Search Results (30,378)

A friction composite material which contains cellulose fiber, carbon fiber, wollastonite, graphite, and resin for use in oil-cooled friction units, hydromechanical boxes, and couplings was developed. The fabrication technique includes the formation of a paper layer based on the mixture of stated fibers via a wet-laid process, impregnation of the layer with phenolic resin, and hot pressing onto a steel carrier. The infrared spectra of the polymeric base (phenolic resin) were studied by solvent extraction. The structural-phase analysis of the obtained material was carried out by the SEM method, and the particle size distribution parameters of the composite components were estimated based on the images of the sample surface. The surface roughness parameters of the samples are as follows: Ra = 5.7 μm Rz = 31.4 μm. The tribotechnical characteristics of the material were tested in an oil medium at a load of 5.0 MPa and a rotation mode of 2000 rpm for 180 min in a pair with a steel 45 counterbody. The coefficient of friction of the developed material was 0.11–0.12; the degree of wear was 6.17 × 10⁻⁶ μm/mm. The degree of compression deformation of the composite is 0.36%, and the compressive strength is 7.8 MPa. The calculated kinetic energy absorbed and power level are 205 J/cm² and 110 W/cm², respectively. The main tribotechnical characteristics of the developed friction material correspond to industrial analogues. Full article

Variegated Cymbidium lancifolium is a highly valued ornamental plant sought after in local and international markets. The commercial production of variegated C. lancifolium through traditional propagation methods faces significant challenges, such as low propagation rates and prolonged growth periods. This study aims to develop effective in vitro propagation techniques for variegated C. lancifolium through asymbiotic seed germination to enhance production efficiency and meet market demand. We examined the effects of various plant growth regulators and coconut water (CW) on in vitro seed germination. The highest germination percentage (46.8%) was recorded in Murashige and Skoog (MS) medium supplemented with 50 mL/L CW, 4.0 µM α-naphthalene acetic acid (NAA), 2.3 µM kinetin (KN), and 2.9 µM gibberellic acid (GA₃). Seed-derived rhizomes were placed on MS medium containing indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), and NAA for proliferation. Among the auxins, NAA was the most effective, significantly increasing rhizome proliferation, with the highest number (17.4) and length (2.1 cm) observed at 5.0 µM. The rhizome explants were cultured in MS medium enriched with kinetin (KN), N⁶-(2-isopentenyl)adenine (2-IP), and N⁶-benzyladenine (BA) to promote plantlet regeneration. Of the cytokinins tested, BA at 10.0 µM resulted in the highest rate of plantlet regeneration (79.4%), the greatest number of plantlets (4.4 per culture), and notable plantlet height (8.5 cm). We obtained plantlets with dark green leaves, light green leaves, and distinct variegation patterns. They were transferred to three different substrate mixtures for acclimatization. The substrate made of orchid stone (30%), wood bark (30%), coconut husk chips (20%), and perlite (20%) supported the highest survival rate (95.9%). This study successfully established optimized in vitro propagation techniques for variegated C. lancifolium, enabling enhanced germination, rhizome proliferation, and plantlet regeneration to meet the growing market demand. Full article

Polycyclic aromatic hydrocarbon (PAH) derivatives, specifically azaarenes and nitrated and oxygenated PAHs, are emerging contaminants of concern due to their increased toxicity and persistence compared to the parent PAHs. Despite their toxicity, their simultaneous analysis in complex matrices, such as in fumes emitted from bituminous mixtures, remains challenging due to limitations of conventional analytical techniques. To address this, an advanced methodology was developed using Ultra-High-Performance Liquid Chromatography coupled with High-Resolution Mass Spectrometry (UHPLC-HRMS Orbitrap Eclipse) equipped with an APCI source for the simultaneous identification and quantification of 14 PAH derivatives. Chromatographic and ionization parameters were optimized to ensure maximum sensitivity and selectivity. Following ICH Q2(R2) guidelines, the method was validated, demonstrating excellent linearity (R² > 0.99), high mass accuracy (≤5 ppm), strong precision (<15%), and excellent sensitivity. Limits of detection (LODs) ranged from 0.1 µg L⁻¹ to 0.6 µg L⁻¹ and limits of quantification (LOQs) ranged from 0.26 µg L⁻¹ to 1.87 µg L⁻¹. The validated method was successfully applied to emissions from asphalt pavement materials collected on quartz filters under controlled conditions, enabling the identification and quantification of all 14 targeted compounds. These results confirm the method’s robustness and suitability for trace-level analysis of PAH derivatives in complex environmental matrices. Full article

Intergrown mixtures of nanostructured manganese oxide phases have been obtained using a highly complexing agent (ethylenediamine) and a weak complexer (urea) during their solvothermal synthesis. In this work, through a detailed structural analysis, it is evidenced the formation of an intergrown mixture of three distinct manganese oxide phases (β-MnO₂, α-Mn₂O₃, and Mn₃O₄). Scanning electron microscopy shows that the products have just one morphology, indicating that the different manganese oxide phases may have grown together, organizing themselves in a 3D crystal network. The reaction mechanisms are discussed in this paper. It is of great interest to produce intergrown mixtures of manganese oxide phases to take advantage of the availability of the different oxidation states of Mn in neighboring crystallites for applications like catalysis. Full article

Addressing the lack of dynamic monitoring methods for assessing the combined toxicity of mixed pesticides, this study developed a fluorescent probe, CCHL, specifically responsive to leucine aminopeptidase (LAP). The probe utilized Cy7-COOH (CCH) as the fluorophore, with fluorescence recovery triggered by enzymatic hydrolysis. Spectral characterization confirmed a linear response between the probe and LAP activity within a concentration range of 0–0.9 μg/mL (R² = 0.992), along with excellent selectivity in the presence of coexisting biomolecules. Application experiments demonstrated that the combination of chlorfenapyr and beta-cyfluthrin significantly reduced LAP activity, revealing a notable antagonistic effect. The novel sensing strategy developed here provides a real-time, visualized analytical tool for evaluating the combined effects of mixed pollutants, demonstrating significant potential for environmental toxicology monitoring. Full article

Biosurfactants (BS) are surface-active compounds synthesized by microorganisms with broad industrial applications. Although BS-producing strains are widely reported, little is known about their production by diazotrophic bacteria. This study investigated, for the first time, the BS produced by Bradyrhizobium sp. ESA 81, a diazotrophic bacterium isolated from the Brazilian semiarid region. The strain was cultivated in the mineral medium using sunflower oil and ammonium nitrate as carbon and nitrogen sources. The compound was chemically characterized using TLC, FAME, FTIR, and mass spectrometry (MALDI-TOF). The results revealed a mixture of glycolipids composed of trehalose linked to fatty acid chains ranging from C9 to C18. The BS exhibited a surface tension of 31.8 mN/m, a critical micelle concentration of 61.2 mg/L, and an interfacial tension of 22.1 mN/m. The BS also showed an emulsification index (EI24) of 55.0%. High stability was observed under extreme conditions of temperature (−20 to 121 °C), pH (2–12), NaCl (5–20%), and sucrose (1–5%). These findings indicate that the trehalolipid BS produced by Bradyrhizobium sp. ESA 81 is a stable and efficient surface-active agent, with promising potential for use in biotechnological and industrial processes. Full article

This study presents the development of a reliable predictive model for evaluating key physical and mechanical properties of cement-based composites modified with granite powder, a waste byproduct from granite rock cutting. The research addresses the need for more sustainable materials in the concrete industry by exploring the potential of granite powder as a supplementary cementitious material (SCM) to partially replace cement and reduce CO₂ emissions. The experimental program included standardized testing of samples containing up to 30% granite powder, focusing on compressive strength at 7, 28, and 90 days, bonding strength at 28 days, and packing density of the fresh mixture. A multilayer perceptron (MLP) artificial neural network was employed to predict these properties using four input variables: granite powder content, cement content, sand content, and water content. The network architecture, consisting of two hidden layers with 10 and 15 neurons, respectively, was selected as the most suitable for this purpose. The model achieved high predictive performance, with coefficients of determination (R) exceeding 0.9 and mean absolute percentage errors (MAPE) below 6% for all output variables, demonstrating its robustness and accuracy. The findings confirm that granite powder not only contributes positively to concrete performance over time, but also supports environmental sustainability goals by reducing the carbon footprint associated with cement production. However, the model’s applicability is currently limited to mixtures using granite powder at up to 30% cement replacement. This research highlights the effectiveness of machine learning, specifically neural networks, for solving multi-output problems in concrete technology. The successful implementation of the MLP network in this context may encourage broader adoption of data-driven approaches in the design and optimization of sustainable cementitious composites. Full article

This study investigates the flexural performance, tensile splitting strength, and fracture behaviour of self-compacting concrete (SCC) reinforced with polypropylene (PP) and basalt (BF) fibres. A total of eleven SCC mixtures with varying fibre types and volume fractions (0.025–0.25%) were tested at 7 and 28 days. In this study, the term high-performance concrete (HPC) refers to SCC mixtures with a 28-day compressive strength exceeding 60 MPa, as commonly accepted in European standards and literature. The control SCC achieved 68.2 MPa at 28 days. While fibre addition enhanced the tensile and flexural properties, it reduced workability, demonstrating the trade-off between mechanical performance and flowability in high-performance SCC. The experimental results demonstrate that both fibre types improve the tensile behaviour of SCC, with distinct performance patterns. PP fibres, owing to their flexibility and crack-bridging capability, were particularly effective at early ages, enhancing the splitting tensile strength by up to 45% and flexural toughness by over 300% at an optimal dosage of 0.125%. In contrast, BF fibres significantly increased the 28-day toughness (up to 15.7 J) and post-cracking resistance due to their superior stiffness and bonding with the matrix. However, high fibre contents adversely affected workability, particularly in BF-reinforced mixes. The findings highlight a dosage-sensitive behaviour, with optimum performance observed at 0.05–0.125% for PP and 0.125–0.25% for BF. While PP fibres improve crack distribution and early-age ductility, BF fibres offer higher stiffness and energy absorption in post-peak regimes. Statistical analysis (ANOVA and Tukey’s test) confirmed significant differences in the mechanical performance among fibre-reinforced mixes. The study provides insights into selecting appropriate fibre types and dosages for SCC structural applications. Further research on hybrid fibre systems and long-term durability is recommended. The results contribute to sustainable concrete design by promoting enhanced performance with low-volume, non-metallic fibres. Full article

Noise in Pulsar Timing Array (PTA) data is commonly modeled as a mixture of white and red noise components. While the former is related to the receivers, and easily characterized by three parameters (EFAC, EQUAD and ECORR), the latter arises from a mix of hard to model sources and, potentially, a stochastic gravitational wave background (GWB). Since their frequency ranges overlap, GWB search methods must model the non-GWB red noise component in PTA data explicitly, typically as a set of mutually independent Gaussian stationary processes having power-law power spectral densities. However, in searches for continuous wave (CW) signals from resolvable sources, the red noise is simply a component that must be filtered out, either explicitly or implicitly (via the definition of the matched filtering inner product). Due to the technical difficulties associated with irregular sampling, CW searches have generally used implicit filtering with the same power law model as GWB searches. This creates the data analysis burden of fitting the power-law parameters, which increase in number with the size of the PTA and hamper the scaling up of CW searches to large PTAs. Here, we present an explicit filtering approach that overcomes the technical issues associated with irregular sampling. The method uses adaptive splines, where the spline knots are included in the fitted model. Besides illustrating its application on real data, the effectiveness of this approach is investigated on synthetic data that has the same red noise characteristics as the NANOGrav 15-year dataset and contains a single non-evolving CW signal. Full article

Perfluoromethyl vinyl ether (PMVE) has recently emerged as a promising environmentally friendly insulating gas with potential for practical applications in the power industry. When mixed with CO₂, the PMVE/CO₂ mixture exhibits an elevated liquefaction temperature and enhanced insulation performance, making it suitable for engineering use. In this study, density functional theory (DFT) calculations were employed to investigate the reactive sites of PMVE molecules. The results indicate that the C₂–O and C₃–O bonds are the most susceptible to breakage, highlighting their high reactivity. The optimal insulation performance of the PMVE/CO₂ mixture is achieved at a CO₂ concentration of approximately 60%, with significant molecular decomposition observed at temperatures exceeding 2600 K. The primary decomposition products include C₂F₂, COF₃, COF₂, F, C₂F₃, CO, CF₃, and C₂F₄. Both high temperature and elevated CO₂ content accelerate the decomposition process. These findings provide valuable insights into the insulation properties and thermal stability of the PMVE/CO₂ system, offering theoretical support for its potential application in eco-friendly high-voltage insulation technologies. Full article

Emergency diesel is prone to degradation during long-term storage, and experimental evaluations are costly and slow. Three-dimensional computational fluid dynamics (CFD) simulations were employed to model the diesel conditioning process. A physical model based on the actual dimensions of the storage tank was constructed. The volume of fraction (VOF) model tracked the gas–liquid interface, and the species transport model handled mixture transport. A UDF then recorded inlet and outlet flow rates and velocities in each cycle. The study focused on the effects of different baffle structures and numbers on conditioning efficiency. Results showed that increasing the baffle flow area significantly delays the mixing time but reduces the cycle time. Openings at the bottom of baffles effectively mitigate the accumulation of high-concentration conditioning oil caused by density differences. Increasing the number of baffles decreases the effective volume of the tank and amplifies density differences across the baffles, which shortens the mixing time. However, excessive baffle numbers diminish these benefits. These findings provide essential theoretical guidance for optimizing baffle design in practical diesel tanks, facilitating rapid achievement of emergency diesel quality standards while reducing costs and improving efficiency. Full article

Background/Objectives: Vitamin C is frequently used in several dietary supplements due to its proposed health-promoting properties, while phenolic compounds and especially flavonoids have been suggested to provide synergistic antioxidant and cardiovascular benefits. However, the specific interactions between these compounds and their individual contributions to biological activity remain underexplored. This study aimed to evaluate the antioxidant potential and anti-inflammatory and antiplatelet biological effects of a high-dose (1 g) vitamin C–low-dose (50 mg) bioflavonoid (VCF)-based supplement using both in vitro and in vivo approaches in human platelets. Methods: Total phenolic content was quantified and antioxidant capacity was assessed using DPPH, FRAP, and ABTS assays and compared to individual phenolic standard compounds, including (simple phenolics like gallic acid, flavonoids like quercetin and catechin, and polyphenols like curcumin and tannin), and a standard supplement containing only high-dose vitamin C (VC). ATR-FTIR spectroscopy was used to assess molecular interactions between vitamin C and flavonoids. In vitro anti-inflammatory and antiplatelet activities of all supplements and standards were assessed by quantifying their IC₅₀ values against ADP, PAF, and thrombin-induced platelet aggregation. The in vivo evaluation of the efficacy and synergy of VCF supplement versus VC was achieved by a two-arm clinical study in healthy volunteers by quantifying their platelet reactivity, which was measured via EC₅₀ values on the aforementioned platelet agonists (PAF, ADP, and Thrombin) before (t = 0) and after receiving either solely VC or VCF supplementation for four weeks. Results: From all phenolic standards, the flavonoids and especially a mixture of flavonoids (catechin + quercetin) showed higher in vitro antioxidant capacity and anti-inflammatory and antiplatelet efficacy, followed by polyphenols and then simple phenolics. The VCF supplement showed the most potent antioxidant capacity, but also the strongest anti-inflammatory and antiplatelet activities too, in comparison to the VC and the mixture of flavonoids, suggesting higher synergy and thus bio-efficacy as a result of the co-presence of flavonoids and vitamin C in this supplement. Platelet reactivity decreased over time for PAF and thrombin in both arms of the trial, but no significant differences were observed between treatment groups, suggesting that the number of flavonoids used was not sufficient to translate the in vitro findings to the in vivo setting. Conclusions: VC-containing supplements provide antioxidant, anti-inflammatory, and antiplatelet benefits, while the incorporation of flavonoids may provide synergistic health benefits, but more in vivo assessment is needed to fully evaluate the dose efficacy. Full article

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to a well-documented trade-off in existing research, where typically only two of these factors are improved at the expense of the third. Unlike previous studies, the present work achieves simultaneous improvement of all three metrics without hardware modification or ignition timing adjustment, relying solely on the optimization of the air–fuel equivalence ratio $\lambda$. Experiments were conducted on a six-cylinder engine for combined heat and power application, fueled with hydrogen–natural gas blends containing up to 30% hydrogen by volume. By optimizing only the air–fuel equivalence ratio, it was possible to extend the lean-burn limit from $\lambda \approx 1.6$ to $\lambda >1.9$, reduce nitrogen oxide emissions by up to 70%, enhance thermal efficiency by up to 2.2 percentage points, and significantly improve combustion stability, reducing cycle-by-cycle variationsfrom 2.1% to 0.7%. A defined $\lambda$ window was identified in which all three key performance indicators simultaneously meet or exceed the natural gas baseline. Within this window, balanced improvements in nitrogen oxide emissions, efficiency, and stability are achievable, although the individual maxima occur at different operating points. Cylinder pressure analysis confirmed that combustion dynamics can be realigned with original equipment manufacturer characteristics via mixture leaning alone, mitigating hydrogen-induced pressure increases to just 11% above the natural gas baseline. These results position hydrogen as a performance booster for natural gas engines in stationary applications, enabling cleaner, more efficient, and smoother operation without added system complexity. The key result is the identification of a $\lambda$ window that enables simultaneous optimization of nitrogen oxide emissions, efficiency, and combustion stability using only mixture control. Full article

We consider the lossless compression bound of any individual data sequence. Conceptually, its Kolmogorov complexity is such a bound yet uncomputable. According to Shannon’s source coding theorem, the average compression bound is $nH$, where n is the number of words and H is the entropy of an oracle probability distribution characterizing the data source. The quantity $nH\left({\widehat{\theta }}_n\right)$ obtained by plugging in the maximum likelihood estimate is an underestimate of the bound. Shtarkov showed that the normalized maximum likelihood (NML) distribution is optimal in a minimax sense for any parametric family. Fitting a data sequence—without any a priori distributional assumption—by a relevant exponential family, we apply the local asymptotic normality to show that the NML code length is $nH\left({\widehat{\theta }}_n\right)+{\displaystyle \frac{d}{2}}log{\displaystyle \frac{n}{2\pi }}+log{\int }_{\Theta }{\left|I\left(\theta \right)\right|}^{1/2}d\theta +o\left(1\right)$, where d is dictionary size, $\left|I\right(\theta \left)\right|$ is the determinant of the Fisher information matrix, and $\Theta$ is the parameter space. We demonstrate that sequentially predicting the optimal code length for the next word via a Bayesian mechanism leads to the mixture code whose length is given by $nH\left({\widehat{\theta }}_n\right)+{\displaystyle \frac{d}{2}}log{\displaystyle \frac{n}{2\pi }}+log{\displaystyle \frac{|I\left({\widehat{\theta }}_n\right){|}^{1/2}}{w\left({\widehat{\theta }}_n\right)}}+o\left(1\right)$, where $w\left(\theta \right)$ is a prior. The asymptotics apply to not only discrete symbols but also continuous data if the code length for the former is replaced by the description length for the latter. The analytical result is exemplified by calculating compression bounds of protein-encoding DNA sequences under different parsing models. Typically, compression is maximized when parsing aligns with amino acid codons, while pseudo-random sequences remain incompressible, as predicted by Kolmogorov complexity. Notably, the empirical bound becomes more accurate as the dictionary size increases. Full article

Particulate matter (PM), a complex mixture of solid particles and liquid droplets, originates from both natural sources, such as sand, pollen, and marine salts, and anthropogenic activities, including vehicle emissions and industrial processes. While PM itself is not inherently toxic in all its forms, it often acts as a carrier of xenobiotic toxicants, such as heavy metals and organic pollutants, which adhere to its surface. This combination can result in synergistic toxic effects, significantly enhancing the potential harm to biological systems. Due to its small size and composition, PM can penetrate deep into the respiratory tract, acting as a physical “shuttle” that facilitates the distribution and bioavailability of toxic substances to distant organs. The omnipresence of PM in the environment leads to unavoidable and constant exposure, contributing to increased morbidity and mortality rates, particularly among vulnerable populations like the elderly, children, and individuals with pre-existing health conditions. This exposure also imposes a substantial financial burden on healthcare systems, as treating PM-related illnesses requires significant medical resources and leads to higher healthcare costs. Addressing these challenges necessitates effective mitigation strategies, including reducing PM exposure, improving air quality, and exploring novel approaches such as AI-based exposure prediction and nutritional interventions to protect public health and minimize the adverse effects of PM pollution. Full article