jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Roman
Source: Pure and Applied Biology (article, 2023-05-18)
Field: Chemistry — Analytical Chemistry

Prediction of nitrogen status based on agronomic and hyperspectral data at different growth stages of winter wheat Nitrogen management is an essential parameter for monitoring wheat plant growth, yield, and grain quality.Advance control management in wheat production generally requires non-destructive and rapid nitrogen determination.This experiment aimed to examine the relationship between spectral data and nitrogen contents in winter wheat (Triticum aestivum L.) at different growth periods, and develop a regression equation to predict nitrogen status.In this study, quantitative correlations between nitrogen content and canopy hyperspectral reflectance were examined under different nitrogen doses.This study was carried out in 2017 and 2018 at the Daxing experimental research base of the National Water Saving Irrigation Engineering Research Center in Beijing, China.The prediction accuracy of sensitive band 539 nm was maximum at the jointing-booting period with the maximum coefficient of determination(R 2 ) lowest RMSE (Root mean square error) and RE% (Relative error %) (0.546, 0.424, 12.234).The prediction model accuracy of band 697 nm was the highest with maximum R2 and lowest RMSE and RE% (0.609, 0.406, 16.033) at the booting-heading period.The prediction model of band 700nm has the maximum R 2 and lowest RMSE and RE% (0.875, 0.489, 22.445) value at the heading-maturity period.The prediction accuracy of spectral index NDV1canste (normalized difference vegetation index caste) showed the highest prediction accuracy with the maximum R 2 and lowest RMSE and RE% (0.741, 0.411, 13.013) at the jointing-booting period, mND705 (modified normalized difference 705) showed the highest accuracy with the lowest RMSE and RE% (0.741, 0.41,13.013)at the booting-heading period.At heading-maturity, NDVI2 showed the highest accuracy with R 2 , RMSE and RE % value (0.824, 0.356, 16.376) in 2018.At the whole growth period, ND705 (normalized difference 705) performed the best accuracy with maximum R 2 , RMSE, and RE% (0.90, 0.389, 16.453).In conclusion, these hyperspectral prediction models are the best for predicting nitrogen in winter wheat at various growth periods. Hyperspectral imaging ; Winter wheat ; Nitrogen ; Environmental science ; Agronomy ; Mathematics ; Remote sensing ; Biology ; Geography ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Joscha Christmann | Sascha Rohn | Philipp Weller
Source: Lebensmittelchemie (article, 2023-03-01)
Field: Chemistry — Spectroscopy

Exhaust gas monitoring of fermentation processes by gas chromatography‐ion mobility spectrometry (GC‐IMS) Chemistry ; Library science ; Analytics ; Ion-mobility spectrometry ; Bioanalysis ; Mass spectrometry ; Chromatography ; Data science ; Computer science Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Moritz Peters | Nicolas Gaudin | Jan Philipp Thoma | Vianney Lapôtre | Pascal Cotret | Guy Gogniat | Tim Güneysu
Source: arXiv (Cornell University) (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

On The Effect of Replacement Policies on The Security of Randomized Cache Architectures Randomizing the mapping of addresses to cache entries has proven to be an effective technique for hardening caches against contention-based attacks like Prime+Prome. While attacks and defenses are still evolving, it is clear that randomized caches significantly increase the security against such attacks. However, one aspect that is missing from most analyses of randomized cache architectures is the choice of the replacement policy. Often, only the random- and LRU replacement policies are investigated. However, LRU is not applicable to randomized caches due to its immense hardware overhead, while the random replacement policy is not ideal from a performance and security perspective. In this paper, we explore replacement policies for randomized caches. We develop two new replacement policies and evaluate a total of five replacement policies regarding their security against Prime+Prune+Probe attackers. Moreover, we analyze the effect of the replacement policy on the system's performance and quantify the introduced hardware overhead. We implement randomized caches with configurable replacement policies in software and hardware using a custom cache simulator, gem5, and the CV32E40P RISC-V core. Among others, we show that the construction of eviction sets with our new policy, VARP-64, requires over 25-times more cache accesses than with the random replacement policy while also enhancing overall performance. Computer science ; Cache ; Security policy ; Embedded system ; Computer security ; Operating system Covalent Organic Framework Applications ; Security and Verification in Computing ; Caching and Content Delivery
Authors: Jonatã Henrique Rezende-de-Souza | Venancio Ferreira de Moraes Neto - | Geodriane Zatta Cassol | Elem Tamirys dos Santos Caramês | Luciana Kimie Savay‐da‐Silva
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Colorimetric parameters combined with PLS-DA modeling: a new strategy for fish freshness assessment Fish <Actinopterygii> ; Computer science ; Artificial intelligence ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Nataliia Shlyun
Source: Automobile Roads and Road Construction (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

THERMO-MECHANICAL SIMILARITY IN THE EXPERIMENTAL AND THEORETICAL MODELING OF THE THERMAL RESISTANCE OF ROAD MATERIALS AND STRUCTURES Experimental or theoretical modeling of the thermal strength of road materials and structures is usually associated with replacing the study of the effect we are interested in nature (prototype) with the analysis of the solutions of the corresponding mathematical equations or with the consideration of a similar phenomenon on a smaller or larger scale experimental model in special laboratory or experimental conditions with full or partial observance of the correspondence between the physical properties of the objects of nature and the model. The main content of modeling is that, based on the results of mathematical or experimental experiments, it is possible to draw conclusions about the behavior of the system in natural conditions. As a rule, modeling is based on consideration of geometrically or physically similar deformation phenomena of structures. At the same time, it is considered that two bodies are geometrically similar, if the ratio of all corresponding lengths is the same, that is, equal to the coefficient of similarity or scale. Two phenomena are physically similar if the given characteristics of one of them can be used to determine the characteristics of the other with the transition from one system of units to another. As a result of establishing a system of parameters defining a selected class of phenomena, conditions for the similarity of two phenomena can be established. The task of determining similarity conditions for road materials and structures becomes more complicated when they are operated in conditions of variable temperatures, and the number of parameters for which it is necessary to calculate scale factors increases. In this work, they are determined on the basis of the theory of thermoelasticity for geometric characteristics, elasticity parameters (Young's modulus, Poisson's ratio), thermal parameters (linear thermal expansion coefficient, thermal conductivity coefficient). Similarity conditions are defined for the thermal displacement, thermal stress, and temperature functions. Concrete examples also show that methods of similarity theory can be a tool for solving applied problems. Similarity (geometry) ; Scale (ratio) ; Similitude ; Mathematical model ; Thermal ; Variable (mathematics) ; Statistical physics ; Deformation (meteorology) ; Mathematics ; Computer science ; Mathematical analysis ; Materials science ; Physics ; Thermodynamics ; Statistics ; Artificial intelligence ; Image (mathematics) Structural mechanics and materials ; Material Properties and Failure Mechanisms ; Geotechnical and Geomechanical Engineering
Authors: M.A. Barbier | A. Dakiw Piaceski | D. Larouche | K. Ghani | E. Pope | M. Caruso | L. Germain
Source: Journal of Investigative Dermatology (article, 2023-04-17)
Field: Materials Science — Biomaterials

833 Ex vivo gene therapy of recessive dystrophic epidermolysis bullosa skin cells leads to dermo-epidermal adhesion strength restoration in autologous bilayered self-assembled skin substitutes Ex vivo ; Keratinocyte ; Genetic enhancement ; Epidermolysis bullosa dystrophica ; Skin grafting ; Genodermatosis ; Epidermolysis bullosa ; Medicine ; Chemistry ; Dermatology ; In vitro ; Gene ; Surgery ; Biochemistry Silk-based biomaterials and applications ; RNA Interference and Gene Delivery ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Mihir Jain | Kashish Jain | Sandip Mane
Source: arXiv (Cornell University) (preprint, 2023-01-07)
Field: Materials Science — Polymers and Plastics

Machine Learning to Estimate Gross Loss of Jewelry for Wax Patterns In mass manufacturing of jewellery, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewellery. Machine learning is a technology that is a part of AI which helps create a model with decision-making capabilities based on a large set of user-defined data. In this paper, the authors found a way to use Machine Learning in the jewellery industry to estimate this crucial Gross Loss. Choosing a small data set of manufactured rings and via regression analysis, it was found out that there is a potential of reducing the error in estimation from +-2-3 to +-0.5 using ML Algorithms from historic data and attributes collected from the CAD file during the design phase itself. To evaluate the approach's viability, additional study must be undertaken with a larger data set. Wax ; Set (abstract data type) ; Investment (military) ; Computer science ; Data set ; Estimation ; CAD ; Artificial intelligence ; Machine learning ; Engineering drawing ; Industrial engineering ; Engineering ; Data mining ; Materials science Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Abdul Hadi Abd Rahman | Razali Ismail | Umie Asyikin binti Rozali | Khairil Anuar Arshad
Source: Indonesian Journal of Fundamental Sciences (article, 2023-04-01)
Field: Chemistry — Analytical Chemistry

Statistical Analysis and Prediction of Paddy Yield Using Neural Network Abstract. This research uses boxplot, Anova and posthoc to analyse the effect of factors such as urine and phosphorous in rice paddy yield. Then an artificial neural network (ANN) is used to predict paddy yields based on those factors. ANN is also used to predict paddy yields from polybags based on the actual data of paddy yields from rice field. A total of 25 data were used in this study where 70% data were used for training while 15% data each for testing and validation. We use the training model using data from rice field to predict paddy yield in polybags. STATISTICA software was used to run the neural networks. The predictive power of constructed neural networks was measured using accuracy measurement Mean Squared Error. The result shows that prediction can be made through neural network since the performance is very encouraging. Keywords: neural networks, paddy yield, prediction, statistical analysis. Artificial neural network ; Yield (engineering) ; Statistics ; Statistical analysis ; Environmental science ; Mathematics ; Artificial intelligence ; Computer science ; Materials science Spectroscopy and Chemometric Analyses
Authors: Volodymyr Samokhval | Stasko Yevhen | Vasyliev Oleksandr
Source: Collection of scholarly papers of Dniprovsk State Technical University (Technical Sciences) (article, 2023-12-25)
Field: Materials Science — Materials Chemistry

TO THE DEVELOPMENT OF STATISTICAL MODELS OF WEAR ROLLS OF LONG PRODACTS MILLS For long products rolling mills, a change in the indicators of wear and costs in a relatively wide range is characteristic, depending on the type of rolled product, the features of the technology, the materials used in the rolls, and other factors. This largely determines the level of operating costs when rolling profiles. Therefore, increasing the wear resistance of long product mill rolls was and remains an urgent task in metallurgy.&#x0D; The purpose of the work is to analyze the structure of known mathematical models of wear of rolls of rolling mills, features of wear of rolls of individual long-products rolling mills, and to determine a general approach to the construction of statistical models for predicting the wear of such rolls.&#x0D; From the review of the literature, it was established that the structure of most wear models includes technological parameters, that are most significant for a certain state, and an empirical coefficient. For the conditions of long products mill, in each of the stands, most of the parameters change within rather narrow limits due to technological deviations. Therefore, for stands mills in such conditions, it is advisable to use "local" statistical models obtained from the results of wear measurements. The structure of such models can be represented as the product of the empirical coefficient, which indirectly reflects the influence of the main technological parameters, and the friction way, as the main factor of the Archard model.&#x0D; As an empirical coefficient, it is recommended to use the indicative wear intensity of the rolls, defined as the ratio of the amount of wear of the groove roll to the mass of rolled products obtained from this pass. The revealed features of the change in the wear intensity indicator are due to the processes of processing the initial surface and the formation of a certain microstructure. After the rolling of the threshold mass of rolled products, these processes are completed and the indicator of wear intensity reaches a stable value. The initial and permanent value of the wear intensity indicator, as well as the threshold value of the mass of rolled steel, are determined based on empirical data on wear and mass of rolled steel for each of the stands mill.&#x0D; The proposed method of determining wear using the wear intensity indicator as an empirical coefficient that indirectly reflects the influence of all significant factors of the process provides sufficient accuracy of wear prediction and can be used to determine the costs of rolls. Forensic engineering ; Manufacturing engineering ; Engineering ; Metallurgy ; Engineering drawing ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced machining processes and optimization
Authors: Ekrem Taha Güldeste | Ceyhun Bulutay
Source: arXiv (Cornell University) (preprint, 2023-10-29)
Field: Materials Science — Materials Chemistry

Wavelet-based Ramsey magnetometry enhancement of a single NV center in diamond Nitrogen-vacancy (NV) centers in diamond constitute a solid-state nanosensing paradigm. Specifically for high-precision magnetometry, the so-called Ramsey interferometry is the prevalent choice where the sensing signal is extracted from time-resolved spin-state-dependent photoluminescence (PL) data. Its sensitivity is ultimately limited by the photon shot noise, which cannot be sufficiently removed by averaging or frequency filtering. Here, we propose Ramsey DC magnetometry of a single NV center enhanced by a wavelet-denoising scheme specifically tailored to suppress photon shot noise. It simply operates as a classical post-processing applied on a collected PL time series. Our implementation is based on a method that we named template margin thresholding which enables not only frequency but also time-dependent denoising. We computationally benchmark its DC magnetic field sensing signal-to-noise-ratio improvement over the raw PL data around an order of magnitude. Magnetometer ; Diamond ; Wavelet ; Center (category theory) ; Economics ; Business ; Materials science ; Physics ; Computer science ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Magnetic field ; Crystallography Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Atomic and Subatomic Physics Research
Authors: Alejandro Nieto Ramos | Christopher Krapu | Elizabeth M. Cherry | Flavio H. Fenton
Source: arXiv (Cornell University) (preprint, 2023-11-16)
Field: Chemistry — Analytical Chemistry

Employing Gaussian process priors for studying spatial variation in the parameters of a cardiac action potential model Cardiac cells exhibit variability in the shape and duration of their action potentials in space within a single individual. To create a mathematical model of cardiac action potentials (AP) which captures this spatial variability and also allows for rigorous uncertainty quantification regarding within-tissue spatial correlation structure, we developed a novel hierarchical Bayesian model making use of a latent Gaussian process prior on the parameters of a simplified cardiac AP model which is used to map forcing behavior to observed voltage signals. This model allows for prediction of cardiac electrophysiological dynamics at new points in space and also allows for reconstruction of surface electrical dynamics with a relatively small number of spatial observation points. Furthermore, we make use of Markov chain Monte Carlo methods via the Stan modeling framework for parameter estimation. We employ a synthetic data case study oriented around the reconstruction of a sparsely-observed spatial parameter surface to highlight how this approach can be used for spatial or spatiotemporal analyses of cardiac electrophysiology. Gaussian process ; Cardiac electrophysiology ; Spatial correlation ; Markov chain Monte Carlo ; Parameter space ; Prior probability ; Bayesian probability ; Gaussian ; Computer science ; Bayesian inference ; Action (physics) ; Spatial dependence ; Monte Carlo method ; Algorithm ; Statistical physics ; Mathematics ; Artificial intelligence ; Statistics ; Physics ; Electrophysiology Spectroscopy and Chemometric Analyses ; Vehicle emissions and performance ; Quality Function Deployment in Product Design
Authors: Velmani Ramasamy | S Nithyadevi | W Lee | V Alchanatis | C Yang | M Hirafuji | D Moshou | C Li | A Wang | W Zhang | X Wei | J Gao | W Liao | D Nuyttens | P Lootens | J Vangeyte | A Piurica | Y He | J Pieters | E Hamuda | M Glavin | E Jones
Source: International Journal of Progressive Research in Engineering Management and Science (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

DETECTING WEEDS FROM CROP USING IMAGE PROCESSING AND ARDUINO Weed detection is an important task in precision agriculture, as it can help farmers to reduce herbicide use, increase crop yields, and minimize the environmental impact of agriculture.In this paper, we present a system for weed detection using image processing and Arduino.The system consists of a camera mounted on a mobile platform, which captures images of crops and weeds in the field.The images are then processed using a computer vision algorithm to detect and classify weeds based on their color and shape characteristics.The system is controlled by an Arduino microcontroller, which communicates with the camera and processes the image data.The results of the weed detection algorithm are displayed on a graphical user interface (GUI), which allows farmers to visualize and analyze the data.We tested the system on a variety of crops and weed species, and found that it achieved a high level of accuracy and reliability in detecting weeds.The system has the potential to improve the efficiency and sustainability of agriculture, by reducing the use of herbicides and promoting more targeted and precise weed control.It is also useful for researchers and developers who are interested in exploring the potential of computer vision and Arduino in the field of precision agriculture and weed management. Arduino ; Crop ; Image processing ; Agronomy ; Agricultural engineering ; Computer science ; Image (mathematics) ; Artificial intelligence ; Biology ; Engineering ; Embedded system Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: V. O. Okonkwo | C. P. Nwogu | J. I. Obiora | B. A. Njotae
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-02-18)
Field: Materials Science — Materials Chemistry

EFFECTS OF COARSE AGGREGATES GRADINGS ON THE PROPERTIES OF CONCRETE Coarse aggregates gradings have varying effects on the properties of concrete. Coarse aggregates of sizes 10mm, 20mm and 25mm were used for this study. Sieve analysis was conducted on these aggregates. Their coefficients of curvature (Cc) ranged between 1.01 and 1.19, while the coefficients of uniformity (Cu) ranged between 1.24 and 1.73, showing that they are all uniformly graded. 10mm and 25mm coarse aggregates were mixed in various proportions to produce five different concrete mixes (concrete C1, C2, C3, C4 and C5). Concrete C6 was produced with 100% 20mm and was used as a check. Slump test was performed for each mixture, C1 gave the highest slump height of 88mm. Compressive strengths of the cast concrete cubes were determined after 7- and 28-days curing periods. C4 gave maximum compressive strength of 26.33N/mm2 at 28 days. Increase in the percentages of 25mm aggregates resulted in higher compressive strength and decreased workability. Materials science Structural mechanics and materials
Authors: Jacob Harvey | Anastasia Ilgen
Source:  (article, 2023-08-01)
Field: Chemistry — Inorganic Chemistry

Identifying predictive structural characteristics of metal-organic frameworks for the transport and separation of rare-earthed elements using machine learning Materials science ; Computer science ; Metal ; Separation (statistics) ; Artificial intelligence ; Metallurgy ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography