complex mixtures with a huge number of components. To keep tracking the chemical changes during the processes like plastic waste pyrolysis usually different sample states are profiled, but retention time drifts between the chromatograms make the comparability difficult. The aim of this study is to develop a fast and simple method to eliminate the time drifts between the chromatograms using easily accessible priori information. The proposed method is tested on GC chromatograms obtained by analysis of pyrolysis product (Mg/Y catalyst) of shredded real waste HDPE/PP/LDPE mixture. A modified k-means algorithm was developed to account the retention time drifts between samples (different sample states). The outcome of the retention time alignment is an averaged retention time for each peak from all the chromatograms which makes the comparison and further analysis (such as "fingerprinting") easier or possible. Retention time ; Sample (material) ; Chromatography ; Cluster analysis ; Pyrolysis ; A priori and a posteriori ; Comparability ; Computer science ; Gas chromatography ; Algorithm ; Process engineering ; Chemistry ; Mathematics ; Artificial intelligence ; Engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Owen Madin | Michael R. Shirts
Source: ChemRxiv (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The computational expense of these simulations limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This allows for fast evaluation of objective functions, greatly accelerating searches over parameter space. We use an iterative framework which performs global optimization at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 ``Parsley'' force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. In most cases, these parameter sets are transferable to other similar molecules in a test set. This multi-fidelity technique provides a platform for fast optimization against physical properties that can be refined and applied in multiple ways to the development of molecular models. Maxima and minima ; Fidelity ; Force field (fiction) ; Surrogate model ; Computer science ; Field (mathematics) ; Mathematical optimization ; Gaussian ; Set (abstract data type) ; Gaussian process ; Optimization problem ; Parameter space ; Property (philosophy) ; Algorithm ; Mathematics ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Ri He | Bingwen Zhang | Hua Wang | Lei Li | Tang Ping | G. Bauer | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2022-12-29)
Field: Materials Science — Materials Chemistry

Ultrafast switching dynamics of the ferroelectric order in stacking-engineered ferroelectrics The recently discovered ferroelectricity of van der Waals bilayers offers an unconventional route to improve the performance of devices. Key parameters such as switching field and speed depend on the static and dynamic properties of domain walls (DWs). Here we theoretically explore the properties of textures in stacking-engineered ferroelectrics from first principles. Employing a machine-learning potential model, we present results of large-scale atomistic simulations of stacking DWs and Moiré structure of boron nitride bilayers. We predict that the competition between the switching barrier of stable ferroelectric states and the in-plane lattice distortion leads to a DW width of the order of ten nanometers. DWs motion reduces the critical ferroelectric switching field of a monodomain by two orders of magnitude, while high domain-wall velocities allow domain switching on a picosecond-timescale. The superior performance compared to conventional ferroelectrics (or ferromagnets) may enable ultrafast and power-saving non-volatile memories. By twisting the bilayer into a stacking Moiré structure, the ferroelectric transforms into a super-paraelectric since DWs move under ultralow electric fields. Ferroelectricity ; Materials science ; Stacking ; Condensed matter physics ; van der Waals force ; Dielectric ; Optoelectronics ; Physics Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Mariusz Topolski | Marcin Beza
Source: JUCS - Journal of Universal Computer Science (article, 2022-03-28)
Field: Chemistry — Analytical Chemistry

Modification of the Principal Component Analysis Method Based on Feature Rotation by Class Centroids Feature engineering is a branch of science that provides tools to support, for example, the preparation of feature spaces for a pattern recognition task. The present work focuses on the problem of feature extraction. The proposed model is based on the mechanisms of PCA principal component analysis. It fills a gap in the implementation of feature extraction by looking for spaces that best discriminate between classes. This was realized by rotating the features according to the centroids of the classes. In addition, a measure of their consistency was determined which allows precise estimation of the number of features for a particular component. Four experiments were conducted in this study. The first two were done on synthetic datasets, while the next two were conducted on ten real datasets. The synthetic data allowed to determine the characteristics depending on the percentage of informative features, the number of input features, the level of imbalance and the number of output components in the extraction task. The obtained results showed that the developed solution allows for a more precise extraction, thus increasing the quality of classification. Moreover, it was shown that the method based on class centroids allows to construct efficient ensembles of classifiers. Centroid ; Principal component analysis ; Computer science ; Pattern recognition (psychology) ; Feature extraction ; Artificial intelligence ; Feature (linguistics) ; Consistency (knowledge bases) ; Task (project management) ; Rotation (mathematics) ; Measure (data warehouse) ; Data mining ; Class (philosophy) ; Component (thermodynamics) ; Feature vector ; Construct (python library) Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Engineering Diagnostics and Reliability
Authors: Owen Madin | Michael R. Shirts
Source: ChemRxiv (preprint, 2022-12-09)
Field: Materials Science — Materials Chemistry

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The computational expense of these simulations limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This allows for fast evaluation of objective functions, greatly accelerating searches over parameter space. We use an iterative framework which performs global optimization at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 ``Parsley'' force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. In most cases, these parameter sets are transferable to other similar molecules in a test set. This multi-fidelity technique provides a platform for fast optimization against physical properties that can be refined and applied in multiple ways to the development of molecular models. Maxima and minima ; Fidelity ; Force field (fiction) ; Computer science ; Surrogate model ; Field (mathematics) ; Mathematical optimization ; Gaussian ; Set (abstract data type) ; Parameter space ; Property (philosophy) ; Optimization problem ; Gaussian process ; Algorithm ; Mathematics ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Sevim Adige | Rıfat Kurban | Ali Durmuş | Ercan Karaköse
Source: European Journal of Science and Technology (article, 2022-07-01)
Field: Chemistry — Analytical Chemistry

Classification of Apple Varieties by Types Using Image Processing Techniques Gelişen teknoloji ile birlikte son dönemlerde sıkça duymaya başladığımız “Yapay Zekâ” ve “Derin Öğrenme” kavramlarının pek çok uygulama alanları mevcuttur. İnsan zekâsını taklit eden bu yöntemler çevresinden aldığı veri setlerini tıpkı insanlar gibi deneyim yoluyla öğrenir. Bu çalışmada Kayseri’nin Yahyalı ilçesinde yetişen elma çeşitlerinin cinslerine göre sınıflandırılması yapılmıştır. Elma meyvesi Eylül ayında toplandığı için elmaların ağaçtan koparıldığı hasat zamanında elde edilmiştir. Elma üretimi yapan çiftçilerin en büyük problemleri el izi olmadan ve en kısa sürede elmaların çeşitlerine göre sınıflandırılmasıdır. Bu çalışmada, 20 Golden, 20 Arjantin, 20 Buckeye Gala, 20 Galaval, 20 Superchief ve 20 Joremin elma türlerinden toplam 120 görüntü alınmıştır. Görüntüler sabit bir arka fonda aynı açı ve aynı büyüklükte Canon EOS 70D DSLR marka fotoğraf makinası ile çekilmiştir. Görüntü işlemek için MATLAB programının R2021a sürümünden faydalanılmıştır. Elma çeşitlerinin türlerine göre sınıflandırılması için derin öğrenme algoritmalarından yararlanılmıştır. Sınıflandırma problemlerinin çözümünde kullanılan en temel mimarilerinden olan AlexNet ve GoogleNet derin öğrenme algoritmaları kullanılmıştır. Çalışma AlexNet ve GoogleNet yöntemlerinin her ikisinde de 10 epoch değerinde ve sgdm eğitim algoritmasında gerçekleştirilmiştir. Öğrenme oranları AlexNet ve GoogleNet için sırasıyla 0.0001 ve 0.0003 olarak ele alınmıştır. Görüntülerin %70’i eğitim %30’u test amacıyla kullanılmış olup toplam veri seti her çeşitte 20 adet olmak üzere 120 tane görselden oluşmaktadır. AlexNet mimarisi %83.33 başarı oranı, 1 dakika 52 saniyedir. GoogleNet mimarisinin sınıflandırma başarı oranı %91,67’ dir, 2 dakika 14 saniye süre ile en başarılı sınıflandırma işlemini gerçekleştirmiştir. Horticulture ; Physics ; Art ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies