jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Şadiye Ayşe Çelik | Yüksel Kan
Source: Selcuk Journal of Agricultural and Food Sciences (article, 2024-08-29)
Field: Chemistry — Organic Chemistry

Determination of Fatty Acid Profiles and Antioxidant Activities of Some Edible Oils Consumed in Turkey Vegetable-based edible oil in Turkey is produced primarily from olive oil, as well as oils obtained from various plants such as sunflower, corn, cotton, poppy, soybean, safflower and canola which are also among the main food sources. The quality of the oils used in our diet is primarily determined by the ratios of saturated and unsaturated fatty acids. Another important feature that determines oil quality is primarily antioxidant activity and its other biological activity capacities. This study was carried out to determine the fatty acid profiles and antioxidant capacities of 17 (seventeen) different oils of vegetable origin, which are widely consumed in Turkey and have economic importance. Fatty acid compositions were determined with GC-MS method. Antioxidant activities of different edible oils were determined by using 2,2'-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity method. According to the results of this research, the highest scavenging effect was detected at different concentrations was obtained from soybean oil with 94.30±1.57%, while the least scavenging effect was obtained from peanut oil with 32.34±1.00%. Major fatty acid components linoleic acid (C18:2) and linolenic acid (C18:3) were detected in 17 different oils. Also, the presence of beta-sitosterol was examined by Thin Layer Chromatography and it was determined that beta-sitosterol was present in almost all of them. Antioxidant ; Food science ; Chemistry ; Fatty acid ; Edible oil ; Biochemistry Edible Oils Quality and Analysis
Authors: Trisita Novianti | Fitri Agustina | Retno Indriartiningtias
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice Rice is a staple food in Indonesia. Although Indonesia produces a large amount of rice, it cannot meet domestic rice needs. The unpredictable domestic rice supply prompted the government to import rice. Moreover, rice imports are one of the efforts to provide rice stock. On the other hand, importing rice can decrease domestic rice prices because it creates a market competitor. This study uses backpropagation artificial neural networks to develop a prediction system for rice supply crises in Indonesia based on models similar to currency crisis prediction systems. The study identified key variables and indicators for predicting rice supply crises, including rice production, consumption, prices, land area, and population. Data from January 2012 to December 2022 was analyzed. The optimal neural network architecture achieved a Mean Squared Error (MSE) of 0.209192. The analysis revealed that rice consumption, land area, and total population are the most strongly correlated indicators of a rice commodity crisis Backpropagation ; Artificial neural network ; Warning system ; Computer science ; Rice plant ; Artificial intelligence ; Agronomy ; Telecommunications Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Ufaq Fayaz | Syed Zameer Hussain | Bazila Naseer | Gopinath Bej | Abhra Pal | Subrata Sarkar | Nazrana Rafique Wani | Khalid Mushtaq | Salwee Yasmin | B. S. Dhekale | Rishi Richa | Sobiya Manzoor
Source: Food Chemistry X (article, 2024-09-04)
Field: Chemistry — Analytical Chemistry

Innovative technology integration: E tongue, near infrared grain tester &amp; machine vision approaches for amylose content &amp; quality characterization <i>E</i>-tongue, machine vision and NIR systems were used to standardize the quality measurements in twenty rice genotypes grown in Highland Himalayan regions of Kashmir, in order to overcome the constraints of manual measurements. <i>IRCTN-312</i> showed highest amylose content of 20.74 % and 20.70 % using iodometric method and NIR tester, which was validated by the highest norm value of 34.158 by <i>E</i>-tongue. From these results, genotypes such as <i>GSR-43, GS-103, GSR-23B, GSR-60, SR-4, GSR-46, Koshihikari, GSR-64, GSR-32, GSR-49, GSR-4, GSR-42, GS-459, SKUA-494 and SKUA-540</i> were classified as low amylose and <i>C-3, K-332, M4-22 and IRCTN-312</i> were classified as intermediate amylose in the present study. Lowest percentage of damaged grains and chalk ratio was found in <i>GSR-23B. SKUA-494</i> recorded highest L/W ratio using both the systems. Highest head rice yield and elongation ratio was found in <i>GSR-23B</i> and <i>SKUA-494</i> genotypes respectively. Highest lightness (L*) value was recorded for <i>Koshihikari</i> genotype. Amylose ; Electronic tongue ; Quality (philosophy) ; Tongue ; Computer science ; Agricultural engineering ; Artificial intelligence ; Computer vision ; Food science ; Engineering ; Chemistry ; Medicine ; Physics ; Starch ; Pathology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: SK Tiwari | V. Raghu
Source: International Journal of Research in Agronomy (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Identification of optimal hyperspectral wavelength to discriminate maize and sorghum crops using AVIRIS-NG data in Guntur district, Andhra Pradesh, India In this study, we utilized AVIRIS-NG hyperspectral data captured during the Rabi season in February 2018 in a region of Guntur District, Andhra Pradesh, where Maize and Sorghum crops are extensively cultivated. Despite having parallel crop calendars and spectral similarities, distinguishing between Maize and Sorghum crops during similar vegetative growth stages poses a challenge. The high spectral resolution of AVIRIS-NG data allows for precise identification of subtle changes in the objects under study. The key finding of our research is the identification of sensitive spectral bands within specific wavelength regions that facilitate the differentiation of these two visually similar crops. Our results indicate that the spectral range from 1553 nm to 1749 nm of AVIRIS-NG data provides optimal discrimination between Sorghum and Maize crops, irrespective of their growth stages. Notably, the wavelengths at 1649 nm and 1654 nm emerge as particularly suitable for distinguishing between Maize and Sorghum crops, as indicated by statistical separability measures such as Wilks' Lambda and F-Value. At these wavelengths, we observed significant results with Wilks' Lambda values of 0.388 and 0.387, and F-Values of 39.29 and 39.46, respectively, further supporting their efficacy in crop discrimination. Sorghum ; Hyperspectral imaging ; Remote sensing ; Identification (biology) ; Geography ; Environmental science ; Agroforestry ; Agronomy ; Mathematics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Zelei Cheng | Xian Wu | Jiahao Yu | S. Han | Xin-Qiang Cai | Xinyu Xing
Source: arXiv (Cornell University) (preprint, 2024-10-18)
Field: Chemistry — Analytical Chemistry

Soft-Label Integration for Robust Toxicity Classification Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification. Toxicity ; Computer science ; Artificial intelligence ; Chemistry Analytical Methods in Pharmaceuticals
Authors: Basem Qahtan | Yurii P. Ivanov | Nikolaos Livakas | Francesco Donato | Liberato Manna | Giorgio Divitini
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A high-throughput compositional study of nanocrystals using a machine learning-assisted algorithm for STEM hyperspectral datasets Hyperspectral imaging ; Throughput ; Computer science ; Nanocrystal ; Algorithm ; Machine learning ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Nanotechnology ; Telecommunications Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Quantum Dots Synthesis And Properties
Authors: Ghazaleh Mirakhori | Jocelyn Doucet | Saad Chidami | Bruno Blais | Jamal Chaouki
Source:  (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

AI-Enhanced Radioactive Particle Tracking: A Game Changing Methodology for Accelerating Industrial Process Development Traditionally, Radioactive Particle Tracking (RPT) relies on nuclear mathematical models to triangulate the position of a radioactive tracer, a process that involves significant computational resources and manual calibration. This work presents a novel methodology leveraging artificial intelligence and collaborative robotics to overcome these limitations. In this approach, a collaborative robot is employed to maneuver a radioactive tracer, generating a precise and extensive dataset that correlates physical positions with radiation levels measured by surrounding detectors. This dataset is then used to train an Artificial Neural Network (ANN) to reconstruct the particle positions. Results demonstrate that this approach provides superior accuracy in position reconstruction and flow field prediction, compared to traditional methods. The proposed technique offers significant advantages, including reduced computational complexity, faster and more accurate data acquisition, and the ability to handle complex geometries and flow conditions. These improvements make RPT method a promising tool for industrial applications. Tracking (education) ; Process (computing) ; Particle (ecology) ; Computer science ; Environmental science ; Psychology ; Geology Graphite, nuclear technology, radiation studies ; Economic and Technological Developments in Russia ; Engineering Education and Technology
Authors: Ashish Ranjan | Akanksha Dubey
Source: South Eastern European Journal of Public Health (article, 2024-09-02)
Field: Materials Science — Biomaterials

Design And Characterization Of Regenerative Potential In Full Thickness Burn Wound Model Hydrogel is a three-dimensional network of interconnecting polymers that is crosslinked and consists of hydrophilic polymers that can absorb and endure water. Because it mimics the extracellular matrix (ECM), the hydrogel has found widespread use in pharmacological and biological applications. Many types of wound dressings are based on hydrogels due to its many advantages. These dressings include Coseal, Tegaderm, Algisite, and Evicel. Notwithstanding their exceptional ability to cure wounds, they have many drawbacks, including expensive expenses, unfavourable storage conditions, and inadequate mechanical protection for the wound. As we have come to realise the need for dressings with greater performance qualities (e.g., antimicrobial, biodegradable, responsive, and injectable), the field of wound dressings has attracted an increasing amount of interest. The purpose of this work is to evaluate the hydrogel loaded with 7, 8-Dihydroxy flavone's healing capabilities in a full-thickness burn wound model. Burn wound ; Characterization (materials science) ; Burn-in ; Regenerative medicine ; Wound healing ; Biomedical engineering ; Materials science ; Engineering ; Nanotechnology ; Medicine ; Reliability engineering ; Surgery ; Biology ; Cell biology ; Stem cell Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine
Authors: Liying Zhang | Wenda Yan | Dongning He | Yong Fang
Source: Research Square (preprint, 2024-07-05)
Field: Materials Science — Materials Chemistry

Density Functional Theory studies the interaction of neopentane with functionalized porous graphene Neopentane ; Graphene ; Surface modification ; Density functional theory ; Materials science ; Molecule ; Porous medium ; Chemical physics ; Porosity ; Chemistry ; Nanotechnology ; Chemical engineering ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Composite material Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Covalent Organic Framework Applications
Authors: Takuto Jibiki | Hajime Igarashi
Source: IET Science Measurement & Technology (article, 2024-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A fast hysteresis analysis based on neural network and play model Abstract This paper presents a fast hysteresis loss analysis method based on neural network, which predicts the hysteresis loss from the waveform of the magnetic flux density in a magnetic core. The hysteresis loss data used to train the neural network is computed using the play model which can accurately account for hysteresis characteristics such as minor loops and DC bias. The proposed method can perform loss analysis 2,000 times faster than conventional methods. The proposed method is highly effective when harmonic components are superimposed on the input waveforms and when loss analysis is performed for a large number of different waveforms. Hysteresis ; Artificial neural network ; Computer science ; Artificial intelligence ; Physics ; Condensed matter physics Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Force Microscopy Techniques and Applications
Authors: Julie Belleville | Prachi Thureja | Harry A. Atwater
Source: arXiv (Cornell University) (preprint, 2024-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical Analysis of Lensless Imaging with Active Metasurfaces and Single-Pixel Detectors We introduce a conceptual framework for a lensless imaging system which employs an active metasurface as a high-frequency, continuously tunable amplitude and phase modulation aperture, coupled to a discrete single-pixel detector. Using an array factor formalism, we first study fundamental limits in information collection, offering a comparison to existing technologies. We also study the effects of modulation rate and losses on the system acquisition time and signal-to-noise ratio, which place bounds on system performance for set illumination conditions. Considering both an ideal metasurface and the phase and amplitude modulation characteristics of an experimentally realized indium tin oxide-based metasurface operating at 1510 nm, we then simulate image recovery with ~60,000 image points for a 0.2 mm x 0.2 mm active metasurface aperture. We show that aberrations appearing in the simulated images produced by the metasurface can be corrected through post-processing. We further investigate trade-offs between image acquisition time and image quality both through the realization of Hadamard coupling bases and by modifying the k-space width coupling to the detector. Finally, we discuss the technical challenges which remain to be overcome for experimental realization of a lensless single-pixel imaging technology. Detector ; Pixel ; Optics ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Photonic Crystals and Applications
Authors: Patrisius Batarius | Alfry Aristo Jansen Sinlae
Source: Journal of Intelligent System and Computation (article, 2024-05-02)
Field: Materials Science — Polymers and Plastics

Chi-Square Histogram Analysis of Woven Fabric Images Made from Natural Dyes Due to Exposure to Sunlight This research aims to conduct a Chi-square analysis on the histogram of woven fabric images dyed with natural dyes following exposure to sunlight. Woven fabrics dyed with natural dyes have attracted attention in the textile industry due to their sustainability and environmental safety. Continuous sunlight is a significant factor influencing color changes in woven fabric dyed with natural dyes. The methodology involves capturing images of woven fabric pre- and post-sunlight exposure, followed by histogram analysis using Chi-Square testing, mean, mode, and standard deviation. We utilize pre-cropped and resized grayscale images. Research findings demonstrate that sunlight significantly impacts the histogram of woven fabric images dyed with natural dyes, causing shifts in color distribution, standard deviation, and mode. These findings hold critical implications for the textile industry, particularly for manufacturers of woven fabrics dyed with natural dyes. The application of Chi-Square analysis and standard deviation provides guidelines for product design, maintenance procedures, and consumer education regarding the preservation of color quality in fabrics exposed to sunlight. Changes in the quality of woven fabric images under sunlight exposure can offer essential guidance in the care and maintenance of textile products dyed with natural dyes. This research contributes to a deeper understanding of the interplay between natural dyes, sunlight, and woven fabrics, supporting the development of sun-resistant natural dyes. Sunlight ; Histogram ; Square (algebra) ; Natural (archaeology) ; Mathematics ; Statistics ; Art ; Artificial intelligence ; Materials science ; Computer science ; Optics ; Physics ; Geography ; Geometry ; Image (mathematics) ; Archaeology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Mehmet Bi̇ngöl
Source: Research Square (preprint, 2024-07-17)
Field: Chemistry — Analytical Chemistry

Efficient removal of Congo red and cytotoxicity evaluation of biosorbents prepared from chitosan-added watermelon pulp Chitosan ; Congo red ; Pulp (tooth) ; Pulp and paper industry ; Chemistry ; Cytotoxicity ; Adsorption ; Botany ; Food science ; Organic chemistry ; Biology ; Engineering ; Biochemistry Dye analysis and toxicity ; Nanocomposite Films for Food Packaging
Authors: Quan Wang | Mingliang Pan | Lucas Kreiß | Saeed Samaei | Stefan A. Carp | Johannes D. Johansson | Yuanzhe Zhang | Melissa M. Wu | Roarke Horstmeyer | Mamadou Diop | David Li
Source: arXiv (Cornell University) (preprint, 2024-05-18)
Field: Chemistry — Analytical Chemistry

A comprehensive overview of diffuse correlation spectroscopy: theoretical framework, recent advances in hardware, analysis, and applications Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry into this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, fibers, and correlators), as well as new data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed, and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research. Computer science ; Data science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: J.A. Ogunsuyi | Babajide O. Ajayi | Y. Ogunrinde | Kehinde OLONISAKIN
Source: Environmental Technology and Science Journal (article, 2024-12-12)
Field: Materials Science — Polymers and Plastics

Sustainable development of cement-bonded boards: evaluating physico-mechanical properties of waste paper and bagasse composites The overexploitation of forest resources for construction purposes has led to the utilization of waste materials for recycled composites. This study examined the sustainable development of Cement Bonded Board from bagasse and waste paper. Three levels of mixing ratios (MR) 2:1, 3:1, and 4:1, were used for this study. Particles from these wastes were milled, pre-treated, and dried, after which they were weighed and put in polythene. Particles were mixed with a mixing machine and the mould was spread on plates which have been previously covered with polythene sheets. The plate is then transferred to the wooden press to press at a pressure of 1.23N/mm2. After pressing for 24 hours, the mould plate is removed from the press and mould and allowed to set for 28 days. Boards produced were cut into test samples. Test were done according to ASTM D 1037-06a and ASTM D 7519-11. The results showed that MOE and MOR increased with an increase in MR. For paper, the MOE recorded showed that 4:1 has the highest value of 4154.97 N/mm2 and 2:1 with the lowest value of 2173.93 N/mm2 while for MOR 4:1 also has a value of 4.74 N/mm2 and 2:1 has the lowest value of 9.07 N/mm2. For bagasse, the MOE recorded showed 4:1/486.06 N/mm2 and 2:1/271.53 N/mm2 while for MOR 4:1/ 2.05 N/mm2 and 2:1/ 0.82 N/mm2. However, TS and WA had an inverse relationship MR for bagasse (2:1/ 6.11 %, 4:1/4.8 %) while for paper TS (2:1/2.24 %, 4:1/1.75 %). WA for bagasse (4:1 /28.30 %, 2:1/ 35.56 %) while for paper (4:1 /25.60 %, 2:1/ 39.02 %). The strongest and most dimensionally stable boards were produced at the highest mixing ratio of bagasse and paper. These two waste materials can be utilized in the production of sustainable materials that can be used in eco-friendly construction. Bagasse ; Materials science ; Composite material ; Cement ; Mixing (physics) ; Pressing ; Flexural strength ; Pulp and paper industry ; Waste management ; Engineering Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Recycled Aggregate Concrete Performance
Authors: Yunxiao Lu | Xin Liu | Zhiyong Zhang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-26)
Field: Materials Science — Materials Chemistry

Predicting assembly/disassembly orders of protein complexes using coarse-grained simulations Abstract Assembly of a protein complex is very important to its biological function, which can be investigated by determining assembly/disassembly order of its protein subunits. Although static structures of many protein complexes are available in the protein data bank, their assembly/disassembly orders of subunits are largely unknown. In addition to experimental techniques for studying subcomplexes in the assembly/disassembly of a protein complex, computational methods can be used to predict the assembly/disassembly order. Since sampling is a nontrivial issue in simulating the assembly/disassembly process, coarse-grained simulations are more efficient than atomic simulations are. In this work, we developed computational protocols for predicting assembly/disassembly orders of protein complexes using coarse-grained simulations. The protocols were illustrated using two protein complexes, and the predicted assembly/disassembly orders are consistent with available experimental data. Computer science ; Biological system ; Molecular dynamics ; Function (biology) ; Process (computing) ; Order (exchange) ; Chemistry ; Computational chemistry ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies