Search Results (18,848)

Sperm cryopreservation produces a series of physicochemical phenomena that negatively impact the function and structure of spermatozoa, including the mobilization of cholesterol from the plasma membrane. The use of attenuated total reflection–Fourier-transform infrared spectroscopy (ATR-FTIR) may be useful to measure the cholesterol efflux in goat spermatozoa. Therefore, the objective of this study was to standardize the use of ATR-FTIR to measure the efflux of cholesterol in goat spermatozoa. Standardization of the technique was carried out in three stages: (i) determination of the appropriate sperm concentration to detect cholesterol in the FTIR spectrum; (ii) determination of the minimum percentage of viable spermatozoa required to observe at least five spectral bands in common with pure cholesterol; (iii) assessment of cholesterol removal in frozen–thawed spermatozoa. Possible differences in the areas of the spectral bands were compared by one-way ANOVA. Nineteen spectra were obtained: pure cholesterol, sperm transport medium, five different sperm concentrations, and ten live/dead sperm proportions (heat and cold-killed). The lowest sperm concentration at which spectral bands were clearly identified was 13 × 10⁶ sperm/mL. Regarding viability, the cut-off value was 50%: higher values produced spectral bands clearly detectable, whereas in smaller values, the band’s areas decreased sharply, making it difficult to quantify them. Five areas of the cholesterol bands decreased in thawed samples compared to fresh spermatozoa; an increase in the proportion of frozen–thawed sperm showing Merocyanine brilliant pattern, indicative of high fluidity, as well as an increase in the proportion of CTC AR pattern, indicative of acrosome reaction, support those results. In conclusion, ATR-FTIR is a useful technique for identifying the movement of cholesterol in goat buck spermatozoa. Full article

This work is related to the development of a highly efficient pH-responsive ionic draw solute for forward osmosis applications utilizing microwave-assisted fast heating. This solute is classified as an ionic compound, a sodium salt originating from imidazole, with the scientific acronym 1-acetyl-2-methylbenzimidazole sodium bisulfate (AMBIM-Na). The synthesized compound was analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), as well as additional physical characteristics. The baseline performance was initially evaluated at various molar concentrations against distilled water as the feed solution (FS). The results indicated that the produced solute exhibits elevated osmotic pressure, resulting in a water flux of up to 130 LMH for a 1 M concentration, coupled with the absence of reverse salt flux. The synthesized AMBIM-Na at a concentration of 1 M was utilized as a draw solution (DS) against synthetic brackish water. The water flux declined progressively with the increase in FS concentration, decreasing from 130 LMH with distilled water to 99, 70, and 41 LMH at NaCl concentrations of 5, 10, and 15 g/L, respectively. The regeneration of the draw solute was assessed using pH adjustment, revealing that 100% regeneration occurs by reducing the pH to 2. Full article

With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths and enables the capture of high-frequency signals to reveal finer subsurface structural details. However, the insufficient sampling rate of existing petroleum instruments prevents the effective capture of such high-frequency signals. To address this limitation, we employ high-frequency geophones together with high-density and high-frequency acquisition systems to collect the required data. Meanwhile, conventional processing methods such as Fourier transform-based time–frequency analysis are prone to phase instability caused by frequency interval selection. This instability hinders the accurate representation of subsurface structures and reduces the precision of shallow-layer phase identification. To overcome these challenges, this paper proposes a denoising method for high-sampling-rate seismic data based on Variational Mode Decomposition (VMD) optimized by the Starfish Optimization Algorithm (SFOA). The denoising results of simulated signals demonstrate that the proposed method effectively preserves the stability of noise-free regions while maintaining the integrity of peak signals. It significantly improves the signal-to-noise ratio (SNR) and normalized cross-correlation coefficient (NCC) while reducing the root mean square error (RMSE) and relative root mean square error (RRMSE). After denoising the surface mountain drilling-while-drilling signals, the resulting waveforms show a strong correspondence with the low-velocity zone interfaces, enabling clear differentiation of shallow stratigraphic distributions. Full article

The texture of asphalt pavement wears down over time due to traffic polishing, which leads to polished pavement surfaces with lower skid resistance. Three-dimensional (3D) texture parameters can be used to describe the evolution of pavement texture and establish predictive models for skid resistance. In this study, a high-resolution 3D laser scanner and a pendulum friction tester were used to collect 3D texture data and the corresponding friction values of dense-graded asphalt pavement over a period of four years. Fourier transformer and Butterworth filters were applied to decompose the 3D texture data into micro-texture and macro-texture components. Twenty different 3D texture parameters from five categories (height, spatial, hybrid, functional, and feature parameters) were calculated from pavement micro- and macro-textures and optimized using correlation methods to derive an independent set of texture parameters. The performance of a multiple linear regression model and neural network predictive model for predicting skid resistance via selected texture parameters was compared through training and testing. The results indicate that pavement micro-texture contributes more significantly to skid resistance than macro-texture, and neural network models can effectively predict the temporal evolution of skid resistance based on texture data. The neural network model achieves R² values of 0.92 and 0.89 on the training and testing sets, respectively, with RMSE values of 3.37 and 5.45, significantly outperforming the multiple linear regression model (R² = 0.50). Full article

Background: Solid dispersions (SDs) of etodolac (ETD), a poorly water-soluble drug model, were developed to enhance its solubility and dissolution rate by employing various preparation methods and hydrophilic or amphiphilic polymers. Methods: Polyvinylpyrrolidone-poly(vinyl acetate) copolymers (PVP/VA), hydroxypropyl methylcellulose (HPMC) and poloxamer were used as carriers, while cryo-milling and lyophilization were utilized as routine methods to SDs preparation. Obtained SDs were characterized by drug content, solubility, dissolution rate and moisture content. The physical structure of SDs was estimated via scanning electron microscopy (SEM), whereas differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) were employed to assess the potential drug-carrier interactions. Results: SD formulations demonstrated enhanced solubility of ETD in aqueous media, including water and buffers (pH 5.5 and 7.4). DSC analysis confirmed that PVP/VA and poloxamer ensured better ETD dissolution and protection against recrystallization. Furthermore, FTIR indicated the formation of hydrogen bonds between ETD and polymer, particularly in lyophilized dispersions. Conclusions: The optimized SD formulation for ETD contained PVP/VA and/or poloxamer as carriers and was obtained via lyophilization. This SD formulation exhibited the most favorable properties, enhanced the solubility and dissolution of ETD in aqueous media and effectively reduced its crystallinity. Full article

This research investigated the sustainable biosynthesis of silver nanoparticles (AgNPs) using Zinnia elegans L. extracts to demonstrate the potential of plant-based methods in nanotechnology. The antioxidant and antibacterial properties of the plant extracts were evaluated, and the phytocompounds that react as natural reducing agents in the synthesis of AgNPs were characterized. This approach has demonstrated the potential of Zinnia elegans L. as an environmentally friendly source for the production of AgNPs. The biosynthesized AgNPs were characterized based on their optical, structural, and morphological properties using various techniques, including scanning electron microscopy (SEM), attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR), and thermogravimetric and differential thermal analysis (TGA/DTA). X-ray diffraction (XRD) analysis confirmed the presence of pure silver phases exhibiting a face-centered cubic (FCC) crystalline structure. Ultraviolet–visible (UV–Vis) spectroscopy revealed an absorption peak at 462 nm, which is characteristic of the surface plasmon resonance associated with AgNPs. ATR-FTIR analysis identified several vibrational peaks corresponding to the functional groups of the constituents present in the biosynthesized AgNPs. The size distribution of the AgNPs was found to range from 10 to 30 nm, and both SEM and TEM confirmed their predominantly spherical morphology. Energy dispersive X-ray spectroscopy (EDX) analysis corroborated the predominance of silver as the principal element within the composition of the nanoparticles. This technique provided quantitative elemental analysis, confirming the high purity and concentration of silver in the synthesized AgNPs. The study effectively elucidated the synthesis of AgNPs utilizing plant extracts as natural reducing agents. The synthesized AgNPs exhibited significant antibacterial and antioxidant activities, indicating their potential applicability in diverse biomedical and environmental contexts. Employment of the advanced characterization techniques facilitated a thorough understanding of the multifaceted properties of the synthesized AgNPs, thereby enhancing their viability for future research and application in nanomedicine and bioremediation. Using Zinnia elegans L. for the biosynthesis of plant-synthesized AgNPs is a sustainable and eco-friendly technique that offers a viable alternative to conventional chemical processes. Full article

The bloom of cyanobacteria has severely disrupted ecological balances, posing significant risks to human health and safety. However, there is currently a lack of environmentally friendly methods that can sustainably suppress these blooms over the long term. This study integrates untargeted metabolomics, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) to systematically characterize the responses of Microcystis aeruginosa to nano zero-valent iron (nZVI). Exposure to nZVI reprograms lipid and amino acid metabolism, coincides with the suppression of protein biosynthesis, and perturbs central pathways—including the tricarboxylic acid (TCA) cycle, photosynthesis, and carbohydrate metabolism—leading to disruptions in energy balance and metabolic homeostasis. FTIR and SEM provide complementary evidence of membrane compromise, with attenuation of -OH, -C-H, and C=O functional group signals, abnormal cell morphology, and progressive oxidative injury culminating in cell lysis and solute leakage. Together, these results support the inhibitory effect of nZVI on M. aeruginosa and provide insights to guide metabolomics studies of M. aeruginosa using nZVI. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

Steel-reinforced thermoplastic pipe is widely used for water transportation in sour gas fields. However, under the combined effects of corrosive media, internal high pressure, and long-term environmental aging, premature failures such as leakage and bursting often occur. To clarify the failure causes and primary contributing factors of the composite pipes, this study conducted a comprehensive analysis through microscopic morphology examination of different typical failure cases, differential scanning calorimetry, Fourier transform infrared spectroscopy, and mechanical property testing. The main failure mechanisms were investigated, and targeted protective measures are proposed. Key findings reveal that the typical failure modes are ductile cracking, aging-induced brittle cracking, and aging creep cracking. These failures follow a mechanism of degradation of the inner and outer polyethylene protective layers, penetration of the medium and corrosion of the steel wires, reduction in pressure-bearing capacity, and eventual structural damage or leakage propagation through the pipe wall. Notably, oxidation induction time values dropped as low as 1.4–17 min—far below the standard requirement of >20 min—indicating severe antioxidant depletion and material aging. The main controlling factors are poor material quality, external stress or mechanical damage, and long-term aging. The polyethylene used for the inner and outer protective layers is critical to the overall pipe performance; therefore, emphasis should be placed on evaluating its anti-aging properties and on protecting the pipe body during installation to ensure the long-term safety and stable operation of the pipeline system. Full article

For optimizing the operational efficiency and productivity within blast furnace processes, a profound understanding of the viscous flow characteristics of CaO–SiO₂–MgO–Al₂O₃–B₂O₃ slag systems is of paramount importance. In this study, we conducted a comprehensive investigation into the influence of the CaO/SiO₂ and MgO/Al₂O₃ ratios on the viscosity, break point temperature (T_Br), and activation energy (E_η) of low boron-bearing high-alumina slag. Concurrently, we elucidated the underlying mechanisms through which these ratios affect the viscous behavior of the slag by employing a combination of analytical techniques, including X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and thermodynamic modeling using the Factsage software. The experimental findings reveal that, as the CaO/SiO₂ ratio increases from 1.10 to 1.30, the slag viscosity at 1773 K decreases from 0.316 Pa·s to 0.227 Pa·s, while both the T_Br and E_η exhibit an upward trend, rising from 1534 K and 117.01 kJ·mol⁻¹ to 1583 K and 182.86 kJ·mol⁻¹, respectively. Conversely, an elevation in the MgO/Al₂O₃ ratio from 0.40 to 0.65 results in a reduction in slag viscosity at 1773 K from 0.290 Pa·s to 0.208 Pa·s, accompanied by a decrease in T_Br from 1567 K to 1542 K. The observed deterioration in slag flow properties can be attributed to an enhanced polymerization degree of complex viscous structural units within the slag matrix. Ultimately, our study identifies that an optimal viscous performance of the slag is achieved when the CaO/SiO₂ ratio is maintained at 1.25 and the MgO/Al₂O₃ ratio is maintained at 0.55, providing valuable insights for the rational design and control of blast furnace slag systems. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

This study investigates stress detection in dairy cattle by integrating circadian rhythm analysis and deep learning. Behavioral biomarkers, including feeding, resting, and rumination, were continuously monitored using Nedap CowControl sensors over a 12-month period to capture seasonal variability. Circadian features were extracted using the Fast Fourier Transform (FFT), and deviations from expected 24 h patterns were quantified using Euclidean distance. These features were used to train a Long Short-Term Memory (LSTM) neural network to classify stress into three levels: normal, mild, and high. Expert veterinary observations of anomalous behaviors and environmental records were used to validate stress labeling. We continuously monitored 10 lactating Holstein cows for 365 days, yielding 87,600 raw hours and 3650 cow-days (one day per cow as the analytical unit). The Short-Time Fourier Transform (STFT, 36 h window, 1 h step) was used solely to derive daily circadian characteristics (amplitude, phase, coherence); STFT windows are not statistical samples. A 60 min window prior to stress onset was incorporated to anticipate stress conditions triggered by management practices and environmental stressors, such as vaccination, animal handling, and cold stress. The proposed LSTM model achieved an accuracy of 82.3% and an AUC of 0.847, outperforming a benchmark logistic regression model (65% accuracy). This predictive capability, with a one-hour lead time, provides a critical window for preventive interventions and represents a practical tool for precision livestock farming and animal welfare monitoring. Full article

This study explores the photoprotective and antioxidant potential of cosmetic emulsions formulated with Alnus glutinosa (black alder) extracts. Extraction of bioactive compounds was performed using Soxhlet, ultrasound-assisted, and microwave-assisted techniques with ethanol and water as solvents. The phytochemical profiles of the resulting extracts were characterized via UV-Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and liquid chromatography–high-resolution mass spectrometry (LC-HRMS). The extracts were incorporated into oil-in-water emulsions and assessed for antioxidant activity using the DPPH radical scavenging assay, pH and viscosity stability, and color L*a*b* values. Among the extraction methods, ethanol-based Soxhlet extraction yielded the highest concentration of bioactive compounds and demonstrated superior antioxidant and photoprotective efficacy. This is the first report that evaluates the antioxidant properties of A. glutinosa-enriched emulsions, supporting their application as multifunctional, plant-derived cosmeceuticals for skin protection. Full article

The widespread adoption of one-time use masks (OUM) has resulted in a substantial new stream of polymer waste, posing a formidable challenge to circular economy and waste management initiatives. Concurrently, the pavement industry continuously seeks innovative modifiers to enhance the durability and service life of asphalt binders. This study presents a novel approach to waste valorization by systematically investigating the potential of shredded OUM as a polymer modifier for asphalt. The research evaluates the impact of various OUM concentrations (up to 10% by weight) on the binder’s chemical, rheological, and performance characteristics. Fourier-transform infrared spectroscopy (FTIR) indicated that the modification is a physical blending process, with the OUM fibers forming a stable reinforcing network within the asphalt matrix, a finding supported by excellent high-temperature storage stability. Rheological assessments revealed a remarkable enhancement in high-temperature performance, with the Zero-Shear Viscosity (ZSV) increasing by nearly 700% (from approximately 450 Pa·s to about 3500 Pa·s) at 10% OUM content, signifying superior rutting resistance. Furthermore, fatigue life, evaluated via the Linear Amplitude Sweep (LAS) test, improved by up to 168% at a 2.5% strain level. However, these benefits were accompanied by a detrimental effect on low-temperature properties, where creep stiffness at −12 °C increased by over 50% and the m-value dropped below the critical 0.30 threshold, indicating a heightened risk of thermal cracking. The study concludes that OUM is a highly effective modifier for improving high-temperature and fatigue performance, with up to 10% content being viable. This research establishes a promising circular economy pathway, transforming a problematic waste stream into a valuable resource for constructing more resilient and sustainable pavement infrastructure. Full article

Accurate forecasts of the U.S. renewable energy consumption mix are essential for planning transmission upgrades, sizing storage, and setting balancing market rules. We introduce a Bayesian Dirichlet ARMA model (BDARMA) tailored to monthly shares of hydro, geothermal, solar, wind, wood, municipal waste, and biofuels from January 2010 through January 2025. The mean vector is modeled with a parsimonious VAR(2) in additive log ratio space, while the Dirichlet concentration parameter follows an intercept plus five Fourier harmonics, allowing for seasonal widening and narrowing of predictive dispersion. Forecast performance is assessed with a 61-split rolling origin experiment that issues twelve month density forecasts from January 2019 to January 2024. Compared with three alternatives (a Gaussian VAR(2) fitted in transform space, a seasonal naive approach that repeats last year’s proportions, and a drift-free ALR random walk), BDARMA lowers the mean continuous ranked probability score by 15 to 60 percent, achieves componentwise 90 percent interval coverage near nominal, and maintains point accuracy (Aitchison RMSE) on par with the Gaussian VAR through eight months and within 0.02 units afterward. These results highlight BDARMA’s ability to deliver sharp and well-calibrated probabilistic forecasts for multivariate renewable energy shares without sacrificing point precision. Full article