jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rashidah Ruslan | Siti Khairunniza Bejo | Mahirah Jahari | Ibni Hajar Rukunuddin
Source: Advances in Agricultural and Food Research Journal (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Characterisation of Weedy Rice Seeds using Principal Component Analysis The weedy rice contamination in certified rice seed samples greatly impacts the Malaysian rice seed industry. The existing manual process to identify the weedy rice seed is only based on the physical appearance of the seed. The physical characteristics such as morphology, colour and texture image of the seeds were captured and analysed using image processing and the application of machine vision to understand the physical characteristics of the weedy rice seed. The objective of this study is to understand the physical characteristics of the weedy rice seed using Principal Component Analysis (PCA) transformation. A total of 7350 images of cultivated rice seeds from five major varieties and 895 images of weedy rice seeds were acquired using machine vision setup, and 67 features from the three major parameters (morphology, colour, and texture) were extracted. The test of equality of means based on Wilks' Lambda was performed to assess significant differences among the group parameters. PCA transformed data into principal components. The relationships between weedy rice seed and cultivated rice seed samples were examined through the score and loading plots of the PCA analysis. This newly transformed data visualises the experimental data's underlying structure and helps identify the parameters distinguishing between weedy rice and cultivated rice seed. The results on the PCA score plot have shown overlapping areas between the cultivated and weedy rice seeds, indicating high similarities between the seed samples. Weedy rice ; Principal component analysis ; Upland rice ; Seed testing ; Image processing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Iryna Trembus | Anna Hondovska
Source: Herald of Khmelnytskyi National University Technical sciences (article, 2025-03-27)
Field: Materials Science — Biomaterials

USE OF MODIFIED MINERAL SORBENT FOR CELLULOSE MEMBRANE OPERATION У статті розглянуто перспективи використання модифікованого бентоніту як сорбційного матеріалу у складі целюлозних мембран для очищення води. Проведено термічну та кислотну модифікацію природнього бентоніту з метою покращення його адсорбційних властивостей. Методами ІЧ-спектроскопії проаналізовано структурні зміни бентоніту після процесу модифікації. Показано зміну інтенсивності функціональних груп, що свідчить про часткове руйнування кристалічної решітки. Аналіз з дослідження фіксації адсорбуючих матеріалів показав, що матеріал активно затримується на поверхні целюлозних волокон. Целюлозні мембрани було виготовлено на основі целюлози з міскантусу, одержаної окисно-органосольвентним способом делігніфікації стебел рослинної сировини. Целюлозу було модифіковано амінуючою сумішшю, яка містить епіхлоргідрин : триетаноламін у співвідношенні 1 : 1. Показано вплив використання бентоніту природнього та модифікованого на роботу целюлозних мембран. Оцінено сорбційні властивості та продуктивність отриманих мембран у процесі фільтрування модельних розчинів гумату натрію та каоліну. Встановлено, що мембрани, виготовлені з модифікованого волокна з міскантусу та з додаванням наповнювача внутрішньомасно, показують покращені показники залишкової кольоровості перміату порівняно з матеріалом виготовленим зі звичайної модифікованої окисно-органосольвентної целюлози. Зокрема, при використанні модифікованої целюлози з додаванням природного та випаленого бентоніту залишкова кольоровість модельного розчину становить 528.15 та 480.2 градусів, відповідно, що на 4.6% та 13.3% ефективніше. Використання в якості наповнювача кислотно-модифікованого бентоніту при виготовлені мембран, навпаки, погіршує якість перміату за рахунок утворення пористої структури матеріалу та осіданні частинок на поверхні волокна, що перешкоджає сорбції. Отримані результати демонструють потенціал застосування модифікованих целюлозних мембран у водоочисних технологіях. Sorbent ; Cellulose ; Mineral ; Membrane ; Chemical engineering ; Chemistry ; Materials science ; Adsorption ; Organic chemistry ; Engineering ; Biochemistry Advanced Cellulose Research Studies
Authors: Oleksandr Salenko | Василь Борисович Струтинський | Konstantyn Avramov | Vadym Orel | Dmytro Dzhulii | Andrii Havrushkevych | Аnton Kostenko
Source: Eastern-European Journal of Enterprise Technologies (article, 2025-06-27)
Field: Materials Science — General Materials Science

Providing technical and algorithmical support to the predictable properties of honeycomb filler obtained by additive processes The object of this study is the FDM process underlying the 3D printing of thin-walled honeycomb-type shells. A solution to the problem of manufacturing a honeycomb filler for a sandwich panel or a panel as a whole, including a curved one, is based on the built model of the process parametric and functional reliability. To this end, the printer dynamics, the behavior of a non-rigid workpiece, and the peculiarities of filament feeding during printing have been analyzed in detail. It has been shown that the predicted mechanical properties, in particular, [σ]x,y,z, are determined by the conditions of extrudate laying and the adhesive bonds formed between its elements, both in the laying plane and between layers, and depend directly on the laying density and on a number of dynamic and thermal phenomena occurring in the mating area. The layout density parameter ρ can be used as a factor in the strength characteristics of the finished article. The application of rational conditions for the movement of the print head, certain features of the contour traversal, the use of supporting elements, and the correct positioning of the part on the printer’s desktop allows for sufficiently reliable reproduction of cellular shell products with maximum strength. It has been proven that the accuracy of basic dimensions of the designed structures corresponds to accuracy quality 11...12 according to engineering standards, which is acceptable for a wide range of applications. The wall thickness of the cellular elements is determined by the filament laying modes, which includes such parameters as extrusion speed, contour traversal speed and acceleration, extrusion temperature, and stacking pitch, but is not less than 1.5 times the diameter of the extruder nozzle. Such geometric parameters provide reasonable strength and rigidity for articles at operation while minimizing the weight of the honeycomb. It has been established that cellular elements should be printed with the use of additional support means; at the same time, the latter do not exclude the occurrence of a certain number of defects whose evolution is not stable and whose presence must be taken into account when predicting the mechanical properties of the finished article Filler (materials) ; Honeycomb ; Materials science ; Composite material Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Industrial Engineering and Technologies
Authors: Zeenat Khan
Source: Advancements in Civil Engineering & Technology (article, 2025-05-30)
Field: Materials Science — Polymers and Plastics

Transforming Agricultural Residues into Construction Materials: A Sustainable Path Forward Amid growing environmental concerns and the need for sustainable construction, agro-waste materials present a promising alternative to conventional building resources like cement and steel.This study explores the use of agricultural residues such as rice husk ash, sugarcane bagasse, coconut shells and natural fibers in producing eco-friendly construction components including green concrete, bricks, insulation, particleboards and bio-based plastics.These materials offer benefits like reduced density, improved thermal insulation and sufficient structural performance, while treatments such as alkali washing enhance durability and fire resistance.Artificial Intelligence (AI) further supports this innovation by enabling predictive modelling, life cycle analysis and supply chain optimization.However, challenges persist, including material inconsistency, limited standardization and durability concerns.The study recommends future research on flammability reduction, fungal resistance, and thermodynamic behavior to enhance performance and market readiness.Overall, agro-waste integration in construction contributes to environmental conservation, resource efficiency, and alignment with global sustainability goals. Agriculture ; Path (computing) ; Sustainability ; Sustainable development ; Agricultural productivity ; Production (economics) Natural Fiber Reinforced Composites ; Materials Engineering and Processing ; Thermochemical Biomass Conversion Processes
Authors: Bezalel Adainoo | Zili Gao | Lili He
Source: Sustainable Food Technology (article, 2025-12-17)
Field: Chemistry — Analytical Chemistry

Real-time prediction of shelf-life of soymilk using a surface-enhanced Raman spectroscopy (SERS) fiber and convolutional neural networks This study combines SERS headspace analysis and machine learning to predict shelf life in real-time. Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network ; Fiber ; Raman spectroscopy ; Optical fiber Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Samuel James Newcome | Fabio Alexander Gratl | Manuel Lerchner | Abdulkadir Pazar | Manish Kumar Mishra | Hans‐Joachim Bungartz
Source: ArXiv.org (preprint, 2025-05-06)
Field: Chemistry — Physical and Theoretical Chemistry

Algorithm Selection in Short-Range Molecular Dynamics Simulations Numerous algorithms and parallelisations have been developed for short-range particle simulations; however, none are optimally performant for all scenarios. Such a concept led to the prior development of the particle simulation library AutoPas, which implemented many of these algorithms and parallelisations and could select and tune these over the course of the simulation as the scenario changed. Prior works have, however, used only naive approaches to the algorithm selection problem, which can lead to significant overhead from trialling poorly performing algorithmic configurations. In this work, we investigate this problem in the case of Molecular Dynamics simulations. We present three algorithm selection strategies: an approach which makes performance predictions from past data, an expert-knowledge fuzzy logic-based approach, and a data-driven random forest-based approach. We demonstrate that these approaches can achieve speedups of up to 4.05 compared to prior approaches and 1.25 compared to a perfect configuration selection without dynamic algorithm selection. In addition, we discuss the practicality of the strategies in comparison to their performance, to highlight the tractability of such solutions. Selection (genetic algorithm) ; Overhead (engineering) ; Selection algorithm ; Fuzzy logic ; Algorithm design Advanced Physical and Chemical Molecular Interactions ; Fault Detection and Control Systems ; Electrostatics and Colloid Interactions
Authors: Benjamin D. Harding | Ashley Hiett | Marco Tonelli | Songlin Wang | Chad M. Rienstra | Katherine A. Henzler‐Wildman
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-01-10)
Field: Chemistry — Spectroscopy

Backbone and Sidechain <sup>1</sup> H, <sup>15</sup> N and <sup>13</sup> C Resonance Assignments of a Multidrug Efflux Membrane Protein using Solution and Solid-State NMR EmrE is a bacterial membrane-embedded multidrug transporter that functions as an asymmetric homodimer. EmrE is implicated in antibiotic resistance, but is now known to confer either resistance or susceptibility depending on the identity of the small molecule substrate. Here, we report both solution- and solid-state NMR assignments of S64V-EmrE at pH 5.8, below the pKa of critical residues E14 and H110. This includes <sup>1</sup>H, <sup>15</sup>N, and <sup>13</sup>C resonance assignments of the backbone, methyl groups (isoleucine, leucine, valine, threonine and alanine) from solution NMR experiments in bicelles, and backbone and side-chain assignments from solid-state NMR <sup>13</sup>C-detected experiments in liposomes. Efflux ; Chemistry ; Valine ; Alanine ; Solid-state nuclear magnetic resonance ; Threonine ; Isoleucine ; Stereochemistry ; Membrane ; Multiple drug resistance ; Nuclear magnetic resonance spectroscopy ; Crystallography ; Resonance (particle physics) ; Molecule ; Leucine ; Amino acid ; Biochemistry ; Nuclear magnetic resonance ; Organic chemistry ; Antibiotics ; Serine ; Enzyme Advanced NMR Techniques and Applications ; DNA and Nucleic Acid Chemistry ; Solid-state spectroscopy and crystallography
Authors: Mahesh Kumar N | A. Sathiya Priya | Shachindra L. Nargund | V Murugan | Rama Bukka | Nidhi Malviya
Source: GSC Biological and Pharmaceutical Sciences (article, 2025-09-04)
Field: Chemistry — Organic Chemistry

Emerging Role of Benzimidazole-Loaded Nanoparticles in Targeted Drug Delivery and Cancer Therapy: A review Benzimidazole derivatives are a privileged heterocyclic scaffold with well-established pharmacological activities, including antimicrobial, antiviral, and anticancer properties. Despite their therapeutic promise, many benzimidazole compounds face challenges such as poor aqueous solubility, limited bioavailability, and off-target toxicity, which hinder their clinical translation. Recent advances in nanotechnology have provided innovative solutions to these limitations through the development of benzimidazole-loaded nanoparticles. Nanoparticle-based formulations improve the solubility, stability and pharmacokinetic profile of benzimidazoles while enabling targeted and sustained delivery to tumor tissues. This review provides a comprehensive analysis of the emerging role of benzimidazole-loaded nanoparticles in drug delivery and cancer therapy. It covers the pharmacological significance of benzimidazoles, nanocarrier platforms, targeting mechanisms, preclinical evidence, clinical outlook, challenges, and future perspectives. Emphasis is placed on how nanotechnology enhances the therapeutic performance of benzimidazoles through improved bioavailability, tumor targeting via passive and active strategies, and controlled drug release. Benzimidazole ; Cancer therapy ; Drug delivery ; Nanotechnology ; Drug ; Cancer ; Pharmacology ; Medicine ; Chemistry ; Materials science ; Internal medicine Synthesis of Tetrazole Derivatives ; Computational Drug Discovery Methods ; Nanoparticle-Based Drug Delivery
Authors: YoungJae Son | Tiejun Chen | Guangyong Shang | Myeongjin Kim | Sung‐June Baek
Source: Mathematics (article, 2025-12-14)
Field: Chemistry — Analytical Chemistry

Fast Spectral Search Using Improved Preprocessing and Limited Axis Check Efficient and accurate identification of spectra from large databases remains a critical challenge in spectroscopic analysis. Previous coarse-to-fine frameworks, typically combining Principal Component Analysis (PCA)-based preprocessing and k-d tree search, have shown that structured search can reduce computational cost without sacrificing accuracy. Building on this foundation, we propose an enhanced algorithm that integrates an improved preprocessing and a novel limited axis check (LAC) method. The preprocessing stage applies running average filtering, downsampling, and threshold-based noise-cutting, followed by PCA to construct a compact, noise-suppressed spectral representation. In the search stage, the proposed LAC algorithm replaces conventional tree-based structures by performing an axis-wise limited-range search and voting strategy to efficiently locate the candidate spectrum closest to the query within the reduced PCA domain. A subsequent refined search determines the closest spectrum by computing distances to the shortlisted candidates. Experimental results demonstrate that the proposed approach attains accuracy equivalent to that of the full search while markedly reducing computational complexity. These results confirm that the integration of enhanced preprocessing and LAC substantially accelerates the spectral search process. Preprocessor ; Principal component analysis ; Search algorithm ; Pattern recognition (psychology) ; Computational complexity theory ; Identification (biology) ; Weighted voting ; Search tree Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zina-Sabrina Duma | Otto Lamminpää | Jouni Susiluoto | Heikki Haario | Ting Zheng | Tuomas Sihvonen | Amy Braverman | Philip A. Townsend | Reinikainen Satu-Pia
Source: ArXiv.org (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Uncertainty calibration for latent-variable regression models Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets. In their original form, multivariate statistical regression models (partial least-squares regression, PLS, principal component regression, PCR) along with their kernelized versions (kernel partial least-squares regression, K-PLS, kernel principal component regression, K-PCR), do not incorporate uncertainty quantification as part of their output. In this study, we propose a method inspired by conformal inference to estimate and calibrate the uncertainty of multivariate statistical models. The result of this method is a point prediction accompanied by prediction intervals that depend on the input data. We tested the proposed method on both traditional and kernelized versions of PLS and PCR. The method is demonstrated using synthetic data, as well as laboratory near-infrared (NIR) and airborne hyperspectral regression models for estimating functional plant traits. The model was able to successfully identify the uncertain regions in the simulated data and match the magnitude of the uncertainty. In real-case scenarios, the optimised model was not overconfident nor underconfident when estimating from test data: for example, for a 95% prediction interval, 95% of the true observations were inside the prediction interval. Principal component regression ; Principal component analysis ; Calibration ; Multivariate statistics ; Sensitivity analysis ; Regression ; Reliability (semiconductor) ; Kernel (algebra) ; Statistical inference ; Inference Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote Sensing in Agriculture
Authors: Zhu Tao | Zhan Longyang | Zhang Lei | Hu Shaobo | Zhang Jian
Source:  (review, 2025-04-23)
Field: Materials Science — Materials Chemistry

A Review of Recent Advances in Boiler Accident Reconstruction Technology Research Boiler accident reconstruction technology refers to the process of systematically analyzing and evaluating the causes of accidents after they occur, utilizing advanced technological methods. The occurrence of a boiler accident can result in significant economic losses and pose serious risks to human safety, thereby underscoring the critical importance of research in boiler accident reconstruction technology. This article aims to explore the research background and the primary methodologies employed in this domain. It provides a comprehensive review of the key technical approaches to boiler accident reconstruction, including data collection and analysis, computer simulation, fault tree analysis, and event tree analysis. By employing these methods, a detailed simulation of the accident can be conducted, enabling the identification of potential risk factors and thus providing a scientific foundation for improving boiler safety. Computer science Nuclear Materials and Properties
Authors: Saad Ahmed AlAhmed | Ayman Osman Elsayed | Ahmed M. Abdelzaher
Source: BMC Chemistry (article, 2025-08-31)
Field: Chemistry — Analytical Chemistry

Multivariate classical least squares-based model for spectrophotometric determination of celecoxib and Tramadol in their new formulated dosage form Celecoxib (CLB) and tramadol (TRD) are frequently co-administered in clinical practice due to their complementary mechanisms in managing acute and chronic pain. Their combination has recently been formulated into a fixed-dose oral medication, representing the first FDA-approved multimodal analgesic targeting COX-2 and central opioid receptors simultaneously. However, the strong spectral overlap between CLB and TRD complicates their simultaneous determination using traditional spectrophotometric methods. In this study, a chemometric-assisted spectrophotometric method was developed for the simultaneous quantification of CLB and TRD without prior separation. The classical least squares (CLS) were ultimately selected due to its suitability when pure spectra are available, its robustness with small calibration sets, and its greater interpretability for routine quality control. A five-level, two-factor experimental design produced 25 binary mixtures, split into 13 calibration and 12 validation samples. After spectral preprocessing and removal of non-informative regions, the CLS model was applied to 81 variables across the 210-290 nm range. The model achieved mean recovery values of 99.85% for CLB and 99.99% for TRD in the calibration set, and 101.29% for CLB and 99.52% for TRD in the validation set, demonstrating excellent accuracy and consistency across both datasets. Linearity was established in the range of 6-14 µg/mL for both drugs, with detection limits of 0.55 µg/mL (CLB) and 0.67 µg/mL (TRD). The method showed excellent selectivity in the presence of common co-formulated drugs and was successfully applied to determine both analytes in commercial Seglentis<sup>®</sup> tablets. This developed method provides a rapid, accurate, and cost-effective solution for routine quality control of complex pharmaceutical formulations. Celecoxib ; Least-squares function approximation ; Multivariate statistics ; Tramadol ; Partial least squares regression ; Chromatography ; Dosage form ; Mathematics ; Applied mathematics ; Chemistry ; Total least squares ; Statistics ; Pharmacology ; Medicine ; Regression analysis Analytical Methods in Pharmaceuticals ; Veterinary Pharmacology and Anesthesia ; Analytical Chemistry and Chromatography