jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anthony Dosseto | Florian Dux | Clément P. Bataille | Patrice de Caritat
Source: Earth system science data (article, 2025-09-26)
Field: Chemistry — Inorganic Chemistry

A bioavailable strontium isoscape of Australia Abstract. Strontium isotope ratios (87Sr/86Sr) at the Earth's surface offer powerful tools for geological, environmental, and archaeological applications. In minerals and biological materials, 87Sr/86Sr reflects the isoztopic composition of the local bedrock and derived soils. In Australia, however, large regional-scale surveys of bioavailable 87Sr/86Sr remain scarce. Here, we present a new dataset of bioavailable 87Sr/86Sr ratios from 278 catchment outlet (floodplain) sediment samples, spanning inland southeastern Australia (South Australia, New South Wales, Victoria), northern Western Australia, the Northern Territory, Queensland (north of 21.5° S), and the Yilgarn Craton in southern Western Australia. Combined with more than 20 000 global Sr isotope measurements, this dataset was used to generate a high-resolution isoscape of Australia using a well-established random forest spatial regression framework (Bataille et al., 2020). Australian bioavailable 87Sr/86Sr values span a narrower range (0.70501–0.78121) compared to co-located bulk sediment values (0.70480–1.09089) (de Caritat et al., 2022, 2023, 2025b), reflecting the influence of soluble and exchangeable mineral phases and atmospheric inputs such as rain and dust/seaspray. The predicted isoscape reproduces major geological patterns, with higher values over ancient crustal provinces like the Yilgarn Craton and eastern Palaeozoic orogens, and lower values across younger sedimentary basins and coastal margins. Model uncertainty, assessed via quantile random forest regression, is lowest across well-sampled, geologically stable regions where the model is well-trained and highest in poorly-sampled regions and lithologically complex zones. Despite remaining spatial gaps and areas of high prediction uncertainty, our model offers significantly improved coverage and resolution for Australia compared to other global or regional isoscapes. It also provides a scalable framework for updating the Australian isoscape as sampling density increases. This isoscape establishes a robust baseline for applications in provenance research, palaeoecology, and environmental geochemistry. The bioavailable Sr isotope dataset is available from the Geoscience Australia e-Catalogue entry by de Caritat et al. (2025a) on https://doi.org/10.26186/150024 Yilgarn Craton ; Bedrock ; Sediment ; Sedimentary rock ; Archean ; Provenance ; Strontium ; Cenozoic ; Isotopes of strontium ; Craton Radioactive element chemistry and processing ; Radioactive contamination and transfer ; Nuclear Physics and Applications
Authors: Mariusz Adamski | Marcin Herkowiak | Przemysław Marek | Katarzyna Dzida | Magdalena Kapłan | Kamila Klimek
Source: Energies (article, 2025-12-04)
Field: Chemistry — Analytical Chemistry

Lactic Fermentation Spectral Analysis of Target Substrates and Food and Feed Wastes for Energy Applications The article deals with the creation of a calibration model of lactic acid content in an aqueous solution. The research concept included the preparation of a control tool for the process of modifying the properties of the food fraction for methane fermentation bacteria. The thesis was formulated that it is possible to prepare a systemic solution for real-time observation and monitoring of lactic acid secretion during the digestion of a hydrated mixture of food fractions. The scientific aim of the work was to develop and verify a calibration model of lactic acid content in an aqueous mixture with limited transparency for visible light waves. The research methodology was based on near-infrared spectroscopy with multivariate analysis. Stochastic modeling with noise reduction based on orthogonal decomposition was used. A calibration model was created using Gaussian processes (GP) to predict the lactic acid concentration in an aqueous solution or mixture using an NIR-Vis spectrophotometer. The design of the calibration model was based on absorbance spectra and computational data from selected wavelength ranges from 450 nm to 1900 nm. The measurement data in the form of spectra were limited from the initial wider range (400–2250 nm) to reduce interference. The generated calibration model achieved a mean error level not exceeding 2.47 g∙dm−3 of the identified lactic acid fraction. The coefficient of determination R2 was 0.996. The effect of absorbing the emitter waves was achieved despite the limited transparency of the mixture. Lactic acid ; Calibration ; Aqueous solution ; Calibration curve ; Analytical Chemistry (journal) ; Fermentation Spectroscopy and Chemometric Analyses ; Biopolymer Synthesis and Applications ; Advanced Scientific Research Methods
Authors: Tanja Lužaić | Jelena Škrbić | Gjore Nakov | Jovana Petrović | Ranko Romanić
Source: Processes (article, 2025-10-14)
Field: Chemistry — Organic Chemistry

Deep-Frying Performance of Palm Olein and Sunflower Oil Variants: Antioxidant-Enriched and High-Oleic Oil as Potential Substitutes Deep-fat frying remains the predominant method of food preparation; however, increasing concerns regarding health and sustainability have prompted the search for safer alternatives. Palm olein is widely used as a frying medium but its consumption has been questioned due to the presence of contaminants (e.g., 3-monochloropropane-1,2-diol, 3-MCPD) and the challenges associated with its transportation from producing countries, creating a need for healthier and more sustainable alternatives. The present study aimed to assess the oxidative stability, physicochemical properties, and sensory characteristics of various oils used for deep-fat frying, with particular emphasis on identifying suitable replacements for palm olein. Five oils were evaluated: refined sunflower oil (RSO), RSO supplemented with tert-butylhydroquinone (RSO+TBHQ), RSO supplemented with rosemary extract (RSO+RE), high-oleic sunflower oil (HOSO), and palm olein (PO). Samples were evaluated before and after deep-frying of French fries, at 175 °C for 2.5 min, over a total of 12 consecutive frying cycles. The results demonstrated that palm olein and HOSO exhibited the highest oxidative stability (induction period determined by Rancimat method at 100 °C was 27 h and 26.2 h, respectively), whereas the addition of TBHQ (induction period 23.4 h) and rosemary extract (induction period 11.5 h) provided only a modest enhancement of RSO stability (induction period 9.6 h). Hierarchical cluster analysis grouped palm olein and HOSO together, confirming their similar stability, while RSOs formed a distinct cluster. These findings suggest that high-oleic sunflower oil represents the most promising, stable, and nutritionally advantageous alternative to palm olein, simultaneously supporting local production and improved dietary quality. Sunflower oil ; Palm oil ; Palm ; Sunflower ; Deep frying Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis ; Biodiesel Production and Applications
Authors: Guirrati, Khaoula | Carmen Loreto Manuelian Fuste | Elena Albanell Trullas
Source: Zaguan (University of Zaragoza Repository) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Calibración NIRS para perfil bioquímico en terneros amantados con distintos sustitutos lácteos La determinación de los marcadores plasmáticos bioquímicos es fundamental para evaluar el estado metabólico y nutricional de los animales. La espectroscopía de infrarrojo cercano (NIR) se plantea como una alternativa rápida y económica a los métodos tradicionales, aunque su uso en plasma aún es limitado. El objetivo de este estudio fue desarrollar modelos predictivos NIR para la estimación de los biomarcadores plasmáticos glucosa, urea, ALT, AST y creatinina en terneras alimentadas con leche enriquecida con aminoácidos esenciales. Para ello, se utilizaron 25 terneras Angus × Frisona divididas en 5 grupos según el tipo de sustituto lácteo. Los animales fueron alimentados 2 veces al día durante 15 días. El último día se recogieron 175 muestras de plasma en serie tras la toma de la mañana (0, 1, 2, 3, 4, 6 y 8 h). Los espectros NIR se recogieron con 2 dispositivos: un NIR de sobremesa (NIRSystem 5000; rango, 1100–2500 nm) y uno portátil (NIR Aurora; rango, 950–1650 nm). Para el desarrollo de los modelos, se emplearon la PCA y PLS-DA para la clasificación cualitativa, y la PLS y MPLS para la predicción cuantitativa evaluando distintos pretratamientos, eliminación de bandas de agua y la selección de longitudes de onda. Los modelos se validaron mediante 2 estrategias: validación cruzada (5- fold, 3 repeticiones) y = validación externa, dividiendo los datos en subconjunto de calibración (80%) y de validación (20%). La seleción de los modelos se hizo en base al coeficiente de determinación de calibración (R2 cal), de validación cruzada (R2 cv) y de validación externa (R2 val) y la desviación predictiva residual (RPD). Los resultados de la PCA y PLS-DA mostraron una discriminación clara de las muestras según el tiempo de muestreo, especialmente a los tiempos 60 y 120 min, pero no según el tipo de dieta ni el origen proteico de esta. En la predicción cuantitativa, ninguno de los modelos alcanzó niveles satisfactorios para su aplicación práctica (R²val < 0,45 y RPD < 1,5), sin mejoras relevantes tras eliminación de zonas de agua o selección de zonas VIP. El NIRSystem 5000 superó al NIR Aurora en todas las estrategias de validación, pero la capacidad predictiva fue siempre limitada. En conclusión, la espectroscopía NIR permite la discriminación de las muestras del plasma de los cambios metabólicos en plasma a lo largo del tiempo, pero no reemplaza a los métodos bioquímicos convencionales para la estimación cuantitativa de biomarcadores plasmáticos en terneras alimentadas con distintos sustitutos lácteos.<br /><br /> Human physiology ; Food intake ; Body weight ; Milk products Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Silvia Muñoz | Juan R. Herrera | Carlos Vilas
Source: Instrumentation viewpoint (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Combination hyperspectral imaging technology and mathematical models for monitoring the quality of marine - origin foods This work explores the use of hyperspectral imaging (HSI) technology for non-destructive monitoring of fish quality and its combination with mathematical models to predict its evolution. As a proof of concept, we focus on farmed turbot (whole) and wild-caught hake (filleted). Quality is evaluated in terms of four different indicators: bacterial growth, total volatile basic nitrogen (TVB-N), nucleotide catabolism, and sensory assessment. Data obtained from several experiments, carried out at different storage temperatures, were used to: (i) find models that correlate such data with HSI information in order to derive a methodology for a fast, real-time, and non-invasive evaluation of fish quality; (ii) calibrate the mechanistic model describing the evolution of bacterial growth, TVB-N formation, and nucleotide degradation, and (iii) train the machine learning models that will describe sensory evolution. Hyperspectral imaging ; Mathematical model ; Quality (philosophy) ; Turbot ; Fish <Actinopterygii> ; Hake ; Focus (optics) Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Meat and Animal Product Quality
Authors: Bashar Abdulazeez Mahmood | S A Alkubaisy | Esraa Abd Al-Karim Marouf | Enas H. Mohammed
Source: International Journal of Environmental Impacts (article, 2025-10-31)
Field: Chemistry — Analytical Chemistry

Estimation of Some Heavy Metal Concentrations in Selected Dried Fruit Samples Available in Local Markets and Assessment of Their Health Risks Dried fruits are a popular and widely consumed food due to their high nutritional value and long shelf life.However, their contamination with heavy metals poses a health and environmental concern.This study aims to estimate the levels of some heavy metals (copper, nickel, manganese, iron, lead, and chromium) in selected dried fruit samples available in local markets and assess their compliance with international standards approved by the World Health Organization (WHO) and the Codex Alimentarius (CAC).Samples were collected from multiple sources and analyzed using advanced techniques (Atomic Absorption Spectroscopy (AAS)).The results showed that some samples contained concentrations exceeding permissible limits, indicating potential health risks, especially in cases of long-term consumption.The sources of this contamination are often attributed to the use of chemical pesticides and fertilizers, and unsafe drying and packaging methods.The research suggests implementing stricter surveillance of food products and increasing consumer awareness on selecting safe dried fruits.Research for different area basis needs to be pursued for better apprehension of menace. Estimation ; Risk assessment ; Human health ; Health risk ; Health risk assessment Heavy Metals in Plants ; Heavy metals in environment ; Heavy Metal Exposure and Toxicity
Authors: Jia He | Barbara Helm | Franziska Goedtel | Katharina Buechner | Marcel Schilling | Marc A. Schneider | Laura V. Klotz | H. Winter | Britta Velten | Ursula Klingmüller | Junyan Lu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-04)
Field: Chemistry — Spectroscopy

msBayesImpute: A Versatile Framework for Addressing Missing Values in Biomedical Mass Spectrometry Proteomics Data Abstract Advancements in mass spectrometry (MS) technologies have significantly improved the ability to quantify proteins and analyse their modifications. However, MS-based proteomics datasets frequently encounter missing values due to a complex interplay of missing at random (MAR) and missing not at random (MNAR) mechanisms. If unaddressed, such missing data can result in information loss and biased outcomes in data pre-processing, such as normalisation, as well as subsequent analyses and interpretations. Few approaches effectively address both MAR and MNAR, and those that do often necessitate manual tuning of mixture percentages between them or rely on two-group experimental designs. To enhance the handling of missing values, we developed msBayesImpute, an innovative computational method that integrates Bayesian factorization with probabilistic dropout models. We evaluated msBayesImpute against several popular imputation methods using both simulated missing values and those generated through a dilution series experiment on samples from lung cancer patients. Our comprehensive benchmark demonstrated superior performance in reconstructing missing values, estimating normalization factors, and identifying differentially expressed proteins across varying levels of missingness. Notably, msBayesImpute does not require predefined experimental designs and is scalable to large-scale studies. This versatility positions msBayesImpute as an effective and robust tool for enhancing the utility of MS datasets in biological research. Missing data ; Imputation (statistics) ; Normalization (sociology) ; Database normalization ; Bayesian probability ; Benchmark (surveying) ; Probabilistic logic ; Dropout (neural networks) Advanced Proteomics Techniques and Applications
Authors: Júlio César da Silva Dantas | Eduardo Aranha | Thiago Reis da Silva
Source: RENOTE (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Predição de Alunos em Risco de Reprovação: uma Comparação do Impacto de Diferentes Técnicas de Amostragem Entre os campos impactados pela Inteligência Artificial, a educação é um dos mais transformados, com aplicações na automatização de processos, suporte em atividades e, como neste caso, na Predição de Performance de Estudantes (student performance prediction, SPP). Esta pesquisa investiga a eficácia de técnicas de amostragem, como SMOTE, Tomek Links e ADASYN, no problema de desequilíbrio de classes, comum na área de SPP. Trata-se de uma pesquisa aplicada, exploratória e experimental, que alcançou 97,5% de precisão com o uso de Gradient Boosting e SMOTE, identificando até 94,4% dos alunos em risco de reprovação. Esses resultados superam trabalhos anteriores com o mesmo conjunto de dados, sugerindo a eficiência das técnicas de amostragem para lidar com o problema de desequilíbrio de classes. Philosophy Graphite, nuclear technology, radiation studies
Authors: Lukáš Klein | Jan Fulneček | Ondřej Kabot | Jiří Dvorský | Lukáš Prokop
Source: Scientific Reports (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Using synthetic data for pretraining partial discharge detection in overhead transmission lines Accurate detection of partial discharges (PDs) in medium-voltage overhead transmission lines is critical for preemptive maintenance and avoiding costly outages, yet it is challenged by scarce labeled data and pervasive electromagnetic interference. This paper investigates a hybrid simulation-and-data-driven framework in which synthetically generated PD signals are used to pretrain deep neural networks and are subsequently fine-tuned on a limited set of real overhead-line measurements. The synthetic pipeline systematically varies PD repetition rates, amplitude distributions, vegetation-contact scenarios, and noise conditions, producing diverse time-series and spectrogram-like representations that approximate real operating environments. We conduct a comprehensive ablation study across multiple architectures-Convolutional Neural Networks (CNNs), a Vision Transformer (ViT), and a Long Short-Term Memory (LSTM) network-and analyze their sensitivity to granular sweeps of synthetic-data parameters. CNN-based models decisively outperform ViT and LSTM counterparts on the spectrogram-based classification task, while ViT and LSTM fail to learn meaningful representation. For the successful CNNs, pretraining on carefully parameterized synthetic datasets-particularly those reflecting higher PD activity, such as our Datasets 3 and 4-consistently improves downstream performance on real data, boosting the Matthews Correlation Coefficient (MCC) on imbalanced, cost-sensitive test sets by roughly 10-20% compared with training from scratch. At the same time, we show that poorly aligned synthetic data can degrade generalization, underscoring the need for accurate noise calibration and domain-aligned simulation. Overall, the results confirm that (i) architectural choice is pivotal for PD detection in overhead lines and (ii) well-designed synthetic data is a powerful, practical lever for achieving reliable and cost-effective PD monitoring when real labeled data are limited. Synthetic data ; Boosting (machine learning) ; Noise (video) ; Overhead (engineering) ; Artificial neural network ; Transformer ; Electric power transmission ; Data transmission ; Pipeline (software) High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Line Inspection Robots
Authors: Tianshu Sun | Jun Chen
Source:  (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

DR-PDEE-based efficient stochastic dynamical response analysis for high-dimensional nonlinear systems subject to multiplicative non-white excitation Multiplicative stochastic excitation plays an indispensable role in modeling complex nonlinear dynamical systems across multiple fields of science and engineering.In particular, random noise in physical systems is typically not ideal white noise.The coupling between excitation and system responses in multiplicative problems, combined with the non-white nature of the excitation, leads to significant challenges for probabilistic response analysis, especially in high-dimensional dynamical systems.To address these challenges, an efficient method based on the dimension-reduced probability density evolution equation (DR-PDEE) is proposed for analyzing the response probability density of such systems.In this approach, the multiplicative non-white noise excitation is simulated by a linear analog filter, and the augmented system, which combines the original dynamical system with the linear filter, becomes a higher-dimensional system under additive white noise.Subsequently, by combining the quantity of interest with one filter output that possesses a nonzero intrinsic diffusion function, and solving the two-dimensional DR-PDEE for the joint process, the probability density function (PDF) solution of the quantity of interest can be directly obtained.Notably, due to the introduction of the analog filter, the corresponding intrinsic diffusion function in the DR-PDEE for the joint process becomes a known constant.Therefore, in this method, only the intrinsic drift functions of the DR-PDEE need to be determined, which can be reliably estimated through a small number of deterministic analyses.A numerical example is illustrated to validate the proposed method, demonstrating that the DR-PDEE achieves PDF solutions with high tail accuracy by only a small number of deterministic analyses. Multiplicative function ; Nonlinear system ; White noise ; Excitation ; Computer science ; Subject (documents) ; White (mutation) ; Control theory (sociology) ; Applied mathematics ; Mathematics ; Physics ; Mathematical analysis ; Telecommunications ; Artificial intelligence ; Quantum mechanics ; Library science Spectroscopy and Chemometric Analyses ; Structural Health Monitoring Techniques ; Probabilistic and Robust Engineering Design
Authors: Yanyan Huang | Chang Liu | Hongjuan Pan | Dong Wang | Jingjing Wei | Jialiang Zhong
Source: Pharmaceutical Fronts (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Quantitative Analysis of Pravastatin Sodium Polymorphs: a Comparative Study of Chemometric Techniques Combined with Powder X-ray Diffraction, Mid-Infrared, and Raman Spectroscopy Pravastatin sodium (PS) is a hydrophilic statin lipid-lowering drug that reduces low-density lipoprotein levels in the blood by inhibiting the activity of 3-hydroxy-3-methylglutaryl coenzyme A reductase. It is known to exist in 17 crystalline forms, with some different crystalline forms overlapping in the powder diffraction patterns, making it difficult to control the purity of the crystalline forms. In this study, we aimed to determine the purity of PS crystals using powder X-ray diffraction (PXRD), mid-infrared (MIR) spectroscopy, and Raman techniques. The predictive ability of the partial least squares (PLS) model was constructed and assessed using SPXY, K_S, and Random methods at different partitioning ratios. PLS calibration curves were established based on the relationship between PXRD, MIR, and Raman data and the content of a solid forms of PS (PS-A) in different ranges (full and partial spectra) using different preprocessing algorithms such as multiplicative scattering correction, standard normal variable, Savitzky–Golay filtering, and derivative spectroscopy, or a combination of them. The results showed that the calibration model (y = 0.999x + 0.008 with R 2 = 0.999) established using the PXRD method was better, with a low detection limit (1.52%) and quantification limit (4.60%). In addition, by analyzing the testing results of the blind sample, it was found that the confidence intervals of the predicted values of MIR and Raman were wider, indicating a large uncertainty of their parameter estimation. Therefore, it will be better to select the calibration model established by the PXRD method to determine the purity of PS in actual production. This can provide more reliable methodological support for the quality control of pharmaceutical products. Raman spectroscopy ; Powder diffraction ; Infrared spectroscopy ; Materials science ; Infrared ; Analytical Chemistry (journal) ; Spectroscopy ; Diffraction ; X-ray crystallography ; Chemistry ; Crystallography ; Optics ; Chromatography ; Organic chemistry Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions