jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Öznur Özaltın | Nursel Koyuncu
Source: Research Square (preprint, 2023-08-07)
Field: Chemistry — Analytical Chemistry

A Novel Feature Selection Approach Based Sampling Theory on Grapevine Images using Convolutional Neural Networks <title>Abstract</title> Feature selection, reducing number of input variables to develop classification model, is an important process to reduce computational and modelling complexity and affects the performance of image process. In this paper, we have proposed new statistical approaches for feature selection based on sample selection. We have applied our new approaches to grapevine leaves data that possesses properties of shape, thickness, featheriness, and slickness are investigated in images. To analyze such kind of data by using image process, thousands of features are created and selection of features plays important role to predict the outcome properly. In our numerical study, Convolutional Neural Networks (CNNs) have been used as feature extractors and then obtained features from the last average pooling layer to detect the type of grapevine leaves from images. These features have been reduced by using our suggested four statistical methods: Simple random sampling (SRS), ranked set sampling (RSS), extreme ranked set sampling (ERSS), Moving extreme ranked set sampling (MERSS). Then selected features have been classified with Artificial Neural Network (ANN) and we have obtained the best accuracy of 97.33% with our proposed approaches. Based on our empirical analysis, it has been determined that the proposed approach exhibits efficacy in the classification of grapevine leaf types. Furthermore, it possesses the potential for integration into various computational devices. Feature selection ; Convolutional neural network ; Pooling ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Sampling (signal processing) ; Feature (linguistics) ; Selection (genetic algorithm) ; Artificial neural network ; Set (abstract data type) ; Data mining ; Machine learning ; Filter (signal processing) ; Computer vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Min-Shao Shih | Kai-Chun Chang | Shao-An Chou | Tsang‐Sen Liu | Yen‐Chieh Ouyang
Source: Remote Sensing (article, 2023-08-25)
Field: Chemistry — Analytical Chemistry

The Automated Detection of Fusarium Wilt on Phalaenopsis Using VIS-NIR and SWIR Hyperspectral Imaging Phalaenopsis, an essential flower for export, is significantly affected by fusarium wilt, which impacts its export quality. Hyperspectral imaging technology offers the potential to detect fusarium wilt on Phalaenopsis. The goal of this study was to establish an automated platform for the rapid detection of fusarium wilt on Phalaenopsis. In this research, the automatic target generation process (ATGP) method was employed to identify outliers in the hyperspectral spectrum. Subsequently, the Spectral Angle Mapper (SAM) method was utilized to detect signals similar to the outliers. To suppress background noise and extract the region of interest (ROI), the Constrained Energy Minimization (CEM) method was implemented. For ROI classification and detection, a deep neural network (DNN), a support vector machine (SVM), and a Random Forest Classifier (RFC) were employed. Model performance was evaluated using three-dimensional receiver operating characteristics (3D ROC), and the automated identification system was integrated into hyperspectrometers. The proposed system achieved an accuracy of 95.77% with a total detection time of 3380 ms ± 86.36 ms, proving to be a practical and effective tool for detecting fusarium wilt on Phalaenopsis in the industry. Hyperspectral imaging ; Phalaenopsis ; Fusarium wilt ; Support vector machine ; Computer science ; Artificial intelligence ; Outlier ; Pixel ; Pattern recognition (psychology) ; Remote sensing ; Environmental science ; Biology ; Botany ; Fusarium oxysporum Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Prashant Kumar | Alexander Simon | Nicholas A. Kotov
Source: Chirality (article, 2023-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enantiomeric discrimination by chiral electromagnetic resonance enhancement Circularly polarized light interacts preferentially with the biomolecules to generate spectral fingerprints reflecting their primary and secondary structure in the ultraviolet region of the electromagnetic spectrum. The spectral features can be transferred to the visible and near-infrared regions by coupling the biomolecules with plasmonic assemblies made of noble metals. Nanoscale gold tetrahelices were used to detect the presence of chiral objects that are 40 times smaller in size by using plane-polarized light of 550 nm wavelength. The emergence of chiral hotspots in the gaps between 80 nm long tetrahelices differentiate between weakly scattering S- vs R-molecules with optical constants similar to that of organic solvents. Simulations map the spatial distribution of the scattered field to reveal enantiomeric discrimination with selectivity up to 0.54. Chemistry ; Biomolecule ; Enantiomer ; Ultraviolet ; Wavelength ; Scattering ; Chirality (physics) ; Resonance (particle physics) ; Plasmon ; Infrared ; Coupling constant ; Molecular physics ; Molecule ; Optics ; Atomic physics ; Physics ; Stereochemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Emile De Bruyn | Anton Emil Dorn | Olav Zimmermann | Giulia Rossetti
Source: Biology (article, 2023-04-10)
Field: Chemistry — Spectroscopy

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories The disordered nature of Intrinsically Disordered Proteins (IDPs) makes their structural ensembles particularly susceptible to changes in chemical environmental conditions, often leading to an alteration of their normal functions. A Radial Distribution Function (RDF) is considered a standard method for characterizing the chemical environment surrounding particles during atomistic simulations, commonly averaged over an entire or part of a trajectory. Given their high structural variability, such averaged information might not be reliable for IDPs. We introduce the Time-Resolved Radial Distribution Function (TRRDF), implemented in our open-source Python package SPEADI, which is able to characterize dynamic environments around IDPs. We use SPEADI to characterize the dynamic distribution of ions around the IDPs Alpha-Synuclein (AS) and Humanin (HN) from Molecular Dynamics (MD) simulations, and some of their selected mutants, showing that local ion-residue interactions play an important role in the structures and behaviors of IDPs. Biology ; Computational biology ; Evolutionary biology Mass Spectrometry Techniques and Applications ; Atomic and Subatomic Physics Research ; Advanced Proteomics Techniques and Applications
Authors: Tomilola M. Obadiya | Daniel M. Sussman
Source: Physical Review Research (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Using fluid structures to encode predictions of glassy dynamics Data-driven approaches that infer the local structures responsible for plasticity in amorphous materials have made substantial contributions to our understanding of the failure, flow, and rearrangement dynamics of supercooled fluids. Some of these methods, such as the ``softness'' approach, have identified combinations of local structural features in a supercooled particle's environment that predict energy barriers associated with particle rearrangements. This approach also predicts the onset temperature, often characterized as the temperature below which the system's dynamics becomes non-Arrhenius and above which local structures are no longer predictive of dynamical activity. We implement a transfer learning approach in which we first show that classifiers can be trained to predict dynamical activity even far above the onset temperature. We then show that applying these classifiers to data from the supercooled phase recovers the same essential physical information about the relationship between local structures and energy barriers that softness does. Supercooling ; Statistical physics ; Dynamics (music) ; Particle (ecology) ; Amorphous solid ; ENCODE ; Computer science ; Biological system ; Physics ; Thermodynamics ; Chemistry ; Crystallography ; Geology ; Biology Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Rui Chen | Virat Tara | Minho Choi | Jayita Duta | Justin Sim | J. Ye | Zhuoran Fang | Jiajiu Zheng | Arka Majumdar
Source: Research Square (preprint, 2023-12-12)
Field: Materials Science — Materials Chemistry

Deterministic quasi-continuous tuning of phase-change material integrated on a high-volume 300-mm silicon photonics platform Abstract Programmable photonic integrated circuits (PICs) consisting of reconfigurable on-chip optical components have been creating new paradigms in various applications, such as integrated spectroscopy, multi-purpose microwave photonics, and optical information processing. Among many reconfiguration mechanisms, non-volatile chalcogenide phase-change materials (PCMs) exhibit a promising approach to the future very-large-scale programmable PICs, thanks to their zero static power and large optical index modulation, leading to extremely low energy consumption and ultra-compact footprints. However, the scalability of the current PCM-based programmable PICs is still limited since they are not directly off-the-shelf in current commercial photonic foundries. Here, we demonstrate a scalable platform harnessing the mature and reliable 300-mm silicon photonic fab, assisted by in-house wide-bandgap PCM (Sb 2 S 3 ) integration process. We show various non-volatile programmable devices, including micro-ring resonators, Mach-Zehnder interferometers and asymmetric directional couplers, with low loss (~ 0.0044 dB/µm), large phase shift (~ 0.012π/µm) and high endurance (&gt; 1,000 switching events). Moreover, we showcase this platform’s capability to handle relatively complex structures with multiple PIN diode heaters, each independently controlling an Sb 2 S 3 segment. By reliably setting the Sb 2 S 3 segments to fully amorphous or crystalline state, we achieved deterministic multilevel operation. An asymmetric directional coupler with two unequal-length Sb 2 S 3 segments showed the capability of four-level switching, beyond cross and bar binary states. We further showed unbalanced Mach-Zehnder interferometers with equal-length and unequal-length Sb 2 S 3 segments, exhibiting reversible switching and a maximum of 5 (\(N+1, N=4\)) and 8 (\({2}^{N}, N=3\)) equally spaced operation levels, respectively. This work lays the foundation for future programmable very-large-scale PICs with deterministic programmability. Photonics ; Silicon photonics ; Optoelectronics ; Optical switch ; Materials science ; Scalability ; Control reconfiguration ; Astronomical interferometer ; Switching time ; Integrated circuit ; Electronic engineering ; Computer science ; Optics ; Physics ; Engineering ; Interferometry ; Embedded system Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Jozef Bucko | Frank Schäfer | František Herman | Rebekka Garreis | Chuyao Tong | Annika Kurzmann | Thomas Ihn | Eliška Greplová
Source: Physical Review Applied (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Automated Reconstruction of Bound States in Bilayer Graphene Quantum Dots Bilayer graphene is a nanomaterial that allows for well-defined, separated quantum states to be defined by electrostatic gating and, therefore, provides an attractive platform to construct tunable quantum dots. When a magnetic field perpendicular to the graphene layers is applied, the graphene valley degeneracy is lifted, and splitting of the energy levels of the dot is observed. Although bilayer graphene quantum dots have recently been realized in experiments, it is critically important to devise robust methods that can identify the observed quantum states from accessible measurement data. Here, we develop an efficient algorithm for extracting the model parameters needed to characterize the states of a bilayer graphene quantum dot. Specifically, we put forward a Hamiltonian-guided random search method and demonstrate robust identification of quantum states on both simulated and experimental data. Bilayer graphene ; Graphene ; Quantum dot ; Degeneracy (biology) ; Hamiltonian (control theory) ; Quantum ; Maxima and minima ; Physics ; Magnetic field ; Quantum mechanics ; Mathematics Graphene research and applications ; Quantum and electron transport phenomena ; Advanced Memory and Neural Computing
Authors: J.F. Benavente | V. Correcher
Source: Radiation Physics and Chemistry (article, 2023-07-03)
Field: Chemistry — Analytical Chemistry

Thermoluminescence-based simplified criteria for the detection of irradiated sesame seeds using artificial intelligence methods The practical application of unsupervised Artificial Intelligence (AI) numerical methods for analysing unexamined data has gained popularity for solving scientific and technological problems. This paper reports on the implementation of numerical algorithms set based on unsupervised AI methods to discriminate between irradiated and non-irradiated Mexican sesame sample by searching for behaviour patterns in the thermoluminescence (TL) response of polymineral samples adhered to the seeds. Two algorithms were tested, which were able to discriminate between irradiated and non-irradiated samples regardless of whether the whole or initial rise of the TL glow curve was considered. The use of AI algorithms can greatly increase the analytical process by using appropriate models to large datasets. Moreover, free software tools are now available for developers to implement these AI methods in their data analysis code, with Python being the primary language of choice. Python (programming language) ; Thermoluminescence ; Computer science ; Irradiation ; Artificial intelligence ; Sample (material) ; Software ; Machine learning ; Algorithm ; Data mining ; Physics ; Chemistry ; Programming language ; Nuclear physics ; Chromatography Spectroscopy and Chemometric Analyses ; Radiation Effects and Dosimetry ; Sesame and Sesamin Research
Authors: Mahmoud A. E. Ibrahim | Xiangyue Liu | Jesús Pérez‐Ríos
Source: Digital Discovery (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Spectroscopic constants from atomic properties: a machine learning approach We present a machine-learning approach toward predicting spectroscopic constants based on atomic properties. Materials science ; Computer science ; Chemistry Machine Learning in Materials Science ; Fault Detection and Control Systems ; Metabolomics and Mass Spectrometry Studies
Authors: C. Richard A. Catlow
Source: IUCrJ (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Crystal structure prediction: achievements and opportunities The role of computational modelling in structural science was discussed in an Editorial in 2020 (Catlow, 2020).The field is, however, evolving rapidly and it is timely Computer science ; Data science X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Krzysztof Chwastek | P. Gębara | A. Przybył | Roman Gozdur | A. P. S. Baghel | B. Sai Ram
Source: Applied Sciences (article, 2023-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Alternative Formulation of the Harrison Model The approach advanced by Harrison puts in the spotlight the fundamental role of bistability in hysteresis modeling. The description is based on physical premises concerning irreversible thermodynamics. In the original model, the upscaling of irreversible phenomena acting on the micromagnetic level is carried out by the introduction of a phenomenological parameter β. In the present paper, an alternative approach is proposed. The outputs of individual outputs of elementary hysteresis units (hysterons) are considered like in the stop model. A verification of the proposed model is carried out using measurement data for a praseodymium–dysprosium ribbon sample and a cylinder core made of cobalt-based amorphous material. Bistability ; Phenomenological model ; Core model ; Hysteresis ; Statistical physics ; Core (optical fiber) ; Thermodynamics ; Materials science ; Computer science ; Physics ; Mathematics ; Condensed matter physics ; Mathematical analysis Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Shape Memory Alloy Transformations
Authors: Iane Vallanzasca | Sébastien Boutry | Martin Laviale | Eric Quinton | Soizic Morin
Source: Botany Letters (article, 2023-03-08)
Field: Materials Science — Biomaterials

Development of a collaborative web platform documenting the diversity and extent of diatom deformities International audience World Wide Web ; Work (physics) ; Diversity (politics) ; Library science ; Computer science ; Engineering ; Sociology Diatoms and Algae Research ; Freshwater macroinvertebrate diversity and ecology ; Aquatic Invertebrate Ecology and Behavior
Authors: Andrés Aguilar-Ariza | Masanori Ishii | Toshio Miyazaki | Aika Saito | Hlaing Phyoe Khaing | Hnin Wint Phoo | T. Kondo | Toru Fujiwara | Wei Guo | Takehiro Kamiya
Source: Scientific Reports (article, 2023-11-17)
Field: Chemistry — Analytical Chemistry

UAV-based individual Chinese cabbage weight prediction using multi-temporal data The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R<sup>2</sup>) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R<sup>2</sup> greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. Computer science ; Data mining Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Anja Hajnal | Mimololorun Lucy Aiyelari | Martin P. Dirauf | Pavel Gurikov
Source: Advanced Engineering Materials (article, 2023-11-14)
Field: Chemistry — Spectroscopy

Investigation of the Uniformity of Gel Shrinkage by Imaging Tracer Particles Using X‐Ray Microtomography Mass transfer and structural changes in the polymer matrix need to be understood for a rational optimization of the biopolymer‐based aerogels production. Herein, the uniformity of changes that occur in the gel structure during the aerogel production by a newly developed method which is based on the imaging of metal tracer particles using X‐ray microtomography (X‐ray micro‐CT) alongside the application of point pattern techniques such as the K‐nearest‐neighbor (KNN) distances is studied. Agar is initially used as a system of validation for the developed method, after which other polysaccharide and protein‐based gels such as alginate, whey protein isolate (WPI), and gelatine are studied. The KNN distance is seen to reduce from hydrogel to aerogel. Furthermore, by means of the KNN distances, the biopolymers are observed to experience a nonuniform distribution of tracer particles correlating to nonuniform shrinkage. Densification at specific sites of the gel represented by lower KNN distance values could be distinguished for each biopolymer. Finally, the spatially resolved polymer network density profile is estimated from the KNN distance by correlating it to experimentally determined polymer network density. Biopolymer ; Materials science ; Aerogel ; Shrinkage ; TRACER ; Polymer ; Opacity ; Porosity ; Composite material ; Optics Aerogels and thermal insulation ; Advanced Cellulose Research Studies
Authors: Carlos Roberto Xavier | Horácio Guimarães Delgado | Matheus Gomes Rebello | Raí Ramos Lisbôa | Ana Carolina Martins Silva | José Adílson de Castro
Source: Materials Research (article, 2023-01-01)
Field: Materials Science — Metals and Alloys

Numerical Prediction for the Effects of Welding Interpass Temperature on the Thermal History and Microstructure of Duplex Stainless Steels Numerical simulation was used to predict the thermal behavior and the resulting microstructure at the heat-affected zone (HAZ) of a 170 mm diameter and 3.5 mm thickness super duplex stainless steel (SDSS) UNS S32750 tube. In order to evaluate the thermal response from the model, a usual welding situation involving interpass temperature (IT) and its influence on the HAZ microstructure was exploited. Thus, two superimposed autogenous welding passes were simulated, the first clockwise with the tube in the room temperature and the second, counterclockwise, with the tube at the temperature of 250oC. Even subjected to successive thermal cycles and high interpass temperature, the proportion and morphology of the phases at the HAZ and Fusion Zone (FZ) did not present significant differences when comparing the two welding passes. Meanwhile, nitrogen losses should be avoided during welding in order to obtain a balanced microstructure in DSS welds, contributing to guarantee satisfactory toughness in addition to resistance to pitting corrosion. The predictions from the simulation were validated by using experimental results obtained from the autogenous TIG (Tungsten Inert Gas) process. Microstructure ; Welding ; Materials science ; Metallurgy ; Heat-affected zone ; Gas tungsten arc welding ; Tungsten ; Thermal ; Composite material ; Duplex (building) ; Arc welding Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Corrosion Behavior and Inhibition