jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qadir Bux alias Imran Latif | Zubair Ahmed Memon | Zafar Mahmood | Mohsin Usman Qureshi | Abdalrhman Milad
Source: Applied Sciences (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

A Machine Learning Model for the Prediction of Concrete Penetration by the Ogive Nose Rigid Projectile In recent years, research interest has been revolutionized to predict the rigid projectile penetration depth in concrete. The concrete penetration predictions persist, unsettled, due to the complexity of phenomena and the continuous development of revolutionized statistical techniques, such as machine learning, neural networks, and deep learning. This research aims to develop a new model to predict the penetration depth of the ogive nose rigid projectile into concrete blocks using machine learning. Genetic coding is used in Python programming to discover the underlying mathematical relationship from the experimental data in its non-dimensional form. A populace of erratic formulations signifies the rapport amid dependent parameters, such as the impact factor (I), the geometry function of the projectile (N), the empirical constant for concrete strength (S), the slenderness of the projectile (λ), and their independent objective variable, X/d, where X is the penetration depth of the projectile and d is the diameter of the projectile. Four genetic operations were used, including the crossover, sub-tree transfiguration, hoist transfiguration, and point transfiguration operations on supervised test datasets, which were divided into three categories, namely, narrow penetration (X/d &lt; 0.5), intermediate penetration (0.5 ≤ X/d &lt; 5.0), and deep penetration (X/d ≥ 5.0). The proposed model shows a significant relationship with all data in the category for medium penetration, where R2 = 0.88, and R2 = 0.96 for deep penetration. Furthermore, the proposed model predictions are also compared with the most commonly used NDRC and Li and Chen models. The outcome of this research shows that the proposed model predicts the penetration depth precisely, compared to the NDRC and Li and Chen models. Projectile ; Penetration (warfare) ; Genetic programming ; Computer science ; Artificial intelligence ; Machine learning ; Structural engineering ; Mathematics ; Materials science ; Engineering ; Operations research High-Velocity Impact and Material Behavior ; Ion-surface interactions and analysis ; Geophysical Methods and Applications
Authors: Ngo Minh Tri Nguyen | Nai-Shang Liou
Source: Agriculture (article, 2022-12-13)
Field: Chemistry — Analytical Chemistry

Ripeness Evaluation of Achacha Fruit Using Hyperspectral Image Data In this study, spectral data within the wavelength range of 400–780 nm were used to evaluate the ripeness stages of achacha fruits. The ripeness status of achacha fruits was divided into seven stages. Both average and pixel-based approaches were used to assess the ripeness. The accuracy and n-level-error accuracy of each ripeness stage was predicted by using classification models (Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN)) and regression models (Partial Least Square Regression (PLSR) and Support Vector Regression (SVR)). Furthermore, how the curvature of the fruit surface affected the prediction of the ripeness stage was investigated. With the use of an averaged spectrum of fruit samples, the accuracy of the model used in this study ranged from 52.25% to 79.75%, and the one-level error accuracy (94.75–100%) was much higher. The SVM model had the highest accuracy (79.75%), and the PLSR model had the highest one-level error accuracy (100%). With the use of pixel-based ripeness prediction results and majority rule, the accuracy (58.25–79.50%) and one-level-error accuracy (95.25–99.75%) of all models was comparable with the accuracy predicted by using averaged spectrum. The pixel-based prediction results showed that the curvature of the fruit could have a noticeable effect on the ripeness evaluation values of achacha fruits with a low or high ripeness stage. Thus, using the spectral data in the central region of achacha fruits would be a relatively reliable choice for ripeness evaluation. For an achacha fruit, the ripeness value of the fruit face exposed to sunlight could be one level higher than that of the face in shadow. Furthermore, when the ripeness value of achacha fruit was close to the mid-value of two adjacent ripeness stage values, all models had a high chance of having one-level ripeness errors. Thus, using a model with high one-level error accuracy for sorting would be a practical choice for the postharvest processing of achacha fruits. Ripeness ; Support vector machine ; Mathematics ; Hyperspectral imaging ; Statistics ; Mean squared error ; Pattern recognition (psychology) ; Partial least squares regression ; Artificial intelligence ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Phytochemicals and Antioxidant Activities
Authors: Songjian Dan
Source: International Journal on Semantic Web and Information Systems (article, 2022-02-16)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%. Preprocessor ; Computer science ; Artificial intelligence ; Orange (colour) ; Identification (biology) ; Data pre-processing ; Pattern recognition (psychology) ; Feature selection ; Near-infrared spectroscopy ; Machine learning ; Chemistry ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Miguel Tzec-Simá | Jean Wildort Félix | María Inés Granados-Alegría | Mónica Aparicio-Ortiz | Dilery Juárez-Monroy | Damian Mayo-Ruiz | Saraí Vivas-López | Rufino Gómez-Tah | Blondy Canto‐Canché | Maxim V. Berezovski | Ignacio Islas‐Flores
Source: Agronomy (article, 2022-12-14)
Field: Chemistry — Inorganic Chemistry

Potential of Omics to Control Diseases and Pests in the Coconut Tree The coconut palm (Cocos nucifera L.) is a common crop in pantropical areas facing various challenges, one of them being the control of diseases and pests. Diseases such as bud rot caused by Phytophthora palmivora, lethal yellowing caused by phytoplasmas of the types 16SrIV-A, 16SrIV-D or 16SrIV-E, among others, and pests like the coconut palm weevil, Rhynchophorus vulneratus (Coleoptera: Curculionidae), and the horned beetle, Oryctes rhinocerus (Coleoptera: Scarabaeidae), are controlled by applying pesticides, pheromones and cultural control. These practices do not guarantee eradication since some causal agents have become resistant or are imbedded in infected tissues making them difficult to eradicate. This review condenses the current genomics, transcriptomics, proteomics and metabolomics studies which are being conducted with the aim of understanding the pathosystems associated with the coconut palm, highlighting the findings generated by omics studies that may become future targets for the control of diseases and pests in the coconut crop. Weevil ; Biology ; Cocos nucifera ; Rhynchophorus ; Palm ; Crop ; Curculionidae ; Biological pest control ; Biopesticide ; Pest control ; Biotechnology ; Arecaceae ; PEST analysis ; Botany ; Agronomy ; Pesticide ; Toxicology Coconut Research and Applications ; Date Palm Research Studies ; Insect Resistance and Genetics
Authors: Christian Hazael Pérez-Beltrán | Guadalupe Pérez‐Caballero | J.M. Andrade | Luis Cuadros-Rodrı́guez | Ana M. Jiménez–Carvelo
Source: Microchemical Journal (article, 2022-11-05)
Field: Chemistry — Analytical Chemistry

Non-targeted spatially offset Raman spectroscopy-based vanguard analytical method to authenticate spirits: White Tequilas as a case study Partial least squares regression ; Chemometrics ; Linear discriminant analysis ; Artificial intelligence ; Principal component analysis ; Pattern recognition (psychology) ; Support vector machine ; Chemistry ; Statistics ; Chromatography ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Helen Onyeaka | Paolo Passaretti | Taghi Miri | Abarasi Hart | Claudia Favero | Christian Anumudu | P.T. Robbins
Source: Biochemistry and Molecular Biology Education (article, 2022-10-31)
Field: Chemistry — Physical and Theoretical Chemistry

Pre‐lab video demonstrations to enhance students' laboratory experience in a first‐year chemical engineering class The limited capabilities of teaching laboratories, combined with an increasing number of students enrolled in university, require constant augmentation of instructional approaches. By enhancing laboratory demonstrations with digital technology, these structural issues can be addressed while at the same time enhancing student understanding and learning. Our case study focuses on the fermentation lab part of the Reaction Equilibria and Thermodynamics (RET) module, a first-year chemical engineering course at the University of Birmingham. Video demonstrations were used to introduce students to the laboratory set-ups and walk them through each step and technique. The video demonstrations allowed the students to attend the in-person lab sessions having established knowledge and understanding of the processes involved and the outcomes desired, which decreased the burden on the facilities and the staff. A knowledge-based quiz and a student survey conducted at the end of the module showed that the pre-lab videos encouraged more active participation in the laboratory sessions and reinforced learning. Approximately 70% of the students polled in the first survey conducted within this project felt more confident going into the laboratory sessions after watching the pre-lab videos and attempting the knowledge quiz, while 92% of the students polled in the second survey judged the pre-lab video sessions as beneficial to them. Overall, the teaching method has the potential to improve student participation and access, boost confidence and learning, and provided a more structured and flexible approach to laboratory learning outcomes. Polling ; Class (philosophy) ; Set (abstract data type) ; Active learning (machine learning) ; Computer science ; Multimedia ; Mathematics education ; Medical education ; Psychology ; Medicine ; Artificial intelligence Various Chemistry Research Topics ; Experimental Learning in Engineering ; Innovative Teaching Methods
Authors: Reza Taherdangkoo | Miaomiao Tian | Ali Sadighi | Tao Meng | Huichen Yang | Christoph Butscher
Source: Data (article, 2022-10-16)
Field: Materials Science — Biomaterials

Experimental Data on Solubility of the Two Calcium Sulfates Gypsum and Anhydrite in Aqueous Solutions Calcium sulfate exists in three forms, namely dihydrate or gypsum (CaSO4·2H2O), anhydrite (CaSO4), and hemihydrate or bassanite (CaSO4·0.5H2O) depending on temperature, pressure, pH, and formation conditions. The formation of calcium sulfates occurs widely in nature and in many engineering settings. Herein, a dataset containing the experimental solubility data of calcium sulfate minerals, i.e., gypsum and anhydrite, in aqueous solutions is presented. The compiled dataset contains calcium sulfates solubility values extracted from 42 papers published between 1906 and 2019. The dataset can be used for various scientific and engineering purposes such as environmental applications (e.g., gas treatment, wastewater treatment, and chemical disposal), geotechnical applications (e.g., clay-sulfate rock swelling), separation processes (e.g., crystallization, extractive distillation, and seawater desalination), and electrochemical processes (e.g., corrosion and electrolysis). Anhydrite ; Gypsum ; Solubility ; Sulfate ; Calcium ; Aqueous solution ; Chemistry ; Mineralogy ; Inorganic chemistry ; Chemical engineering ; Materials science ; Metallurgy ; Organic chemistry Calcium Carbonate Crystallization and Inhibition ; Mine drainage and remediation techniques ; Concrete and Cement Materials Research
Authors: Christopher Weaver | Adrian Fortuin | Anton Vladyka | Tim Albrecht
Source: Chemical Communications (article, 2022-01-01)
Field: Chemistry — Electrochemistry

Unsupervised classification of voltammetric data beyond principal component analysis In this study, we evaluate different apoproaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) as well as neural networks. To this end, we exploit a form of transfer learning, based on feature extraction in an image recognition network, VGG-16, in combination with PCA, t-SNE or UMAP. Overall, we find that t-SNE performs best when applied directly to numerical data (noise-free case) or to features (in the presence of noise), followed by UMAP and then PCA. Principal component analysis ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Embedding ; Noise (video) ; Feature extraction ; Independent component analysis ; Dimensionality reduction ; Artificial neural network ; Projection (relational algebra) ; Transfer of learning ; Projection pursuit ; Image (mathematics) ; Algorithm Electrochemical Analysis and Applications ; Neural Networks and Applications ; Advanced Chemical Sensor Technologies
Authors: Kai Sun | Evangelos Vassos | Xingzhao Yan | Callum Wheeler | James Churm | Peter R. Wiecha | Simon A. Gregory | Alex Feresidis | C.H. de Groot | Otto L. Muskens
Source: Advanced Optical Materials (article, 2022-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wafer‐Scale 200 mm Metal Oxide Infrared Metasurface with Tailored Differential Emissivity Response in the Atmospheric Windows Abstract Metasurfaces with sub‐wavelength nanoscale features have emerged as a platform to achieve desirable electromagnetic responses. However, it remains technically challenging to fabricate metasurfaces in large size and at low cost for mass production. This work demonstrates a 200 mm wafer‐scale Al:ZnO metasurface coating based on deep‐UV lithography. The metasurfaces are targeted to achieve infrared (IR) reflectivity and emissivity characteristics at bandwidths across the two atmospheric windows in the IR spectrum. The wafers demonstrate a high uniformity of optical response with tailored reflectivity of around 50% at the 3–5 µm mid‐wave IR band and less than 10% at the 8–13 µm long‐wave IR band. This article furthermore shows that the design principle allows achieving a wide range of dual‐band reflectivity values using a single underlying materials stack, offering a versatile platform. The proposed approach is compatible with CMOS‐compatible mass‐production manufacturing and brings IR metasurface coatings closer to commercially relevant and scalable technology. Materials science ; Emissivity ; Wafer ; Optoelectronics ; Infrared ; Lithography ; Optics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Han-Sol Kim | Chang-Min Kim | An-Na Jo | Joo-Eun Kim
Source: Pharmaceuticals (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

Studies on Preformulation and Formulation of JIN-001 Liquisolid Tablet with Enhanced Solubility This study aimed to develop a heat shock protein 90 (Hsp90) inhibitor liquisolid tablet with improved solubility to overcome low bioavailability issues. As an active pharmaceutical ingredient (API), JIN-001, a novel Hsp90 inhibitor, was reported to have substantial in vitro antiproliferative and in vivo antitumor activity; however, JIN-001 was a crystalline solid with very low solubility in an aqueous solution, and therefore, Capryol 90, which has excellent solubilization ability, was selected as an optimal liquid vehicle based on solubility studies. JIN-001 liquisolid (JLS) powder was successfully prepared by dissolving JIN-001 in Capryol 90 and mixing colloidal silicon dioxide (CSD) used as an oil adsorption agent. The prepared JLS was confirmed to be amorphous. Based on the result of the solubility test of JLS, compared to JIN-001, the solubility of the former was significantly improved in all solvents regardless of pH. JLS tablets were prepared through wet granulation using JIN-001 and stable excipients based on the compatibility test. The developed JLS tablet significantly increased the drug release rate in all tested solutions; however, the liquisolid method had no significant effect on bioavailability in the pharmacokinetics study in beagle dogs. In conclusion, the liquisolid system influenced the solubility and dissolution rate of JIN-001. Solubility ; Bioavailability ; Dissolution ; Chemistry ; Chromatography ; Nuclear chemistry ; Materials science ; Pharmacology ; Organic chemistry ; Medicine Crystallization and Solubility Studies ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Sherien N. Elkateb
Source: Fibres and Textiles in Eastern Europe (article, 2022-07-01)
Field: Materials Science — Polymers and Plastics

Prediction of Mechanical Properties of Woven Fabrics by ANN Abstract This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties. Yarn ; Ultimate tensile strength ; Materials science ; Composite material ; Artificial neural network ; Bending ; Stiffness ; Elongation ; Polyester ; Woven fabric ; Structural engineering ; Bending stiffness ; Engineering ; Computer science ; Artificial intelligence Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color perception and design
Authors: Xiao‐Shuang Cai | Xiao‐Juan Zhao | Wenbo Miao | Zhongwei Wu | Hua‐Min Liu | Xue‐De Wang
Source: Journal of Oleo Science (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Optimization and Kinetics Modeling of Microwave-Assisted Subcritical &lt;i&gt;n&lt;/i&gt;-Butane Extraction of Tigernut Oil In this study, tigernut oil was extracted from tigernut meal by subcritical n-butane extraction with the assistance of microwave pretreatment. Effects of microwave pulse duration, particle size of tigernut meal, and subcritical extraction variables (temperature, time, solid-liquid ratio, number of extraction cycles) on extraction efficiency were examined by single-factor experiments and Response Surface Methodology (RSM) modeling. The results indicate that microwaving (560 W, 6 min) significantly increased the subcritical extraction efficiency. The variation of extraction yield could be interpreted as a nonlinear function of extraction time, temperature and liquid-solid ratio. Changing the independent variables could affect the oil extraction efficiency. The subcritical extraction of tigernut oil with a liquid-solid ratio of 3.62 kg/(kg of tigernut meal) at a temperature of 52°C for 32 min after three extraction cycles produced the most oil, and a maximum yield (24.736%) of tigernut oil was achieved. The ratio of unsaturated to saturated fatty acids (4.68 UFA/SFA), low acid value (3.30 mg KOH/g oil), low peroxide value (0.28 meq.kg<sup>-1</sup>), and preponderance of oleic acid indicate a high-quality oil. To describe the extraction kinetics, a modified Brunner's mathematical model was used. The model fit the experimental data well over the entire operating range, and the explanation coefficient exceeds 96%. Our results can be used to develop an optimized method for subcritical fluid extraction of tigernut oil and can move industry further toward implementing microwave-assisted subcritical extraction in oil processing. Extraction (chemistry) ; Response surface methodology ; Butane ; Yield (engineering) ; Chromatography ; Chemistry ; Materials science ; Peroxide value ; Microwave ; Analytical Chemistry (journal) ; Organic chemistry ; Composite material Diffusion Coefficients in Liquids ; Botany, Ecology, and Taxonomy Studies ; Analytical Chemistry and Chromatography
Authors: Abdulilah Mohammad Mayet | Karina Shamilyevna Nurgalieva | Ali Awadh Al-Qahtani | Igor M. Narozhnyy | Hala H. Alhashim | Ehsan Nazemi | I. M. Indrupskiy
Source: Mathematics (article, 2022-08-13)
Field: Chemistry — Analytical Chemistry

Proposing a High-Precision Petroleum Pipeline Monitoring System for Identifying the Type and Amount of Oil Products Using Extraction of Frequency Characteristics and a MLP Neural Network Setting up pipelines in the oil industry is very costly and time consuming. For this reason, a pipe is usually used to transport various petroleum products, so it is very important to use an accurate and reliable control system to determine the type and amount of oil product. In this research, using a system based on the gamma-ray attenuation technique and the feature extraction technique in the frequency domain combined with a Multilayer Perceptron (MLP) neural network, an attempt has been made to determine the type and amount of four petroleum products. The implemented system consists of a dual-energy gamma source, a test pipe to simulate petroleum products, and a sodium iodide detector. The signals received from the detector were transmitted to the frequency domain, and the amplitudes of the first to fourth dominant frequency were extracted from them. These characteristics were given to an MLP neural network as input. The designed neural network has four outputs, which is the percentage of the volume ratio of each product. The proposed system has the ability to predict the volume ratio of products with a maximum root mean square error (RMSE) of 0.69, which is a strong reason for the use of this system in the oil industry. Artificial neural network ; Pipeline transport ; Detector ; Pipeline (software) ; Multilayer perceptron ; Mean squared error ; Frequency domain ; Computer science ; Volume (thermodynamics) ; Process engineering ; Engineering ; Artificial intelligence ; Mathematics ; Statistics ; Mechanical engineering ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis