jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhuo Chen | Ningning Wang | Wenbo Jin | D D Li
Source: Energy Engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines. The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%, respectively. Additionally, it shows a higher prediction accuracy compared to the ENN prediction model. In fact, the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process, which can overcome the shortcomings of the ENN prediction model, such as weak generalization ability and tendency to fall into the local minimum value, so that it has the advantages of strong implementation and high prediction accuracy. Pipeline transport ; Crude oil ; Generalization ; Wax ; Artificial neural network ; Deposition (geology) ; Petroleum engineering ; Process (computing) ; Computer science ; Artificial intelligence ; Algorithm ; Engineering ; Mathematics ; Environmental engineering ; Materials science ; Geology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques
Authors: M. Ortega | Alexander Saynisch | Bahar-Merve Yurtseven | Thomas Gries
Source: Preprints.org (review, 2024-01-17)
Field: Materials Science — Polymers and Plastics

A Review on False-Twist Texturing The annual demand for fibres continues to rise worldwide. Consequently, more and more fibres must be produced to meet this demand, most of which are melt-spun polymeric man-made fibres. [1] Smooth filaments made of polymers are mainly used for technical applications in industry. For use in clothing or home textiles, for example, a texturing process is used to give the filaments a crimp and thus a feel like that of natural fibres. In this state, they can be processed together with natural fibres and used in textiles. Partially oriented yarns (POY) are of great importance in tex-turing. The yarns are mainly crimped with the help of the so-called false-twist texturing process (FTTP). [2] Since POY accounts for about 60 % of the melt-spun filament yarn produced world-wide, the FTTP is the most important texturing process in the textile industry [3]. In this paper, the main components of false-twist texturing (FTT) machines are explained along with the state of the art and research for each component and its influence on the process. Relevant patents are dis-cussed as well as process optimisation techniques, innovative polymers and yarn types recently used in FTT, followed by a conclusion and an outlook for the process. Crimp ; Yarn ; Process (computing) ; Materials science ; Composite material ; Weaving ; Textile ; Polymer ; Textile industry ; Polymer science ; Engineering drawing ; Computer science ; Mechanical engineering ; Engineering Textile materials and evaluations
Authors: Talha Alperen Çengel | Bunyamin Gencturk | Elham Tahsin Yasin | Müslüme Beyza Yıldız | İlkay Çınar | Osman Özbek | Murat Köklü
Source: Selcuk Journal of Agricultural and Food Sciences (article, 2024-12-16)
Field: Chemistry — Analytical Chemistry

Classification of Orange Features for Quality Assessment Using Machine Learning Methods Oranges are a member of the citrus family and are eaten in large quantities due to their high vitamin C content, sweet and tart taste, and useful fiber and antioxidant qualities. Orange quality assurance is essential to market competitiveness and customer satisfaction. Conventional approaches to evaluating quality are costly and susceptible to mistakes made by people. This research aims to investigate how well different machine learning algorithms automate and improve the orange quality assessment procedure. A dataset containing 241 samples and 11 features (size, weight, sweetness (Brix), acidity (pH), and color) was used to evaluate the effectiveness of the Random Forest (RF), XGBoost, and k-Nearest Neighbors (k-NN) algorithms. According to the findings, k-NN acquired the maximum accuracy of 69.38%, with RF coming in second at 67.34% and XGBoost third at 63.26%. These results demonstrate how machine learning models may be used to improve quality control in the orange industry by offering a more dependable and effective approach. According to this study, machine learning can greatly improve the quality control procedures for oranges, resulting in higher-quality goods for customers and more productivity for providers. The orange sector can enhance product quality and expedite operations by utilizing these technologies, which will eventually benefit both producers and consumers. Orange (colour) ; Quality assurance ; Machine learning ; Artificial intelligence ; Computer science ; Quality (philosophy) ; Sweetness ; Marketing ; Food science ; Business ; Chemistry ; Taste Spectroscopy and Chemometric Analyses
Authors: Gabriela Drabik | Mariusz Radoń
Source: ChemRxiv (preprint, 2024-01-24)
Field: Chemistry — Organic Chemistry

Approaching the Complete Basis Set Limit for Transition Metal Spin–State Energetics Convergence to the complete basis set (CBS) limit is analyzed for the problem of transition metal (TM) spin-state energetics by taking under scrutiny a benchmark set of 18 energy differences between spin states for 13 chemically diverse TM complexes. The performance of conventional CCSD(T) and explicitly correlated CCSD(T)-F12a/b calculations in approaching the CCSD(T)/CBS limits is systematically studied. An economic computational protocol is developed based on the CCSD-F12a approximation and (here proposed) modified scaling of the perturbative triples term, (T#). This computational protocol recovers relative spin–state energetics of the benchmark set in excellent agreement with the reference CCSD(T)/CBS limits (mean absolute deviation 0.4, mean signed deviation 0.2, and maximum deviation 0.8 kcal/mol) and enables performing canonical CCSD(T) calculations for mononuclear TM complexes sized up to ca. 50 atoms, which is illustrated by application to heme-related metalloporphyrins. Furthermore, a good transferability of the basis set incompleteness error (BSIE) is demonstrated for spin–state energetics calculated using CCSD(T) and other wave-function methods (MP2, CASPT2, CASPT2/CC, NEVPT2, MRCI+Q), which justifies efficient focal-point approximations and simplifies construction of multi-method benchmark studies. Energetics ; Limit (mathematics) ; State (computer science) ; Basis (linear algebra) ; Transition metal ; Spin (aerodynamics) ; Set (abstract data type) ; Basis set ; Spin states ; Statistical physics ; Physics ; Condensed matter physics ; Theoretical physics ; Chemistry ; Computer science ; Mathematics ; Quantum mechanics ; Thermodynamics ; Algorithm ; Geometry ; Mathematical analysis ; Density functional theory Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Photochemistry and Electron Transfer Studies
Authors: Izabela Cristina de Oliveira | Ricardo Gava | Dthenifer Cordeiro Santana | Ana Carina da Silva Cândido Seron | Larissa Pereira Ribeiro Teodoro | Mayara Fávero Cotrim | Regimar Garcia dos Santos | Rita de Cássia Félix Alvarez | Carlos Antônio da Silva | Fábio Henrique Rojo Baio | Paulo Eduardo Teodoro
Source: Algorithms (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

Detection of Irrigated and Non-Irrigated Soybeans Using Hyperspectral Data in Machine-Learning Models The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in the 2023/24 harvest in the experimental area of the Federal University of Mato Grosso do Sul, Câmpus Chapadão do Sul, Mato Grosso do Sul, and it was conducted in a strip scheme with seven cultivars subjected to irrigated and rainfed management. Sixty days after crop emergence, three leaves per plot were collected for evaluation by the hyperspectral sensor. The spectral data was then separated into 28 bands to reduce dimensionality. In this way, two databases were generated: one with all the spectral information provided by the sensor (WL) and one with the 28 spectral bands (SB). Each database was subjected to different machine-learning models to ascertain the improved accuracy of the models in distinguishing the different eucalyptus species. The models tested were artificial neural networks (ANN), decision trees (DT), linear regression (LR), M5P algorithm, random forest (RF), and support vector machine (SVM). The results demonstrate the effectiveness of machine-learning models in differentiating soybean management under rainfed and irrigated conditions, highlighting the advantage of hyperspectral data (WL) over selected spectral bands (SB). Models such as the support vector machine (SVM) showed the best levels of accuracy when using the entire available spectrum. On the other hand, artificial neural networks (ANN) performed well with spectral band data, demonstrating their ability to work with smaller data sets without compromising the classification. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Artificial neural network ; Random forest ; Decision tree ; Machine learning ; Spectral bands ; Curse of dimensionality ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Banana Cultivation and Research
Authors: Andreas D. Demou | Nikos Savva
Source: Preprints.org (preprint, 2024-03-22)
Field: Materials Science — Surfaces, Coatings and Films

Hybrid AI-Analytical Modeling of Droplet Dynamics on Inclined Heterogeneous Surfaces This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset. Dynamics (music) ; Materials science ; Mechanics ; Nanotechnology ; Computer science ; Physics ; Acoustics Surface Modification and Superhydrophobicity ; Vehicle emissions and performance ; Particle Dynamics in Fluid Flows
Authors: L. Salgado | Paula C. Cifuentes-Delgado | Juan Camilo Orozco | Carolina Muñoz-Camargo | Luis H. Reyes | Valentina Quezada | Juan C. Cruz
Source: Frontiers in Bioengineering and Biotechnology (article, 2024-12-02)
Field: Materials Science — Biomaterials

Evaluating the impact of cell-penetrating motif position on the cellular uptake of magnetite nanoparticles Cell-penetrating peptides (CPPs) have been employed to enhance the cellular uptake and intracellular delivery of various nanocarriers. Among them, nanoparticles (NPs) have been used as suitable vehicles for delivering different bioactive molecules in the treatment of a diverse range of diseases. Given the pivotal role of the conjugation method of CPPs, this study aims to evaluate the impact of the position of a cell-penetrating motif (LFVCR) on the biocompatibility, cellular uptake, and endosomal escape of magnetite NPs. The designed peptide's physicochemical properties suggest they are well-suited for efficient cell penetration with minimal cytotoxicity. The resulting designed nanoconjugates were characterized using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), dynamic light scattering (DLS), and transmission electron microscopy (TEM). The results indicate that motif position significantly impacts the cellular uptake and endosomal escape of the designed nanobioconjugates. Key findings suggest that motif exposure enhances endocytosis-mediated cell internalization and improves endosomal escape efficiency. These results were compared with nanobioconjugates displaying previously reported CPPs. The selected nanobioconjugate demonstrated superior performance in endosomal escape and comparable cell uptake to the reference nanobioconjugates. These results, along with the nanobioconjugate's physicochemical characteristics and high biocompatibility, position the nanocarrier as a suitable candidate for delivering diverse bioactive molecules. Magnetite Nanoparticles ; Magnetite ; Nanoparticle ; Motif (music) ; Chemistry ; Cell biology ; Biophysics ; Computational biology ; Magnetic nanoparticles ; Nanotechnology ; Materials science ; Biology ; Physics Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoparticles: synthesis and applications
Authors: Ren Ji-wen | Yuming Xiong | Xinyu Chen | Yong Hao
Source: Preprints.org (preprint, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method. Quality (philosophy) ; Product (mathematics) ; Agriculture ; Computer science ; Machine learning ; Artificial intelligence ; Algorithm ; Mathematics ; Geography Spectroscopy and Chemometric Analyses
Authors: Amin Alibakhshi | Lars V. Schäfer
Source: ChemRxiv (preprint, 2024-07-16)
Field: Chemistry — Organic Chemistry

On the Theoretical Quantification of Radii of Atoms in Molecules Despite the fundamental importance of radii of atoms in molecules for numerous applications in physics and chemistry, comprehensive methods for their theoretical evaluation are still scarce. Here, we present quantum chemistry-based approaches for evaluation of radii of atoms in molecules and assess their robustness by studying the agreement of van der Waals and solvent-excluded surfaces constructed by them with reference molecular surfaces. By studying a large dataset of 1235 molecules, we show that estimation of radii via effective and free atomic volumes can accurately take the dependence of atomic radii on the chemical environment into account. Molecule ; Atoms in molecules ; Materials science ; Physics ; Quantum mechanics Chemical Thermodynamics and Molecular Structure ; History and advancements in chemistry ; Thermal and Kinetic Analysis
Authors: Shih-Hung Lin | Yuan-Ting Wang | Y. C. Wang
Source: Sensors and Materials (article, 2024-01-23)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Techniques Applied to Development of Flexible Electronic Antireflective Film In this study, we aim to optimize the process for developing antireflective and refractiveindex-matching films on flexible substrates.The development of these films is crucial in light of the increasing demand in the market for flexible electronics, which are poised to be the next emerging technology and application after semiconductors and flat panel displays.Our research involves the production of these films by physical vapor deposition and sputtering techniques, which can also be applied to various optical thin films.The effectiveness of our coating process has been verified and refined on the basis of feedback.The results of this study are applicable to related industrial technologies and will contribute to improving industry competitiveness. Anti-reflective coating ; Computer science ; Development (topology) ; Materials science ; Nanotechnology ; Mathematics Optical Coatings and Gratings ; Surface Roughness and Optical Measurements
Authors: Marko Stojanović | Mirjana B. Čolović | Jovana Lalatović | Aleksandra Milosavljević | Nada D. Savić | Kilian Declerck | Branimir Radosavljević | Mila Ćetković | Tamara Kravic‐Stevovic | Tatjana N. Parac‐Vogt | Danijela Krstić
Source: International Journal of Molecular Sciences (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Monolacunary Wells-Dawson Polyoxometalate as a Novel Contrast Agent for Computed Tomography: A Comprehensive Study on In Vivo Toxicity and Biodistribution Polyoxotungstate nanoclusters have recently emerged as promising contrast agents for computed tomography (CT). In order to evaluate their clinical potential, in this study, we evaluated the in vitro CT imaging properties, potential toxic effects in vivo, and tissue distribution of monolacunary Wells-Dawson polyoxometalate, α<sub>2</sub>-K<sub>10</sub>P<sub>2</sub>W<sub>17</sub>O<sub>61</sub><sup>.</sup>20H<sub>2</sub>O (mono-WD POM). Mono-WD POM showed superior X-ray attenuation compared to other tungsten-containing nanoclusters (its parent WD-POM and Keggin POM) and the standard iodine-based contrast agent (iohexol). The calculated X-ray attenuation linear slope for mono-WD POM was significantly higher compared to parent WD-POM, Keggin POM, and iohexol (5.97 ± 0.14 vs. 4.84 ± 0.05, 4.55 ± 0.16, and 4.30 ± 0.09, respectively). Acute oral (maximum-administered dose (MAD) = 960 mg/kg) and intravenous administration (1/10, 1/5, and 1/3 MAD) of mono-WD POM did not induce unexpected changes in rats' general habits or mortality. Results of blood gas analysis, CO-oximetry status, and the levels of electrolytes, glucose, lactate, creatinine, and BUN demonstrated a dose-dependent tendency 14 days after intravenous administration of mono-WD POM. The most significant differences compared to the control were observed for 1/3 MAD, being approximately seventy times higher than the typically used dose (0.015 mmol W/kg) of tungsten-based contrast agents. The highest tungsten deposition was found in the kidney (1/3 MAD-0.67 ± 0.12; 1/5 MAD-0.59 ± 0.07; 1/10 MAD-0.54 ± 0.05), which corresponded to detected morphological irregularities, electrolyte imbalance, and increased BUN levels. Polyoxometalate ; In vivo ; Biodistribution ; Iohexol ; Nanoclusters ; Chemistry ; Iodine ; Toxicity ; Medicine ; Creatinine ; Hemoglobin ; Pharmacology ; Nuclear medicine ; Internal medicine ; Renal function ; In vitro ; Biochemistry Lanthanide and Transition Metal Complexes ; Polyoxometalates: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Kamal Choudhary
Source: ChemRxiv (preprint, 2024-09-03)
Field: Materials Science — Materials Chemistry

SLMat: A Comprehensive Serverless Toolkit for Advanced Materials Design SLMat is a serverless, browser-based toolkit that revolutionizes computational materials science by offering a scalable and efficient alternative to traditional server-based platforms like Google Colab. By eliminating the need for server management and providing persistent setups, SLMat enhances productivity and security, enabling researchers to focus on innovation rather than infrastructure. The toolkit integrates seamlessly with materials databases, supports AI model development, and offers advanced features like AI-guided coding and chatbot integration. With its streamlined workflow, SLMat accelerates production time, promotes reproducibility, and democratizes access to powerful computational resources. This makes SLMat an essential tool for modern materials science, offering a versatile and cost-effective solution for researchers across various domains. Code: https://github.com/deepmaterials/slmat WebPage: https://deepmaterials.github.io/slmat/ Computer science ; Software engineering Catalytic Processes in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Cloud Data Security Solutions