jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liset Cayhualla Amaro | Sebastian Rau Reyes | María Acuña Meléndez | Christian Ovalle
Source: IAES International Journal of Artificial Intelligence (article, 2024-10-08)
Field: Chemistry — Analytical Chemistry

Systematic review of artificial intelligence with near-infrared in blueberries The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries. Computer science ; Chemometrics ; Quality (philosophy) ; Support vector machine ; Machine learning ; Systematic review ; Quality assessment ; Product (mathematics) ; Artificial intelligence ; Convolutional neural network ; Evaluation methods ; Mathematics ; Reliability engineering ; Chemistry ; MEDLINE Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Horban VF
Source: Journal of Advanced Materials Science and Engineering (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Comparison of The Yield Strength of High-Entropy Solid Solutions Based on BCC and FCC Lattices Obtained in the Experiment and Calculation Based on the literature data, high-entropy alloys based on BCC or FCC phases were selected for which the ultimate strength from 214 to 2863 MPA was determined. Knowing the chemical composition of the selected high-entropy alloys, the average elastic modulus, distortion level, and mixing enthalpy were calculated. The use of formulas that use only their atomic radius and elastic modulus, mixing enthalpy to determine the theoretical level of hardness made it possible to establish the yield strength based on the ratio s0,2=0,3H. High entropy alloys ; Materials science ; Yield (engineering) ; Solid solution ; Condensed matter physics ; Thermodynamics ; Statistical physics ; Physics ; Metallurgy ; Alloy Solidification and crystal growth phenomena ; Radiative Heat Transfer Studies ; Metallurgical Processes and Thermodynamics
Authors: Mallika Adhatrao
Source: Journal of Pharma Insights and Research. (article, 2024-10-04)
Field: Chemistry — Analytical Chemistry

Contemporary Trends in Analytical Spectroscopy and Integrating Qbd Principles for Enhanced Method Development Analytical spectroscopy has undergone significant advancements in recent years, driven by technological innovations and the demand for more robust, efficient, and reliable methods. The integration of Quality by Design (QbD) principles in spectroscopic method development has emerged as a powerful approach to enhance performance, reliability, and regulatory compliance. Contemporary trends in UV-Visible, Infrared (IR), Near-Infrared (NIR), Raman, Nuclear Magnetic Resonance (NMR), and Mass Spectrometry showcase remarkable progress in sensitivity, selectivity, and applicability. QbD implementation in method development, optimization, and validation has led to improved robustness and reduced method failures. Hyphenated techniques, coupling spectroscopy with separation methods, have expanded the analytical capabilities, enabling more comprehensive characterization of complex samples. Chemometrics and advanced data analysis techniques play a crucial role in extracting meaningful information from large spectral datasets. Regulatory agencies increasingly recognize the value of QbD in analytical methods, encouraging its adoption in the pharmaceutical and other regulated industries. Despite these advancements, challenges remain in standardizing QbD approaches and adapting to emerging technologies. Future directions point towards increased automation, miniaturization, and real-time monitoring capabilities in spectroscopic techniques Spectroscopy ; Computer science ; Biochemical engineering ; Management science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Srinivasulu Reddy Motireddy
Source: Journal of pharmacology & clinical research (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Toxicity of Nanoparticles in Medical Applications, Cytotoxicity and Oxidative Stress: Future Challenges Through the introduction of a novel class of materials and consumer goods in numerous fields, nanotechnology has completely changed the world.Nanotechnologies have shown numerous uses in a variety of domains over the past 20 years, including biomedical sciences, electronics, catalysis, detection, and sensing.It has been discovered that certain nanoparticles, including TiO 2 , copper, silver, and others, have harmful effects on aquatic life, plants, and humans.The public and governments around the world are becoming increasingly concerned about the health effects of nanoparticles.In order to evaluate the health effects of nanoparticles, the majority of nanotoxicity research to date has concentrated on exposures to the respiratory tract.Given the ongoing exposure, it is vital to fully comprehend the possible short-term and long-term negative effects of nanoparticles on people.With an emphasis on mechanistic understanding of nanoparticle toxicity at the organ, tissue, cell, and biomolecular levels, we examine and highlight the current status of nanotoxicology research in this review.The main methods for determining cytotoxicity are attempted to be compiled in this review.Although the number of in vitro studies of nanoparticles has been steadily growing, in vivo studies have not yet developed a unified system.Validated standard methods and predictive models are essential.Nowadays, a wide range of human illnesses, such as cancer and autoimmune diseases, are diagnosed and treated with nanoparticles (NPs).Therefore, in order to demonstrate the effectiveness of NPs and their influence on health, trustworthy data regarding their effects on different organs is required.This review discusses the body of knowledge already available on the topic, which should help us better prepare to handle these difficulties.We have described the state of toxicological research on nanoparticles, including its achievements, shortcomings, and upcoming difficulties. Cytotoxicity ; Oxidative stress ; Nanoparticle ; Toxicity ; Nanotechnology ; Chemistry ; Materials science ; Biochemistry ; In vitro ; Organic chemistry Nanoparticles: synthesis and applications
Authors: Rajendar Dommeti
Source: INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS (article, 2024-08-30)
Field: Chemistry — Analytical Chemistry

MACHINE LEARNING-BASED ASSESSMENT OF OPERATIONAL EFFICIENCY AND SUSTAINABILITY IN THE POULTRY PROCESSING INDUSTRY USING SVR AND GPR REGRESSION The poultry industry in the United Kingdom is characterized by large-scale, highly integrated production systems that require the efficient coordination of resources to meet growing demand while maintaining sustainability, quality, and animal welfare standards.M-Park UK, a leading poultry processing company, operates in a complex environment where production efficiency is influenced by numerous interdependent operational parameters, including feed supply, energy consumption, labor hours, processing time, and quality control measures. This study aims to analyze the relationships between these key production variablesand develop accurate predictive models for production output using advanced machine learning techniques.Descriptive statistical analysis and correlation assessment were initially employed to understand the data distribution and interdependencies among the operational parameters.Two machine learning approaches -Support Vector Regression (SVR) and Gaussian Process Regression (GPR) -were used to predict production outcomes.The models were evaluated using standard evaluation metrics including R, explained variance score, mean squared error, root mean squared error, Machine Learning-Based Assessment of Operational Efficiency and Sustainability in the Poultry Processing Industry Using SVR and GPR Regression https://iaeme.com/Home/journal/IJITMIS163 editor@iaeme.comand mean absolute error, which were applied to both training and test data.SVR demonstrated superior predictive accuracy and a strong ability to generalize beyond the training data, while GPR showed near-perfect performance on the training data and robust results on new data, with the added advantage of providing probability-based predictions.This study contributes to the growing body of research on data-informed decision-making tools for the agri-food sector and illustrates how machine learning techniques can be effectively implemented in industrial-scale poultry processing facilities. Production (economics) ; Support vector machine ; Sustainability ; Kriging ; Quality (philosophy) ; Regression analysis ; Regression ; Statistical process control ; Reliability (semiconductor) Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Forecasting Techniques and Applications
Authors: Stephen Coleman | Lisa M. Breckels | Ross F. Waller | Kathryn S. Lilley | Chris Wallace | Oliver M. Crook | Paul Kirk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-12)
Field: Chemistry — Spectroscopy

Semi-supervised Bayesian integration of multiple spatial proteomics datasets Abstract The subcellular localisation of proteins is a key determinant of their function. High-throughput analyses of these localisations can be performed using mass spectrometry-based spatial proteomics, which enables us to examine the localisation and relocalisation of proteins. Furthermore, complementary data sources can provide additional sources of functional or localisation information. Examples include protein annotations and other high-throughput ‘omic assays. Integrating these modalities can provide new insights as well as additional confidence in results, but existing approaches for integrative analyses of spatial proteomics datasets are limited in the types of data they can integrate and do not quantify uncertainty. Here we propose a semi-supervised Bayesian approach to integrate spatial proteomics datasets with other data sources, to improve the inference of protein sub-cellular localisation. We demonstrate our approach outperforms other transfer-learning methods and has greater flexibility in the data it can model. To demonstrate the flexibility of our approach, we apply our method to integrate spatial proteomics data generated for the parasite Toxoplasma gondii with time-course gene expression data generated over its cell cycle. Our findings suggest that proteins linked to invasion organelles are associated with expression programs that peak at the end of the first cell-cycle. Furthermore, this integrative analysis divides the dense granule proteins into heterogeneous populations suggestive of potentially different functions. Our method is disseminated via the mdir R package available on the lead author’s Github. Author summary Proteins are located in subcellular environments to ensure that they are near their interaction partners and occur in the correct biochemical environment to function. Where a protein is located can be determined from a number of data sources. To integrate diverse datasets together we develop an integrative Bayesian model to combine the information from several datasets in a principled manner. We learn how similar the dataset are as part of the modelling process and demonstrate the benefits of integrating mass-spectrometry based spatial proteomics data with timecourse gene-expression datasets. Proteomics ; Computer science ; Inference ; Computational biology ; Proteogenomics ; Flexibility (engineering) ; Bayesian probability ; Data mining ; Artificial intelligence ; Genomics ; Biology ; Genome ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Metabolomics and Mass Spectrometry Studies
Authors: Miroslav Yosifov | Thomas Lang� | Virginia Florian | Stefan Gerth | Jan De Beenhouwer | Jan Sijbers | Johann Kastner | Christoph Heinzl
Source: Research Square (preprint, 2024-02-08)
Field: Chemistry — Analytical Chemistry

Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI Degradation (telecommunications) ; Computer science ; Biological system ; Biochemical engineering ; Artificial intelligence ; Chemistry ; Engineering ; Biology ; Telecommunications Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Microbial Metabolism and Applications
Authors: Ondrej Dyck | Aisha Okmi | Kai Xiao | Sidong Lei | Andrew R. Lupini | Stephen Jesse
Source: arXiv (Cornell University) (preprint, 2024-07-02)
Field: Materials Science — Polymers and Plastics

Your Clean Graphene is Still Not Clean Efforts aimed at scaling fabrication processes to the level of single atoms, dubbed atom-by-atom fabrication or atomic fabrication, invariably encounter the obstacle of atomic scale cleanliness. When considering atomic fabrication, cleanliness of the base material and purity of the source reservoir from which atomic structures will be built are invariable constraints imposed by laws of physics and chemistry. As obvious as such statements may be, and regardless of the inevitable consequences for successful atomic fabrication, there is a poignant lack of understanding of the "dirt" (contamination/impurities). Here, we examine hydrocarbon contamination on graphene. Graphene has formed the base substrate for many e-beam-based atomic fabrication studies and many strategies for cleaning graphene have been presented in the literature. One popular method is heating to high temperatures (&gt;500 °C). It is usually inferred that volatile hydrocarbons evaporate into the microscope vacuum system leaving behind pristine graphene. Here, we show through direct image intensity analysis that what appears to be clean graphene can be coated with a thin layer of dynamically diffusing hydrocarbons. This result holds significant implications for approaches to e-beam based atomic fabrication, updates the conceptual model of e-beam induced hydrocarbon deposition, and may extend to hot surfaces generally. Clean-up ; Clean technology ; Clean water ; Clean energy ; Graphene ; Nanotechnology ; Environmental science ; Chemistry ; Materials science ; Engineering ; Waste management ; Political science ; Environmental engineering ; Chromatography Textile materials and evaluations
Authors: Ruoqi Zhao | Yingjie Wang | Jiali Gao | Jun Zhang
Source: Journal of Chemical Theory and Computation (article, 2024-11-26)
Field: Chemistry — Organic Chemistry

Method and Implementation of Projected Hybrid Orbitals for Treating Multiple Covalent Bonds in Combined QM/MM Calculations The projected hybrid orbital (PHO) method for treatment of multiple boundary atoms introduces a novel solution for handling the covalent connection between quantum mechanical (QM) and molecular mechanical (MM) regions in QM/MM calculations. By projecting the QM basis, typically adequately large for computational accuracy, onto a secondary minimal basis set on the boundary atom, it preserves electronic interactions between the two regions without system-specific parameters. Applicable in both ab initio wave function theory and density functional theory, PHO has been shown to maintain structural and electronic integrity across various systems. It offers key advantages over traditional QM/MM methods, such as avoiding modification of MM charge distribution and energy corrections, while being flexible for biochemical systems and potentially broader QM/QM embedding frameworks. Covalent bond ; Atomic orbital ; QM/MM ; Computer science ; Molecular orbital ; Computational chemistry ; Chemistry ; Physics ; Molecule ; Molecular dynamics ; Quantum mechanics ; Electron Supramolecular Chemistry and Complexes ; Advanced NMR Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Atsushi Nakamula | Kiori Obuse | N. Sawado | K. Shimasaki | Yasunobu Shimazaki | Yutaro Suzuki | Kouichi Toda
Source: arXiv (Cornell University) (preprint, 2024-12-28)
Field: Chemistry — Spectroscopy

Inelastic Scattering, Emergent Interactions of Solitons in the Zakharov-Kuznetsov Equation through Conservative and non-Conservative Physics-Informed Neural Networks The Zakharov-Kuznetsov equation, originally a three dimensional mathematical model of plasma with a uniform magnetic field, is a direct extension of the KdV equation into higher dimensions and is a typical quasi-integrable system. Physics-Informed Neural Networks (PINNs) are used to study the collision of soliton solutions in the 2+1 dimensional Zakharov-Kuznetsov equation. PINNs are able to successfully solve the equations in the forward process, and the solutions are obtained using a mesh-free approach and automatic differentiation, taking into account conservation laws. In the inverse process, the proper form of the equation can be successfully derived from a given training data. However, the situation becomes intractable in the collision process. The forward analysis result no longer adheres to the laws of conservation, and is better described as a dynamically incompatible field configuration (DIFC) than a solution to the system. Conservative PINNs have thus been introduced for this purpose, and in this paper we succeed in obtaining solutions that satisfy conservation laws. The inverse analysis suggests a different equation in which the coefficients exhibit significant changes, implying an emergence of temporary interactions. With these modulated coefficients, we recalculate the equation and confirm that the adherence to the laws of conservation has unquestionably improved. Physics ; Classical mechanics ; Theoretical physics Spectroscopy and Laser Applications ; Seismic Waves and Analysis ; Nonlinear Photonic Systems
Authors: M. A. Jawale | P. William | N. K. Darwante | Ved Prakash Verma | S. S. Ingle | Dilip Kumar Roy
Source: Journal of Nano- and Electronic Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing the Implementation and Reliability of Nanomaterial Detectors through Deep Learning Optimization Journal of Nano- and Electronic Physics. Scientific journal. ISSN: 2077-6772. Journal abbreviation: J. Nano- Electron. Phys. No page charges. All articles are freely available on-line. Issued 4 times per year. Publisher: Sumy State University. (Sumy, Ukraine) Reliability (semiconductor) ; Computer science ; Nanomaterials ; Reliability engineering ; Deep learning ; Artificial intelligence ; Nanotechnology ; Materials science ; Engineering ; Physics Machine Learning in Materials Science
Authors: Cecilia M. S. Alvares | Rocío Semino
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Chemistry — Inorganic Chemistry

Coarse grained modeling of a metal-organic framework/polymer composite and its gas adsorption at the nanoparticle level Simulations have acted as a cornerstone to understand MOF/polymer interface structure, however, no molecular-level simulation has yet been performed at the nanoparticle scale. In this work, a hybrid MARTINI/Force Matching (FM) force field was developed and successfully implemented to model the ZIF-8/PVDF composite at a coarse grained resolution. Inter-phase interactions were modeled using FM potentials, which strive to reasonably reproduce the forces from an atomistic benchmark model, while intraphase interactions are modeled using the general-purpose MARTINI potentials. Systems made of a ZIF-8 nanoparticle embedded into a PVDF matrix were considered to evaluate the effect of nanoparticle size and morphology in the polymer structure and in the CO2 adsorption. Results show that simulations at the nanoparticle level are crucial for depicting the polymer penetration. Notably, the smallest nanoparticle exhibited the least extent of polymer penetration, while the cubic nanoparticle exhibited the highest amount. Polymer conformation and local density values change similarly in all ZIF-8/PVDF systems depending on whether the polymer lies inside or outside of the nanoparticle domain. All composite models present more significant CO2 adsorption in the nanoparticle domain than in the PVDF phase, in agreement with experiments. More remarkably, the small rhombic dodecahedron ZIF-8/PVDF system presents a larger equilibrium amount of gas adsorbed at ambient condition compared to the other two systems, in alignment with the observed polymer penetration trend. On the other hand, the amount of CO2 adsorbed at equilibrium is lower for the rhombic dodecahedron morphology than for the cubic one, contrary to the intuitive expectation founded in the polymer penetration trend. This result could be a reflection of a difference in the number of surface adsorption sites. Adsorption ; Composite number ; Nanoparticle ; Metal-organic framework ; Materials science ; Polymer ; Metal ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques
Authors: Atul Kumar and Mahipal Singh
Source: Nanotechnology Perceptions (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Impact Of Grain Size On Thermal Conductivity Of Nanomaterials Nanomaterials have unique thermal properties due to atomic dimensions, high surface-to-volume ratios and quantum confinement effects. Thermal conductivity (κ) is an important consideration in determining the suitability of nanomaterials in a various domain, from electronics to energy, and thermoelectric systems. In this paper we present the theoretical and computational background of thermal conductivity in perspective of nanomaterial, on size effects, structure and surface interactions. The theoretical model illustrates size dependence of thermal properties, while emphasizing quantum confinement, surface effects and the structural elements. The thermal conductivity of nano cylindrical rods of different nanomaterials have been calculated and compared with other theoretical models and previously published experimental data. The comparison with experimental results is consistent with our theoretical predictions and provides implications for the applicability of nano materials for application in thermal management, thermos-electrics and materials engineering. Nanomaterials ; Grain size ; Thermal conductivity ; Materials science ; Thermal ; Condensed matter physics ; Nanotechnology ; Physics ; Metallurgy ; Thermodynamics ; Composite material Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Thermal properties of materials