jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. Boonma | C. Suwanprasit | S. Homhuan | . Shahnawaz
Source: International Journal of Geoinformatics (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

Classification of Northern Thai Rice Varieties Using Random Forest (RF) and Support Vector Machine (SVM) on Google Earth Engine with Sentinel Imagery: A Case Study in Buak Khang Subdistrict, San Kamphaeng District, Chiang Mai Province Rice is a crucial agricultural product for Thailand's economy, as the majority of the country's agricultural sector primarily cultivates rice for both domestic consumption and international demand. This research focuses on land use analysis, specifically on rice cultivation areas, and the classification of different rice varieties. The study significantly contributes to the achievement of the Sustainable Development Goals (SDGs) concerning food security. This research utilizes Sentinel-2 Spectral Instrument, Level 2A, and Sentinel-1 polarization VV and VH satellite images from 2023, covering the planting season (June to November). The data processing and analysis are conducted on the cloud platform Google Earth Engine (GEE), employing Support Vector Machine (SVM) and Random Forest (RF) classification methods for both land use analysis and rice variety classification. The analysis results indicate that the RF classification method has higher accuracy than the SVM method. Specifically, for land use analysis, the RF and SVM classification methods achieved accuracy values of 0.91 and 0.87, and kappa values of 0.89 and 0.85, respectively. For rice variety classification, the RF and SVM methods achieved accuracy values of 0.88 and 0.83, and kappa values of 0.73 and 0.61, respectively. Support vector machine ; Random forest ; Geography ; Remote sensing ; Cartography ; Forestry ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Nydia Roxana Varela-Rosales | Michael Engel
Source: arXiv (Cornell University) (preprint, 2024-08-04)
Field: Materials Science — Biomaterials

Computational Self-Assembly of a Six-Fold Chiral Quasicrystal Quasicrystals are unique materials characterized by long-range order without periodicity. They are observed in systems such as metallic alloys, soft matter, and particle simulations. Unlike periodic crystals, which are invariant under real-space symmetry operations, quasicrystals possess symmetry that requires description by a space group in reciprocal space. In this study, we report the self-assembly of a six-fold chiral quasicrystal using molecular dynamics simulations of a two-dimensional particle system. The particles interact via the Lennard-Jones-Gauss pair potential and are subjected to a periodic substrate potential. We confirm the presence of chiral symmetry through diffraction patterns and order parameters, revealing unique local motifs in both real and reciprocal space. The quasicrystal's properties, including the tiling structure and symmetry and the extent of diffuse scattering, are strongly influenced by substrate potential depth and temperature. Our results provide insights into the mechanisms of chiral quasicrystal formation and the role and potential of external fields in tailoring quasicrystal structures. Quasicrystal ; Fold (higher-order function) ; Crystallography ; Computer science ; Chemistry Supramolecular Self-Assembly in Materials ; Quasicrystal Structures and Properties ; Nanocluster Synthesis and Applications
Authors: Mohammed A. Almeshaal | Govindasamy Mahendran | A. Chandrashekhar | Sumanth Ratna Kandavalli | Ravishankar Sathymurthy | R. Balamurugan | G. Puthilibai | Vijayan Venkatraman | Ramkumar Kathalingam
Source: Thermal Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigation on the impact of carbon nanoplatelets on the thermal behavior of magnesium composites The Mg/2.4 wt.% CNP composite has a specific heat capacity improvement of 174%, according to DSC testing results. The TGA data reveals a 2.4% decrease in mass when comparing the Mg/2.4 wt.% CNP composite to pure Mg. The coefficient of heat transfer, the effectiveness of the fin, and the heat conduction via fin were all evaluated with a pin-fin transferring of heat under conditions of both free and forced convection. When carbon nanoplatelets are added to a Mg matrix, the resulting composites have better heat transfer characteristics when subjected to both natural and artificial convection. Materials science ; Composite material ; Magnesium ; Thermal ; Carbon fibers ; Metallurgy ; Composite number ; Meteorology Magnesium Oxide Properties and Applications ; Aluminum Alloys Composites Properties ; Material Properties and Applications
Authors: Adalberto Santana | Emídio Cantídio Almeida de Oliveira | Josiclêda Domiciano Galvíncio | Edilane Aparecida da Silva | Rodrigo de Queiroga Miranda | Júlio Araújo
Source: Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences (article, 2024-12-10)
Field: Chemistry — Analytical Chemistry

Assessment of leaf nitrogen diagnostics on sugarcane crop using spectroradiometry The objective of this study was to identify the spectral variation that enables the implementation of mathematical adjustments for the estimation of the nutritional diagnosis of nitrogen (N) in sugarcane leaves. The experiment was conducted in a field setting, with two varieties of sugarcane subjected to seven doses of N (0 at 200 kg ha-1). At 45, 90, 135, and 235 days after planting (DAP), the N content and spectral measurements of reflectance with a spectral range of 350-2,500 nm were evaluated in the diagnostic leaf using a portable spectroradiometer. The wavelengths that most effectively represented the variation in the N content in the leaves were those of the medium infrared band, particularly the wavelengths 1,405, 1,651, 1,721, and 1,857 nm. The natural and logarithmic exponential transformation facilitated enhanced sensitivity for the detection of variations, with models that explain 89% of the total variation in the N content observed in the diagnostic leaf. The models demonstrated high accuracy, with a root-mean-square error of 0.19, which corresponds to 10.2% of error in relation to the average content of N estimated in the diagnostic leaves. The estimation models created using the medium infrared wavelengths can be used to accurately estimate the nutritional diagnosis of N in sugarcane at different stages of the cycle. Crop ; Nitrogen ; Agronomy ; Environmental science ; Agroforestry ; Agricultural engineering ; Biology ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Eric Lepowsky | David Snyder | Alexander Glaser | Anirudha Majumdar
Source: arXiv (Cornell University) (preprint, 2024-02-27)
Field: Materials Science — Materials Chemistry

Privacy-Preserving Map-Free Exploration for Confirming the Absence of a Radioactive Source Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially secret nature of observable features, we propose a robotic verification procedure that provides map-free exploration to perform a source verification task without requiring, nor revealing, any task-irrelevant, site-specific information. We provide theoretical guarantees on the privacy and correctness of our approach, validated by extensive simulated and hardware experiments. Computer science ; Environmental science Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Yuki K. Wakabayashia | Yoshiharu Krockenberger | Kosuke Takiguchi | Hideki Yamamoto | Yoshitaka Taniyasu
Source: arXiv (Cornell University) (preprint, 2024-10-29)
Field: Materials Science — Materials Chemistry

Role of Ion Milling Angle in Determining Conducting and Insulating States on SrTiO3 Surfaces SrTiO3 (STO), a promising wide-bandgap semiconductor for high-k capacitors and photocatalysis, requires precise surface control for device fabrication. This study investigates the impact of ion milling on STO's surface conductivity. We find that ion milling at incident angles below 10 degree preserves the insulating state, while ion milling at larger angles induces a conducting surface with high electron mobility (5000-11000 cm2/Vs). This transition is attributed to the milling penetration depth exceeding the STO lattice constant (3.905 Å). Our results provide valuable insights for optimizing STO-based device fabrication, enabling precise control over surface properties while maintaining desired insulating characteristics. Materials science ; Ion ; Ion milling machine ; Engineering physics ; Nanotechnology ; Optoelectronics ; Chemistry ; Engineering Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Analytical Chemistry and Sensors
Authors: Wencheng Ji | Massimo Pica Ciamarra | Matthieu Wyart
Source: arXiv (Cornell University) (preprint, 2024-05-01)
Field: Materials Science — Materials Chemistry

The Role of Excitations in Supercooled Liquids: Density, Geometry, and Relaxation Dynamics Low-energy excitations play a key role in all condensed-matter systems, yet there is limited understanding of their nature in glasses, where they correspond to local rearrangements of groups of particles. Here we introduce an algorithm to systematically uncover these excitations up to the activation energy scale relevant to structural relaxation. We use it in a model system to measure the density of states on a scale never achieved before, confirming that this quantity shifts to higher energy under cooling, precisely as the activation energy does. Secondly, we show that the excitations' energetic and spatial features allow one to predict with great accuracy the dynamic propensity, i.e. the location of future relaxation dynamics. Finally, we find that excitations have a core whose properties, including the displacement of the most mobile particle, scale as a power-law of their activation energy and are independent of temperature. Additionally, they exhibit an outer deformation field that depends on the material's stability and, therefore, on temperature. We build a scaling description of these findings. Overall, our analysis supports that excitations play a crucial role in regulating relaxation dynamics near the glass transition, effectively suppressing the transition to dynamical arrest predicted by mean-field theories while also being strongly influenced by it. Supercooling ; Energy (signal processing) ; Geometry ; Physics ; Activation energy ; Materials science ; Condensed matter physics ; Thermodynamics ; Chemistry ; Mathematics ; Quantum mechanics ; Physical chemistry Material Dynamics and Properties
Authors: Dr. Ayesha Tariq | Dr. Iffat Tahira Siddique
Source: Research Corridor Journal of Engineering Science (article, 2024-08-24)
Field: Materials Science — Biomaterials

Biomimicry in Engineering: Learning from Nature to Solve Complex Problems Biomimicry in engineering represents a transformative approach to solving complex challenges by emulating nature's time-tested solutions. This interdisciplinary field leverages biological principles and processes to inspire innovative engineering designs and technologies. Nature, having evolved through millions of years of optimization, offers unparalleled insights into efficient, sustainable, and resilient solutions for a myriad of engineering problems. This abstract reviews the concept of biomimicry, emphasizing its application in developing novel materials, structures, and systems that mimic biological functions and processes. The integration of biomimicry into engineering has led to the creation of advanced materials with exceptional properties, such as self-healing materials inspired by biological systems' regenerative abilities. Innovations like gecko-inspired adhesives and lotus leaf-inspired water-repellent surfaces showcase how biomimetic designs can improve functionality and performance. Furthermore, biomimicry contributes to sustainability by offering eco-friendly alternatives to traditional engineering practices, such as energy-efficient buildings modeled after termite mounds' natural ventilation systems. Despite its promising potential, the application of biomimicry in engineering faces challenges, including the need for a deep understanding of complex biological systems and translating biological principles into practical engineering solutions. Research and development in this field are ongoing, with efforts focused on overcoming these challenges to fully realize biomimicry's potential in addressing modern engineering problems. In conclusion, biomimicry provides a valuable framework for innovation in engineering by drawing inspiration from nature. As this field continues to evolve, it holds the promise of delivering groundbreaking solutions that enhance performance, sustainability, and resilience in engineering applications. Biomimetics ; Biomimetic materials ; Field (mathematics) ; Biological systems engineering ; Transformative learning ; Synthetic biology Calcium Carbonate Crystallization and Inhibition ; Adhesion, Friction, and Surface Interactions ; Plant and Biological Electrophysiology Studies
Authors: Chandramani D Waghmare
Source: Journal of Scientific & Industrial Research (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Remote Sensing Detection of Root-Knot Nematode Infestation in Mungbean under Various Management Strategies In response to the pervasive threat of Meloidogyne incognita infestation in mungbean crops, this study explores the application of hyperspectral remote sensing methodology to detect and assess root-knot nematode presence, while investigating the efficacy of diverse management strategies. Spectral remote sensing was used to record the reflectance pattern of the Root-knot nematode (RKN), M. incognita infestation on mungbean plants under various management methods under glasshouse pot conditions. Leaf reflectance was measured on plant canopies of mungbean which revealed that reflectance varies with different inoculum levels of root-knot nematode. The spectral reflectance pattern was maximum at the highest inoculum level of 6000 J2/ pot with respect to the wavelength of 250–750 nm and minimum at 0 inoculum level or 100 J2/ pot corresponding to a wavelength of 800–1600 nm. In response to nematode management, mungbean plant canopy reflectance showed that at a lower wavelength of 350–750 nm, spectral reflectance were mixed and can be determined easily for low or high pattern. However, at wavelengths &gt;800–1600 nm, the lowest and median reflectance was found with carbofuran 3G @ 1 kg a.i/ha and 2 kg a.i/ha, respectively. Similarly, high and low reflectance pattern for Trichoderma harzianum was observed at the concentration of 2 and 1% (w/w) respectively but the reflectance pattern was similar for neem seed oil at two different concentrations of 1 and 2% (v/w). The highest reflectance was observed with untreated un-inoculated control. The valuable information on the use of hyperspectral data gathered from the hand-held spectrometer and small unmanned aerial systems for large-scale mapping of the root-knot nematode infestation could be possible. Hence, the effect of M. incognita on the root system of mungbean can be non-invasively diagnosed by using hyperspectral data at the early growth stage of aerial plant canopy. Hyperspectral imaging ; Infestation ; Root-knot nematode ; Knot (papermaking) ; Nematode ; Remote sensing ; Biology ; Agronomy ; Engineering ; Geography ; Ecology ; Pulp and paper industry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jacquerie, Kathleen | Tyulmankov, Danil | Sacré, Pierre | Drion, Guillaume
Source: Open Repository and Bibliography (University of Liège) (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Memory consolidation facilitated by burst-driven late-phase plasticity peer reviewed Plasticity ; Consolidation (business) ; Memory consolidation ; Developmental plasticity ; Neuroplasticity Shape Memory Alloy Transformations ; Magnetic properties of thin films ; Ferroelectric and Negative Capacitance Devices
Authors: Carter Rhea | Julie Hlavacek-Larrondo | Justine Giroux | Auriane Thilloy | Hyunseop Choi | Laurie Rousseau-Nepton | Marie-Lou Gendron-Marsolais | Mario Pasquato | S. Prunet
Source: RAS Techniques and Instruments (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Reconstructing robust background integral field unit spectra using machine learning ABSTRACT In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artefacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. SITELLE is an imaging Fourier transform spectrometer located at the Canada-France-Hawaii Telescope, which produces a three-dimensional data cube containing the position of the emission (two dimensions) and the spectrum of the emission. SITELLE has a wide field of view (11 arcmin × 11 arcmin), which makes the background emission particularly challenging to model. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a Star-formation, Ionized Gas and Nebular Abundances Legacy Survey galaxy, NGC 4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modelling approach with standard methods used in the literature and find that our method better captures the emission from H ii regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the data cube. While the approach is applied explicitly to SITELLE data in this study, we argue that it can be readily adapted to any integral field unit style data, enabling the user to obtain more robust measurements on the flux of the emission lines. Physics ; Data cube ; Galaxy ; Astrophysics ; Emission spectrum ; Principal component analysis ; Astronomy ; Artificial intelligence ; Spectral line ; Computer science Spectroscopy and Laser Applications ; Galaxies: Formation, Evolution, Phenomena ; Stellar, planetary, and galactic studies
Authors: Anar Akkenzheyeva | Akkenzhe Bussurmanova | Viktors Haritonovs | Asyltas Kuangali
Source: Yessenov Science Journal (article, 2024-12-10)
Field: Chemistry — Analytical Chemistry

MODIFICATION OF OIL BITUMENS WITH POLYMERIC MATERIALS Currently, in the world, due to the recent push for recycling, many road authorities are considering using recycled plastic on roads. This paper presents the modification of bitumen with recycled plastics from waste. Bitumen modification for roads in Kazakhstan is considered one of the most suitable and popular approaches. This research paper describes the details of using plastic waste as bitumen modifiers, with a particular focus on recycled plastics and how they can potentially be used to improve bitumen performance and road durability. The main physical and mechanical characteristics of the modified bitumen were determined after routine tests, penetration and plasticity, softening temperature, and Fraas brittleness temperature. Materials science ; Polymer science ; Chemical engineering ; Engineering Petroleum Processing and Analysis ; Epoxy Resin Curing Processes
Authors: Bere Benjamin Bantchına | Kemal Sulhi Gündoğdu
Source: Journal of Applied Remote Sensing (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Machine learning-based maize (Zea mays L.) extraction at parcel level using Sentinel 2A-derived spectral indices Crop type classification is crucial for policymaking and precision agriculture applications. This study aimed to develop a parcel-based maize (Zea mays L.) extraction approach using Sentinel 2A-derived spectral indices and machine learning (ML) in two distinct study sites: Yeşilova and Ormankadı villages in Bursa Province, Turkey. Employing 13 widely recognized spectral indices, the investigation implemented 4 ML classifiers: support vector machines, random forest, K-nearest neighbors, and bootstrap aggregating. The training-test methodology was explored using two scenarios: Yeşilova as the training set and Ormankadı as the test set, and vice versa. The models calibrated on Yeşilova and validated on Ormankadı maintained the accuracy of the model, with an overall accuracy (OA) ranging from 79.3% to 89.9%, precision between 72.8% and 80.1%, recall between 82.1% and 84.9%, F1-score between 77.4% and 82.2%, and a Matthews correlation coefficient (MCC) ranging from 58.9% to 68.3%. Furthermore, the models consistently demonstrated good performance when Ormankadı served as the training set and Yeşilova as the test set, with commendable OA (78.7% to 84.8%), precision (85.5% to 88.0%), recall (88.0% to 91.1%), F1-score (86.2% to 89.5%), and MCC (68.2% to 76.0%). This study demonstrated the potential of using high-resolution remote sensing and ML for effective maize crop extraction using diverse datasets. Zea mays ; Extraction (chemistry) ; Computer science ; Artificial intelligence ; Remote sensing ; Agronomy ; Geology ; Biology ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture