jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diego Rincon-Yanez | Chahinez Ounoughi | Bassem Sellami | Tarmo Kalvet | Marek Tiits | Sabrina Senatore | Sadok Ben Yahia
Source: arXiv (Cornell University) (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms. Embedding ; Notation ; Representation (politics) ; Knowledge graph ; Computer science ; Graph ; Process (computing) ; Knowledge representation and reasoning ; Knowledge integration ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Knowledge management ; Data science ; Knowledge engineering ; Political science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Complex Network Analysis Techniques
Authors: Davide Ronchi | Elena M. Tosca | Roberta Bartolucci | Paolo Magni
Source: Research Square (preprint, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Go beyond the limits of Genetic Algorithm in daily covariate selection practice Abstract Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic (PK/PD) model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search and on the assumption of independent covariate effects. Moreover, it is manly designed to test few covariate-parameter relationships, often chosen ad hoc or based on prior knowledge. The application of genetic algorithms (GA) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new genetic algorithm for covariate selection to address these challenges. The GA was first developed on a simulated case study; then, its performances were evaluated on a real-world problem related to Remifentanil. The implemented GA showed good results both in terms of correctness of the selected model and fitness optimization, and it was able to outperform the SCM in both scenarios. Furthermore, the heuristics introduced to overcome the limitations affecting currently available GA approaches allowed to reduce convergence times, obtain replicable results, and limit the number of selected covariates. Covariate ; Selection (genetic algorithm) ; Computer science ; Heuristics ; Correctness ; Genetic algorithm ; Identification (biology) ; Population ; Machine learning ; Model selection ; Mathematical optimization ; Data mining ; Mathematics ; Algorithm ; Medicine ; Biology Analytical Methods in Pharmaceuticals ; Hemodynamic Monitoring and Therapy ; Advanced Statistical Process Monitoring
Authors: Muayad M. Hasan | Firas K. Al-Zuhairi | Anfal H. Sadeq | Rana Abbas Azeez
Source: Fluid dynamics & materials processing (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Assessment of Nanoparticle-Enriched Solvents for Oil Recovery Enhancement Solvents are generally used to reduce the viscosity of heavy crude oil and ultimately enhance oil recovery. Recently, a new method has been introduced where nanoparticles (NPs) are exploited to induce enhanced oil recovery owing to their ability to improve the mobility ratio, dampen the interfacial tension, and alter rock wettability. This study investigated the integration of nano-alumina (Al2O3) NPs with an n-hexane solvent. In particular, a Brookfield viscometer has been used to measure the crude oil viscosity and it has been found that NPs can effectively lead to a significant decrease in the overall oil viscosity (70 cp using the solvent only, 45 cp when NPs are added). Nanoparticle ; Chemical engineering ; Chromatography ; Chemistry ; Materials science ; Nanotechnology ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques
Authors: Ibrahim Sabry | Abdel‐Hamid I. Mourad
Source: Journal of Hunan University Natural Sciences (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Experimental Investigation of Self-Cleaning Behavior of 3D-Printed Textile Materials with Different Printing Parameters The ability to eliminate contaminants from the surface without an external source is a characteristic feature of self-cleaning textile fabrics. This advanced application of science is environmentally friendly because it saves on laundry, water, electricity, and other resources and money. In the past, the self-cleaning feature was typically acquired through chemical coatings, which enhance the roughness and lower the surface energy of the fabric, thereby causing the particle or droplet pollutants to float over the surface layer instead of adhering to it. This technique works well for fabrics manufactured using conventional woven-based textile technology. The production of textile fabrics has changed recently, thanks to paid advances in technologies such as 3D printing. However, the earlier chemical coating-based technique cannot clean printed fabrics in such a way. A developed regression model to study the relationship between the secondary 3D printing parameters and the self-cleaning capabilities of various polymeric textiles allowed a deeper investigation. This study aims to examine the effects of the primary printing factors, such as the flow rate, printing temperature, and speed printing, on the self-cleaning characteristics of the printed textile. We measured the self-cleaning behavior of the chosen material, polyurethane fabric, used the trial findings to build a regression polynomial, and empirically validated models to show the critical values of the main parameters considered for the ideal self-cleaning behavior. The outcome indicated the processing-property relationship of the parts, including determining the most vital parameters that influence the fabric’s wettability. Keywords: self-cleaning, 3D printing, polyurethane, design of experiments, optimization. https://doi.org/10.55463/issn.1674-2974.50.9.8 Textile ; 3D printing ; Materials science ; Process engineering ; Environmentally friendly ; Surface roughness ; Surface finish ; Inkwell ; Coating ; Composite material ; Mechanical engineering ; Engineering Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: İsmail Özlüsoylu
Source: BioResources (article, 2023-08-17)
Field: Materials Science — Polymers and Plastics

The effect of varnish type, glue amount, and density on the surface properties of low density particleboards produced from waste wood bark Particleboards, which are widely used in various fields, are commonly coated with solid and liquid surface coating materials to achieve better physical, mechanical, and aesthetic results. This study produced low-density particleboards from waste wood bark using different adhesive mixtures and densities. These particleboards were then coated with three types of varnish: water-based (Aq), polyurethane-based (Pu), and oil/wax-based (Ow). The color, gloss, and surface roughness values of the coated boards were determined to investigate the effect of varnish type, total adhesive usage, and density on these properties. In the board groups produced with the same glue ratio and density, the roughness values obtained with Ow varnish application were mostly higher compared to Aq and Pu. In the Ow varnish type, the roughness (Rmax) decreased linearly with increasing total adhesive amount in particleboards produced at low density (320 kg/m3). The highest color change (ΔE*) values for all variations were obtained in the Ow varnish type, while the highest gloss values were achieved at 85° and in the B2 (4%-420 kg/m3) board group. It was concluded that higher density should be preferred for smoother and glossier surfaces, which are important in terms of surface properties and aesthetics. Overall, these findings highlight the preference for higher density to achieve smoother and glossier surfaces in areas where surface properties and aesthetics are significant. Varnish ; Gloss (optics) ; Materials science ; Adhesive ; Composite material ; Surface roughness ; Polyurethane ; GLUE ; Surface finish ; Wax ; Coating ; Layer (electronics) Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Textile materials and evaluations
Authors: Carl Zeiss Industrial Metrology | Herminso Villarraga-Gómez | Dustin Sirny | Carl Zeiss X-ray Microscopy | Masako Terada | Mansoureh Norouzi Rad | Allen Gu
Source: e-Journal of Nondestructive Testing (article, 2023-02-25)
Field: Materials Science — Surfaces, Coatings and Films

Workflows for assessing electronic devices with 3D X-ray microscopy and nanoscale computed tomography Modern 2D and 3D X-ray technologies are among the most useful non-destructive testing (NDT) methods that allow inspection of an object’s internal features without cutting or disassembling a sample. This paper presents workflows that combine 3D X-ray microscopy and nanoscale tomography to generate a detailed three-dimensional visualization of the interior of electronic devices and assemblies, without destroying them, to enable the study of internal components for failure analysis (FA). These imaging workflows can be run independently or complementary to other multiscale correlative microscopy evaluations and provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging, possibly complemented with additional correlative microscopy investigations, can speed development time, increase cost efficiency, and simplify FA and quality inspection of semiconductor packaging, printed circuit boards (PCBs), and electronic devices assembled with new emerging technologies. Workflow ; Microscopy ; Electronic component ; Electronics ; Computer science ; Nanoscopic scale ; Tomography ; Characterization (materials science) ; Visualization ; Sample (material) ; Materials science ; Nanotechnology ; Artificial intelligence ; Mechanical engineering ; Electrical engineering ; Engineering ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques
Authors: Vanya Slavova | Ewa Ropelewska | Kadir Sabancı
Source: European Food Research and Technology (article, 2023-09-08)
Field: Chemistry — Analytical Chemistry

The application of fluorescence spectroscopy and machine learning as non-destructive approach to distinguish two different varieties of greenhouse tomatoes Abstract The application of interdisciplinary non-invasive diagnostic methods combining fluorescence spectroscopy with multiple machine learning algorithms as tools for rapid application in tomato breeding programs is essential when crossing specific genotypes or parental samples to obtain representatives with better performance. Non-destructive distinguishing tomato species is of great importance for the preservation of product quality. This study aimed at combining fluorescence spectroscopic data and machine learning algorithms for distinguishing greenhouse tomatoes. The models for the discrimination of greenhouse tomato samples were built based on selected spectroscopic data using different machine learning algorithms from the groups of Meta, Functions, Bayes, Trees, Rules, and Lazy. The confusion matrices with accuracy for each sample, average accuracy, time taken to build the model, Kappa statistic, mean absolute error, root mean squared error and relative absolute error were determined. The greenhouse tomato samples were discriminated with an accuracy reaching 100% for the models built using Multi-Class Classifier (Meta), Logistic (Function), Bayes Net (Bayes), PART (Rules), and J48 (Trees). In the case of these algorithms, Kappa statistic was 1.0 and mean absolute error, root mean squared error and relative absolute error were equal to 0. Mean squared error ; Greenhouse ; Machine learning ; Mean absolute error ; Mathematics ; Artificial intelligence ; Bayes' theorem ; Statistics ; Classifier (UML) ; C4.5 algorithm ; Statistic ; Computer science ; Naive Bayes classifier ; Algorithm ; Support vector machine ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Fatemeh Dadgar Pakdel | Javad Dadgar Pakdel | Ali Najmeddin | Afshin Peirovi | Mohammad Hossein Nicknam | Farid Abedin Dorkoosh
Source: Turkish Journal of Pharmaceutical Sciences (article, 2023-08-28)
Field: Materials Science — Biomaterials

A novel controlled release implant of insulin based on Poly (3- hydroxybutyrate-co-3-hydroxyvalerate) polymer prepared by extrusion process In this study, for the first time, a controlled release implant of insulin has been developed based on a PHBV polymer. In this method, the extrusion process has been used, which provides the possibility of preparing implants on an industrial scale in the future. Also, their development appears to be a promising treatment for diabetic patients and leads to the elimination of frequent drug injections and then more adherence of the patients to the continuation of the treatment process. Extrusion ; Polymer ; Materials science ; Insulin ; Chemical engineering ; Process (computing) ; Chemistry ; Composite material ; Biotechnology ; Computer science ; Engineering biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Piyush Haluai | Adrià Marcos Morales | Matan Leibovich | Mai Tan | Joshua Vincent | Carlos Fernandez‐Granda | Peter A. Crozier
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Extracting High Spatio-Temporal Information Using Machine Learning from Pt Nanoparticles in CO Gas Environment Journal Article Extracting High Spatio-Temporal Information Using Machine Learning from Pt Nanoparticles in CO Gas Environment Get access Piyush Haluai, Piyush Haluai School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, Arizona, United States Corresponding author: phaluai@asu.edu Search for other works by this author on: Oxford Academic Google Scholar Adrià Marcos Morales, Adrià Marcos Morales New York University, New York, United States Search for other works by this author on: Oxford Academic Google Scholar Matan Leibovich, Matan Leibovich New York University, New York, United States Search for other works by this author on: Oxford Academic Google Scholar Mai Tan, Mai Tan School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, Arizona, United States Search for other works by this author on: Oxford Academic Google Scholar Joshua Vincent, Joshua Vincent School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, Arizona, United States Search for other works by this author on: Oxford Academic Google Scholar Carlos Fernandez-Granda, Carlos Fernandez-Granda New York University, New York, United States Search for other works by this author on: Oxford Academic Google Scholar Peter A Crozier Peter A Crozier School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, Arizona, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1930–1931, https://doi.org/10.1093/micmic/ozad067.999 Published: 22 July 2023 State (computer science) ; Tempe ; Library science ; Energy (signal processing) ; Art history ; Engineering physics ; Artificial intelligence ; Engineering ; History ; Computer science ; Chemistry ; Physics ; Algorithm Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Mass Spectrometry Techniques and Applications
Authors: Dongliang Jin
Source: International Journal of Advanced Computer Science and Applications (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Develop an Olive-based Grading Algorithm using Image Processing Olives come in a number of external and internal varieties. The Shengeh kind, which is available in three colours—green, brown, and black—was chosen at random by the researchers to ensure that the sample was diverse. To avoid discoloration throughout the experiment, 150 healthy olives were harvested and stored correctly. These olives had not been subjected to any external harm, such as crushing or milling. The particular kind of olives were kept chilled at 2°C and preserved in water. This study investigates the possibility of grading Shengeh cultivars from olives that have different uses, based on color using image processing. After preparing images of olives using MATLAB software and image processing techniques, olives are graded based on their color in three categories: immature with green, semi-ripe with brown, and ripe with black. The results showed that image processing technology can be used to grade olives of the Shengeh type in terms of their ripeness as a single-color grain with acceptable accuracy. The HSV color space is one of the best color spaces to separate the colors of the olive cultivar. The accuracy of the software for detecting olives with the mentioned degrees is 98%, 96%, and 100%, respectively. Ripeness ; Computer science ; Image processing ; Color space ; Grading (engineering) ; Software ; Artificial intelligence ; Computer vision ; Cultivar ; Image (mathematics) ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Smart Agriculture and AI
Authors: Marcus Djokic | José L. Mendoza-Cortés
Source: ChemRxiv (preprint, 2023-06-19)
Field: Chemistry — Inorganic Chemistry

MultiBinding Sites United in Covalent-Organic Frameworks (MSUCOF) for H2 Storage and Delivery at Room Temperature The storage of hydrogen gas (\ce{H2}) has presented a significant challenge that has hindered its use as a fuel source for transportation. To meet the Department of Energy's ambitious goals of achieving 50 g L$^{-1}$ volumetric and 6.5 wt \% gravimetric uptake targets, materials-based approaches are essential. Designing materials that can efficiently store hydrogen gas requires careful tuning of the interactions between the gaseous \ce{H2} and the surface of the material. Metal-Organic Frameworks (MOFs) and Covalent-Organic Frameworks (COFs) have emerged as promising materials due to their exceptionally high surface areas and tunable structures that can improve gas-framework interactions. However, weak binding enthalpies have limited the success of many current candidates, which fail to achieve even 10 g L$^{-1}$ volumetric uptake at ambient temperatures. To overcome this challenge, We utilized quantum mechanical (QM) based force fields (FF) to investigate the uptake and binding enthalpies of 3 linkers chelated with 7 different transition metals (TM), including both precious metals (Pd and Pt) and first row TM (Co, Cu, Fe, Ni, Mn), to design 24 different COFs in-silico. By applying QM-based FF with grand canonical Monte Carlo (GCMC) from 0-700 bar and 298 K, We demonstrated that Co-, Ni-, Mn-, Fe-, Pd-, and Pt-based MSUCOFs can already achieve the Department of Energy's hydrogen storage targets for 2025. Surprisingly, the COFs that incorporated the more affordable and abundant first-row TM often outperformed the precious metals. This promising development brings us one step closer to realizing a hydrogen-based energy economy. Hydrogen storage ; Gravimetric analysis ; Metal-organic framework ; Covalent bond ; Nanotechnology ; Transition metal ; Materials science ; Hydrogen ; Energy storage ; Binding energy ; Chemistry ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Physics ; Thermodynamics ; Catalysis ; Atomic physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Xueting Ma | Jiean Liao | Jinfei ZHAO | Linqiao XI
Source: Food Science and Technology (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Knowledge mapping of research on spectral technology in the fruit field using CiteSpace (1981-2021) To explore the development process and trends of spectral technology in the fruit field, the quantitative and visual analysis of 849 documents from China National Knowledge Infrastructure (CNKI) and 4791 documents from Web of Science (WoS) in the fruit field was carried out in terms of the annual publication, authors and institutions, and keywords based on CiteSpace. According to the results of visual analysis and some important documents, the main research hotspots of spectral technology in the fruit field were discussed and summarized. The research based on the bibliometrics visualization tool CiteSpace expounds on the research of spectrum in the fruit field from a macroscopic and microscopic perspective. The document information is comprehensive and the results are intuitive, which can help researchers to understand the research progress, academic exchange, and grasp of the research in this field. Field (mathematics) ; Geography ; Remote sensing ; Mathematics Spectroscopy and Chemometric Analyses