jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Laura Rosina Torres Ortega | Héctor Guillén‐Alonso | Raúl Alcalde-Vázquez | Enrique Ramı́rez-Chávez | Jorge Molina‐Torres | Robert Winkler
Source: Metabolites (article, 2022-12-08)
Field: Chemistry — Spectroscopy

In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction Volatile organic compounds (VOCs) comprises a broad class of small molecules (up to ~300 g/mol) produced by biological and non-biological sources. VOCs play a vital role in an organism's metabolism during its growth, defense, and reproduction. The well-known 6-pentyl-α-pyrone (6-PP) molecule is an example of a major volatile biosynthesized by <i>Trichoderma atroviride</i> that modulates the expression of PIN auxin-transport proteins in primary roots of <i>Arabidopsis thaliana</i> during their relationship. Their beneficial relation includes lateral root formation, defense induction, and increased plant biomass production. The role of 6-PP has been widely studied due to its relevance in this cross-kingdom relationship. Conventional VOCs measurements are often destructive; samples require further preparation, and the time resolution is low (around hours). Some techniques enable at-line or real-time analyses but are highly selective to defined compounds. Due to these technical constraints, it is difficult to acquire relevant information about the dynamics of VOCs in biological systems. Low-temperature plasma (LTP) ionization allows the analysis of a wide range of VOCs by mass spectrometry (MS). In addition, LTP-MS requires no sample preparation, is solvent-free, and enables the detection of 6-PP faster than conventional analytical methods. Applying static statistical methods such as Principal Component Analysis (PCA) and Discriminant Factorial Analysis (DFA) leads to a loss of information since the biological systems are dynamic. Thus, we applied a time series analysis to find patterns in the signal changes. Our results indicate that the 6-PP signal is constitutively emitted by <i>T. atroviride</i> only; the signal shows high skewness and kurtosis. In <i>A. thaliana</i> grown alone, no signal corresponding to 6-PP is detected above the white noise level. However, during <i>T. atroviride-A. thaliana</i> interaction, the signal performance showed reduced skewness and kurtosis with high autocorrelation. These results suggest that 6-PP is a physiological variable that promotes homeostasis during the plant-fungal relationship. Although the molecular mechanism of this cross-kingdom control is still unknown, our study indicates that 6-PP has to be regulated by <i>A. thaliana</i> during their interaction. Chemistry ; Mass spectrometry ; In vivo ; Chromatography ; Biological system ; Biology ; Biotechnology Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Fermentation and Sensory Analysis
Authors: Alberto Miguel-Torcal | Jaime Abad-Arredondo | F. J. Garcı́a-Vidal | Antonio I. Fernández‐Domínguez
Source: Nanophotonics (article, 2022-08-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse‐designed dielectric cloaks for entanglement generation We investigate the generation of entanglement between two quantum emitters through the inverse-design engineering of their photonic environment. By means of a topology-optimization approach acting at the level of the electromagnetic Dyadic Green's function, we generate dielectric cloaks operating at different inter-emitter distances and incoherent pumping strengths. We show that the structures obtained maximize the dissipative coupling between the emitters under extremely different Purcell factor conditions, and yield steady-state concurrence values much larger than those attainable in free space. Finally, we benchmark our design strategy by proving that the entanglement enabled by our devices approaches the limit of maximum-entangled-mixed-states. Quantum entanglement ; Inverse ; Photonics ; Topology (electrical circuits) ; Benchmark (surveying) ; Physics ; Dissipative system ; Photonic crystal ; Common emitter ; Concurrence ; Limit (mathematics) ; Coupling (piping) ; Quantum mechanics ; Quantum ; Optoelectronics ; Materials science ; Mathematics ; Mathematical analysis ; Electrical engineering ; Engineering Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Brian Flynn | Antonio A. Gentile | Nathan Wiebe | Raffaele Santagati | Anthony Laing
Source: New Journal of Physics (article, 2022-04-21)
Field: Materials Science — Materials Chemistry

Quantum model learning agent: characterisation of quantum systems through machine learning Abstract Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices. Physics ; A priori and a posteriori ; Quantum ; Hamiltonian (control theory) ; Protocol (science) ; Artificial intelligence ; Ising model ; Physical system ; Machine learning ; Computer science ; Statistical physics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Quantum many-body systems
Authors: Mohamed Yousuff | M. Rajasekhara Babu
Source: Earth Science Informatics (article, 2022-12-23)
Field: Chemistry — Analytical Chemistry

Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space Dimensionality reduction ; Curse of dimensionality ; Embedding ; Artificial neural network ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Spectral space ; Nonlinear system ; Chemometrics ; Machine learning ; Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: R. C. Dharmik | Sushilkumar Chavhan | Shashank Gotarkar | Arjun Pasoriya
Source: 3C TIC Cuadernos de desarrollo aplicados a las TIC (article, 2022-12-29)
Field: Chemistry — Analytical Chemistry

Rice quality analysis using image processing and machine learning Object Detection and its analysis are used in various fields. Rice quality evaluation subtask in Agricultural industries is not exception for object Detection. Manual identification using image processing techniques, Machine Learning Techniques and Deep Learning is also used for the rice quality analysis. Due to Feature identification challenge machine Learning and Deep Learning are in the demand. As rice is mostly used agricultural product so it is important to have the proper analysis of the crops. In this study we proposed the used of image processing method with the help of Machine Learning model. Rice grain morphological characteristics are what define a grain’s quality analysis. The suggested method can operate efficiently with little expense. Artificial intelligence ; Computer science ; Quality (philosophy) ; Machine learning ; Feature (linguistics) ; Identification (biology) ; Image processing ; Product (mathematics) ; Object (grammar) ; Agriculture ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Mathematics ; Geography Spectroscopy and Chemometric Analyses
Authors: Yeong-Mo Yeon | Wookjin Lee | Ki-Nam Hong
Source: Applied Sciences (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Finite Element Analysis of Reinforced Concrete Beams Prestressed by Fe-Based Shape Memory Alloy Bars Prestressing of concrete structures using Fe-based shape memory alloys has been investigated extensively by experiments in the last decade. However, detailed investigations on the stress produced by the Fe-based shape memory alloys and its influence on concrete damage during deformation of concrete structure has not been investigated yet. In this study, the prestressing effect by Fe-based shape memory alloy bars on bending behavior of reinforced concrete beam was investigated numerically. A finite element simulation model was developed to investigated the bending responses of the beams including nonlinear material properties such as concrete cracking and crushing as well as the plastic deformation of the Fe-based shape memory alloy. The model is able to capture the bending behavior of the beam prestressed with the Fe-based shape memory alloy bars. Based on the numerical and experimental results, the prestressing effect by the shape memory alloy bars was investigated in detail. Although the developed model slightly overestimated the experimentally obtained bending load-deflection curves of the concrete beams, it was shown that the developed model can be used for an optimization study to select the best possible design parameters for prestressing the concrete beam with the Fe-based shape memory alloy bars. A possible reason for the overestimation is the idealized perfect bonding assumption between Fe-SMA and concrete used in the model, while slip at the interface occurred in the experiments. Shape-memory alloy ; Materials science ; Structural engineering ; Bending ; Cracking ; Finite element method ; Deflection (physics) ; Beam (structure) ; Composite material ; Alloy ; Slip (aerodynamics) ; Engineering Shape Memory Alloy Transformations ; Concrete Corrosion and Durability ; Structural Behavior of Reinforced Concrete
Authors: Zhaoyang Han | Bin Li | Qiu Wang | Akun Yang | Yande Liu
Source: Journal of Spectroscopy (article, 2022-07-12)
Field: Chemistry — Analytical Chemistry

Detection Storage Time of Mild Bruise’s Loquats Using Hyperspectral Imaging Bruise may cause spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, it is crucial to detect whether a loquat is bruised and when it is bruised to save storage and transportation costs. At present, the bruise of loquats is mainly discriminated by the operator’s naked eye, which is affected by personal habits, light intensity, and subjective psychological factors. The detection method is time-consuming, inaccurate, inefficient, and difficult to identify the bruise’s time of loquats. Due to the fact that the color features can be used to perform the conditions of the darkened and brownish regions in bruise’s loquats, the combined spectral information and the color features method is proposed to accurately detect the storage time of mild bruise’s loquats in this study. In order to reduce economic losses, different methods are used to deal with the loquats at the corresponding bruise’s time. Loquats with four types of bruise’s time, including 6, 12, 24, and 36 h, are studied. Models with four types of characteristics, including spectral information, RGB features combined with spectral information, HSI features combined with spectral information, and mixed color features combined with spectral information (mixed-spectral), are established based on linear discriminant analysis (LDA), support vector machine (SVM), and least-squares support vector machine (LS-SVM). The investigated 400 independent samples with four bruise’s time conditions are utilized to assess the classification ability of the proposed methods. The results indicate that the Mixed-RBF-LS-SVM model has the lowest errors, and the accuracies of storage time of mild bruise’s loquats at 6, 12, 24, and 36 h are 100%, 92%, 92%, and 100%, respectively. The overall accuracy of the LS-SVM model based on mixed-spectral is 96%, and it demonstrates that the combined spectral information and color features method can be used to accurately detect the bruise’s time of loquats. Finally, the LS-SVM model based on mixed-spectral is optimized by UVE, SPA, CARS, and GA, respectively; it is found that the UVE-LS-SVM model based on mixed-spectral is the best, and the overall accuracy is 92%. It also lays a foundation for future studies about detecting the bruise’s time of fruits with a high-precision, rapid, and nondestructive measurement. Bruise ; Artificial intelligence ; Hyperspectral imaging ; Support vector machine ; Pattern recognition (psychology) ; Computer science ; RGB color model ; Computer vision ; Linear discriminant analysis ; Mathematics Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Food Safety and Hygiene
Authors: Ju Qiu | Ion Stiharu
Source: Science and Engineering of Composite Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Vibration and noise reduction of pipelines using shape memory alloy Abstract In this article, the pipeline design is introduced. The new pipe structure is made of new fiber metal laminates with the unidirectional composite and sheet metal (Ti–Ni alloy). Many pipe structures are in the heating environment such as in or around the engine, which will also cause the heating-up structure. If the shape memory alloy (SMA) fiber is added to the composite lamination, it can be seen that with the increase of temperature, the stiffness of the structure is increased and so is its frequency. The changed frequency of the structure can avoid the excitation frequency in this way, which effectively inhibits the resonance. In dynamic analysis, it can also show that the pipeline with the SMA fiber has good performance for vibration reduction and noise attenuation. Additionally, the convergence of the meshing model and the effect of the thickness of the SMA material on vibration and noise reduction are also discussed. Materials science ; Shape-memory alloy ; Reduction (mathematics) ; Vibration ; SMA* ; Lamination ; Noise (video) ; Pipeline (software) ; Composite material ; Stiffness ; Fiber ; Noise reduction ; Structural engineering ; Acoustics ; Mechanical engineering ; Computer science ; Engineering Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Structural Health Monitoring Techniques
Authors: Md. Saiful Hoque | Md. Imranul Shahid | Abdullah Al Parvez | Md. Jakir Hossain | Sheenrina Miyan Sheen
Source: AATCC Journal of Research (article, 2022-09-28)
Field: Materials Science — Polymers and Plastics

Knitted Denim Fabrics: Fabrication Process and Fibrous Influence on Several Properties of the Fabric Denim-like knitted fabrics are getting popular for their several favorable properties, such as flexibility, comfort, and ease of manufacturing. This study aimed to manufacture knitted denim fabrics made from various blend ratios of cotton, polyester, and spandex fibers. Seven fabrics with a fiber blend ratio ranging from 95% cotton/5% spandex to 30% cotton/65% polyester/5% spandex were developed using a weft circular knitting machine. First, the twill effect of the knitted denim fabric was brought by following a cross terry knitting structure to produce each fabric sample of this study. After that, the fabric performance was analyzed by characterizations, such as areal density, pilling and abrasion resistance, dimensional stability, stretch, and recovery, tear strength, bursting strength, air permeability, vertical wicking, liquid absorbency capacity, and different colorfastness tests. The results showed that different cotton, polyester, and spandex fiber compositions did not significantly affect knitted denim fabrics’ weight per unit area, abrasion resistance, and different color fastness properties. However, the elongation and vertical wicking test data showed that the knitted denim fabrics with a higher cotton fiber ratio were better. However, the results from shrinkage, spirality, pilling, recovery, strength, air permeability, and liquid absorbency capacity tests revealed the benefits of having a higher polyester fiber ratio in the cotton/polyester/spandex blended knitted denim fabrics. One-way analysis of the variance test was also performed on the generated data of this study and reported in the respective section of the article. Denim ; Air permeability specific surface ; Materials science ; Composite material ; Polyester ; Fiber ; Breaking strength Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Erhan Baysal | Oğuz Koçar | Engin Kocaman | Uğur Köklü
Source: Metals (article, 2022-10-24)
Field: Materials Science — Materials Chemistry

An Overview of Deformation Path Shapes on Equal Channel Angular Pressing In recent years, research on ultra-fine grain materials has gained attention. While attempts have been made to improve the properties of the material, it has also become increasingly important to decrease the costs. Studies on improving material properties have revealed new production methods or have required the revision of existing production methods. In this direction, severe plastic deformation methods have come to the fore as a good alternative, and by improving the methods with new variations, materials with grain sizes below 1 µm have been obtained. In addition, this method positively affects the mechanical properties of the material. In this study, the Equal Channel Angular Pressing (ECAP) method, one of the severe plastic deformation methods, which has attracted great attention among researchers, was examined and the development stages of the method were investigated according to recent studies. The effective parameters in the method were examined and the effects of these parameters on the grain structure and mechanical properties of the material were discussed. Channel shapes, which are open to innovation and increase the efficiency of the ECAP method, were kept in the foreground among the prominent parameters in the ECAP process, and the results of the design changes made with new variations were examined. Pressing ; Materials science ; Severe plastic deformation ; Deformation (meteorology) ; Channel (broadcasting) ; Production (economics) ; Grain size ; Path (computing) ; Mechanical engineering ; Composite material ; Computer science ; Engineering Microstructure and mechanical properties ; Metal Alloys Wear and Properties ; Metallurgy and Material Forming
Authors: Mpho Mafata | Jeanne Brand | Martin Kidd | Andrei Medvedovici | Astrid Buica
Source: Beverages (article, 2022-10-21)
Field: Chemistry — Analytical Chemistry

Exploration of Data Fusion Strategies Using Principal Component Analysis and Multiple Factor Analysis In oenology, statistical analyses are used for descriptive purposes, mostly with separate sensory and chemistry data sets. Cases that combine them are mostly supervised, usually seeking to optimize discrimination, classification, or prediction power. Unsupervised methods are used as preliminary steps to achieving success in supervised models. However, there is potential for unsupervised methods to combine different data sets into comprehensive, information-rich models. This study detailed stepwise strategies for creating data fusion models using unsupervised techniques at different levels. Principal component analysis (PCA) and multiple factor analysis (MFA) were used to combine five data blocks (four chemistry and one sensory). The model efficiency and configurational similarity were evaluated using eigenvalues and regression vector (RV) coefficients, respectively. The MFA models were less efficient than PCA, having gradual distributions of eigenvalues across model dimensions. The MFA models were more representative than PCA, as indicated by high RV coefficients between MFA and each individual block. Therefore, MFA approaches were better suited for multi-modal data than PCA. This work approached data fusion systematically and showed the type of decisions that must be made and how to evaluate their consequences. Proper integration of data sets, instead of concatenation, is an important aspect to consider in multi-modal data fusion. Principal component analysis ; Concatenation (mathematics) ; Computer science ; Artificial intelligence ; Sensor fusion ; Pattern recognition (psychology) ; Data mining ; Sparse PCA ; Machine learning ; Predictive modelling ; Mathematics Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Advanced Chemical Sensor Technologies
Authors: Junichi Kurihara | Toshikazu Yamana
Source: Remote Sensing (article, 2022-03-15)
Field: Chemistry — Analytical Chemistry

Detection of Apple Valsa Canker Based on Hyperspectral Imaging Approximately half of the world’s apple production occurs in East Asia, where apple Valsa canker (AVC) is a prominent disease. This disease affects the bark of the tree, ultimately killing it and resulting in significant economic loss. Visual identification of the diseased area of the bark, particularly in the early stages, is extremely difficult. In this study, we conducted hyperspectral imaging of the trunks and branches of AVC-infected apple trees and revealed that the diseased area can be identified in the near-infrared reflectance, even when it is difficult to distinguish visually. A discriminant analysis using the Mahalanobis distance was performed on the normalized difference spectral index (NDSI) obtained from the measured spectral reflectance. A diagnostic model for discriminating between the healthy and diseased areas was created using the threshold value of NDSI. An accuracy assessment of the diagnostic model presented the overall accuracy as &gt;0.94 for the combinations of spectral bands at 660–690 nm and 720–760 nm. This simple diagnostic model can be applied to other tree bark canker diseases. Canker ; Hyperspectral imaging ; Bark (sound) ; Mahalanobis distance ; Horticulture ; Botany ; Biology ; Remote sensing ; Computer science ; Artificial intelligence ; Geology ; Ecology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Remote Sensing in Agriculture