Search Results (175)

In this paper, a water-based aerosol particle counter was used to measure aerosol number concentrations with high temporal resolution at a meteorological tower and on the ground, and the ultrasonic anemometer on the meteorological tower measured the data of the three-dimensional wind speed. The power spectrum of the aerosol particle number concentration fluctuation was obtained by using a Fourier transform, and the characteristics of the power spectrum were deeply analyzed. The results show that the aerosol concentration fluctuation power spectrum satisfies the Monin–Obukhov law in the low-frequency (0.02–0.25 Hz) part of the inertial subregion, which is consistent with the characteristics of atmospheric turbulent motion. Significant attenuation occurs in the high-frequency (0.3–5 Hz) range, which is mainly caused by the attenuation of the aerosol concentration by the intake pipe. Using the similarity of the power spectrum in the low-frequency part, using the “−5/3” line as a standard, the characteristic time of the measurement system is obtained by fitting the transfer function. The results show that in the flux measurement experiments in this paper, the characteristic time is usually less than 1 s. Finally, this paper uses the Fourier transform and wavelet transform to correct the high-frequency attenuation in the fluctuation of aerosol concentration and obtains the corrected aerosol flux. The results show that the effect of high-frequency attenuation on the flux is approximately 1–4% in this experiment. Full article

(1) Background: Over 90% of hop crops are currently used in beer production, with a small part used in the cosmetics and pharmaceutical industries. Spent hops as a waste product contain one of the strongest antioxidants, xanthohumol. The aim of the study was to purify spent hop extracts by magnetic dispersive extraction using iron oxide nanoparticles (IONP) to obtain pure xanthohumol; (2) Methods: The extract from the waste product obtained after supercritical carbon dioxide extraction of hops was prepared by ultrasound-assisted extraction utilizing different solvents, i.e., ethyl acetate, propanol, acetone, 80% methanol, ethyl acetate-methanol (1:1, v/v), and propanol-methanol (1:1, v/v). The hydrodynamic diameters and zeta potential of IONPs before and after incubation were measured by dynamic light scattering (DLS). The extracts were analyzed by reversed-phase high-performance liquid chromatography (HPLC). Isolated xanthohumol was identified based on the DAD spectrum in the range of 200–600 nm and by Fourier transform infrared spectroscopy/attenuated total reflectance (FT-IR/ATR); The antioxidant activity of extracts before and after incubation with IONPs was assessed using SNPAC (Silver Nanoparticle Antioxidant Capacity), DPPH (2,2-diphenyl-1-picrylhydrazyl radical), and FRAP (Ferric Reducing Antioxidant Power) assays, as well as total phenolic content (TPC) and total flavonoid content (TFC). (3) Results: The amount of added IONPs, the kind of solvent, and the contact time of the extract with nanoparticles were optimized. We found that 80% MeOH extract after incubation with IONPs (865 µg IONPs/g of spent hops) at room temperature for 48 h contains 74.61% of initial xanthohumol content, providing a final xanthohumol concentration of 43 µg mL⁻¹. (4) Conclusions: The proposed method of magnetic dispersive extraction using IONPs allows for the purification of spent hops extract and obtaining a pure product, namely xanthohumol, with a wide potential for practical applications in medicine, pharmacy, cosmetics, and agriculture. This is clear evidence of the usefulness of IONP as an effective sorbent. The method allows the use of residues from the brewing industry, i.e., the biomass of used hop cones to obtain a valuable substance. Full article

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater–sea surface–air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from −114 dB to −157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments. Full article

In this paper, we present the logic designs of two FPGA hardware programming algorithms implemented for a Coherent Doppler Lidar system used in wind sensing. The first algorithm divides the received time-domain signals into segments, each corresponding to a specific spatial resolution. It then calculates the power spectrum for each segment and accumulates these spectra over 10,000 pulse returns. The second algorithm computes the autocorrelation of the received signals and accumulates the results over the same number of pulses. Both signal pre-processing algorithms are initially developed as logic designs and compiled using the Xilinx System Generator toolset to produce a hardware VLSI image. This image is subsequently programmed into an FPGA. However, the hardware implementation of these algorithms presents several challenges: (1) bit growth: multiplication operations in the binary number system significantly increase the number of bits, complicating hardware implementation. (2) Memory constraints: onboard RAM arrays of sufficient size are lacking for accumulating vectors of the calculated Fast Fourier Transforms (FFTs) or autocorrelations. (3) Signal drive issues: large fan-out in the logic design leads to significant capacitance, restricting the driving capabilities of transistor output signals. This article discusses the solutions devised to overcome these challenges. Additionally, it presents atmospheric wind measurements obtained using the two algorithms. Full article

Background/Objectives: Bee venom (BV), as a natural product, is one of the foundations of the pharmaceutical industry, through which many diseases, including serious ones, can be effectively treated. The BV nanofilm is an effective antidote delivered into the human body to target the affected area and address the issue without major side effects. In this study, we investigated the intriguing therapeutic effects of apitoxin (bee venom) used in isolation, combined with the powerful properties of zinc oxide nanoparticles. Methods and Results: BV nanofilm was evaluated using Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). The BV extract was analyzed using mass spectrometry (MS), which identified 84 active components present at varying concentrations. BV was treated with both polyvinyl alcohol (PVA) and zinc oxide nanoparticles (ZNPs) to increase the intensity of OH and CH₂ groups and to enhance the dispersion of C=O. BV has demonstrated anti-type 2 diabetes activity by inhibiting α-amylase and α-glucosidase, which are starch-degrading enzymes. The nanofilm is an active mixture of BV, PVA, and ZNPs, which exhibited the highest antidiabetic activity with IC₅₀ values of 30.33 μg/mL and 5.55 μg/mL for the inhibition of α-amylase and α-glucosidase, compared to IC₅₀ of 51.69 µg/mL and IC₅₀ of 7.30 µg/mL for BV, respectively. The nanofilm also showed higher anti-inflammatory activity by inhibiting red blood cell (RBC) hemolysis, with an IC₅₀ of 16.99 μg/mL in comparison to IC₅₀ of 72.99 µg/mL for BV alone. The nanofilm demonstrated broad-spectrum antimicrobial activity, effectively targeting both Gram-positive (Staphylococcus aureus ATCC 6538 and Bacillus subtilis ATCC 6633) and Gram-negative bacteria (Salmonella typhi ATCC 6539, Escherichia coli ATCC 8739). Furthermore, increased antioxidant activity was recorded by inhibiting the 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging effect with an IC₅₀ of 4.26 μg/mL and 19.43 μg/mL for nanofilm and BV, respectively. BV was found to be more toxic to liver tissue (HepG2 cell line) than nanofilm, with IC₅₀ values of 18.5 ± 0.08 μg/mL and 52.27 ± 0.7 μg/mL, respectively. The BV extract displayed higher toxicity to liver tissue (2.3%) with 97.7% viability at 250 μg/mL, compared to nanofilm, which showed 0.09% toxicity and 99.9% viability at the same concentration. Conclusions: the BV nanofilm emerges as a promising alternative medicine, offering an innovative solution for treating various diseases through its high concentration of therapeutically active compounds and effortless targeting delivery. Full article

On-chip Fourier transform spectrometer (FTS) is a promising technology due to the compact size, low cost, and relatively high throughput. In this work, we design, fabricate, and characterize a Mach-Zehnder interferometer (MZI) FTS based on SU-8 polymer waveguide. The optical path length difference of MZI is tuned by a heater with a maximum power consumption of 2.2 W. The interference signal is analyzed by Fourier transform algorithm, to retrieve the spectrum of light source. The footprint of the fabricated FTS device is only 2 × 12 mm², with the spectral bandwidth of ~100 nm and resolution <20 nm. Full article

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets. Full article

The proliferation of IoT using heterogeneous wireless technologies within the unlicensed spectrum has intensified cross-technology interference (CTI) in wireless local area networks (WLANs). As WLANs increasingly adopt time-triggered transmission methods to support real-time services, this interference affects throughput, packet loss, and latency. This paper presents a CTI-aware rate adaptation framework designed to mitigate interference in WLANs without direct coordination with heterogeneous wireless devices. The framework includes a CTI identification model and CTI-aware rate selection algorithms. Leveraging short-time Fourier transform, the identification model captures the time–frequency–power characteristics of CTI signals, enabling the estimation of the average power of various heterogeneous wireless technologies employed by interfering devices. The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. Experimental results demonstrated that the lightweight CTI identification model accurately estimated the average power of each type with an error margin of ±1.414 dBm, achieving this in under 1 ms on the target hardware. Additionally, applying the proposed framework to explicit interference mitigation enhanced goodput by 20.67%, reduced packet error rate by 2.38%, and decreased the probability of packets exceeding 1 ms latency by 0.932% compared to conventional methods. Full article

This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes. Full article

Background: For a single exposure in radiography, a dual-layer flat-panel detector (DFD) can provide spectral images and efficiently utilize the transmitted X-ray photons to improve the detective quantum efficiency (DQE) performance. In this paper, to acquire high DQE performance, we present a registration method for X-ray images acquired from a DFD, considering only spatial translations and scale factors. The conventional registration methods have inconsistent estimate accuracies depending on the captured object scene, even when using entire pixels, and have deteriorated frequency performance because of the interpolation method employed. Methods: The proposed method consists of two steps; the first step is conducting a spatial translation according to the Fourier shift theorem with a subpixel registration, and the second step is conducting a scale transformation using cubic interpolation to process the X-ray projections. To estimate the subpixel spatial translation, a maximum-amplitude method using a small portion of the slant-edge phantom is used. Results: The performance of the proposed two-step method is first theoretically analyzed and then observed by conducting extensive experiments and measuring the noise power spectrum and DQE. An example for registering chest images is also shown. For a DFD, the proposed method shows a better registration result than the conventional one-step registration. The DQE improvement was more than 56% under RQA 9 compared to the single flat-panel detector case. Conclusions: The proposed two-step registration method can efficiently provide aligned image pairs from the DFD to improve the DQE performance at low doses and, thus, increase the accuracy of clinical diagnosis. Full article

This article studies the general properties of the Stokes drift field. This name is commonly used for the correction added to the mean Eulerian velocity for describing the averaged transport of the material particles by the oscillating fluid flows. Stokes drift is widely known mainly in connection with another feature of oscillating flows known as steady streaming, which has been and remains the focus of a multitude of studies. However, almost nothing is known about Stokes drift in general, e.g., about its energy or helicity (Hopf’s invariant). We address these quantities for acoustic drift driven by simple sound waves with finite discrete Fourier spectra. The results discover that the mean drift energy is partly localized on a certain resonant set, which we have described explicitly. Moreover, the mean drift helicity turns out to be completely localized on the same set. We also present several simple examples to discover the effect of the power spectrum and positioning of the spectral atoms. It is revealed that tuning them can drastically change both resonant and non-resonant energies, zero the helicity, or even increase it unboundedly. Full article

Background: Zinc oxide nanoparticles are safe, non-toxic, and biocompatible. These NPs are used in food packaging materials, self-cleaning glass, ceramics, deodorants, sunscreens, paints, coatings, ointments, lotions, and as preservatives. This study explored the biological potential of ZnO nanoparticles synthesized using H. rariflorum. Methods: In vitro antibacterial and antifungal activities against Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi, Candida albicans, Penicillium notatum, Aspergillus flavus, Aspergillus niger and Aspergillus solani were determined. Antioxidant activity was explored using the DPPH radical scavenging method. In vivo analgesic, antipyretic and sedative potential of synthesized nanoparticles was investigated using a mouse model. Results: SEM with various magnification powers showed that some particles were spherical while some were aggregated, flake-shaped, and hexagonal with rough and irregular surfaces. The EDX analysis revealed Zn (12.63%), O (22.83%) and C (63.11%) with trace quantities of Si (0.40%), Ca (0.54%) and P (0.49%). The XRD pattern indicated an amorphous state, with no peaks observed throughout the spectrum. The UV–visible spectrophotometry revealed a characteristic absorption peak at 375 nm, indicating the presence of ZnO nanoparticles. Fourier Transform Infrared Spectroscopy (FTIR) displayed several small peaks between 1793 and 2370 cm⁻¹, providing evidence of the presence of different kinds of organic compounds with different functional groups. ZnO-NPs showed dose-dependent antibacterial and antifungal potential against all strains. Staphylococcus aureus and Candida albicans were the most susceptible strains. The nanoparticles exhibited a maximum antioxidant effect of 85.28% at 100 μg/mL. In this study, the acute toxicity test showed no mortality, and normal behavior was observed in mice at ZnO-NP doses of 5, 10, and 20 mg/kg. For analgesic and antipyretic activities, a two-way ANOVA revealed that dose, time, and the interaction between dose and time were significant. In contrast, the samples had a non-significant effect on sedative activity. Conclusions: This innovative study suggests a potential use of plant resources for managing microbes and treating various diseases, providing a scientific basis for the traditional use of H. rariflorum. Full article

Waveform design is a potentially significant approach to improve the performance of an imaging or detection system. Photoacoustic imaging is a rapidly developing field in recent years; however, photoacoustic waveform design has not been extensively investigated. This paper considers the problem of photoacoustic waveform design for parameter estimation under constraints on input energy. The use of information theory is exploited to formulate and solve this optimal waveform design problem. The approach yields the optimal waveform power spectral density. Direct inverse Fourier transform of the optimal waveform frequency spectrum amplitude is proposed to obtain a real waveform in the time domain. Absorbers are assumed to be stochastic absorber ensembles with uncertain duration and location parameters. Simulation results show the relationship between absorber parameter distribution and the characteristics of optimal waveforms. Comparison of optimal waveforms for estimation, optimal waveforms for detection (signal-to-noise ratio) and other commonly used waveforms are also discussed. The symmetry properties of the forward and inverse Fourier Transforms are used to analyze the time and frequency properties and provide a heuristic view of how different goals affect the choice of waveform. Full article

The development of a suitable set of input ground motions is crucial for dynamic time history analyses. The US Nuclear Regulatory Commission (NRC) requires that these motions generate response spectra closely matching the plant’s design spectrum. Additionally, the NRC recommends verifying the motions’ power spectral densities (PSDs) against a target function to ensure sufficient energy across all frequencies. Current NRC guidelines in Standard Review Plan (SRP) provide a general method for creating target PSDs for any design spectrum. However, this method does not explicitly consider the influence of strong motion duration on the relationship between PSD and response spectrum. This article proposes an improved approach that incorporates the expected strong motion duration into the target PSD generation process. The method first constructs a Fourier amplitude spectrum (FAS) compatible with both the design spectrum and the expected strong motion duration. Subsequently, a large set of synthetic motions based on this FAS is used to construct the target PSD function. It is shown that current target PSD functions tabulated in SRP 3.7.1 implicitly infer an expected strong motion duration of approximately 9 s. The proposed method can be used to construct target PSDs tailored to different strong motion durations. Full article

We used numerical methods to define the normative structure of the different stages of sleep and wake (W) in a pilot study of 19 participants without pathology (18–64 years old) using a double-banana bipolar montage. Artefact-free 120–240 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped into frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum (PS) was calculated via fast Fourier transform and used to compute the areas for the delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands, which were log-transformed. Furthermore, Pearson’s correlation coefficient and coherence by bands were computed. Differences in logPS and synchronization from the whole scalp were observed between the sexes for specific stages. However, these differences vanished when specific lobes were considered. Considering the location and stages, the logPS and synchronization vary highly and specifically in a complex manner. Furthermore, the average spectra for every channel and stage were very well defined, with phase-specific features (e.g., the sigma band during N2 and N3, or the occipital alpha component during wakefulness), although the slow alpha component (8.0–8.5 Hz) persisted during NREM and REM sleep. The average spectra were symmetric between hemispheres. The properties of K-complexes and the sigma band (mainly due to sleep spindles—SSs) were deeply analyzed during the NREM N2 stage. The properties of the sigma band are directly related to the density of SSs. The average frequency of SSs in the frontal lobe was lower than that in the occipital lobe. In approximately 30% of the participants, SSs showed bimodal components in the anterior regions. qEEG can be easily and reliably used to study sleep in healthy participants and patients. Full article