jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paula Pongrac | Mitja Kelemen | Katarina Vogel‐Mikuš | Primož Vavpetič | Primož Pelicon | Paula Žurga | Nikolina Vidović | Marija Polić Pasković | Goreta Smiljana | Igor Lukić | Igor Pasković
Source: Plant Physiology and Biochemistry (article, 2022-12-06)
Field: Chemistry — Analytical Chemistry

Tissue-specific calcium and magnesium allocation to explain differences in bulk concentration in leaves of one-year-old seedlings of two olive (Olea europaea L.) cultivars Olive tree (Olea europaea L.) leaves have recently been recognised as a valuable source in cosmetic and pharmaceutical industry as well as in preparation of health-supporting beverages. Little is known about the element composition of olive leaves and almost nothing about tissue-specific allocation of elements. Element composition and tissue-specific distribution were determined in leaves of two olive cultivars, Leccino and Istarska bjelica using micro-particle induced X-ray emission (micro-PIXE). In leaves of the Istarska bjelica cultivar larger bulk concentrations of potassium, sodium, molybdenum and boron, but smaller concentrations of calcium and magnesium were found than in leaves of the Leccino cultivar. Tissue-specific investigation revealed that larger concentration of calcium in epidermis and in leaf blade tissues (secondary veins, palisade and spongy mesophyll) contributed to the larger leaf bulk calcium concentration in the Leccino cultivar. For magnesium, all leaf tissues, except the bundle sheath cells and consequently the main vascular bundle, contributed to the larger bulk concentration in the Leccino cultivar. Potassium was not predominant in any of the leaf tissues examined, while sodium and molybdenum were below the limit of detection, and boron not detectable by micro-PIXE. The results indicate that sinks for calcium and magnesium are stronger in specific leaf tissues of the Leccino than of the Istarska bjelica cultivar. The new understanding of tissue-specific allocation of elements in leaves of olive will serve as a basis for detailed studies into the effects of foliar and/or soil fertilisers in olive. Olea ; Cultivar ; Magnesium ; Calcium ; Vascular bundle ; Potassium ; Chemistry ; Palisade cell ; Horticulture ; Botany ; Sodium ; Agronomy ; Biology Heavy Metals in Plants ; Edible Oils Quality and Analysis ; Nuts composition and effects
Authors: Nicolas Leconte | Srivani Javvaji | Jiaqi An | Appalakondaiah Samudrala | Jeil Jung
Source: Physical review. B./Physical review. B (preprint, 2022-09-12)
Field: Materials Science — Materials Chemistry

Relaxation effects in twisted bilayer graphene: A multiscale approach We present a multiscale density functional theory (DFT) informed molecular dynamics and tight-binding approach to capture the interdependent atomic and electronic structures of twisted bilayer graphene. We calibrate the flat band magic angle to be at ${\ensuremath{\theta}}_{\mathrm{M}}=1.{08}^{\ensuremath{\circ}}$ by rescaling the interlayer tunneling for different atomic structure relaxation models as a way to resolve the indeterminacy of existing atomic and electronic structure models whose predicted magic angles vary widely between $0.{9}^{\ensuremath{\circ}}$ and $1.{3}^{\ensuremath{\circ}}$. The interatomic force fields are built using input from various stacking and interlayer distance-dependent DFT total energies including the exact exchange and random phase approximation ($\mathrm{EXX}+\mathrm{RPA}$). We use a Fermi velocity of ${\ensuremath{\upsilon}}_{\mathrm{F}}\ensuremath{\simeq}{10}^{6}$ m/s for graphene that is enhanced by $\ensuremath{\sim}15%$ over the local density approximation (LDA) values. Based on this atomic and electronic structure model we obtain high-resolution spectral functions comparable with experimental angle-resolved photoemission spectroscopy. Our analysis of the interdependence between the atomic and electronic structures indicates that the intralayer elastic parameters compatible with the DFT-LDA, which are stiffer by $\ensuremath{\sim}30%$ than widely used reactive empirical bond order force fields, can combine with $\mathrm{EXX}+\mathrm{RPA}$ interlayer potentials to yield the magic angle at $\ensuremath{\sim}1.{08}^{\ensuremath{\circ}}$ without further rescaling of the interlayer tunneling. Bilayer graphene ; Graphene ; Wannier function ; Tight binding ; Physics ; Condensed matter physics ; Random phase approximation ; Density functional theory ; Relaxation (psychology) ; Electronic structure ; Electronic band structure ; Quantum mechanics Graphene research and applications ; Surface and Thin Film Phenomena ; Quantum and electron transport phenomena
Authors: Mario Peláez-Fernández | Bruno Majérus | Daniel Funes-Hernando | Romain Dufour | Jean‐Luc Duvail | Luc Henrard | Raúl Arenal
Source: Nanophotonics (article, 2022-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward laser‐induced tuning of plasmonic response in high aspect ratio gold nanostructures High aspect-ratio gold nanostructures sustain Fabry-Perot-like surface plasmon responses from infrared to visible light energies. We show that some resonances can be tuned by means of laser irradiation, where low energy modes stay unperturbed. After laser irradiation, gold nanowires' tips are transformed into nanoparticles of various sizes joint to gold nanowires, producing high aspect-ratio half-dumbbells and dumbbells structures. The plasmonic behaviour of both the nanowires and the newly created nanostructures has been characterised by in-depth monochromated electron energy loss spectroscopy (EELS) developed in a transmission electron microscope (TEM) and state-of-the-art discrete dipole approximation (DDA) calculations. All these analyses serve as experimental proof of the selective tuning (or robustness) of the plasmonic modes of the nanostructures in a specific spectral range, which is of critical interest regarding applications for sensing devices, nano-sources or nanophotonic waveguide, as well as optical remote control. Plasmon ; Materials science ; Nanowire ; Discrete dipole approximation ; Nanophotonics ; Nanostructure ; Optoelectronics ; Laser ; Electron energy loss spectroscopy ; Nanomaterials ; Nanotechnology ; Aspect ratio (aeronautics) ; Spectroscopy ; Transmission electron microscopy ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Molecular Junctions and Nanostructures
Authors: Qing Huang | Heru Xue | Jiangping Liu | Xinhua Jiang
Source: Journal of Spectroscopy (article, 2022-11-22)
Field: Chemistry — Analytical Chemistry

A New Method for Spectral Wavelength Selection Based on Multiple Linear Regression Combined with Ant Colony Optimization and Genetic Algorithm Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimization (MLR-ACO). In each iteration, the absolute value of the regression coefficient corresponding to each wavelength of the individual with the highest fitness value is used as the basis for a pheromone update. The crossover operator is introduced in MLR-ACO (MLR-ACO-GA), and the individuals with the top 100 fitness values in MLR-ACO are used as the initial population of the genetic algorithm (GA). A selected frequency of wavelengths greater than the threshold among MLR-ACO individuals is calculated. A number of coarse interval points are generated according to the selected frequency, and a coarse crossover operation is performed at the coarse interval points. Fine crossover points are randomly generated within the coarse interval, and fine crossover operations are performed within the coarse interval to exploit the potential of combining excellent individuals in MLR-ACO with each other as much as possible. MLR-ACO can well solve the problem of traditional ACO initial pheromone scarcity, and MLR-ACO-GA can avoid MLR-ACO falling into a local optimum to a certain extent and be more flexible in the selection of the number of wavelengths, which can give full play to the advantages of MLR-ACO. Crossover ; Ant colony optimization algorithms ; Linear regression ; Mathematics ; Genetic algorithm ; Selection (genetic algorithm) ; Population ; Algorithm ; Statistics ; Polynomial regression ; Mathematical optimization ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Mladen Jurišić | Dorijan Radočaj | Ivan Plašćak | Daria Galić Šubašić | Davor Petrović
Source: Poljoprivreda (article, 2022-12-20)
Field: Chemistry — Analytical Chemistry

The evaluation of the rgb and multispectral camera on the unmanned aerial vehicle (uav) for the machine learning classification of maize U ovoj studiji istražena je klasifikacija usjeva i tla korištenjem algoritma strojnoga učenja Random Forest, temeljenoga na crveno-zeleno-plavoj (RGB) i multispektralnoj kameri integriranoj na bespilotnome zrakoplovu. Područje istraživanja obuhvaćalo je dva podskupa poljoprivredne čestice kukuruza dimenzija 10 x 10 m u blizini Koške. Najveća ukupna točnost klasifikacije postignuta je u kombinaciji rubnoga crvenog (RE), bliskoga infracrvenog (NIR) kanala i indeksa normalizirane vegetacijske razlike (NDVI) u oba podskupa, s ukupnom točnošću od 99,8 %, odnosno 91,8 %. Provedena analiza pokazala je da je RGB kamera postigla dovoljnu točnost i da je prihvatljivo rješenje za klasifikaciju tla i vegetacije. Međutim, multispektralna kamera i spektralna analiza omogućile su detaljniju analizu, prvenstveno za spektralno slična područja. Ovaj je postupak temelj i za izračun gustoće usjeva i za otkrivanje korova s pomoću bespilotnih zrakoplova. Kako bi se osigurala učinkovitost klasifikacije usjeva u praktičnoj primjeni, potrebno je dodatno uključiti klase korova u trenutačnu klasu vegetacije i podijeliti ih na klase usjeva i korova. Physics ; Humanities ; Art Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI
Authors: Axel Bral | Lode Daelemans | Joris Degroote
Source: Textile Research Journal (article, 2022-11-25)
Field: Materials Science — Polymers and Plastics

A novel technique to simulate and characterize yarn mechanical behavior based on a geometrical fiber model extracted from microcomputed tomography imaging Textiles and their production machines are increasingly designed using simulations of the textile production process. These simulations require reliable structural yarn models. In practice, these models are often based on simplifying assumptions concerning the underlying geometry. However, in recent years more realistic geometrical fiber models have been developed, but have not been used to estimate the structural properties of yarns. The current contribution presents a new methodology to obtain a structural yarn model through numerical simulations based on a high-fidelity geometrical yarn model. Starting from microcomputed tomography data of a real-life natural fiber yarn used in air-jet weaving, a geometrical yarn model is constructed by tracing the individual fibers. This geometry is then incorporated in a computational finite-element analysis framework to obtain the yarn tensile and bending behavior through a simulated tensile test and Peirce cantilever test. Finally, the results are validated by comparison with experimental data. It is shown that this new technique succeeds to estimate the tensile and bending behavior. The presented methodology allows one to gain fundamental insight into the internal deformations and stresses in the yarn. Moreover, it is an important first step toward statistical studies on the structural yarn behavior on the microscale level and toward homogenized macro-models originating from a fully digital workflow. Yarn ; Weaving ; Microscale chemistry ; Spinning ; Fiber ; Finite element method ; Ultimate tensile strength ; Bending ; Tensile testing ; Materials science ; Computer science ; Structural engineering ; Composite material ; Engineering ; Mathematics Textile materials and evaluations ; Material Properties and Processing ; Mechanical Behavior of Composites
Authors: Chengjun Li | Zhongjia Chen | Qingchun Wang
Source: Energies (article, 2022-01-24)
Field: Materials Science — Polymers and Plastics

Effect of Temperature on Densification Pressure and the Unit Density in Production of Corn Straw Pellet with a Post-Heating Method In the biomass densification process, a lot of energy is needed to prevent its spring-back and to overcome the friction between the material and the channel surface of the die so as to build up pressure. However, moisture, particle size, temperature, and type of material all influence the densification pressure, pellet quality, and the density. The research was aimed to find out how the densification pressure and density were affected by the die temperature based on an open-end densification method using a self-developed pellet extruder. When the piston fixed in the front of the extruder moved in a reciprocating way, the raw material was pressed into the opening die, which had a heater wrapped up. Crushed corn straw with a particle size under 4 mm and with a given moisture content of 10% was used to be compacted by the extruder. The results showed that with the increase of the die temperature, the value of densification pressure decreased; however, the quality of pellets was not good at temperatures higher than 140 °C. The proper temperature for corn straw densification was 100 °C, and the value of the unit density was above 1.0 g/cm³. Meanwhile, the surface quality of the pellet was comparatively good. The reference values of the friction coefficient at different temperatures were provided. Pellet ; Materials science ; Pellets ; Composite material ; Straw ; Die (integrated circuit) ; Piston (optics) ; Raw material ; Bulk density ; Water content ; Particle size ; Moisture ; Plastics extrusion ; Environmental science ; Chemistry Natural Fiber Reinforced Composites ; Biofuel production and bioconversion ; Thermochemical Biomass Conversion Processes
Authors: Puneet Mishra | Maxime Metz | Federico Marini | Alessandra Biancolillo | Douglas N. Rutledge
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-04-05)
Field: Chemistry — Analytical Chemistry

Response oriented covariates selection (ROCS) for fast block order- and scale-independent variable selection in multi-block scenarios Multi-block datasets are widely met in the chemometrics domain, and several data fusion approaches have recently been proposed to treat them. Apart from exploratory and predictive modelling, a key task in this context is feature selection which involves finding key complementary variables across multiple data blocks that jointly provide a good explanation of the response variables, revealing the key variables of the system. In that direction, a new method called response-oriented covariate selection (ROCS) is proposed here. ROCS is a direct extension of the covariance selection (CovSel) approach to multi-block scenarios, where the choice is based on a competition between variables in different blocks, as is done in the response-oriented sequential alternation (ROSA) method. The uniqueness of the ROCS method is its simplicity, fast execution speed, insensitivity to block order and scale-invariance. The evaluation of ROCS is presented using several multi-block modelling cases and by comparison with other variable selection methods. Block (permutation group theory) ; Computer science ; Selection (genetic algorithm) ; Covariate ; Feature selection ; Context (archaeology) ; Scale (ratio) ; Data mining ; Mathematics ; Algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hye-Young Song | Byeong-Hyo Cho | Yonghyun Kim | Kyoung-Chul Kim
Source: Korean Journal of Agricultural Science (article, 2022-03-01)
Field: Chemistry — Analytical Chemistry

Development of a classification model for tomato maturity using hyperspectral imagery Hyperspectral imaging ; Maturity (psychological) ; Geography ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Cartography ; Remote sensing ; Political science Spectroscopy and Chemometric Analyses
Authors: Xiaobin Yan | Yibo Wang | Xiyin Zhang | Zhenhua Wang | Song Yang | Yanxia Li | Chenbo Yang | Meichen Feng | X.Y. SONG | Min Zhang | L.J. XIAO | S. FAHAD | W.D. YANG | C. WANG
Source: Applied Ecology and Environmental Research (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

PERFORMANCE OF PRETREATMENTS AND MULTIVARIATE METHOD ON THE HYPERSPECTRAL ESTIMATION OF SOIL MOISTURE CONTENT Soil moisture controls the exchange of energy between the land surface and the atmosphere and is a significant factor affecting plant growth and productivity. Hyperspectral monitoring of soil water fraction could provide a theoretical basis for real-time estimation of spatial and temporal variations in soil moisture. To quantitatively evaluate the hyperspectral monitoring of soil moisture content (SMC): the SMC and its corresponding spectral reflectance were measured in the laboratory. In addition, the original spectral data was pre-processed by single and multiple transformations to study the effect of spectral preprocessing methods on the quantitative evaluation of soil moisture. The successive projections algorithm (SPA) was used to extract the corresponding wavelengths of soil moisture and the spectral monitoring model was established by using the partial least squares (PLS). The results show that (1) SMC and spectral reflectance show an obvious negative correlation, and the spectral reflectance gradually decreases with the increase of SMC. (2) Appropriate pretreatment methods can improve the correlation between SMC and spectral reflectance and improve the accuracy of the SMC monitoring model, of which T19 (R 2 + SNV + FD) is the best spectral pretreatment method. (3) The optimal SMC monitoring model is T19-SPA-PLS (R 2 v = 0.986, RMSEv = 1.824, RPD = 8.239). This study provided a reference for spectral data processing and an effective method for the accurate estimation of SMC using hyperspectral remote sensing. Hyperspectral imaging ; Water content ; Environmental science ; Multivariate statistics ; Estimation ; Soil science ; Mathematics ; Remote sensing ; Statistics ; Geology ; Economics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Assem Akbayeva | Gulzhan Muratbekova | Zhanar Altayeva | І. І. Бондар | Серик Абибуллаев | Сауле Бекжанова | Михаил Квашнин
Source: Eastern-European Journal of Enterprise Technologies (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

Development of safety methods on artificial structures of railway lines The numerous cases of deformation of technogenic objects in the transport industry under increasing axial loads and speeds of motion aggravate the need to solve the problems of early recognition of the nature and causes of deformations of structural elements. The need for this is due to the fact that destructions and accidents, resulting from deformation processes, cause enormous economic, social and environmental damage, incomparable with the funds spent on protective measures. The object of the study in the article is a railway trestle, consisting of two spans of a ribbed reinforced concrete beam. Determination of the residual resource in terms of bearing capacity and load-carrying capacity of the railway trestle is the main task. The authors have obtained bending deformations (stresses), frequencies of natural vibrations and operating modes of structures of railway trestle spans. The damage degree of the railway overpass spans structures may also be judged by the deviation of the calculated values of the amplitude-phase-frequency characteristics (AFFR) from the standard values. The peculiarity of this research is the fact, that the natural frequencies of railway overpass vibrations are determined either by «tails» of experimental vibrograms (oscillograms) after the load release from the span structure. During the express-diagnostics of railway overpasses, the relative deformations (stresses) of girder spans in the middle of the span, the first frequency (period) of natural vibrations of girder reinforced concrete spans of railway overpasses are used as parameters characterizing the technical condition of the spans. The results of tests and examinations of overpasses to ensure their safe operation have been given Structural engineering ; Vibration ; Girder ; Deformation (meteorology) ; Span (engineering) ; Load bearing ; Bearing (navigation) ; Engineering ; Bending ; Residual ; Beam (structure) ; Natural frequency ; Computer science ; Geology ; Physics Material Properties and Failure Mechanisms ; Advanced Energy Technologies and Civil Engineering Innovations ; Industrial Engineering and Technologies
Authors: Víctor M. Fernández‐Cabanás | Dolores Pérez‐Marín | Tom Fearn | Joadil Gonçalves de Abreu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-09-24)
Field: Chemistry — Analytical Chemistry

Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models. The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100-250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds. Algorithm ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Agricultural Engineering and Mechanization