jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdul N. Hamood | Samsuri Abdullah | A Alwazir | Abdul Amir H. Kadhum | Waleed Khalid Al‐Azzawi | Tayser Sumer Gaaz | Ahmed A. Al‐Amiery | B Mahdi | M Abbass | M Mohsin | W Al-Azzawi | M Hanoon | M Alkaabi | L Shaker | A Al-Amiery | W Isahak | A Kadhum | M Takriff | Q Jawad | D Zinad | R Salim | T Gaaz | S Al-Baghdadi | F Noori | W Ahmed | A Resen | S Junaedi | A Kadihum | A Mohamad | A Alamiery | H Aljibori | H Al-Asadi | F Hashim | A Salam | T Abed | K Reda | A Al Adily | F Sayyid | R Al-Azzawi | M Kzar | H Jawoosh | A Mustafa | N Betti | Y Abdulsahib | A Eltmimi | S Alhabeeb | T Allami | A Khudhair | T Gazz | A
Source: International Journal of Corrosion and Scale Inhibition (article, 2023-12-24)
Field: Chemistry — Organic Chemistry

Experimental and theoretical investigation of 5-(5-bromothiophen-2-yl)oxazole as a highly effective corrosion inhibitor for mild steel in 1 M hydrochloric acid The present research article focuses on the utilization of 5-(5-bromothiophen-2-yl)oxazole as a potent corrosion inhibitor for mild steel in 1 M hydrochloric acid (HCl) solution. The corrosion inhibition behavior of the inhibitor was thoroughly examined using experimental gravimetrical measurements, and density functional theory (DFT) studies. To investigate the effect of varying conditions, the inhibitor's performance was studied at different temperatures (303, 313, 323, and 333 K) and immersion periods (1, 5, 10, 24, and 48 hours). Remarkably, 5-(5-bromothiophen-2-yl)oxazole exhibited an outstanding inhibition performance of 93.8% at an optimized inhibitor concentration of 0.5 mM, effectively mitigating mild steel corrosion in the aggressive 1 M HCl environment. To gain deeper insights into the inhibitor's inhibitive properties, quantum chemical calculations were performed. Frontier Molecular Orbitals (HOMO and LUMO), energy gap (E = ELUMO-EHOMO), electronegativity, chemical softness, hardness, and the number of electron transfers (N) were meticulously determined and analyzed. The findings revealed that nitrogen, oxygen, and sulfur atoms were the most favorable sites for coordination with iron atoms on the mild steel surface through electron donation to unoccupied d-orbitals of Fe atoms. The experimental data also supported the Langmuir adsorption isotherm model for the inhibitor's behavior. This comprehensive investigation provides valuable insights into the inhibitive mechanism of 5-(5-bromothiophen-2-yl)oxazole, bridging the gap between Hydrochloric acid ; Oxazole ; Corrosion ; Corrosion inhibitor ; Chemistry ; Carbon steel ; Nuclear chemistry ; Materials science ; Metallurgy ; Inorganic chemistry ; Stereochemistry Inorganic and Organometallic Chemistry ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Zainab Harbi
Source: Wasit Journal of Pure sciences (article, 2023-09-30)
Field: Chemistry — Analytical Chemistry

Automatic Diagnosis of Ovarian Cancer Based on Relative Entropy and Neural Network Ovarian Cancer is one of the most common causes of death for women in developing countries. Screening and early diagnoses of OC are urgently needed. Early diagnosis would help in consequence procedures and treatment. Mass spectrometry (MS) data is been used as an effective component of cancer diagnosis tools. However, these valuable data have a large number of dimensions that can affect the learning process in addition to time-consuming considerations. Feature selection plays an important role in reducing information redundancy, and deals with the invalidation that occurs in basic classification algorithms when there are too many features and huge datasets. To improve the automatic system diagnosis accuracy, entropy-based selection features are proposed. These features are combined with the novel learning capabilities of neural networks to achieve higher diagnostic accuracy. Experiments have been performed using different feature selection algorithms and machine learning classification approaches. Experimental results have proved that the proposed system performs better based on the measure of accuracy. Computer science ; Feature selection ; Medical diagnosis ; Machine learning ; Artificial intelligence ; Artificial neural network ; Entropy (arrow of time) ; Redundancy (engineering) ; Data mining ; Pattern recognition (psychology) ; Medicine Spectroscopy and Chemometric Analyses
Authors: Ana-Maria Udrea
Source: FARMACIA (article, 2023-12-28)
Field: Chemistry — Organic Chemistry

CHARACTERIZATION OF NOVEL CARPROFEN DERIVATIVES AS INHIBITORS OF THE SEROTONIN 5-HT2C RECEPTORS AND CLK4-KD PROTEIN KINASE FOR THE DEVELOPMENT OF PHARMACOLOGICALLY ACTIVE COMPOUNDS In search of novel compounds with multiple biological activities, nine carprofen derivatives (1a-i) have been investigated by molecular docking to explore their interactions with target proteins involved in different diseases, from cancer to inflammation associated pathologies (viral infections, pain, neurodegenerative conditions).The docking studies have been performed to study the dual specificity for the protein kinase CLK4 and serotonin 2C (5-HT2C) receptor.Our molecular docking predictions indicate that the highest predicted binding energy has been observed in the case of the 1b derivative for the 5-HT2C receptor and respectively in the case of derivative 1h for CLK4-KD.Therefore, these two derivatives show a promising potential for the development of multi-pharmacologically active compounds. RezumatÎn vederea descoperirii de noi compuși cu activități biologice multiple, nouă derivați ai carprofenului (1a-i) au fost investigați prin andocare moleculară pentru a explora interacțiunile lor cu proteinele țintă implicate în diferite boli, de la cancer la patologii asociate inflamației (infecții virale, durere, afecțiuni neurodegenerative).Studiile de andocare au fost efectuate pentru a studia specificitatea duală pentru receptorul proteinkinazei CLK4 și cel al serotoninei 2C (5-HT2C).Predicțiile noastre de andocare moleculară indică faptul că cea mai mare energie de legare prezisă a fost observată în cazul derivatului 1b pentru receptorul 5-HT2C și, respectiv, în cazul derivatului 1h pentru receptorul CLK4-KD.Prin urmare, acești doi derivați arată un potențial promițător pentru dezvoltarea compușilor cu potențial farmacologic multivalent. Pharmacology ; Chemistry ; Serotonin ; Receptor ; Kinase ; Medicine ; Biochemistry Synthesis and Reactions of Organic Compounds
Authors: Rinske M. Alkemade | Frank Smallenburg | Laura Filion
Source: arXiv (Cornell University) (preprint, 2023-01-30)
Field: Materials Science — Materials Chemistry

Improving the prediction of glassy dynamics by pinpointing the local cage The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To do this we introduce a method to pinpoint the cage state of the initial configuration -- i.e. the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime. Cage effect ; Computer science ; Dynamics (music) ; State (computer science) ; Statistical physics ; Simple (philosophy) ; Work (physics) ; Artificial intelligence ; Machine learning ; Algorithm ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Pickering emulsions and particle stabilization
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Nanoscale, 2023, 15, 11303 DOI: 10.1039/D3NR90135A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; World Wide Web ; Computer security ; Chemistry ; Psychology ; Social psychology Quantum Dots Synthesis And Properties
Authors: Beata E. Kowal | Krzysztof M. Graczyk | Artur M. Ankowski | Rwik Dharmapal Banerjee | Hemant Prasad | J. T. Sobczyk
Source: arXiv (Cornell University) (preprint, 2023-12-28)
Field: Materials Science — Surfaces, Coatings and Films

Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the bootstrap approach and the Monte Carlo dropout approach. In our analysis, the $χ^2$ defines the loss function, including point-to-point and normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7$%. To test these models, we compare their predictions to test datasets excluded from the training process and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and theoretical calculations. We also perform a comparison to a dataset lying beyond the covered kinematic region, and find that the bootstrap approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm. Extrapolation ; Statistical physics ; Monte Carlo method ; Interpolation (computer graphics) ; Physics ; Computer science ; Mathematics ; Artificial intelligence ; Statistics Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Medical Imaging Techniques and Applications
Authors: Lorenzo Vallegi | Marco Scutari | Federico Mattia Stefanini
Source: arXiv (Cornell University) (preprint, 2023-08-11)
Field: Chemistry — Analytical Chemistry

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statistical and machine learning models, including mixed-effect models, random coefficients models, random forests, and deep learning architectures, have been devised to predict maize yield. These models consider factors such as genotype, environment, genotype-environment interaction, and field management. However, the existing models often fall short of fully exploiting the complex network of causal relationships among these factors and the hierarchical structure inherent in agronomic data. This study introduces an innovative approach integrating random effects into Bayesian networks (BNs), leveraging their capacity to model causal and probabilistic relationships through directed acyclic graphs. Rooted in the linear mixed-effects models framework and tailored for hierarchical data, this novel approach demonstrates enhanced BN learning. Application to a real-world agronomic trial produces a model with improved interpretability, unveiling new causal connections. Notably, the proposed method significantly reduces the error rate in maize yield prediction from 28% to 17%. These results advocate for the preference of BNs in constructing practical decision support tools for hierarchical agronomic data, facilitating causal inference. Interpretability ; Bayesian network ; Artificial intelligence ; Machine learning ; Computer science ; Causal inference ; Bootstrapping (finance) ; Cluster analysis ; Probabilistic logic ; Bayesian probability ; Random effects model ; Field (mathematics) ; Causal model ; Bayesian inference ; Random forest ; Data mining ; Mathematics ; Econometrics ; Statistics Spectroscopy and Chemometric Analyses
Authors: Cagla Deniz Bahadir | Benjamin Liechty | David J. Pisapia | Mert R. Sabuncu
Source: ArXiv.org (preprint, 2023-10-05)
Field: Chemistry — Spectroscopy

Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no ``gold-standard'' independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise levels and human subjectivity. In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner. We train a probabilistic diffusion model to synthesize patches of cell nuclei for a given mitosis label condition. Using this model, we can then generate a sequence of synthetic images that correspond to the same nucleus transitioning into the mitotic state. This allows us to identify different image features associated with mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity and high contrast between the nucleus and the cell body. Our approach offers a new tool for pathologists to interpret and communicate the features driving the decision to recognize a mitotic figure. Probabilistic logic ; Diffusion ; Computer science ; Statistical model ; Statistical physics ; Artificial intelligence ; Physics Molecular spectroscopy and chirality
Authors: Fayas Asharindavida | Omar Nibouche | James Uhomoibhi | Jun Liu | Hui Wang
Source:  (article, 2023-01-05)
Field: Chemistry — Analytical Chemistry

Machine Learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study Food quality analysis can be carried out by spectral data acquired from spectrometers with its advantage of non-destructive way of testing. Portable and miniature spectroscopy can be a suitable solution when it meets the specifications such as portability, cost, and short processing time requirements, to enable ordinary citizens to use such a device in the fight against food fraud. Compared to more expensive, bulky, and non-portable devices, the data collected using miniature and portable spectrometers is of a lower quality and thus adversely affect the quality of the analysis. Research have been carried out to use machine learning (ML) classifiers on spectral data analysis for food quality assessment. The present work focuses on two aspects: firstly, preliminary exploratory statistical analysis is conducted on the real spectral data on different food products including oils, fruits and spices acquired from such miniature devices, which aims to evaluate and illustrate the distinctive characteristics of such spectral data, data distribution and difference in the spectra across multiple data acquisitions etc. along with a summary of the key challenges to face and explore. Secondly, a case study for the differentiation of extra virgin olive from adulterated with vegetable oil is provided to analyze and evaluate how some commonly used ML classifiers can be used for classification, while the impact of different preprocessing methods to improve the accuracy and efficiency is also provided. The case study demonstrates the good potential of using data analytics for spectral data from miniature device, although the overall performance of those ML classifiers is not exceptional (the classification rates of up to 83.32%) which is partially due to the quality of data, and partially due to limiting to only some classifiers. More elaborate data pre-processing and cleaning methods can be used to address the key challenges of the spectral data from miniature device, and other types of classifiers can be also explored further in future work. Software portability ; Computer science ; Preprocessor ; Quality (philosophy) ; Data quality ; Data pre-processing ; Data analysis ; Spectrometer ; Data processing ; Food quality ; Artificial intelligence ; Data mining ; Machine learning ; Engineering ; Database Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ivan Maliyov | Jia Yin | Jia Yao | Chao Yanɡ | Marco Bernardi
Source: arXiv (Cornell University) (preprint, 2023-11-13)
Field: Materials Science — Materials Chemistry

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation Nonequilibrium dynamics governed by electron-phonon (e-ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e-ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials. Boltzmann equation ; Physics ; Non-equilibrium thermodynamics ; Relaxation (psychology) ; Phonon ; Electron ; Thermalisation ; Excitation ; Momentum (technical analysis) ; Computational physics ; Statistical physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Thermal properties of materials
Authors: TanJinting
Source: International Journal of Education and Social Science Research (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

IMPERFECT GRAIN IDENTIFICATION OF WHEAT BASED ON IMAGE PROCESSING AND CNN In order to realize the rapid, comprehensive and nondestructive identification of imperfect grains of wheat, image processing and CNN recognition methods can be applied by collecting the imperfect grains and perfect grains mentioned in the national quality standard of wheat. Image processing can make the output image have better effect, which is convenient for image analysis and recognition. Image preprocessing includes image acquisition, graying, median filtering, image segmentation and so on. The classical classification network LeNet-5 in convolutional neural network (CNN) takes the preprocessed image as the input and adds batch normalization (BN). BN algorithm can speed up the decline speed of training gradient, increase the convergence speed of the model, and increase the stability of the model. It can recognize the image and evaluate the performance with the accuracy of the test set. It avoids complex feature extraction steps and effectively improves the recognition rate of wheat grains, which is of great significance to the intelligent detection and recognition of wheat. Normalization (sociology) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Preprocessor ; Convolutional neural network ; Image processing ; Image segmentation ; Image quality ; Feature extraction ; Digital image processing ; Feature (linguistics) ; Segmentation ; Artificial neural network ; Image (mathematics) ; Computer vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Xiaoyu Zhang | Chuanhou Gao | Denis Dochain
Source: arXiv (Cornell University) (preprint, 2023-03-28)
Field: Chemistry — Spectroscopy

Capturing persistence of delayed complex balanced chemical reaction systems via decomposition of semilocking sets With the increasing complexity of time-delayed systems, the diversification of boundary types of chemical reaction systems poses a challenge for persistence analysis. This paper focuses on delayed complex balanced mass action systems (DeCBMAS) and derives that some boundaries of a DeCBMAS can not contain an $ω$-limit point of some trajectory with positive initial point by using the method of semilocking set decomposition and the property of the facet, further expanding the range of persistence of delayed complex balanced systems. These findings demonstrate the effectiveness of semilocking set decomposition to address the complex boundaries and offer insights into the persistence analysis of delayed chemical reaction network systems. Persistence (discontinuity) ; Decomposition ; Computer science ; Chemistry ; Geology ; Geotechnical engineering ; Organic chemistry Analytical Chemistry and Chromatography ; Gene Regulatory Network Analysis ; Chemical Synthesis and Analysis
Authors: Lucien Roach | David Gonzalez‐Rodriguez | Jie Gao | Éric Laurichesse | Alexánder Castro Grijalba | Reïko Oda | Véronique Schmitt | Émilie Pouget | Mona Tréguer‐Delapierre | Glenna L. Drisko
Source: ChemRxiv (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

The effect of solvent on convectively‑driven silicaparticle assembly: Decoupling surface tension,viscosity, and evaporation rate The process of convectively self‑assembling particles in films suffers from low reproducibility due to its high dependency on particle concentration, as well as a variety of interactions and physical parameters. Inhomogeneities in flow rates and instabilities at the air‑liquid interface are mostly responsible for reproducibility issues. These problems are aggravated by adding multiple components to the dispersion, such as binary solvent mixtures or surfactant/polymer additives, both common approaches to control stick‑slip behavior. When an additive is used, not only does it change the surface tension, but also the viscosity and the evaporation rate. Worse yet, gradients can be formed in these three properties, which then lead to Marangoni currents. Here, we use a series of alcohols to study the role of viscosity independently of other solvent properties, to show its impact on stick‑slip behavior and interband distances. We show how mixtures of glycerol and alcohol, or poly(acrylic acid) and alcohol lead to more complex patterning. Marangoni currents are not always observed in co‑solvent systems, being dependent on the rate of solvent evaporation. To produce homogenous particle assemblies and control stick‑slip behavior, gradients must be avoided, and the surface tension and viscosity need both be carefully controlled. Surface tension ; Marangoni effect ; Viscosity ; Solvent ; Materials science ; Decoupling (probability) ; Chemical engineering ; Slip (aerodynamics) ; Chemistry ; Composite material ; Thermodynamics ; Organic chemistry Pickering emulsions and particle stabilization ; Surface Modification and Superhydrophobicity ; Electrowetting and Microfluidic Technologies