jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siddharth Baskar | Karan Vikyath Veeranna Rupashree | Daniel L. Pimentel-Alarcón
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Deep-Union Completion Large amounts of missing data are becoming increasingly ubiquitous in modern high-dimensional datasets. Unfortunately, classical completion methods like low-rank, high-rank, or deep matrix completion (LRMC/HRMC/DMC) are often unable to handle real data that does not fall under their respective models. Here we propose a novel completion strategy that generalizes all these models. The main idea is to find a Union of Subspaces (UoS) that can fit a non-linear embedding of the original data, and complete the data according to this latent UoS. This embedding is obtained through a novel pseudo-completion layer in a deep architecture, and the UoS structure is identified in closed-form through an intermediate clustering layer. Our design reduces the exponential memory requirements that are typically induced by uneven patterns of missing data. We give exact details of our architecture, model, loss functions, and training strategy. Our experiments on over 10 real datasets show that our method consistently outperforms the state-of-the-art accuracy by more than a staggering 40%. Completion (oil and gas wells) ; Geology Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials
Authors: Anderson Gomide Costa | Leonardo Soares Da Silva | João Célio L. de Carvalho | Túlio de Almeida Machado
Source: Engenharia Agrícola (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

ESTIMATION OF TOMATO FRUIT FIRMNESS USING DIGITAL IMAGING ABSTRACT omputer vision systems have proven to be a promising alternative for assessing fruit quality attributes in a non-invasive, instantaneous, and accurate manner. This study aimed to use colorimetric characteristics extracted from digital images to estimate tomato fruit firmness through multivariate modeling. Images of 80 tomato fruits at four ripening stages were acquired using two digital cameras, enabling the extraction of average intensity values for the red, green, blue, and near-infrared bands, followed by the calculation of colorimetric indices. Reference firmness values were measured using a digital fruit penetrometer. Colorimetric indices were employed to estimate fruit firmness using principal component regression. Principal component analysis enabled the dimensionality to be reduced to a single principal component (explanatory percentage of the data variance of 97.06%), which was used to generate firmness estimation equations. The application of the model to the validation dataset yielded an R2 = 0.937 and a mean standard error (SE) of 2.05 N, demonstrating that the protocol based on colorimetric characteristics extracted from digital images is suitable for estimating tomato firmness. Estimation ; Horticulture ; Computer science ; Artificial intelligence ; Mathematics ; Biology ; Economics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Weibing Dong | Hongxin Wang | Saeed Kamali | David R. Tyler | Yisong Guo | Li‐Fen Yan | Chantal G. Balesdent | J.L. Crossland | David A. Case | Yoshitaka Yoda | Jiyong Zhao | Stephen P. Cramer
Source: ChemRxiv (preprint, 2025-05-23)
Field: Chemistry — Inorganic Chemistry

Wavelength dependent photochemistry of an iron dinitrogen hydride complex via multiple spectroscopies - Competing ejection of axial ligands Nitrogenase (N2ase) is a critical enzyme which catalyzes the reaction of N2 --> NH3 in nature. Studies on the spectroscopy and photochemistry of trans-[FeII(DMeOPrPE)2(N2)H][BPh4](1) and its isotopomers provide a possible first step to evaluate the geometries and properties of the real N2ase-N2 structure(s). In this article, we have used FT-IR, FT-Raman, and synchrotron-based nuclear resonant vibrational spectroscopy (NRVS) to examine and assign the normal modes of these complexes. In addition, we have monitored their wavelength dependent photochemistry using mid-IR, near-IR, NRVS, and Mössbauer spectroscopies. Two distinct photolysis pathways are observed with mid-IR at (nominal) 4K - 1) the cleavage of Fe-N2 bond at UV or visible light photolyses, which presents a unipolar disappearance of the N2 peak at 2094 cm-1; 2) the ejection of trans hydrogen atom with UV irradiation, which has a pair of bipolar peaks with the disappearance of N2 at 2094 cm-1 and the appearance of a new species at 2056 cm-1 which is aligned with the N2 peak in a similar Fe(I) complex. To assist the detailed investigation, NRVS, near-IR and Mössbauer were performed – NRVS finds a low spin Fe(II) and no H detachment in a visible photolysis while the latter two studies reveal an additional low spin Fe(I) species in the UV photolysis product. In summary, the N2 and H dissociations are in competition with each other in UV or UV-inclusive photolyses of this dinitrogen hydride complex. This wavelength dependent photochemical work is the first one on a reaction active N2ase-N2 model complex, which for sure offers valuable information for future studies on real N2ase-N2 and its photolysis products. Hydride ; Photochemistry ; Wavelength ; Chemistry ; Materials science ; Hydrogen ; Organic chemistry ; Optoelectronics Metal-Catalyzed Oxygenation Mechanisms ; Free Radicals and Antioxidants ; Chemistry and Stereochemistry Studies
Authors: Takafumi Aizawa
Source: Technologies (article, 2025-01-28)
Field: Materials Science — Polymers and Plastics

A New Bundling and Packaging Method Using Nonwoven Polylactide Based on Polymer Shrinkage by Carbon Dioxide This study proposes the exposure of nonwoven fabrics to carbon dioxide for bundling and packaging purposes. The proposed process, which utilizes the shrinking property of the nonwoven fabric during carbon dioxide exposure, is demonstrated on a polylactic acid (PLA) nonwoven fabric produced by the melt-blown method. Evaluating the shrinkage induced by carbon dioxide in PLA nonwoven fabrics with varying degrees of crystallinity, it was found that increasing the crystallinity decreases both the speed and amount of shrinkage. This process is potentially applicable as a simple, inexpensive, and environmentally friendly approach for packaging food and drug products. Shrinkage ; Carbon dioxide ; Materials science ; Polymer ; Composite material ; Polymer science ; Carbon fibers ; Process engineering ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; Polymer Foaming and Composites
Authors: Ting Zhang | Ying Xu | Kai Cao | Xiude Chen | Qiaolian Liu | Weikuan Jia
Source: Horticulturae (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

An Accurate Optimized Contour Segmentation Model for Green Spherical Fruits Accurate fruit detection in complex orchard environments remains challenging due to variable lighting conditions and weather factors. This paper proposes an optimized contour segmentation model for green spherical fruits (apples and persimmons) based on the E2EC framework. The model employs DLA34 as the backbone network for feature extraction enhanced by a path aggregation balanced feature pyramid network (PAB FPN) with embedded attention mechanisms to refine feature representation. For contour segmentation, we introduce a Cycle MLP Aggregation Deformation (CMAD) module that incorporates cycleMLP to expand the receptive field and improve contour accuracy. Experimental results demonstrate the model’s effectiveness, achieving average precision (AP) and average recall (AR) of 75.5% and 80.4%, respectively, for green persimmons and 57.8% and 64.0% for green apples, outperforming previous segmentation methods. These advancements contribute to the development of more robust smart agriculture systems. Segmentation ; Artificial intelligence ; Botany ; Horticulture ; Computer vision ; Mathematics ; Pattern recognition (psychology) ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Leqing Lin | Haoyang Lin | Hong Guo | Jianfeng He | Lihao Wang | Ruobin Zhuang | Wenguo Zhu | Yongchun Zhong | Jianhui Yu | Huadan Zheng
Source: Sensors (article, 2025-11-12)
Field: Chemistry — Spectroscopy

A Compact 2.3 μm DFB-Laser CO Sensor Using MPC-LITES for Real-Time Monitoring of Cigarette Smoke A compact and high-sensitivity carbon monoxide (CO) detection system based on multi-pass cell enhanced light-induced thermoelastic spectroscopy (MPC-LITES) was developed for real-time monitoring. A 2.3 μm distributed feedback (DFB) diode laser targeting the CO absorption line at 4300.699 cm<sup>-1</sup> was employed, offering strong line intensity and minimal interference from H<sub>2</sub>O, CO<sub>2</sub>, NO<sub>2</sub>, and SO<sub>2</sub>. The optimal modulation depth of 0.76 cm<sup>-1</sup> produced the maximum second harmonic (2<i>f</i>) signal. Experimental results demonstrated excellent linearity (R<sup>2</sup> = 0.998) and a minimum detection limit of 230 ppb at 1 s, further reduced to 47 ppb at 367 s by Allan deviation analysis. Application tests were carried out for real-time monitoring of cigarette smoke in a 20 m<sup>2</sup> indoor environment. Under closed conditions, the CO concentration rapidly increased to approximately 165 ppm, while in ventilated conditions, it peaked at 45 ppm and decayed quickly due to air exchange. The results confirm that the proposed MPC-LITES sensor enables accurate, real-time detection of transient CO variations, demonstrating strong potential for indoor air quality evaluation, environmental safety, and public health protection. Detection limit ; Carbon monoxide ; Indoor air quality ; Diode ; Linearity ; Smoke ; Laser ; Cigarette smoke ; Absorption spectroscopy ; Sensitivity (control systems) Spectroscopy and Laser Applications ; Advanced Fiber Laser Technologies ; Advanced Fiber Optic Sensors
Authors: Xuanyi An | Xia Zhang | Yufei Cai | Cong Liu | Zongwei Zhang
Source: Thermal Science (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Study on measurement and prediction methods of nonlinear thermal conductivity of high-temperature resistant porous insulation High temperature insulation materials are critical components of thermal protection systems for hypersonic vehicles, gas turbines, and other advanced technologies. In these contexts, the assessment of thermal insulation performance through the measurement of thermal conductivity is essential. This study measures the effective thermal conductivity of high dimensional S blanket, aluminum silicate cotton needle felt, and nanoaerogel blanket insulation fibers using the heat flux meter method under two environmental conditions: dry and 60% relative humidity. The experiments covered hot surface temperatures ranging from 50-550?C, encompassing 90 distinct operational conditions. The results elucidate the variation patterns of both the effective and true thermal conductivity of these materials. The findings indicate that: fitting models for the effective thermal conductivity of the high dimensional S blanket, aluminum silicate cotton needle felt, and nanoaerogel blanket provided accurate predictions. Humidity significantly affected both the effective and true thermal conductivity at high temperatures for the high dimensional S blanket and aluminum silicate cotton needle felt, but had a relatively minor impact on the nanoaerogel blanket. Incorporating true thermal conductivity allowed for accurate predictions of material performance in 42 experimental conditions, with strong agreement between calculated values and experimental data. Materials science ; Thermal conductivity ; Nonlinear system ; Composite material ; Porosity ; Conductivity ; Thermal conductivity measurement ; Porous medium ; Physics Aerogels and thermal insulation
Authors: Omar Arias‐Gaguancela | Carmen G. Palii | Mehar Un Nissa | Marjorie Brand | Jeffrey A. Ranish
Source: PROTEOMICS (article, 2025-09-04)
Field: Chemistry — Spectroscopy

QuickProt: A Bioinformatics and Visualization Tool for DIA and PRM Mass Spectrometry‐Based Proteomics Datasets Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results, remains cumbersome due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming. Proteomics ; Stable isotope labeling by amino acids in cell culture ; Computer science ; Proteome ; Computational biology ; Quantitative proteomics ; Workflow ; Isobaric labeling ; Bioinformatics ; Biology ; Database ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Chromatin Dynamics
Authors: Md Asaduzzaman
Source: ChemRxiv (preprint, 2025-07-16)
Field: Chemistry — Physical and Theoretical Chemistry

The Tareq Index (TI): A Novel Graph-Based Descriptor for Inorganic Acids Topological indices play a significant role in chemical graph theory by providing quantitative measures of molecular structures. Most existing indices are focused on organic compounds, particularly hydrocarbons, with limited applicability to inorganic acids. In this work, we propose a novel graph-theoretical index, the Tareq Index (TI), specifically designed to reflect bonding patterns in inorganic acid molecules. This index incorporates bond multiplicity and molecular connectivity, capturing characteristics often overlooked by traditional indices like the Zagreb or Randić indices. A dataset of over 20 inorganic acids was analyzed, with the TI compared against pKa values and other topological descriptors. Preliminary trends indicate a potential correlation between TI values and acid strength. This contribution offers a new avenue for the structural interpretation of inorganic compounds and lays the foundation for further expansion into quantitative structure–activity/property relationships (QSAR/QSPR) involving inorganic systems. Index (typography) ; Graph ; Chemistry ; Mathematics ; Computer science ; Combinatorics ; World Wide Web History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Fatima Musbah Abbas | Ahlam Khalofa | Hanan Musbah Abbas | Zehbh Ali Al-Ahamd | Abubaker Elsheikh Abdelrahman
Source: Material and Mechanical Engineering Technology (article, 2025-12-30)
Field: Materials Science — Polymers and Plastics

Carbon Pellets Prepared from Self-Adhesive Properties of Pre-Carbonized Date Palm Leaves (Phoenix dactylifera L.): X-ray DiffractionAnalysis and Applications Carbon pellets (CPs) were prepared from the self-adhesive properties of date palm leaves (Phoenix dactylifera L.) and converted into pellets by applying (5 to 21) metric tons of pressure, before being carbonized at 1000 °C in a nitrogen environment using a multi-stipe heating profile. Characterizations of CPs produced were carried out in terms of Young's modulus (YM) and crystallite dimensions, specific surface area, and porosity. A microscopic cross-linking model based on crystallite dimensions (d002, Lc, and La) was also used to estimate YM, spring length (Sp) between two cubic granulars of the carbon microstructure structure, specific surface area, and porosity as a class of X-ray diffraction application, by assuming that the changes in YM for carbon pellets as a function of compression occur similarly as a function of heat treatment temperature. Results of the X-ray diffraction pattern show that the structure of the CPs is turbostatic, and its crystallite dimensions are improved by compressive pressure. The estimated YM from a microscopic cross-linking model was in good agreement with that measured by ultrasonic techniques, showing that the behavior of the YM is related to the crystallite dimensions. The results also found that the degree of stiffness as a function of compression pressure is equal to that of heat treatment temperature, proving that the changes in YM as a function of compression pressure occur similarly as a function of heat treatment temperature. The results also show that the data of specific surface area and porosity, estimated from microscopic cross-linking, are in good agreement with the measured data. The Sp is reduced with increasing compression pressure, and above 12 metric tons of pressure the CP observed the stress broadening behavior. These results can conclude that the crystal dimensions can play an essential role in the phenotypic and description of CP pellets, indicating the applicability of the microscopic cross-linking model to estimate the physical properties of carbon materials. Crystallite ; Pellets ; Carbon fibers ; Carbonization ; Compression (physics) ; Porosity ; Microstructure ; Diffraction Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Thermochemical Biomass Conversion Processes
Authors: Jian Zhang | Yunhao Cui | Haibin Yang | Liuting Wang | Jun Qian
Source: Forests (article, 2025-01-02)
Field: Materials Science — Polymers and Plastics

Roughness Evaluation of Bamboo Surfaces Created by Abrasive Belt Sanding Mechanical belt sanding is critical in the manufacturing of bamboo and bamboo products, where surface roughness is commonly used to quantitatively evaluate the surface quality. In this study, flattened bamboo workpieces were sanded using P80 and P120 abrasive belts to create different surfaces. The linear roughness parameters, namely Rz, Ra, Rq, Rsk, Rku, and Rmr(c), were measured using both a stylus profilometer and a 3D profilometer. Statistical t-tests were conducted to determine the significance of differences between the two methods. Additionally, roughness profiles were analyzed in the frequency domain using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods. A Random Forest (RF) regression model was also developed to predict the roughness values and figure out the dominant factors between granularity and measurement methods. The results revealed that both the stylus and 3D profilometers provided reliable comparisons of Rz, Ra, Rq, and Rmr (50%) for different grit sizes. However, resolution differences between the two methods were found to be critical for accurately interpreting roughness values. Variations in Rsk and Rku highlighted differences in sensitivity and detection range, particularly at finer scales, between the two methods. The stylus profilometer, with its higher spatial resolution and finer sampling density, demonstrated greater sensitivity to finer surface details. This was consistent with the FFT and PSD analyses, which showed that the stylus profilometer captured higher-frequency surface components more effectively. Furthermore, the RF model indicated that the choice of measurement method had negligible impact on the evaluation of the selected roughness parameters, suggesting that standardizing measurement techniques may not be essential for consistent roughness assessments of sanded bamboo surfaces. Stylus ; Profilometer ; Surface finish ; Surface roughness ; Materials science ; Abrasive ; Optics ; Acoustics ; Composite material ; Physics Textile materials and evaluations ; Surface Roughness and Optical Measurements ; Industrial Vision Systems and Defect Detection
Authors: You‐Lin Qiao | Rui Li | Bo Chang | Wenbin Zhou | Xiaoyu Tian | Lan Chen | J.J. WANG
Source: Applied Ecology and Environmental Research (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

RESEARCH ON HYPERSPECTRAL MONITORING OF FOXTAIL MILLET BIOMASS In the context of the need for efficient agricultural monitoring under climate change, this study aimed to achieve rapid and non-destructive monitoring of foxtail millet aboveground biomass (AGB), AGB data and canopy spectral data were measured under varying sowing dates.Preprocessed using Standard Normal Variate (SNV), Multiple Scattering Correction (MSC), and First Derivative (1ST) methods.Based on this, foxtail millet AGB monitoring models were constructed using the full spectrum, characteristic wavelengths, and optimized spectral indices, and their accuracy was evaluated.The results showed that all three preprocessing methods improved the correlation between spectral reflectance and foxtail millet AGB.The best spectral index was 626, 697), which exhibited a correlation coefficient of 0.863 with foxtail millet AGB.Among the constructed models, the 1ST-PLS model based on the full spectrum exhibited higher accuracy (R 2 =0.834;RMSE=1.443t/hm 2 ; RPD=2.507).This study confirms the feasibility of spectral monitoring for foxtail millet AGB and provides a reference for rapid, non-destructive monitoring of foxtail millet AGB, and provide a robust framework for precision agriculture, enabling efficient foxtail millet management and yield optimization. Foxtail ; Hyperspectral imaging ; Biomass (ecology) ; Environmental science ; Agronomy ; Remote sensing ; Geography ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Xinyan Wang | Yong Ding | Rongfeng Zou | Hang Zheng
Source: ChemRxiv (preprint, 2025-07-15)
Field: Chemistry — Organic Chemistry

Terminal-flip Monte Carlo: An Effective Tool for Accelerating the Convergence of Complex Alchemical Free Energy Calculations Free energy perturbation (FEP) methods have become indispensable for predicting relative binding free energies (RBFE) in lead optimization, yet they often suffer from slow convergence due to high-energy barriers in ligand conformational sampling. In this work, we introduce and validate a terminal-flip Monte Carlo move specifically designed to accelerate conformational sampling in alchemical simulations. We integrated terminal-flip Monte Carlo into alchemical RBFE calculations of protein–ligand complexes and benchmarked it on MCL1, JNK1, and Factor Xa systems. Terminal-flip Monte Carlo yields a 2-5× faster convergence of key dihedral distributions and improves ∆∆G estimates by up to 1 kcal/mol compared with conventional MD-based FEP. Our method removes sampling biases in scaffold-hopping transformations and is straightforward to implement in existing FEP workflows. Monte Carlo method ; Convergence (economics) ; Terminal (telecommunication) ; Flip ; Energy (signal processing) ; Computer science ; Statistical physics ; Alchemy ; Physics ; Mathematics ; Telecommunications ; Statistics ; Economics Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Various Chemistry Research Topics