jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heshalini Rajagopal | N. Christian | Devika Sethu | Mohd Azwan bin Ramlan | Hanis Farhah Jamahori | Mardhiah Awalludin | Norul Ashikin Norzain | Renuka Devi Rajagopal | Narayanan Ganesh
Source: TELKOMNIKA (Telecommunication Computing Electronics and Control) (article, 2025-06-01)
Field: Chemistry — Organic Chemistry

Deep learning approaches for accurate wood species recognition Wood species identification is a crucial task in various industries, including forestry, woodworking, and conservation. Traditional methods rely on manual expertise, which can be time-consuming and error prone. Hence, an automatic wood species recognition system is developed in this study using deep learning (DL) models. In this study, three deep convolutional neural network (CNN) architectures, SqueezeNet, GoogLeNet, and ResNet-50 was tailored for wood species classification. The accuracy of the DL models was evaluated in recognizing fifty different wood species. Additionally, the wood species images were altered using JPEG Compression, Gaussian Blur, Salt and Pepper, and Speckle noises to assess the models' performance in identifying the wood species from the distorted images. Results show that the ResNET-50 based wood recognition system is the most accurate model to recognise the wood species. The implications of this research extend to forestry management, quality control in woodworking industries, and the preservation of endangered wood species in conservation efforts. Artificial intelligence ; Computer science Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Remote Sensing and LiDAR Applications
Authors: Anirban Ghosh | Chandan Adhikary
Source: Amitrakshar International Journal of Interdisciplinary and Transdisciplinary Research (article, 2025-04-30)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical Foundations for Understanding Students’ Misconceptions in Chemistry Amitrakshar International Journal of Interdisciplinary and Transdisciplinary Research (AIJITR) ISSN: 3049-0278 (Online) [(A Social Science, Science, Literature and Indian Knowledge Systems Perspective) DOI (Crossref) Prefix: 10.63431] [Bi-Monthly, International Peer Reviewed & Refereed Journal, Open Access Journal] Mathematics education ; Chemistry ; Epistemology ; Engineering ethics ; Psychology ; Engineering ; Philosophy Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: Wei Tang | Tengfei Fu | Mingming Guo | Xixian Ji | W. Liu | Renhui Qiu | Demei Yu
Source: Materials (article, 2025-12-23)
Field: Materials Science — Biomaterials

A Mechanistic Investigation on Cation-Modified Cellulose Nanofibrils–Reinforced Cement Composite Cellulose nanofibril (CNF), as a renewable biomass material, has the characteristics of low density, high strength, and high hydrophilicity. It can also overcome shortcomings of traditional inorganic nano materials, such as difficult dispersion, high cost, and high health risks. In this work, CNF was modified with a cationic surfactant to further enhance the compatibility with hydrating cement. The effects on cement paste were assessed via compressive and flexural strength, heat of hydration, and restrained ring cracking. The reinforcing mechanisms were analyzed by microhardness test, XRD, and BSE-SEM/EDS. Results showed that cation-modified CNF improved mechanical performance, with an optimal dosage of 0.15 wt.% (by binder). Restrained ring test showed that cation-modified CNF-cement composite delayed crack initiation. An isothermal calorimetry test revealed that cation-modified CNF can increase hydration rate in early age. Microstructural analysis confirmed promotion of denser hydration products. A comprehensive consideration of experimental results indicates internal curing and "short-circuit diffusion" are likely the enhancing mechanism. Composite number ; Curing (chemistry) ; Cellulose ; Compatibility (geochemistry) ; Flexural strength ; Pulmonary surfactant ; Calorimetry Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Concrete and Cement Materials Research
Authors: Shuran Yang | Qianrui Huang | Yu Lv | Xiaohua Zhou | Mingxing Hou | Y. W. Bao
Source: Research Square (preprint, 2025-09-09)
Field: Chemistry — Analytical Chemistry

Geochemical Differences, Source apportionment, and Origin Discrimination of Mineral Elements in Soil and Coffee Beans — A Case Study of Puer and Baoshan, the Largest Coffee Producing Regions in China Mineral ; Soil water ; Soil test ; Principal component analysis ; Bioavailability ; Soil classification ; Soil fertility ; Soil quality Heavy Metals in Plants ; Heavy metals in environment ; Geochemistry and Geologic Mapping
Authors: R. Shivashankar | Shivaraj R. Dharennavar | A Meenakshi | Balachandra Paramashwaraiah | Geetanjali S. Guggari | Harish Kumar | T. P. Jeevan
Source: Tribology in Industry (article, 2025-06-01)
Field: Materials Science — Polymers and Plastics

Tribological Advancements in Glass Fabric Epoxy: Effects of Halloysite Nanotubes Additives In this study, the effects of halloysite nanotubes (Hnts) on the dry sliding wear behavior and coefficient friction (CoF) of E-glass fabric-reinforced epoxy (G/E) composites will be examined. It aims to identify the ideal Hnts weight percentage for improving the wear performance of G/E composites for demanding applications. In the study, G/E composites with varying Hnts weight percentages (2, 4, and 6) were produced and their tribological characteristics were assessed. Under various load and speed conditions (25 N, 3 m/s to 75 N, 5 m/s), the specific wear rate (SWR) and CoF respectively were measured. To identify wear mechanisms, worn surface micrographs were examined by scanning electron microscopy. In line with the study, adding Hnts to G/E composites greatly increases their resistance to wear and lowers their CoF. The composite with 6 wt % Hnts had the lowest SWR (27.45 × 10-6 mm³/N m), which was 26.4 % lower than the unfilled G/E composite. With CoF values of 0.31 at 25 N, 3 m/s and 0.48 at 75 N, 5 m/s, the 6 wt % Hnts composite had the lowest values. Wear mechanisms for unfilled and Hnts-filled composites are different; unfilled G/E exhibits significant matrix damage, whereas Hnts-filled composites exhibit enhanced transfer film formation, fiber thinning, and matrix protection. This study is unique in that it investigates the use of Hnts as filler in G/E composites. By highlighting the promising properties of Hnts for enhancing the friction performance and wear resistance of epoxy-based composites reinforced with E-glass fabric, it offers fresh perspectives on the possible uses of Hnts in high-performance composite materials. Halloysite ; Epoxy ; Tribology ; Materials science ; Composite material Polymer Nanocomposites and Properties ; Clay minerals and soil interactions
Authors: Rui Li | Chunhui Xie | Lu Liu | Yang You | Qin Chen | Haibo Xie | Yunqi Li
Source: Macromolecular Rapid Communications (article, 2025-04-28)
Field: Materials Science — Polymers and Plastics

Enclose Biobased Content into Polyurethane Elastomers: A Summary of Synthetic Routes and an Inverse Prediction of their Percentages Biobased polyurethane elastomers (BPUEs) got vigorous exploration in recent decade, clarifying how biobased content affect their mechanical and thermal properties becomes critical. Here, a comprehensive BPUE dataset with 506 splines associated with a brief summary for the transformation of biobased content into polyurethane are presented. Distributions for typical mechanical and thermal properties including Young's modulus, tensile strength, elongation at break, and glass transition temperature (T<sub>g</sub>) are clarified. A linear relationship T<sub>g</sub> <sup>DMA</sup> = 0.98*T<sub>g</sub> + 19.43K is found for T<sub>g</sub>'s measured by differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Then, a binary classification model with an accuracy of 0.80 to distinguish PUEs with and without biobased content, and a regression model with the coefficient of determination of 0.89 to predict the mass percentage of biobased content (BBC%) are constructed. Based on these predictive models, important correlations are observed, including the strong dependence of Tg and tanδ on BBC%, and the weak preference to use longer spline in tensile test, lower heating rates in DSC and DMA measurements for BPUEs with higher BBC%. These inverse predictive models provide a cost-effective way to quantify the biobased content in PUE products, without prior knowledge of their exact composition and formulas. Differential scanning calorimetry ; Glass transition ; Materials science ; Ultimate tensile strength ; Polyurethane ; Elastomer ; Dynamic mechanical analysis ; Composite material ; Hexamethylene diisocyanate ; Inverse ; Polymer science ; Mathematics ; Polymer ; Thermodynamics ; Physics Polymer composites and self-healing ; Lignin and Wood Chemistry ; biodegradable polymer synthesis and properties
Authors: Jue Wang | Xinyi Xu | Changxin Ye | Wei Huangfu
Source: Algorithms (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Point-Wise Full-Field Physics Neural Mapping Framework via Boundary Geometry Constrained for Large Thermoplastic Deformation Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Boundary (topology) ; Point cloud ; Consistency (knowledge bases) ; Deformation (meteorology) ; Point (geometry) ; Artificial neural network ; Boundary representation ; Ground truth Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced machining processes and optimization
Authors: Simon Spelthann | Lea Koetters | Rajesh Komban | Christoph Gimmler | M. Steinke
Source: Nanoscale Horizons (article, 2025-09-15)
Field: Chemistry — Physical and Theoretical Chemistry

Unambiguous calibration of power dependence in ratiometric luminescent nanothermometry through multiple intensity ratios and symbolic regression Ratiometric luminescence nanothermometry carries the potential to measure temperature in situations for which established methods are unsuitable. The precision of nanothermometry depends on the excitation power, so calibration and monitoring of the optical power is mandatory-a requirement that complicates optical setups and limits nanothermometry in scenarios where precise power control or measurement is impractical or unfeasible. Here, we use Er<sup>3+</sup>-activated nanothermometers and, besides the well-known 525/545 nm ratio, define a second luminescence intensity ratio involving the emission at 660 nm to achieve a power-calibration-free nanothermometry. The intensity of this emission is strongly correlated with the power and is available anyways when using standard spectroscopic instrumentation. We apply symbolic regression to find an unambiguous mathematical expression that describes the experimental data. From this mathematical expression, we determine the mean temperature deviation resulting from the fitting error to be 0.16 K and a maximum temperature precision as small as 6 mK (0.22 K on average). In summary, our approach makes excitation power measurements in ratiometric luminescent nanothermometry superfluous. Luminescence ; Calibration ; Intensity (physics) ; Excitation ; Power (physics) ; Measure (data warehouse) ; Temperature measurement ; Optical power ; Radiant intensity thermodynamics and calorimetric analyses ; Advanced Chemical Sensor Technologies
Authors: Norman Seeba | L. Tani | Hardi Vanaveski | Joosep Pata | Torben Lange
Source:  (article, 2025-01-09)
Field: Materials Science — Surfaces, Coatings and Films

End-to-end tau reconstruction and identification using transformers Tau leptons play a key role in studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. The precise reconstruction and identification of hadronically decaying tau leptons is essential for present and upcoming high energy physics experiments. Driven by the advancements in jet tagging, we demonstrate that tau lepton reconstruction can be broken down into tau identification, kinematic reconstruction, and decay mode classification using a multi-task machine learning approach. Based on a new publicly available electron-positron collision dataset with full detector simulation and reconstruction, we demonstrate that standard jet tagging architectures can effectively perform these tasks. Our models achieve momentum resolutions of 2–3%, while the accuracy for reconstructing individual decay modes ranges from 80–95%. Computer science ; Transformer ; End-to-end principle ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage Electron and X-Ray Spectroscopy Techniques
Authors: Yukun Zhang | Xueqing Zhou
Source: ArXiv.org (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers We propose a novel framework, Continuous_Time Attention, which infuses partial differential equations (PDEs) into the Transformer's attention mechanism to address the challenges of extremely long input sequences. Instead of relying solely on a static attention matrix, we allow attention weights to evolve over a pseudo_time dimension via diffusion, wave, or reaction_diffusion dynamics. This mechanism systematically smooths local noise, enhances long_range dependencies, and stabilizes gradient flow. Theoretically, our analysis shows that PDE_based attention leads to better optimization landscapes and polynomial rather than exponential decay of distant interactions. Empirically, we benchmark our method on diverse experiments_demonstrating consistent gains over both standard and specialized long sequence Transformer variants. Our findings highlight the potential of PDE_based formulations to enrich attention mechanisms with continuous_time dynamics and global coherence. Transformer ; Dimension (graph theory) ; Mechanism (biology) ; Benchmark (surveying) ; Exponential function ; Control theory (sociology) Machine Learning in Materials Science
Authors: Fabio Andrés Ávila | John Y. Park | Leanna Feder | Damon P. Little
Source: New Phytologist (article, 2025-08-11)
Field: Chemistry — Analytical Chemistry

<i>Herbariograph</i>: a deep‐learning tool to classify specimen images Herbarium specimens are the physical evidence of plant diversity world-wide and a rich source of research data. With c. 402 M items, distributed in 3.9 k registered herbaria, in 182 countries, it is estimated that c. 12.8% have associated digital images. Despite this low proportion, information in these images facilitates numerous avenues of research aimed at understanding the hidden complexity and variation of plants. The first step in harvesting this information is to sort images by the types of items represented within them. To address this challenge, we present Herbariograph, a new open image dataset and deep-learning model designed to automatically recognize all the image types commonly stored in collection databases. The dataset consists of 17 image categories with 12 288 images per category gathered from 43 institutions. A Convolutional Neural Network was trained on the Herbariograph dataset. The trained model produces a test macro F<sub>1</sub> score of 0.9611. Herbariograph will help to automate specimen image processing to make targeted dataset creation faster, better, and more accessible. Convolutional neural network ; Herbarium ; Computer science ; sort ; Artificial intelligence ; Deep learning ; Image (mathematics) ; Digital image ; Pattern recognition (psychology) ; Sorting ; Artificial neural network ; Image processing ; Machine learning ; Information retrieval Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: 嘉燕 杨
Source: Hans Journal of Food and Nutrition Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Research on the Differences in Aroma Components and the Construction of Flavor Wheel for Qinan Agarwood Tea Based on Dynamic Sensory Evaluation 为了了解奇楠沉香叶制茶的潜力，本研究以奇楠沉香嫩叶为原料，采用不同制茶工艺(绿茶、黄茶、红茶、白茶)制备沉香茶，通过静态与动态感官评价系统分析其香气成分差异及感官特性。静态感官评价结果表明，绿茶综合评分最高(91.2分)，茶汤清澈、香气清新、滋味甘甜爽口；黄茶次之(90.1分)，以醇厚口感和高爽香气为特征。动态感官评价显示，红茶涩感显著(第一泡得分为7~8)，白茶青涩明显，绿茶与黄茶则呈现顺滑柔和的口感。研究首次构建了沉香茶风味轮，将感官属性分为滋味、汤色、香气等五大类共65个描述词，实现了感官属性的标准化。结果表明，奇楠沉香叶具备开发为高品质茶饮的潜力，为沉香资源的综合利用及茶产品创新提供了理论依据。To explore the potential of Qi-Nan agarwood leaves for tea, the tender leaves of Qi-Nan agarwood were utilized as raw material to prepare agarwood tea through various processing techniques, including green tea, yellow tea, black tea, and white tea. The differences in aroma components and sensory characteristics were analyzed using both static and dynamic sensory evaluation systems. The static sensory evaluation results indicated that green tea received the highest comprehensive score of 91.2 points, characterized by a clear tea liquor, fresh aroma, and a sweet, refreshing taste. Yellow tea closely followed with a score of 90.1 points, distinguished by its mellow taste and brisk aroma. Dynamic sensory evaluation revealed that black tea exhibited significant astringency, with an astringency score of 7~8 in the first infusion, while white tea was notably grassy and astringent. In contrast, green tea and yellow tea presented a smooth and mild taste profile. For the first time, this study constructed a flavor wheel for agarwood tea, categorizing sensory attributes into five major groups with a total of 65 descriptors, thereby standardizing the sensory attributes. The results indicate that the leaves of Qi-Nan agarwood have the potential to be developed into high-quality tea beverages, providing a theoretical basis for the comprehensive utilization of agarwood resources and the innovation of tea products. Agarwood ; Aroma ; Flavor ; Sensory system ; Food science ; Mathematics ; Biology ; Medicine Wood and Agarwood Research ; Fermentation and Sensory Analysis ; Plant biochemistry and biosynthesis
Authors: Marc Allain | Selin Aslan | Wim Coene | Sjoerd Dirksen | J. Dong | Julien Flamant | Mark Iwen | Felix Krahmer | Tristan van Leeuwen | Oleh Melnyk | Andreas Menzel | Allard P. Mosk | Viktor Nikitin | Gerlind Plonka | Palina Salanevich | Matthias Wellershoff
Source: ArXiv.org (preprint, 2025-05-21)
Field: Materials Science — Materials Chemistry

Phasebook: A Survey of Selected Open Problems in Phase Retrieval Phase retrieval is an inverse problem that, on one hand, is crucial in many applications across imaging and physics, and, on the other hand, leads to deep research questions in theoretical signal processing and applied harmonic analysis. This survey paper is an outcome of the recent workshop Phase Retrieval in Mathematics and Applications (PRiMA) (held on August 5--9 2024 at the Lorentz Center in Leiden, The Netherlands) that brought together experts working on theoretical and practical aspects of the phase retrieval problem with the purpose to formulate and explore essential open problems in the field. Phase retrieval ; Phase (matter) ; Inverse problem ; Signal processing ; Harmonic ; Inverse X-ray Diffraction in Crystallography
Authors: Shenao Chen | Lixia Li | Xianghao Chen | Qingyu Zeng | Xiaoning Yu | Xiaoqi Yue | Shu‐Ping Wan
Source: Journal of Food Process Engineering (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

Walnut Quality Classification Based on X‐Ray and Multi‐Domain Feature Fusion ABSTRACT During the processing and storage of walnuts, defects such as shriveled and moldy nuts can significantly diminish product quality and impede processing efficiency. An intelligent walnut quality classification method based on X‐ray imaging and multi‐domain feature fusion is proposed to address this issue. After extracting spatial, Fourier, and wavelet features from X‐ray images of normal, shriveled, and moldy walnuts, single and fused feature sets were used to build and evaluate Extreme Learning Machine (ELM), One‐vs‐All Logistic Regression (OVALR), and K‐Nearest Neighbors (KNN) models. The optimal feature‐model combination was then determined based on classification metrics. Subsequently, three dimensionality reduction methods were applied to the optimal fused features, and classification models were reconstructed with the reduced datasets. SHAP analysis was used to interpret feature contributions and identify the most discriminative features. Finally, the Arithmetic Optimization Algorithm (AOA), White Shark Optimizer (WSO), and Cuckoo Search Algorithm (CSA) were used to fine‐tune model hyperparameters. Experimental results demonstrated that the VISSA‐CSA‐OVALR model achieved the best performance, with accuracy, precision, and specificity on the test set reaching 94.72%, 94.79%, and 97.36%, respectively. This study provides a theoretical foundation for developing high‐precision, non‐destructive, and online quality sorting equipment for walnuts and other nuts. Pattern recognition (psychology) ; Discriminative model ; Feature (linguistics) ; Cuckoo search ; Sorting ; Extreme learning machine ; Set (abstract data type) ; Curse of dimensionality Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Nuts composition and effects