jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Somnath Bharech | Yangyiwei Yang | Michael Selzer | Britta Nestler | Bai‐Xiang Xu
Source: Scientific Data (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

ML-extendable framework for multiphysics-multiscale simulation workflow and data management using Kadi4Mat As material modeling and simulation has become vital for modern materials science, research data with distinctive physical principles and extensive volume are generally required for full elucidation of the material behavior across all relevant scales. Effective workflow and data management, with corresponding metadata descriptions, helps leverage the full potential of data-driven analyses for computer-aided material design. In this work, we propose a research workflow and data management (RWDM) framework to manage complex workflows and resulting research (meta)data, while following FAIR principles. Multiphysics-multiscale simulations for additive manufacturing investigations are treated as showcase and implemented on Kadi4Mat - an open source research data infrastructure. The input and output data of the simulations, together with the associated setups and scripts realizing the simulation workflow, are curated in corresponding standardized Kadi4Mat records with extendibility for further research and data-driven analyses. These records are interlinked to indicate information flow and form an ontology-based knowledge graph. Automation scheme for performing high-throughput simulations and post-processing integrated with the proposed RWDM framework is also presented. Workflow ; Computer science ; Multiphysics ; Metadata ; Workflow management system ; Scripting language ; Leverage (statistics) ; Interoperability ; Data management ; Workflow technology ; Database ; Automation ; Data mining ; Software engineering ; Programming language Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Research Data Management Practices
Authors: Shuke Jia | Fanxu Kong | Bo Jin | Chenyang Jin | Zeli Que
Source: Applied Sciences (article, 2025-12-11)
Field: Chemistry — Organic Chemistry

Wood-YOLOv11: An Optimized YOLOv11-Based Model for Real-Time Pith Detection in Sawn Timber The precise localization of the pith within sawn timber cross-sections is essential for improving downstream processing accuracy in modern wood manufacturing. Existing industrial workflows still rely heavily on manual interpretation, which is labor-intensive, error-prone, and unsuitable for real-time quality control. However, automatic pith detection is challenging due to the small size of the pith, its visual similarity to knots and cracks, and the dominance of negative samples (boards without visible pith) in practical scenarios. To address these challenges, this study develops Wood-YOLOv11, a task-adapted YOLOv11-based pith detection model optimized for real-time and high-precision operation in wood processing environments. The proposed approach incorporates: (1) a dedicated sawn-timber cross-section dataset including multiple species, mixed imaging sources, and clearly annotated pith positions; (2) a negative-sample-aware training strategy that explicitly leverages pithless boards and weighted binary cross-entropy to mitigate extreme class imbalance; (3) a high-resolution (840 × 840) input configuration and optimized loss weighting to improve small-target localization; and (4) a comprehensive evaluation protocol including false-positive analysis on pithless boards and comparison with mainstream detectors. Validated on a comprehensive, custom-annotated sawn timber dataset, our model demonstrates excellent performance. It achieves a mean Average Precision (mAP@0.5) of 92.1%, a Precision of 95.18%, and a Recall of 87.72%, proving its ability to handle high-texture backgrounds and small target sizes. The proposed Wood-YOLOv11 model provides a robust, real-time, and efficient technical solution for the intelligent transformation of the wood processing industry. Pith ; Weighting ; Precision and recall ; Transformation (genetics) ; Workflow ; Similarity (geometry) Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Wood Treatment and Properties
Authors: Sajal Agarwal | Laxmi Kant Mishra | Sadhana Sachan | Dheeraj Ahuja | Ankur Gaur
Source: Research Square (preprint, 2025-11-11)
Field: Materials Science — Biomaterials

Impact of cellulose and nanocellulose obtained from Sugarcane Bagasse on mechanical properties and durability of concrete Nanocellulose ; Durability ; Bagasse ; Cellulose ; Filler (materials) ; Cementitious ; Fourier transform infrared spectroscopy ; Nanomaterials Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Polymer-Based Agricultural Enhancements
Authors: Nazar Prykhodko | О. І. Трубаєв
Source: Bulletin of the National Technical University «KhPI» Series Dynamics and Strength of Machines (review, 2025-09-03)
Field: Materials Science — Materials Chemistry

Modern Approaches to Mathematical Modeling of Pipeline System Lifetime Considering Corrosion-Erosion Wear: A Review This article presents a review of modern mathematical models used to predict the service life of pipeline systems, taking into account corrosion-erosion wear. The main approaches to modeling pipeline degradation processes are analyzed, including analytical and numerical methods based on both deterministic and statistical frameworks. Special attention is paid to the influence of hydrodynamic and chemical factors on wear rate and defect formation. The review covers models of localized (pitting) corrosion, uniform thinning, and erosion caused by fluid flow. Analytical models allow for rapid estimation of residual wall thickness or critical pressure but have limited accuracy under complex operating conditions. Numerical modeling, particularly finite element methods (FEM) and computational fluid dynamics (CFD), provides more accurate assessments of the stress–strain state and wear processes in pipelines. Probabilistic approaches, including those based on various distributions (Weibull, normal, log-normal) and Monte Carlo simulations, make it possible to incorporate variability in parameters such as defect depth, length, and growth rate, which is critical for failure risk assessment. The article also examines modern approaches to estimating time to failure and residual service life. Special emphasis is placed on modeling random fields of defects, parameterization of the Weibull distribution, and its adaptation to inspection data. Based on the review, promising research directions are outlined, including the integration of monitoring data with mathematical models, the development of digital twins for pipeline systems, and the application of hybrid stochastic approaches to improve predictive accuracy. Pipeline (software) ; Erosion ; Corrosion ; Erosion corrosion ; Forensic engineering ; Computer science ; Environmental science ; Engineering ; Petroleum engineering ; Geology ; Materials science ; Metallurgy ; Mechanical engineering ; Geomorphology Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Non-Destructive Testing Techniques
Authors: Tao Wen | Jing Tang | Alvin Chan | Bryan Hooi | Baolong Bi | Nanyun Peng | Yuansheng Liu | Yiwei Wang
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

How to Make Large Language Models Generate 100% Valid Molecules? Molecule generation is key to drug discovery and materials science, enabling the design of novel compounds with specific properties.Large language models (LLMs) can learn to perform a wide range of tasks from just a few examples.However, generating valid molecules using representations like SMILES is challenging for LLMs in few-shot settings.In this work, we explore how LLMs can generate 100% valid molecules.We evaluate whether LLMs can use SELFIES, a representation where every string corresponds to a valid molecule, for valid molecule generation but find that LLMs perform worse with SELFIES than with SMILES.We then examine LLMs' ability to correct invalid SMILES and find their capacity limited.Finally, we introduce SmiSelf, a cross-chemical language framework for invalid SMILES correction.SmiSelf converts invalid SMILES to SELFIES using grammatical rules, leveraging SELFIES' mechanisms to correct the invalid SMILES.Experiments show that SmiSelf ensures 100% validity while preserving molecular characteristics and maintaining or even enhancing performance on other metrics.SmiSelf helps expand LLMs' practical applications in biomedicine and is compatible with all SMILES-based generative models.Code is available at https: //github.com/wentao228/SmiSelf. Language model ; Natural language ; Feature (linguistics) ; Language identification ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Yupeng Wu | Kai Ma | Wu Zhou | Wenxi Zhang
Source: Sensors (article, 2025-11-13)
Field: Chemistry — Spectroscopy

Intensity Noise Suppression in Photonic Detector Systems for Spectroscopic Applications Spectral measurement technology has found extensive applications across a diverse range of fields, including chemical analysis, environmental monitoring, precise measurement, and laser frequency stabilization. However, the accuracy of spectral measurement results is often constrained by the power noise and frequency jitter inherent in the light source. In contrast to the traditional differential amplification method for acquiring spectral signals, our study introduces a novel approach. By employing a power correction quotient, we effectively suppress common-mode noise. Additionally, we introduce a novel composite differential method that, in theory, is capable of performing closed-loop processing on spectral signals to stabilize the laser frequency. This innovative method not only constructs a stable laser source but also yields high-quality spectral signals simultaneously. In an experiment involving iodine molecule absorption spectroscopy, the algorithm we propose demonstrated remarkable efficacy in mitigating distortions caused by modulated signals and significantly enhanced the signal-to-noise ratio. This algorithm is versatile and can be applied to the signal processing of any spectral signal sensor that employs dual-path light. Jitter ; Photonics ; Noise (video) ; Detector ; Laser ; SIGNAL (programming language) ; Signal processing ; Spectral density ; Differential (mechanical device) Spectroscopy and Laser Applications ; Quantum optics and atomic interactions ; Advanced Fiber Laser Technologies
Authors: Cláudia Cândida Silva | Paulo de Tarso Barbosa Sampaio | Anne Borges de Bastos | Lucas Gabriel Correa de Souza | Lucia Maria Pereira de Oliveira | Caio Vinicius Menezes Saldanha | Pedro Medrado Krainovic
Source: International Contemporary Management Review (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

CHEMOMETRIC STUDY OF X, RAY FLUORESCENCE DATA FOR ROSEWOOD BRANCH SAMPLES Through qualitative and quantitative analyses performed by WDXRF, it was noted that the distribution of elements in the samples was not uniform, with chemical species unique to each location, highlighting differences in the composition of the branches. Low concentrations of nutrients were observed in both locations, which may be explained by the influence of the soil type (terra firme), as it is a territory of low fertility and nutrient deprivation. The study of inorganic antioxidant elements revealed the absence of selenium and copper. Despite the low concentration percentages, the presence of the antioxidants Zn and Mn, important for the cosmetics industry, provides preliminary information for further studies, aiming to add value to this already highly valued raw material. According to the Shapiro, Wilk test, none of the variables in the data set follow a normal distribution. Tukey's HSD test, applied with a simplified comparison of each row with itself, showed the non, additivity of the variables. Levene's test indicated that none of the variables exhibited homogeneity of variance. PCA analysis suggested a clear separation between the groups in the reduced, dimensional space. Furthermore, the top 20 variables with the highest loadings for the first three principal components were identified, providing insight into the specific spectral characteristics driving this separation. These analyses, taken together, proved important for differentiating the inorganic composition of the branches of these two regions, highlighting the relevance of chemometric techniques in data interpretation. Fluorescence ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Optics ; Environmental chemistry Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis
Authors: S. Dhakal | Tim D. Nalder | Susan N. Marshall | Colin J. Barrow
Source: Marine Drugs (review, 2025-08-30)
Field: Chemistry — Spectroscopy

Analytical Approaches to the Rapid Characterisation of Marine Glycolipids in Bioproduct Discovery Glycolipids are structurally diverse amphiphilic molecules with potential as non-petrochemical-derived bioproducts, including surfactants, emulsifiers, and antioxidants. The different bioactivities associated with this range of glycolipid structures also present opportunities for dietary supplements, cosmetics, and pharmaceuticals. Marine glycolipids are underexplored due to challenges with purification and structural characterisation. Analytical approaches enabling efficient sample purification, isolation, and identification of target glycolipids are crucial to determining the bioactivity and functions of organisms such as shellfish and seaweed. This review summarises advances in analytical methods applicable to marine glycolipids, including extraction and enrichment methods tailored to specific subclasses. Thin-layer chromatography (TLC)-based rapid detection techniques developed for specific subclasses in complex biological samples are discussed, alongside structure identification methods based on liquid chromatography (LC)-electrospray ionisation (ESI)-tandem mass spectrometry (MS/MS). Hydrophilic interaction liquid chromatography (HILIC), reverse-phase liquid chromatography (RPLC), and supercritical fluid chromatography (SFC) coupled with MS detection are reviewed for their application to glycolipids. The application of two-dimensional liquid chromatography (2D-LC) and advanced MS-based approaches that facilitate both the rapid resolution and comprehensive characterisation of molecular species are also reviewed. Glycolipid ; Computational biology ; Drug discovery ; Chemistry ; Biology ; Biochemistry Analytical Chemistry and Chromatography ; Marine Toxins and Detection Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Cong Wang | Chen, Jiahong | Renjie Yang | Dong Li | Wang, Zhibin | Zongyu Yang | Zhijun Wang | Yixiong Wei | Liu, Zhaoyang | Hu, Chenshu | Jing Li
Source: ArXiv.org (preprint, 2025-02-13)
Field: Chemistry — Spectroscopy

Integrated Data Analysis of Plasma Electron Density Profile Tomography for HL-3 with Gaussian Process Regression An integrated data analysis model based on Gaussian Process Regression is proposed for plasma electron density profile tomography in the HL-3 tokamak. The model combines line-integral measurements from the far-infrared laser interferometer with point measurements obtained via the frequency-modulated continuous wave reflectometry. By employing Gaussian Process Regression, the model effectively incorporates point measurements into 2D profile reconstructions, while coordinate mapping integrates magnetic equilibrium information. The average relative error of the reconstructed profile obtained by the integrated data analysis model with normalized magnetic flux is as low as 3.60*10^(-4). Additionally, sensitivity tests were conducted on the grid resolution, the standard deviation of diagnostic data, and noise levels, providing a robust foundation for the real application to experimental data. Bayesian probability ; Gaussian process ; Plasma ; Electron density ; Process (computing) ; Tomography ; Electron tomography ; Gaussian ; Computer science ; Physics ; Statistical physics ; Artificial intelligence ; Optics ; Nuclear physics ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Analytical Chemistry and Sensors
Authors: Poorti Sharma | Moniza Nurez Khan | Manika Gupta
Source: International Journal of Aquatic Research and Environmental Studies (article, 2025-05-20)
Field: Materials Science — Biomaterials

Impact of manmade inputs on ocean alkalinity and effect on calcifying marine organisms Biological procedures in the surface waters of the Southern Ocean (SO) significantly influence global productivity and the storage of oceanic carbon dioxide (CO2). This study illustrates that biological calcification in the SO significantly influences the worldwide distribution of alkalinity (AK). The hallmark of SO calcification is manifested in observations as a reduction of potential AK in Subantarctic Phase and Intermediate Waters sections. Studies utilizing an ocean overall circulation modeling demonstrate that the calcification and consequent descent of biogenic carbonate in this area efficiently facilitates the movement of AK across the upper and lower units of the meridional overturning cycle. The calcification in the SO sequesters AK in the deep sea; a reduction in calcification allows greater AK to escape from the SO, resulting in elevated AK in the upper cell and low-latitude waterways. These processes affect carbon partitioning between the upper atmosphere and the ocean due to manmade inputs. Decreases in calcification in the SO augment the buffering capacity of surface waters worldwide, improving the ocean's capability to sequester atmospheric carbon. This investigation emphasizes the essential function of SO calcification in shaping worldwide AK patterns, indicating that alterations in this process have extensive repercussions on the air-sea exchange of CO2. Alkalinity ; Oceanography ; Environmental science ; Marine invertebrates ; Ecology ; Geology ; Biology ; Chemistry Calcium Carbonate Crystallization and Inhibition
Authors: Hao Zheng | Li Sun | Yue Wang | Yang Han | Shuwen Zhang
Source: Horticulturae (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Image-Based Detection of Chinese Bayberry (Myrica rubra) Maturity Using Cascaded Instance Segmentation and Multi-Feature Regression The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Segmentation ; Margin (machine learning) ; Modular design ; Maturity (psychological) ; Pattern recognition (psychology) ; Regression ; Channel (broadcasting) ; Support vector machine ; Artificial neural network Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Plant Pathogens and Fungal Diseases
Authors: Zhaoqi Xie | Siqing Fan | Junyu Xu | Haijing Xiao | Jiaxin Yang | Min Guo | Chunsong Cheng
Source: PeerJ (article, 2025-07-23)
Field: Chemistry — Organic Chemistry

Identification and classification of <i>Aquilaria</i> (Thymelaeaceae): inferences from a phylogenetic study based on matK sequences In the realm of <i>Aquilaria</i> classification and grading, a persistent market uncertainty persists, questioning whether the basis should be geographical distribution or biological origin. In this study, the effectiveness of matK molecular markers, particularly through eight stable polymorphic loci (<i>e.g.</i>, +249C for Chinese origin, +435G for <i>Aquilaria sinensis</i>), emerges as a decisive tool for differentiating <i>Aquilaria</i> species. The integration of matK and trnL-trnF not only validates this efficacy but also streamlines the systematic categorization of 34 agarwood products into four biogeographic pedigrees: Chinese (C1: <i>A. sinensis</i>; C2: <i>A. malaccensis</i>), Indonesian (<i>A. cumingiana</i>), and Indochinese (<i>A. rugosa</i>). Molecular clock analyses trace the genus's divergence to 6.78 million years ago (Ma) (<i>A. hirta</i>), with recent speciation of commercially pivotal species (<i>A. sinensis</i>: 0.9 Ma; <i>A. malaccensis</i>: 1.0 Ma). Notably, the redefined placement of <i>Gyrinops walla</i> (5.75 Ma) within <i>Aquilaria</i> challenges prior taxonomic assumptions, suggesting revised genus boundaries. The Median-Joining network further visualized these haplotypes, showing key evolutionary transitions, particularly from <i>A. crassna</i> to <i>A. rugosa</i> and <i>A. malaccensis</i>. These findings provide robust tools for species differentiation, insights into evolutionary history, and practical guidance for conservation and trade applications within the field of botany. Agarwood ; Biology ; Phylogenetic tree ; Evolutionary biology ; Genus ; Botany Wood and Agarwood Research ; Identification and Quantification in Food ; Forest Ecology and Biodiversity Studies