jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinkang Li | Zirui Chen | Lijun Tang | Jingjing Guo | Baoqiong Li
Source: Symmetry (article, 2024-07-18)
Field: Chemistry — Analytical Chemistry

The Application of Multilinear Regression Model for Quantitative Analysis on the Basis of Excitation-Emission Matrix Spectra and the Release of a Free Graphical User Interface Multivariate regression is a fundamental supervised chemometric method for developing the relationship between the independent variables and quantitative response, and it has been widely applied for data analysis in many research fields. In this study, we propose an effective method for the quantitative determination of target compounds in traditional Chinese medicine, specifically Mongolia, using excitation-emission matrix (EEM) spectra with partial overlap. The accuracy and reliability of the established model have been validated, demonstrating that the proposed method can realize the accurate quantitative analysis purpose. In order to facilitate the calculation easier, the authors have developed a friendly graphical user interface (GUI). The GUI offers the procedures for data imputation, model establishment, model optimization and results presentation. Graphical user interface ; Computer science ; Multilinear map ; Data mining ; Principal component analysis ; Imputation (statistics) ; Multivariate statistics ; Basis (linear algebra) ; Matrix (chemical analysis) ; Regression analysis ; Interface (matter) ; Regression ; Graphical model ; Experimental data ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Statistics ; Mathematics ; Chemistry ; Missing data Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: MG Blanco-Barrientos | M. Melissa Rojas-Downing | AM Rojas-González | JA Elizondo-Salazar
Source: Smart Agricultural Technology (article, 2024-12-19)
Field: Chemistry — Analytical Chemistry

Estimating dry matter in African stargrass (Cynodon nlemfuensis) forage with in-field hyperspectral proximal sensing • Hyperspectral data is cost-effective for estimating dry matter in African Stargrass. • The PLSR model for estimating dry matter achieved high statistical performance. • Hyperspectral sensing enhances forage monitoring and livestock production efficiency. Hyperspectral remote sensing, applied to forage monitoring, stands as an innovative technology promising to enhance livestock production by providing real-time results at reduced costs. Currently, forage quality testing in Costa Rica is predominantly conducted using destructive and time-consuming methods such as wet chemistry or laboratory spectrometry. This study aims to estimate the dry matter content of African Star Grass ( Cynodon nlemfuensis ) at the Alfredo Volio Mata Experimental Station (EEAVM) employing field spectroscopy with a multitemporal approach and a partial least squares regression (PLSR) mathematical model. Data were collected from two experimental plots managed for mowing and grazing at the EEAVM of the University of Costa Rica from June to September 2022. During this period, two growth cycles were considered for mowing management and three cycles for grazing. Forage quality tests were conducted using wet chemistry at the EEAVM bromatology laboratory to provide a reference dataset to create the model. Additionally, auxiliary data such as forage height and biomass were collected to correlate with spectral data and identify outliers in the data set. Spectral data were acquired using an ASD FieldSpec4 spectroradiometer with 1 nm resolution and a spectral range of 350–2,500 nm, corresponding to visible and near-infrared. The ChemFlow Galaxy platform was used to develop 18 dry matter estimation models for forage applying PLSR to two pretreatment combinations and different spectral eliminations on three data sets corresponding to the ones collected on the management plots (mowing, grazing, and both combined). Results revealed that dry matter did not exhibit the expected behavior due to the unusual development of forage within the experimental plot. The optimal model for obtaining dry matter quality parameter demonstrated robust statistical performance, with r 2 cv = 0.81, RMSECV=1.41, r 2 c =0.89, RMSEC=1.16, and an RPD of 2.31, classified as a highly satisfactory model. These findings have significant implications for forage monitoring and livestock production, as they provide a more efficient and cost-effective method for estimating dry matter content at the EEAVM. This model can be scaled up to a broader scale by replicating the experiment in different regions to collect more data and generate a more robust model, while taking into consideration the use of alternative remote sensing platforms. Cynodon ; Forage ; Dry matter ; Field (mathematics) ; Mathematics ; Biology ; Agronomy Spectroscopy and Chemometric Analyses
Authors: Xiaofeng Yang | Guangming Xiao | Qi Liu | Cai Xingkao | Liu Lei | Yanxia Du | Dong Wei | Xianxu Yuan | Zhigong Tang
Source: Scientia Sinica Technologica (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Numerical prediction, flight verification, and targeted regulation of multiscale thermal effects in thermal protection structures <p indent="0mm">Composite materials and complex structures are integral to the thermal protection systems of near-space high-speed vehicles, exhibiting macro/microscale thermal effects. These multiscale effects introduce prediction biases in heat transfer characteristics of materials/structures, complicating the evaluation of thermal protection performance. This paper introduces the MMM–CVFVM cross-scale heat transfer prediction method to address these challenges. By integrating a microstructure intelligent reconstruction algorithm, the method effectively predicts multiscale thermal effects in composite materials. The prediction model has been validated through flight experiments with space vehicle models. Using multiscale analysis of directional energy transport, a new ceramic lattice superstructure for thermal protection was designed. Supported by numerical simulations and ground and flight experiments, this design achieves directional weight reduction and temperature control. The multiscale thermal effect prediction model established in this paper provides refined evaluation tools and design paradigms for thermal protection systems. It provides crucial theoretical and technical support for advancing next-generation near-space high-speed vehicles in China. Computer science Thermal properties of materials ; Gas Dynamics and Kinetic Theory ; Thermal and Kinetic Analysis
Authors: Hanwei Zhu | Xiangjie Sui | Baoliang Chen | Xuelin Liu | Peilin Chen | Yuming Fang | Shiqi Wang
Source: arXiv (Cornell University) (preprint, 2024-02-02)
Field: Chemistry — Analytical Chemistry

2AFC Prompting of Large Multimodal Models for Image Quality Assessment While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs. Quality (philosophy) ; Computer vision ; Artificial intelligence ; Image (mathematics) ; Image quality ; Multimodal therapy ; Quality assessment ; Computer science ; Psychology ; Engineering ; Reliability engineering ; Epistemology ; Philosophy ; Evaluation methods ; Psychotherapist Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods ; Industrial Vision Systems and Defect Detection
Authors: Benjamin Minixhofer | Edoardo Maria Ponti | Ivan Vulić
Source: arXiv (Cornell University) (preprint, 2024-05-13)
Field: Chemistry — Organic Chemistry

Zero-Shot Tokenizer Transfer Language models (LMs) are bound to their tokenizer, which maps raw text to a sequence of vocabulary items (tokens). This restricts their flexibility: for example, LMs trained primarily on English may still perform well in other natural and programming languages, but have vastly decreased efficiency due to their English-centric tokenizer. To mitigate this, we should be able to swap the original LM tokenizer with an arbitrary one, on the fly, without degrading performance. Hence, in this work we define a new problem: Zero-Shot Tokenizer Transfer (ZeTT). The challenge at the core of ZeTT is finding embeddings for the tokens in the vocabulary of the new tokenizer. Since prior heuristics for initializing embeddings often perform at chance level in a ZeTT setting, we propose a new solution: we train a hypernetwork taking a tokenizer as input and predicting the corresponding embeddings. We empirically demonstrate that the hypernetwork generalizes to new tokenizers both with encoder (e.g., XLM-R) and decoder LLMs (e.g., Mistral-7B). Our method comes close to the original models' performance in cross-lingual and coding tasks while markedly reducing the length of the tokenized sequence. We also find that the remaining gap can be quickly closed by continued training on less than 1B tokens. Finally, we show that a ZeTT hypernetwork trained for a base (L)LM can also be applied to fine-tuned variants without extra training. Overall, our results make substantial strides toward detaching LMs from their tokenizer. Shot (pellet) ; Zero (linguistics) ; Transfer (computing) ; Physics ; Computer science ; Chemistry ; Philosophy ; Linguistics Organoboron and organosilicon chemistry
Authors: Steven Rowan | Ronald W. Breault
Source:  (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

The Advanced Scale Up Reactor Experiment (ASURE) Facility: A Testbed for Demonstration of Advances in Biomass and Waste Co-Gasification Systems. 2024 AIChE Annual Meeting conference presentation detailing the planned Advanced Scale Up Reactor Experiment (ASURE) facility planned for construction on the NETL site in Morgantown, WV. Testbed ; Biomass gasification ; Biomass (ecology) ; Waste management ; Environmental science ; Scale (ratio) ; Process engineering ; Engineering ; Biofuel ; Aerospace engineering ; Physics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Jianxia Du | Mingjie Yuan | Haoze Du | Pan Huang | M. Zhu
Source:  (article, 2024-12-06)
Field: Materials Science — Polymers and Plastics

Digital modeling and grading evaluation of fabric softness for online trading Fabric softness is a very important tactile index to characterize clothing comfort. With the development of online trading, web-based, contactless clothing evaluation are demanded. But the softness evaluation of clothing is highly dependent on physical contact, and the evaluation method mainly relies on consumers' subjective evaluation. At present, there is a lack of classification standards and non-contact evaluation means for fabric softness. Through objective and subjective experiments, 18 objective mechanical property indexes and subjective softness evaluation results of 200 kinds of fabrics were obtained. Correlation analysis, variance analysis and linear regression were performed on the objective data and subjective results to gradually explore the relationship between the softness and mechanical indexes of different types of fabrics, and finally establish a digital evaluation model for fabric softness. Then the five grade classification standard of the softness are established. The sample of this study includes underwear, sports, suits, and the results have a wide range of adaptation. This digital evaluation model and grading standard make it possible to evaluate the subjective softness by objective means; meanwhile it provides a non-contact softness evaluation standard for online transactions, which facilitates the communication between merchants and users, reduces the differences caused by subjective evaluation, and is of great significance for the evaluation of clothing comfort for online transactions. Computer science ; Grading (engineering) ; Engineering ; Civil engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: José L. Romero‐Béjar | Francisco Javier Esquivel | José Antonio Esquivel Guerrero
Source: Mathematics (article, 2024-12-29)
Field: Chemistry — Analytical Chemistry

Variables Selection from the Patterns of the Features Applied to Spectroscopic Data—An Application Case Spectroscopic data allows for the obtaining of relevant information about the composition of samples and has been used for research in scientific disciplines such as chemistry, geology, archaeology, Mars research, pharmacy, and medicine, as well as important industrial use. In archaeology, it allows the characterization and classification of artifacts and ecofacts, the analysis of patterns, the characterization and study of the exchange of materials, etc. Spectrometers provide a large amount of data, the so-called “big data” type, which requires the use of multivariate statistical techniques, mainly principal component analysis, cluster analysis, and discriminant analysis. This work is focused on reducing the dimensionality of the data by selecting a small subset of variables to characterize the samples and presents a mathematical methodology for the selection of the most efficient variables. The objective is to identify a subset of variables based on spectral features that allow characterization of the samples under study with the least possible errors when performing quantitative analyses or discriminations between different samples. The subset is not predetermined and, in each case, is obtained for each set of samples based on the most important features of the samples under study, which allows for a good fit to the data. The reduction of the number of variables to an important performance based on the previously chosen difference between features, with a great fit to the raw data. Thus, instead of 2151 variables, a minimum optimal subset of 32 valleys and 31 peaks is obtained for a minimum difference between peaks or between valleys of 20 nm. This methodology has been applied to a sample of minerals and rocks extracted from the ECOSTRESS 1.0 spectral library. Selection (genetic algorithm) ; Feature selection ; Computer science ; Data science ; Data mining ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: С. С. Виноградова
Source: IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENIY KHIMIYA KHIMICHESKAYA TEKHNOLOGIYA (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

PREDICTION OF ELECTROCHEMICAL PROCESSES OF LOCAL DISSOLUTION OF STAINLESS STEELS. PART 1. SPECTRAL –FREQUENCY ANALYSIS OF POTENTIAL FLUCTUATIONS The propagation of localized corrosion of stainless steels was investigated by spectral frequency analysis (HPA) of potential fluctuations in the modes of stationary and cyclic galvanostatic polarization. Corrosion-resistant austenitic and austenitic-ferritic chromium-nickel steels were studied: 12X18N10T, 10X17N13M2T, 08X22N6T, resistant to pitting corrosion, in an aqueous solution of 0.1 M NaCl with a cyanide-containing complexing agent, a corrosion stimulator - 0.01 g/l (K3[Fe(CN)6]). The studies were carried out in the range of low densities (0.5–10 µA /cm2) due to the small values of the limiting diffusion current density in oxygen. To find the time of the origin of macropittings, it is proposed to use the NAV of chronopotentiograms with their subsequent statistical processing (Fourier transform). It has been established that fluctuations in the boundary polarization regime are characteristic of the process of the origin, development and passivation of macropittings. In the mode of stationary galvanostatic polarization, frequency characteristics were studied in the range of 1.0 ± 0.5 µA/cm2. When analyzing spectral density graphs, it was found that the dominant frequency characterizes the time of formation of the macropitting, and the sections of the chronopotentiogram should be divided into sections with a duration of no more than 50-100 s. In non-stationary conditions of galvanostatic polarization in the range of 1.0-10 µA/cm2 in a cyclic mode, it was found that the processes of nucleation and passivation of macropittings can be traced with a changing value of the polarizing current. Based on the frequency range of chronopotentiograms, it was found that the average macropitting lifetime is 250-300 s (frequency range 0.033 - 0.004 Hz). The dominant frequencies are determined to identify the characteristic dependencies of the envelopes along the upper (fhg) and lower (fhg) boundaries of the potential change, based on them, an algorithm is proposed for selecting the values of the parameters of the prediction mode of the formation of macropitting processes. Dissolution ; Electrochemistry ; Spectral analysis ; Materials science ; Metallurgy ; Chemistry ; Physics ; Electrode ; Physical chemistry ; Spectroscopy Material Properties and Failure Mechanisms ; Thermography and Photoacoustic Techniques ; Engineering Diagnostics and Reliability
Authors: Madeleine Darbyshire | Elizabeth Sklar | Simon Parsons
Source: arXiv (Cornell University) (preprint, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds, or the precision application of fertilizer only to undernourished crops, rather than to the entire field. The approach promises to maximize yields while minimizing resource use and harm to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method that simultaneously determines leaf count (as an identifier of plant growth)and locates weeds within an image. In particular, our approach aims to improve the segmentation of smaller instances like the leaves and weeds by incorporating focal loss and boundary loss. Not only does this result in competitive performance, achieving a PQ+ of 81.89 on the standard training set, but we also demonstrate we can improve leaf-counting accuracy with our method. The code is available at https://github.com/madeleinedarbyshire/HierarchicalMask2Former. Panopticon ; Segmentation ; Boundary (topology) ; Computer science ; Artificial intelligence ; Sociology ; Mathematics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Hao Song | Xuelian Zhang | Jian Wang | Yanming Sun | Guo Ping Wang
Source: arXiv (Cornell University) (preprint, 2024-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bound state in the continuum and polarization-insensitive electric mirror in low-contrast metasurface High-contrast refractive indexes are pivotal in dielectric metasurfaces for inducing various exotic phenomena, such as the bound state in the continuum (BIC) and electric mirror (EM). However, the limitations of high-index materials are adverse to the practical applications, thus low-contrast metasurfaces with comparable performance are highly desired. Here we present a low-contrast dielectric metasurface comprising radially anisotropic cylinders, which are SiO2 cylinders doped with a small amount of WS2. The cylinder exhibits unidirectional forward superscattering originating from the overlapping of the electric and magnetic dipole resonances. When normal illumination by a near-infrared plane wave, the metasurface consisting of the superscattering constituents manifests a polarization-insensitive EM. Conversely, when subjected to an in-plane incoming wave, the metasurface generates a symmetry-protected BIC characterized by an ultrahigh Q factor and nearly negligible out-of-plane energy radiation. Our work highlights the doping approach as an efficient strategy for designing low-contrast functional metasurfaces and sheds new light on the potential applications in photonic integrated circuits and on-chip optical communication. Contrast (vision) ; Polarization (electrochemistry) ; Physics ; Optics ; Bound state ; Electric field ; Quantum mechanics ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Quantum optics and atomic interactions
Authors: Andrey Kurkin | А. Е. Хробостов | В. В. Андреев | Olga Andreeva
Source: Materials (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Assessing and Forecasting Fatigue Strength of Metals and Alloys under Cyclic Loads Within the scope of this research, patterns of changes in the fatigue life and limit of metals under cyclic stress were identified and the most informative parameters were determined as the basis for developing a method for the universal transformation of experimental data on the fatigue of metals and alloys for their subsequent generalization. Experimental data on metal fatigue, obtained by a large number of authors for a wide range of grades of steels and alloys, under the influence of various combinations of factors, were systematized. A generalized dependence of the recalculated parameters of fatigue life and limit was obtained, its characteristics were assessed, and a sensitivity analysis was performed, confirming the universal nature of the obtained dependence. A system of parameters has been proposed making it possible to consider and forecast high-cycle fatigue processes for a wide range of metals and alloys, under the conditions of various combinations of operating factors, from unified positions and a more general point of view. Fatigue limit ; Generalization ; Materials science ; Goodman relation ; Limit (mathematics) ; Range (aeronautics) ; Transformation (genetics) ; Structural engineering ; Sensitivity (control systems) ; Metallurgy ; Mathematics ; Engineering ; Mathematical analysis ; Stress concentration ; Composite material ; Fracture mechanics Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Engineering Diagnostics and Reliability
Authors: Jing Wu | Encheng Zhou | An Huang | Hongbin Zhang | Meichun Hu | Guangzhao Qin
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Deep-Potential Enabled Multiscale Simulation of Interfacial Thermal Transport in Boron Arsenide Heterostructures High thermal conductivity substrate plays a significant role for efficient heat dissipation of electronic devices, and it is urgent to optimize the interfacial thermal resistance. As a novel material with ultra-high thermal c... | Find, read and cite all the research you need on Tech Science Press Boron ; Heterojunction ; Materials science ; Thermal ; Gallium arsenide ; Thermal conductivity ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics ; Thermodynamics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications