Search Results (2,113)

Detailed description of phase composition and geochemistry of the Muschelkalk carbonate rocks of the Upper Silesian Province in Poland were presented in this article. The tests were carried out to determine mineralogical features and geochemical properties. The samples were collected from the formations of the Lower Muschelkalk (Gogolin Unit), Middle Muschelkalk (Diplopore Dolomite Unit) and Upper Muschelkalk (Tarnowice Unit, Boruszowice Unit). The following research methods were used: macroscopic description, X-Ray Diffraction, Fourier transform infrared spectroscopy, X-Ray Fluorescence and Atomic spectrometry with plasma intensification. The following carbonate phases were identified: a low-Mg calcite, a high-Mg calcite, a proto-dolomite, an ordered dolomite and a huntite. The results of XRD analysis allowed the determination of the chemical formulas of the mineral phases. XRF and ICP AES analyses allowed to establish the content of following trace elements: Sr, Ba, Al, Si, Fe, Mn, K, Na, S, Cl, Ti, Cr, Ni, Zn, Rb, Zr, Pb, As, V, Be, B, Co, Cu, Br, Mo and Cd. Apart from Sr and Ba, they are not fundamental components of carbonate rocks. They indicate the presence of minerals such as silicates, aluminosilicates, oxides and sulfides. Full article

Objectives: This study aimed to evaluate the reproducibility of artificial intelligence (AI) in identifying cephalometric landmarks, comparing its performance with manual tracing by an experienced orthodontist. Methods: A high-quality lateral cephalogram of a 26-year-old female patient, meeting strict inclusion criteria, was selected. Eighteen cephalometric landmarks were identified using the WebCeph software (version 1500) in three experimental settings: AI tracing without image modification (AInocut), AI tracing with image modification (AI-cut), and manual tracing by an orthodontic expert. Each evaluator repeated the procedure 10 times on the same image. X and Y coordinates were recorded, and reproducibility was assessed using the coefficient of variation (CV) and centroid distance analysis. Statistical comparisons were performed using one-way ANOVA and Bonferroni post hoc tests, with significance set at p < 0.05. Results: AInocut achieved the highest reproducibility, showing the lowest mean CV values. Both AI methods demonstrated greater consistency than manual tracing, particularly for landmarks such as Menton (Me) and Pogonion (Pog). Gonion (Go) showed the highest variability across all groups. Significant differences were found for the Posterior Nasal Spine (PNS) point (p = 0.001), where AI outperformed manual tracing. Variability was generally higher along the X-axis than the Y-axis. Conclusions: AI demonstrated superior reproducibility in cephalometric landmark identification compared to manual tracing by an experienced operator. While certain points showed high consistency, others—particularly PNS and Go—remained challenging. These findings support AI as a reliable adjunct in digital cephalometry, although the use of a single radiograph limits generalizability. Broader, multi-image studies are needed to confirm clinical applicability. Full article

Purity analysis of parent gases used to produce reference gas mixtures is fundamental to assure the metrological traceability of the certified gas composition, and the use of purity data in the calculation of the mixture composition should be performed in accordance with the requirements of international standards. Purity analysis can be difficult to realize since limited measurement standards are available for the determination of trace levels of gaseous compounds. The first step of purity analysis is the definition of the impurities considered critical or significant to the final composition of a mixture. In this work, we present the activity carried out for the identification and quantification of impurities of carbon dioxide and water in some ultrapure gases used for the preparation of primary certified reference gas mixtures of carbon dioxide at atmospheric amount fraction (400–800 µmol·mol⁻¹), by means of different spectroscopic techniques (Fourier Transform IR, Non-Dispersive IR and Cavity Ring-Down). Dynamic dilution was used for the generation of reference mixtures for the calibration of the analyzers by using calibrated Mass Flow Controllers. The certified reference gas mixtures produced with the tested pure gases will also be applied to characterization studies and calibration protocols for gas sensors used both for outdoor and indoor monitoring. Full article

In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally demanding. Machine learning (ML) offers an alternative, but its application to direct reconstruction of full pressure traces remains limited. This study evaluates three strategies for reconstructing cylinder pressure under unmeasured operating conditions, establishing a machine learning-assisted framework that generates the complete pressure–crank angle (P–CA) trace. The framework treats crank angle and operating conditions as inputs and predicts either pressure directly or apparent heat release rate (HRR) as an intermediate variable, which is then integrated to reconstruct pressure. In all approaches, discrete pointwise predictions are combined to form the full P–CA curve. Direct pressure prediction achieves high accuracy for overall traces but underestimates HRR-related combustion features. Training on HRR improves combustion representation but introduces baseline shifts in reconstructed pressure. A hybrid approach, combining non-combustion pressure prediction with combustion-phase HRR-based reconstruction delivers the most robust and physically consistent results. These findings demonstrate that ML can efficiently reconstruct in-cylinder pressure at unmeasured conditions, reducing experimental requirements while supporting combustion diagnostics, calibration, and digital twin applications. Full article

Due to the increasing demand for accurate and robust GNSS positioning for location-based services (LBS) in urban regions, the impacts prevalent in metropolitan areas, like multipath reflections and various interferences, have become persistent challenges. Consequently, developing effective strategies to address these sophisticated influences has become both a primary research focus and a shared priority. In this paper, the authors explore an approach to identify and mitigate the drawbacks arising from multipath effects in urban positioning. Unlike conventional ways for building complex models, an adaptive data-driven methodology is proposed to identify the fingerprints of a multipath in GNSS observations. This approach utilizes the Fourier transform (FT) to examine code multipath and other error sources in terms of frequency, as represented by the power spectrum. Wavelet decomposition and signal spectrum methods are subsequently applied to seek traces of code multipath in multilayer decompositions. Based on the exhibited multipath features, the impacts of multipath in GNSS observations are detected and mitigated in the reconstructed observations. The proposed method is validated for both static and dynamic positioning scenarios, demonstrating seamless integration with existing positioning models. The feasibility has been verified through a series of experiments and tests under urban environments using navigation terminals and smartphones. Full article

Background: Metabolic syndrome is a constellation of medical conditions including central obesity, high blood sugar, and high triglycerides known to increase the risk of heart disease, stroke, and type 2 diabetes, with significant sex differences in the syndrome’s incidence and prevalence. These clinical symptoms may be accompanied by cognitive impairment. Methods: In the present experiment, we fed rabbits a diet high in fat and sugar (HFSD), assessed symptoms, and measured changes in cognition using trace eyeblink conditioning. Results: We show that a range of symptoms of metabolic syndrome resulted from HFSD in male and female rabbits and obesity, high blood sugar, and glucose intolerance were higher in male than female rabbits. Specifically, HFSD male rabbits gained more weight and had a higher body-mass index, more body fat, higher fasting glucose levels, and greater glucose intolerance. Importantly, using trace and delay eyeblink conditioning, we show that there was cognitive impairment because of the high-fat and high-sugar diet in both male and female rabbits, but this was greater in HFSD male rabbits than HFSD female rabbits. Conclusions: Metabolic syndrome modeled in rabbits fed a diet high in fat and sugar reflects trends in the adult population including central obesity, high blood sugar, and high triglycerides and cognitive impairment and provides an important model and test bed for assessing interventions. Full article

Rare earth elements (REEs) have significant application value in the quality control of nuclear materials and in traceability research in nuclear forensics. Methods were developed for the determination of REEs in uranium-bearing nuclear materials. The digestion parameters for uranium oxides and uranium ores, such as the digestion acid, digestion temperature, and digestion time, were optimized and reported. The optimized digestion parameters for uranium oxides were 2 mL HNO₃ at 160 °C for 3 h, and those for uranium ores were 7 mL mixed acid (HNO₃–HClO₄–HF = 5:5:3) at 180 °C for 36 h. Two digestion methods were demonstrated to be effective for the quantitative recovery of REEs. The suitable system and specifications for different resin columns were investigated to achieve a high decontamination factor of U (10⁵) by UTEVA resin. The corresponding loading system was 10 mL 4 M HNO₃, and the elution system was 6 mL 4 M HNO₃. Additionally, the analysis of ultra-trace REEs in high-uranium matrices was accomplished using two UTEVA resins. The developed methods were subjected to the Cochran test and the Grubbs test, and the relative standard deviation (RSD) for all REEs was below 6%. In uranium oxide samples with different spiked amounts, the recovery of REEs exceeded 80% in all cases, and the RSDs were all less than 10%. The method’s detection limits were below 10 ppt for all REEs (except for Ce), ensuring the accurate measurement of REEs in uranium-bearing nuclear materials. Full article

Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd³⁺ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis. Full article

Background/Objectives: Synaptic plasticity is fundamental to learning and memory, yet classical models such as Hebbian learning and spike-timing-dependent plasticity often overlook the distributed and wave-like nature of neural activity. We present a computational framework grounded in Scale Relativity Theory (SRT), which describes neural propagation along fractal geodesics in a non-differentiable space-time. The objective is to link nonlinear wave dynamics with the emergence of structured memory representations in a biologically plausible manner. Methods: Neural activity was modeled using nonlinear Schrödinger-type equations derived from SRT, yielding complex wave solutions. Synaptic plasticity was coupled through a reaction–diffusion rule driven by local activity intensity. Simulations were performed in one- and two-dimensional domains using finite difference schemes. Analyses included spectral entropy, cross-correlation, and Fourier methods to evaluate the organization and complexity of the resulting synaptic fields. Results: The model reproduced core neurobiological features: localized potentiation resembling CA1 place fields, periodic plasticity akin to entorhinal grid cells, and modular tiling patterns consistent with V1 orientation maps. Interacting waveforms generated interference-dependent plasticity, modeling memory competition and contextual modulation. The system displayed robustness to noise, gradual potentiation with saturation, and hysteresis under reversal, reflecting empirical learning and reconsolidation dynamics. Cross-frequency coupling of theta and gamma inputs further enriched trace complexity, yielding multi-scale memory structures. Conclusions: Wave-driven dynamics in fractal space-time provide a hypothesis-generating framework for distributed memory formation. The current approach is theoretical and simulation-based, relying on a simplified plasticity rule that omits neuromodulatory and glial influences. While encouraging in its ability to reproduce biological motifs, the framework remains preliminary; future work must benchmark against established models such as STDP and attractor networks and propose empirical tests to validate or falsify its predictions. Full article

This study presents a multidisciplinary forensic experiment evaluating the destructive effects of household chemical agents on animal bone and soft tissue analogues, with a particular focus on traumatic lesion persistence and trace evidence detection. A total of 59 domestic pig rib fragments, subjected to standardized lesions inflicted with either an axe or a ceramic knife, were immersed in acidic, basic, and oxidizing solutions for over two months. Samples were monitored through macroscopic scoring, serological species identification, and X-ray fluorescence (XRF) analysis. Results showed marked differences in tissue degradation depending on the chemical, with lesion persistence ranging from rapid obliteration to prolonged detectability. Axe-induced wounds generally remained visible longer than ceramic knife injuries, which tended to be erased earlier. XRF analysis revealed differential residue detection, with metallic traces persisting only under certain conditions, while serological testing demonstrated varying levels of protein preservation despite advanced tissue degradation. These findings underscore the forensic relevance of recognizing lesion persistence and chemical-specific degradation patterns, contributing to the assessment of chemical body disposal attempts and to the development of experimental training models for forensic practice. Full article

This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil. Full article

The diagnosis of early-stage osteoarthritis (eOA) is important in disease management and outcomes. Herein we report the clinical validation of a blood test for the diagnosis of eOA in a large patient cohort using trace-level glycated and oxidized amino acid analytes. Subjects were recruited and enrolled in two study groups: subjects with eOA of the hip (n = 110) and asymptomatic controls (n = 120). Their plasma was analyzed for glycated and oxidized amino acids by quantitative liquid chromatography–tandem mass spectrometry. Algorithms were developed using plasma hydroxyproline and 12 glycated and oxidized amino acid analyte features to classify the subjects with eOA and asymptomatic controls. The accuracy was defined as the percentage of the subjects correctly classified in the test set validation. The minimum number of analyte features required for the optimum accuracy was five glycated amino acid analytes: N_ω-carboxymethyl-arginine, hydroimidazolones derived from glyoxal, methylglyoxal and 3-deoxyglucosone, and glucosepane. The classification performance metrics included an accuracy of 95%, sensitivity of 96%, specificity of 94%, area under the curve of the receiver operating characteristic curve of 99%, and positive and negative predictive values of 94% and 97%. We concluded that an assay of five trace-level glycated amino acids present in plasma can provide a simple blood test for the screening of eOA. This is predicted to improve the case identification for expert referral 9-fold. Full article

Major depressive disorder (MDD) is a leading contributor to global disability. Despite advances in neurobiological research, no reliable peripheral biomarkers are currently available for diagnosis or monitoring. Copper (Cu), an essential trace element involved in redox balance and monoamine metabolism, has been repeatedly associated with MDD, though evidence remains inconsistent. To systematically evaluate and quantify differences in serum Cu concentrations between individuals with MDD and healthy controls, and to explore potential moderators, including sex, age, and analytical methodology. We conducted a systematic review and meta-analysis of observational studies reporting serum Cu levels in MDD patients versus controls. Data were extracted regarding diagnostic criteria, measurement methods, sample characteristics, and study quality. Subgroup and sensitivity analyses were performed based on demographic and methodological variables. Twenty-four studies, including 8617 participants (2736 MDD, 5881 controls), were analyzed. The pooled analysis revealed significantly higher Cu levels in MDD patients (Mean Difference (MD) = 2.22 µmol/L; 95% CI: 0.97–3.48; p = 0.001), although heterogeneity was high (I² = 98.6%). Sub-analysis in females confirmed the association (MD = 1.39 µmol/L; 95% CI: 0.65–2.12; p = 0.009). Results remained robust in sensitivity analyses. Begg’s test did not indicate possible publication bias. Our findings support an association between altered Cu homeostasis and MDD. Elevated Cu levels were observed in most studies, including among females and in subclinical cases, suggesting a potential role as a trait biomarker. Standardization in measurement and longitudinal designs is needed to confirm Cu’s clinical utility. Full article

Aortic valve (AV) disease affects about 5% of the aging population, with AV replacement as the only treatment option. Histopathology indicates that accumulation of extracellular matrix (ECM) proteoglycans correlates with dysfunctional AVs. Proteoglycan content is controlled by ECM proteolytic cleavage, with the cleaved and intact forms of the proteoglycan Versican (VCAN) occupying different cell lineage-specific regions throughout AV development. To test the hypothesis that VCAN cleavage is required for lineage specific cell behaviors and ECM stratification, the cardiac neural crest (CNC) lineage was traced in mice with global inactivation of the proteoglycan protease Adamts5. By mid-gestation, Adamts5^−/− mice exhibited disorganized CNC patterning with excess VCAN and enlarged semilunar valve (SLV) morphology. Use of the Adamts5 floxed mice indicated that Adamts5 was required in the endothelial cells and their mesenchymal derivatives (EndoMT lineage) to prevent VCAN accumulation, initiate ECM stratification, and promote normal SLV morphology. These data suggest that the ECM remodeling event of VCAN cleavage may orchestrate cell lineage distinct behaviors and interactions to control proteoglycan levels throughout AV development and to prevent disease. Understanding mechanisms that regulate VCAN content may lead to the discovery of effective pharmacological targets for the treatment of AV disease. Full article

Cardiopulmonary bypass (CPB) is one of the most groundbreaking medical innovations in history, enabling safe and effective heart surgery by temporarily replacing the function of the heart and lungs. This review starts with ancient concepts of cardiopulmonary function and then traces the evolution of CPB through important physiological and anatomical discoveries, culminating in the development of the modern heart–lung machine. In addition to examining the contributions of significant figures like Galen, Ibn al-Nafis, William Harvey, and John Gibbon, we also examine the ethical and technical challenges faced in the early days of open heart surgery. Modern developments are also discussed, such as miniature extracorporeal systems, off-pump surgical techniques, and the increasing importance of extracorporeal membrane oxygenation (ECMO) and extracorporeal life support (ECLS), while the evolving role of perfusionists in diverse cardiac teams and the variations in global access to CPB technology are also given special attention. We look at recent advancements in CPB, including customized methods, nanotechnology, artificial intelligence-guided perfusion, and organ-on-chip testing, emphasizing CPB’s enduring significance as a technological milestone and a living example of the cooperation of science, medicine, and human inventiveness because it bridges the gap between the past and the future. Full article