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This study explores the influence of solar irradiance (Ir) and ambient temperature (T) on photovoltaic (PV) production (P) by combining statistical analysis and deep learning techniques. A strong positive correlation was found between irradiance and PV output with Pearson coefficient (R=0.9211), while temperature exhibited a moderate effect (R=0.4477). A two-way analyze of variance (ANOVA) confirmed the statistical significance of these environmental factors. Furthermore, an autoencoder-based model was developed to capture complex nonlinear relationships and outperformed classical regression models in terms of accuracy and generalization. These results highlight the potential of machine learning methods for improving the understanding and optimization of PV systems in variable climatic conditions. Combines statistical analysis and deep learning to assess PV production factors Employs an autoencoder model to capture nonlinear relationships more effectively The model integrates a new interaction term $I_r.T$ that increases sensitivity to joint environmental variations.

In this study a new approach of cosmogenic radionuclide (CRN) isochron burial dating was developed for locations where the maximum clast size of the sediment does not reach the size of using single cobble samples. This modification makes use of the presumption that different grain size fractions have diverse pre-burial histories and thus are deposited with diverse amount of CRNs. Accordingly, a bulk sample is taken at a certain sediment depth, from which several grain size fractions are selected. These are treated as individual samples for isochron burial age determination. To test our hypothesis, a fluvial sediment succession of the Paleo-Danube River in the Western Pannonian Basin, with an assumed Pliocene age was sampled at two sample depths. The different grain size fractions (0.25-0.5; 1-2; 4-8; 10-20; and 40-60 mm; n=7) provided variable ²⁶Al and ¹⁰Be concentrations, supporting the validity of our assumption. The difference between the lowest and largest concentrations was limited making age determination and outlier identification more challenging compared to an isochron with more variable CRN concentrations. After a careful outlier identification, χ² minimisation inverse modelling yielded 3.9 ± 0.7 Ma as the age of fluvial sedimentation. The offered sampling strategy opens a window towards further application of the CRN isochron burial dating. The burial age of fluvial sediments that were hitherto undatable due to their limited thickness and small clast size can be determined. After the presented adjustment of the sampling strategy the age of deposition can be calculated by the standard ways of isochron burial dating.

The use of Bacillus Calmette-Guérin (BCG) vaccine against tuberculosis (TB) spans more than a century. Besides protection against severe paediatric TB, randomized trials and novel advances within innate immunology documented that BCG has beneficial non-specific effects, providing protection against non-TB infections. Since paediatric intradermal BCG vaccination has proved unable to contain adult pulmonary tuberculosis, several novel TB vaccines are under development, most of which build upon BCG. BCG's status as an essential remedy against TB will therefore be maintained, but despite many decades of near-universal intradermal use, the local responses to BCG in the skin have not been thoroughly elucidated. We therefore developed appropriate methods to capture the localised skin events at the cellular and molecular level after intradermal BCG vaccination. This work informs future studies to identify the immunological events induced following administration of BCG to accelerate development of improved or new vaccines against Mycobacterium tuberculosis (Mtb). We employed advanced technologies such as spatial transcriptomics on skin tissue punch biopsies and cell-free plasma transcriptomics (liquid biopsies) to characterize both the local (in situ) skin and systemic (peripheral blood) response to BCG via:•Whole blood transcriptomics and epigenetic analysis•Blood immune cell characterization•Plasma proteomic and metabolomic analysis.

Microplastic (MP) contamination in soil media presents growing analytical challenges. We introduce a validated method for MP mass quantification using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), targeting polyethylene (PE), polypropylene (PP), and polystyrene (PS) in synthetic and environmental soils. The method achieved low limits of detection (0.02-0.44 µg), strong linearity (R² > 0.995), and high recovery rates-86.1% (sandy), 90.7 % (loamy), and 99.6 % (sandy-loam). Cryomilling improved sample homogeneity and quantification accuracy (+3.2 %). Fourier transform infrared spectroscopy (FTIR) was used to confirm polymer identity with >85% match. The protocol was successfully applied to environmental samples from urban and agricultural soils in Auckland, New Zealand, demonstrating its robustness and field applicability. This practical workflow offers a reproducible, high-sensitivity approach suitable for routine microplastics monitoring across diverse soil matrices. Py-GC/MS method achieved over 90 % accuracy for microplastic quantification. Extraction protocols demonstrated recovery efficiencies of up to 99.6 % FTIR complemented Py-GC/MS, confirming polymer identification with >85 % accuracy.

This article provides an update to a previously published data article and associated data set regarding Eu isotope ratio of NIST3117a Eu chemical solution, determined over the period 2017-2024. The Eu isotope ratios were measured via MC-ICP-MS using various Sm isotope pairs -¹⁴⁷Sm-¹⁴⁹Sm, ¹⁴⁷Sm-¹⁵⁴Sm, ¹⁴⁹Sm-¹⁵⁴Sm and ¹⁵⁰Sm-¹⁵⁴Sm-based on the exponential law over the eight-year period. Measurements were conducted under both wet plasma conditions (using spray chamber) and dry plasma conditions (using Aridus II system). The mean δ¹⁵³Eu values for all Sm isotope pairs are close to 0 ‰, and their 2SD intervals include zero, indicating no systematic bias from the choice of Sm pair or the sample-introduction system. While the precision of δ¹⁵³Eu values for NIST 3117a varies slightly among different Sm isotope pairs, it remains generally consistent. These results demonstrate that Sm-based internal normalization yields robust and reproducible Eu isotope ratios by MC-ICP-MS. The dataset can serve as reference for studying Eu isotope fractionation in geological rocks and meteorites. 36 • No measurable bias in the Eu isotopic composition of the NIST 3117a standard solution is observed with respect to the selection of Sm isotope pairs for normalization or the sample introduction technique.

Volatile organic compounds (VOCs) are a significant class of air pollutants, as they pose a risk to human health and the environment. Precise quantification of VOCs sampled onto sorbent tubes requires gas phase standards at suitable concentrations. Herein, we present a: - gas standard preparation method for benzene, toluene, ethylbenzene and xylene (BTEX) prepared from liquid standards by means of evaporation and dynamic dilution with nitrogen - demonstration of the applicability of the LiqMix Cascade gas mix system. Repeatability and reproducibility were first successfully demonstrated via direct GC-MS analysis of generated gas mixtures. Then µg levels of generated gas standards were adsorbed onto sorbent tubes employed in active sampling and ng levels were collected onto Radiello cartridges used in passive sampling, followed by liquid extraction or thermal desorption and GC-MS analysis. Accuracy was verified by a BTEX certified reference material (99 % recovery). The LiqMix Cascade gas mix system was shown to produce precise and repeatable concentrations of BTEX gas phase standards for sorbent tube sampling applications for the first time.

This study proposes a real-time tool wear monitoring approach for dry milling of AISI 304 stainless steel using a Microflown PU sensor and the I-Kaz™ statistical feature. A Taguchi L18 orthogonal array was adopted to optimize cutting speed, feed rate, depth of cut and tool type (uncoated and AlCrN-coated WC-Co inserts) based on surface roughness and flank wear responses. ANOVA revealed that tool type was the most significant factor, contributing approximately 85 % to flank wear variation, followed by depth of cut and cutting speed. The optimal combination: AlCrN-coated insert, cutting speed of 1250 rpm, depth of cut of 0.50 mm and feed rate of 0.04 mm/rev minimized both responses. The proposed I-Kaz-based monitoring approach established a strong inverse power-law relationship between the I-Kaz coefficient and flank wear Z₂ ^α =a(VB)^-n, achieving R²>0.96, indicating high accuracy and stability across repetitions.•This study introduces the first application of a Microflown PU sensor for near-field acoustic monitoring in milling operations.•The I-Kaz™ feature is demonstrated as a computationally efficient and accurate method for real-time tool wear prediction.•The research integrates cutting parameter optimization and predictive monitoring within a single experimental framework.