Search Results (80)

The increasing use of electronic cigarettes (e-cigarettes) highlights the need for accessible and reliable biomarkers to assess nicotine exposure. Fingernails represent a non-invasive and stable keratin matrix capable of capturing the long-term incorporation of xenobiotics such as cotinine, the primary metabolite of nicotine. This study aimed to optimise cotinine extraction from fingernails using Response Surface Methodology (RSM) with a central composite design prior to quantification by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Three extraction variables were evaluated: NaOH concentration, extraction temperature, and extraction time. Numerical optimisation identified the optimal conditions as 3.74 M NaOH, 50 °C, and 40 min, yielding a predicted recovery of 84.06% with a high desirability value of 0.973. The calibration curve demonstrated excellent linearity (R² = 0.9998), with a limit of detection of 14.5 µg kg⁻¹ and a limit of quantification of 43.8 µg kg⁻¹. The RSM model exhibited strong predictive performance, with an R² of 0.9990, an adjusted R² of 0.9982, and a predicted R² of 0.9958, supported by a non-significant lack of fit and robust residual diagnostics. Application of the optimised protocol to real fingernail samples successfully differentiated e-cigarette smokers from non-smokers based on characteristic cotinine-associated FTIR spectral features and quantitative measurements, demonstrating the practical utility of the proposed method. Overall, this study establishes a rapid, chromatography-free, and cost-effective analytical approach for monitoring long-term nicotine exposure using keratin-based matrices. Full article

The structural health monitoring (SHM) of microcracking in cryogenic hydrogen storage tanks is a critical aspect for ensuring long-term safety and operational reliability. Early detection of such damage can prevent leaks and structural failure, making the development of sensitive, non-intrusive diagnostic techniques essential. In this study, a series of experimental tests were conducted to evaluate the feasibility of using thermal expansion behavior as a potential SHM indicator. The material under investigation was a carbon–epoxy composite laminate (M21/IMA) with a [0/90]_2s layup, representative of those used in cryogenic aerospace applications. Artificial microcracks were introduced at cryogenic temperatures (approximately 20 K), followed by thermal expansion and gas permeability measurements. The objective was to explore the correlation between induced damage and measurable physical changes, with the aim of assessing the viability of this approach for future SHM strategies in liquid hydrogen tank systems. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

Smart vesicle therapeutics represent a transformative frontier in nanomedicine, offering precise, biocompatible, and adaptable platforms for drug delivery and theranostic applications. This review explores recent advances in the design and engineering of liposomes, niosomes, polymersomes, and extracellular vesicles (EVs), emphasizing their capacity to integrate therapeutic and diagnostic functions within a single nanoscale system. By tailoring vesicle size, composition, and surface chemistry, researchers have achieved improved pharmacokinetics, reduced immunogenicity, and fine-tuned control of drug release. Stimuli-responsive vesicles activated by pH, temperature, and redox gradients, or external fields enable spatiotemporal regulation of therapeutic action, while hybrid bio-inspired systems merge synthetic stability with natural targeting and biocompatibility. Theranostic vesicles further enhance precision medicine by allowing real-time imaging, monitoring, and adaptive control of treatment efficacy. Despite these advances, challenges in large-scale production, reproducibility, and regulatory standardization still limit clinical translation. Emerging solutions—such as microfluidic manufacturing, artificial intelligence-guided optimization, and multimodal imaging integration—are accelerating the development of personalized, high-performance vesicular therapeutics. Altogether, smart vesicle platforms exemplify the convergence of nanotechnology, biotechnology, and clinical science, driving the next generation of precision therapies that are safer, more effective, and tailored to individual patient needs. Full article

This study presents a methodological investigation of laser-induced breakdown spectroscopy (LIBS) for elemental analysis of quinoa and amaranth pseudocereals using a TEA CO₂ laser. Solid samples were prepared as pressed pellets, and reference data were obtained by ICP–OES. Synthetic solid standards were developed for calibration of selected elements (Ca, Fe, Zn, and Mg). Laser parameters were optimized based on the signal-to-noise ratio of characteristic spectral lines and applied to both pseudocereal samples. Emission lines of Mg, Ca, Fe, K, P, Zn, Al, Sr, and Cu were identified, and limits of detection were determined. Quantitative analysis used calibration curves from analyte-to-internal standard line intensity ratios, showing good linearity and agreement with reference values. Plasma diagnostics under optimized conditions revealed an average temperature of ~11,000 K and electron number densities of ~5 × 10¹⁶ cm⁻³ for both samples. Numerical plasma simulations confirmed the experimental results and provided additional insight into plasma composition and behavior. The developed LIBS methodology proved effective for multi-elemental analysis of pseudocereals and shows potential for application to other cereal and plant-based materials with similar composition. It should be noted that this methodology was demonstrated on pelletized samples prepared under controlled laboratory conditions; adaptation to rapid or field-based measurements would require alternative sample preparation strategies. This work provides a methodological framework and experimental validation for LIBS application in food compositional and nutritional analysis. Full article

Baltimore’s historic row-home neighborhoods face escalating risks to energy, heat, and durability under intensifying climate stress. This study develops a Housing-Performance Atlas that quantifies multi-hazard performance for eight representative archetypes using DesignBuilder/EnergyPlus Version 7.3.1.003, under Baltimore TMY3 boundary conditions. Performance is evaluated across the following four adaptation domains: energy use intensity, passive survivability during 72 h outage events, roof overheating exposure (>150 °F exceedance hours), and material service life derived from ISO 15686 and synthesized into Lean and Full Deficit Indices for comparative resilience ranking. Results show that EUI ranged from 46.7 to 67.6 kBtu ft⁻²·yr⁻¹, survivability from 0 to 23 h, and roof temperatures exceeded 150 °F for 150–210 h, shortening roof service life by up to 10 years. Composite Lean and Full Deficit Indices ranged 7.8–92.4, ranking Model 5 (end-unit, flat roof, two-story with basement) as the most resilient configuration and Model 8 (end-unit, pitched roof, three-story above-grade) as the least resilient due to compounded overheating and energy losses. Heat-related domains accounted for nearly 70% of overall resilience deficits, confirming thermal safety and roof reflectivity as retrofit priorities. The Housing-Performance Atlas establishes a reproducible diagnostic framework linking simulation, service life, and resilience metrics to guide cost-effective, climate-responsive retrofits in Baltimore’s aging urban housing stock. Full article

Subclinical hypocalcemia (SCH) is one of the most prevalent metabolic disorders in early-lactation dairy cows, yet its multifaceted physiological effects are often overlooked due to the absence of clinical symptoms. This study aimed to characterize SCH through an integrative assessment of blood biochemical markers, in-line milk composition, and sensor-derived behavioral traits. Seventy-five Holstein cows between 2 and 21 days in milk were classified into hypocalcemic (group 1) (Ca < 2.0 mmol/L; n = 20) and healthy (group 2) groups (n = 55). Blood samples, milk data, and rumination metrics were evaluated, and group differences were analyzed using Welch’s t-test and Pearson correlations. Cows with SCH exhibited significantly lower concentrations of Ca, PHOS, Mg, ALB, TP, GLUC, and Fe, indicating disruptions in mineral balance, protein metabolism, and energy status. Hepatic indicators (AST, ALT, GGT) did not differ between groups, whereas CREA was significantly lower in hypocalcemic cows, suggesting altered muscle metabolism rather than impaired renal function. Although differences in milk yield, composition, and rumination time did not reach statistical significance, hypocalcemic cows showed consistent biological tendencies toward reduced milk components and lower milk temperature. Correlation analysis revealed strong physiological linkages among Ca, ALB, P, TP, and Fe, underscoring the interconnected nature of mineral and protein metabolism in early lactation. These findings demonstrate that SCH is associated with coordinated biochemical and behavioral changes even in the absence of clinical signs. Integrating blood biomarkers with real-time sensor data provides a more comprehensive understanding of calcium-related metabolic challenges and highlights the potential of precision-livestock technologies for early detection. Future studies incorporating ionized calcium and longitudinal sampling are needed to refine diagnostic thresholds and improve predictive monitoring of SCH in dairy herds. Full article

This study introduces GAT-CAMDA, a novel framework for the structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation (DA) techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with a domain-discriminative adversarial model, the framework achieves scalable alignment of feature distributions across temperature domains, ensuring robust damage detection. A simple yet at the same time efficient data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies the optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset, but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments. Full article

Generalized frosts have a significant impact on the Pampa Húmeda of Argentina, particularly those without persistence (0DP), defined as events that do not last more than one day, and are the most frequent generalized frosts. This study investigates the dynamical and physical mechanisms that sustain these events, emphasizing the nonlinear interactions represented by the Rossby Wave Source (RWS) equation. Composite analysis of pressure, temperature, wind and geopotential height fields were performed, showing that 0DP events are related to abrupt cold air intrusion linked to the enhancement of upper levels troughs over the eastern Pacific Ocean and transient surface anticyclones over South America. This linear analysis only showed a lack of persistent upper-level maintenance and did not explain the dynamics of the rapid weakening of the circulation. For this reason, a nonlinear analysis based on the decomposition of the RWS equation into its advective and divergent terms is performed. The advective term only acts as an initial trigger, deepening troughs and favoring meridional cold air advection, while the divergent term dominates the events, representing 63–67% of the affected area. This term reinforces ridges, promotes subsidence and favors clear sky conditions that enhance nocturnal radiative cooling and frost formation. Positive anomalies of the divergent RWS term strengthen the ridge and advect cold air over the Pampa Húmeda, whereas subsequent negative anomalies over the southwestern Atlantic act as sinks of wave activity, leading to the rapid dissipation of the synoptic configuration. Consequently, the same mechanism that generates favorable conditions for frost development also determines their lack of persistence. These findings demonstrate that the short-lived nature of 0DP frosts is not due to the absence of dynamical forcing, but rather to nonlinear processes that both enable and constrain frost occurrence. This highlights the importance of incorporating nonlinear diagnostics, such as the RWS, to improve the understanding of short-lived atmospheric extremes. Full article

This paper introduces innovative approaches to the design of railway point machines, with particular emphasis on the implementation of multi-component AlNiCoFe alloys, classified as semi-hard magnetic materials. A comprehensive review of existing mechanisms for mechanical force transmission—from the electric motor to the throwing bar—was conducted. The inherent limitations of conventional dry friction clutches, commonly used in current point machine designs, are critically analyzed. Furthermore, the feasibility of employing multi-component AlNiCoFe alloys as functional materials in hysteresis magnetic clutches is examined, with a view toward enhancing the reliability and performance of railway point actuation systems. A review of diagnostic methods for railway point machines was conducted to evaluate the potential application of a novel magnetic torque limiter as a means to eliminate maintenance activities typically required for systems utilizing dry friction clutches. Experimental research was performed on AlNiCoFe alloys employed as the hysteresis layer in the proposed torque limiter. Microstructural and compositional analyses were carried out using scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and X-ray Diffraction (XRD) to determine the crystallographic structure, chemical composition, and selected physical properties of the tested materials. The hysteresis loops of the tested materials were measured using a Vibrating Sample Magnetometer (VSM) over a wide temperature range. A prototype magnetic clutch, functioning as a torque limiter in a railway point machine, was developed and presented. The operational characteristics—specifically the throwing force as a function of time—were recorded for a railway point machine equipped with an electromechanical module incorporating the new magnetic torque limiter. The advantages of the proposed solution in terms of force transmission and overall system performance in railway point machine design were analyzed and discussed. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

The Gadouras Reservoir, Rhodes Island’s primary water source, experiences recurrent whiting events—milky turbidity from calcium carbonate precipitation—that challenge treatment operations, with impacts compounded by a major 2023 wildfire in this fire-prone Mediterranean setting. To elucidate these dynamics, a pragmatic, multi-source monitoring framework integrates archived Sentinel-2 and Landsat imagery with treatment-plant records (2017–mid-2025). Unitless spectral indices (e.g., AreaBGR) for whiting detection and chlorophyll-a proxies are combined with laboratory measurements of turbidity, pH, total organic carbon, manganese, and hydrological metrics, analyzed via spatiotemporal Hovmöller diagrams, Pearson correlations, and interrupted time-series models. Two seasonal whiting regimes are identified: a biogenic summer mode (southern origin; elevated chlorophyll-a; water temperature > 15 °C; pH > 8.5) and a non-biogenic winter mode (northern inflows). Following the wildfire, the system exhibits characteristics that could be related to possible hypolimnetic anoxia, prolonged whiting, a ~50% rise in organic carbon, and a manganese excursion to ~0.4 mg L⁻¹ at the deeper intake. Crucially, the post-fire period shows a decoupling of AreaBGR from turbidity (r ≈ 0.233 versus ≈ 0.859 pre-fire)—a key diagnostic finding that confirms a fundamental shift in the composition and optical properties of suspended particulates. The manganese spike is best explained by the confluence of a wildfire-induced biogeochemical predisposition (anoxia and Mn mobilization) and a consequential operational decision (relocation to a deeper, Mn-rich intake). This framework establishes diagnostic baselines and thresholds for managing fire-impacted reservoirs, supports the use of remote sensing in data-scarce systems, and informs adaptive operations under increasing climate pressures. Full article

In this study, we present analysis of TESS photometry, spectral energy distribution (SED), high-resolution spectroscopy, and spot modeling of the ${\alpha }^2$ CVn-type star AL Col (HD 46462). The primary objective is to determine its fundamental physical parameters and investigate its surface activity characteristics. Using TESS short-cadence (120 s) SAP flux, we identified a rotational frequency of 0.09655 ${\mathrm{d}}^{-1}$ ($P_{\mathrm{rot}}=10.35733$ d). Wavelet analysis reveals that while the amplitudes of the harmonic components vary over time, the strength of the primary rotational frequency remains stable. A SED analysis of multi-band photometric data yields an effective temperature ($T_{\mathrm{eff}}$) of 11,750 K. High-resolution spectroscopic observations covering wavelengthrange 4500–7000 Å provide refined estimates of $T_{\mathrm{eff}}$ = 13,814 ± 400 K, $logg$ = 4.09 ± 0.08 dex, and $\upsilon sini$ = 16 ± 1 km s⁻¹. Abundance analysis shows solar-like composition of O ii, Mg ii, S ii, and Ca ii, while helium is under-abundant by 0.62 dex. Rare earth elements (REEs) exhibit over-abundances of up to 5.2 dex, classifying the star as an Ap/Bp-type star. AL Col has a radius of $R=3.74\pm 0.48{\mathrm{R}}_{\odot }$, with its H–R diagram position estimating a mass of $M=4.2\pm 0.2{\mathrm{M}}_{\odot }$ and an age of $0.12\pm 0.01$ Gyr, indicating that the star has slightly evolved from the main sequence. The TESS light curves were modeled using a three-evolving-spot configuration, suggesting the presence of differential rotation. This star is a promising candidate for future investigations of magnetic field diagnostics and the vertical stratification of chemical elements in its atmosphere. Full article

A new method of diagnostics of technological parameters of the material agglomeration process and a design of a new matrix for compacting fine-grained materials were presented. Two different dies were used in the agglomeration process to carry out the tests. One of them is a new prototype design of an opening matrix, which allows for its quick opening and closing and easier extraction of the briquette and measurement. Comparative tests were carried out for four materials with different chemical compositions. The experimental part showed changes in the temperature of samples subjected to agglomeration. Analysis of the results for the Electric Arc Furnace Dust (EAFD) mixture showed that the average temperature in the middle of the material was higher by 1.3 °C than the highest temperature on the external surface. Comparing the test results for the hydrated lime obtained in the opening matrix with the results obtained for the classic closed matrix, it can be seen that the results are very similar, which indicates the advantage of the prototype solution of the opening matrix. The prototype solution of the opening matrix significantly speeds up the agglomeration process. The obtained results confirm the possibility of using thermography diagnostics in the agglomeration processes of fine-grained materials and the usefulness of the newly designed die. Thermography examination can be practically used to control the agglomeration process, product quality, and, indirectly, roller wear. Full article

Background/Objectives: Magnetic nanoparticles, particularly iron oxide-based materials, such as magnetite (Fe₃O₄), have gained significant attention as contrast agents in medical imaging This study aimsto syntheze and characterize Fe₃O₄-based core–shell nanostructures, including Fe₃O₄@TiO₂ and Fe₃O₄@SiO₂, and to evaluate their potential as tunable contrast agents for diagnostic imaging. Methods: Fe₃O₄, Fe₃O₄@TiO₂, and Fe₃O₄@SiO₂ nanoparticles were synthesized via co-precipitation at varying temperatures from iron salt precursors. Fourier transform infrared spectroscopy (FTIR) was used to confirm the presence of Fe–O bonds, while X-ray diffraction (XRD) was employed to determine the crystalline phases and estimate average crystallite sizes. Morphological analysis and particle size distribution were assessed by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and transmission electron microscopy (TEM). Magnetic properties were investigated using vibrating sample magnetometry (VSM). Results: FTIR spectra exhibited characteristic Fe–O vibrations at 543 cm⁻¹ and 555 cm⁻¹, indicating the formation of magnetite. XRD patterns confirmed a dominant cubic magnetite phase, with the presence of rutile TiO₂ and stishovite SiO₂ in the coated samples. The average crystallite sizes ranged from 24 to 95 nm. SEM and TEM analyses revealed particle sizes between 5 and 150 nm with well-defined core–shell morphologies. VSM measurements showed saturation magnetization (Ms) values ranging from 40 to 70 emu/g, depending on the synthesis temperature and shell composition. The highest Ms value was obtained for uncoated Fe₃O₄ synthesized at 94 °C. Conclusions: The synthesized Fe₃O₄-based core–shell nanomaterials exhibit desirable structural, morphological, and magnetic properties for use as contrast agents. Their tunable magnetic response and nanoscale dimensions make them promising candidates for advanced diagnostic imaging applications. Full article