Search Results (4,145)

The accurate classification of staple crops is of great significance for scientifically promoting food production. Crop classification methods based on deep learning models or medium/low-resolution images have been applied in plain areas. However, existing methods perform poorly in complex mountainous scenes with rugged terrain, diverse planting structures, and fragmented farmland. This study introduces the Complex Scene Crop Classification U-Net+ (CSCCU+), designed to improve staple crop classification accuracy in intricate landscapes by integrating supplementary spectral information through an additional branch input. CSCCU+ employs a multi-branch architecture comprising three distinct pathways: the primary branch, auxiliary branch, and supplementary branch. The model utilizes a multi-level feature fusion architecture, including layered integration via the Shallow Feature Fusion (SFF) and Deep Feature Fusion (DFF) modules, alongside a balance parameter for adaptive feature importance calibration. This design optimizes feature learning and enhances model performance. Experimental validation using GaoFen-2 (GF-2) imagery in Xifeng County, Guizhou Province, China, involved a dataset of 2000 image patches (256 × 256 pixels) spanning seven categories. The method achieved corn and rice classification accuracies of 89.16% and 88.32%, respectively, with a mean intersection over union (mIoU) of 87.04%, outperforming comparative models (U-Net, DeeplabV3+, and CSCCU). This research paves the way for staple crop classification in complex land surfaces using high-resolution imagery, enabling accurate crop mapping and providing robust data support for smart agricultural applications. Full article

Spectral reflectance of plants can be readily associated with physiological and biochemical parameters. Thus, relating spectral data to amino acid contents in different genetic materials provides an innovative and efficient approach for understanding and managing genetic diversity. Therefore, this study had two objectives: (I) to differentiate genetic materials according to amino acid contents and spectral reflectance; (II) to establish the relationship between amino acids and spectral bands derived from hyperspectral data. The research was conducted with 32 soybean genetic materials grown in the field during the 2023–2024 crop year. The experimental design involved randomized blocks with four replicates. Leaf spectral data were collected 60 days after plant emergence, when the plants were in full bloom. Three leaf samples were collected from the third fully developed trifoliate leaf, counted from top to bottom, from each plot. The samples were taken to the laboratory, where reflectance readings were obtained using a spectroradiometer, which can measure the 350–2500 nm spectrum. Wavelengths were grouped as means of representative intervals and then organized into 28 bands. Subsequently, the leaf samples from each plot were subjected to quantification analyses for 17 amino acids. Then, the soybean genotypes were subjected to a PCA–K-means analysis to separate the genotypes according to their amino acid content and spectral behavior. A correlation network was constructed to investigate the relationships between the spectral variables and between the amino acids within each group. The groups formed by the different genetic materials exhibited distinct profiles in both amino acid composition and spectral behavior. Leaf reflectance data proved to be efficient in identifying differences between soybean genotypes regarding the amino acid content in the leaves. Leaf reflectance was effective in distinguishing soybean genotypes according to leaf amino acid content. Specific and high-magnitude associations were found between spectral bands and amino acids. Our findings reveal that spectral reflectance can serve as a reliable, non-destructive indicator of amino acid composition in soybean leaves, supporting advanced phenotyping and selection in breeding programs. Full article

The vortex-induced vibration (VIV) dynamics of commercial-scale deep-sea mining risers with complex component arrangements (pumps, buffer stations, buoyancy modules) remain insufficiently explored, especially for 6000 m systems with nonlinear tension. This study investigates VIV control strategy by adjusting tension for a nonlinear riser system using the Iwan-Blevins wake oscillator model integrated with Morison equation-based analysis. An analytical model incorporating four typical current profiles was established to quantify the dynamic response under different flow velocities, internal flow density, and structural parameters. Increased buffer station mass effectively suppressed drift distance (over 35% reduction under specific conditions) by regulating axial tension. Dynamic comparisons demonstrated distinct VIV energy distribution patterns under different current conditions. Spectral analysis revealed that the vibration follows Strouhal vortex shedding lock-in principles. Spatial modal differentiation was observed due to nonlinear variations in velocity profiles, pipe diameters, and axial tension, accompanied by multi-frequency resonance, coexistence of standing and traveling waves, and broadband resonance with amplitude surges under critical velocities (1.75 m/s in Current-B). This study proposes to control the VIV amplitude by adjusting internal flow density and buffer mass, which is proved effective for reducing vibrations in upper (0–2000 m) risers. It validates vibration amplitude and frequency control through current velocity, buffer mass and slurry density regulation in a nonlinear riser system. Full article

Although cyclic adenosine monophosphate (cAMP) is not a major secondary messenger in the visual transduction cascade in vertebrates, it may modulate photoreceptor functions. The effects of cAMP have been extensively studied in rods; however, its role in cones remains less understood. The aim of this study was to investigate the effects of increased levels of cAMP on the photoresponses of isolated blue- and green-sensitive cones in adult zebrafish (Danio rerio). To examine the effects of elevated cAMP on individual cone spectral types, photoreceptor currents were recorded using a suction pipette method. The adenylate cyclase activator forskolin was used to increase intracellular cAMP levels. Sensitivity and photoresponse parameters were compared before and after forskolin application. An increase in cAMP levels has similar effects on photoresponses of blue- and green-sensitive cones. Forskolin application to both types of cones resulted in a slight increase in sensitivity, with significant slowing of the phototransduction cascade shutdown processes and a marked increase in the integration time of photoresponses. These findings suggest that intracellular cAMP levels, which fluctuate in the retina during the diurnal cycle, can modulate cone function. The observed effects of cAMP are consistent with its action on one of its main putative targets, opsin kinases. 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

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

This article presents the results of single-station microtremor measurements conducted in Diyarbakır Province. To develop shear wave velocity profiles and determine dynamic soil parameters for the region, measurements were carried out at eight strong-motion accelerometer stations located within the provincial boundaries and operated by the Disaster and Emergency Management Authority (AFAD). Data were recorded in three components over a 30 min period. For analysis, the Rayleigh wave ellipticity method was employed in combination with inversion techniques, along with the Horizontal-to-Vertical (H/V) Spectral Ratio method. These analyses yielded shear wave velocity profiles for each station, from which Vs values, predominant ground frequency, and amplification factors were obtained. Based on the average shear wave velocity in the upper 30 m (Vs30), ground classifications were made. In the final stage, earthquake acceleration records were analyzed and compared with the microtremor results. The findings indicate that the predominant frequency values range from 3.64 to 16.61 Hz, while ground amplification values vary between 1.20 and 2.85. The lowest Vs30 value was 555 m/s, and the highest damage vulnerability index (Kg), calculated from the H/V analysis, was 1.90. Full article

Ultra-high-energy cosmic rays (UHECRs) with energies exceeding ${10}^{19}$ eV are believed to originate from extragalactic environments, potentially associated with relativistic jets in active galactic nuclei (AGN). Among AGNs, blazars, particularly those detected in very-high-energy (VHE) gamma rays, are promising candidates for UHECR acceleration and high-energy neutrino production. In this work, we investigate three blazar sources, AP Librae, 1H 1914–194, and PKS 0735+178, using multiwavelength spectral energy distribution (SED) modeling. These sources span a range of synchrotron peak classes and redshifts, providing a diverse context to explore the physical conditions in relativistic jets. We employ both leptonic and lepto-hadronic models to describe their broadband emission from radio to TeV energies, aiming to constrain key jet parameters such as magnetic field strength, emission region size, and particle energy distributions. Particular attention is given to evaluating their potential as sources of UHECRs and high-energy neutrinos. Our results shed light on the complex interplay between particle acceleration mechanisms, radiative processes, and multi-messenger signatures in extreme astrophysical environments. Full article

The Poincaré plot was introduced as a tool to analyze heart rate variations caused by arrhythmias. Later, it was applied to time series with normal beats. The plot shows the relationship between the inter-beat interval (IBI) of one beat to the next. Several parameters were developed to characterize this relationship. The short and long axis of the fitting ellipse, SD1 and SD2, respectively, their ratio, and their product are used. The difference between the IBI of a beat and m beats later are also studied, SD1(m) and SD2(m). We studied the mathematical relations between heart rate variability measures and the Poincaré measures in the time (standard deviation of IBI, SDNN, root mean square of successive differences, RMSSD) and frequency domain (power in low and high frequency band, and their ratio). We concluded that SD1 and SD2 do not provide new information compared to SDNN and RMSSD. Only the correlation coefficient r(m) provides new information for m > 1. Novel findings are that ln(SD2(m)/SD1(m)) = tanh⁻¹(r(m)), which is an approximately normal distributed transformation of r(m), and that SD1(m) and SD2(m) can be calculated by multiplying the power spectrum by a weighing function that depends on m, revealing the relationship with spectral measures, but also the relationship between SD1(m) and SD2(m). Both lagged parameters are extremely difficult to interpret compared to low and high frequency power, which are more closely related to the functioning of the autonomic nervous system. Full article

In this work, we propose and demonstrate a novel approach to suppressing stimulated Raman scattering in an oscillating–amplifying integrated fiber laser (OAIFL) by changing the spectral bandwidth of the output-coupler fiber Bragg gratings (OC-FBGs). The reflectance bandwidth of the fiber Bragg grating (FBG) in the oscillating section was systematically investigated as a critical parameter for SRS mitigation. Three types of long-period FBGs with distinct reflectance bandwidths (1.2 nm, 1.3 nm, and 2 nm) were comparatively studied as output couplers. The experimental results demonstrated a direct correlation between FBG bandwidth and SRS suppression efficiency, with the configuration of the OC-FBG with a 2 nm bandwidth achieving optimal suppression performance. Concurrently, the output power was enhanced to 5.02 kW with improved power scalability. And excellent beam quality was obtained with M² < 1.3. Remarkably, in the architecture of this laser, increasing the bandwidth of the output couplers in the oscillating section had a relatively minor effect on the optical-to-optical (O-O) efficiency, which reached up to 78%. Additionally, this modification also reduced the 3 dB bandwidth of the laser output, thereby achieving a beam output with enhanced monochromaticity. Full article

Vehicle suspension significantly influences the safety of cargo transportation. This study presents a 14-degree-of-freedom vehicle–cargo coupling model that explicitly incorporates the frequency-dependent stiffness of air springs. Systematic parametric investigations of air spring orifice resistance, loading mass, and cargo stiffness reveal the following: (a) Compared with leaf spring suspension, air suspension vehicles attenuated the first peak of acceleration power spectral density by over 50%, while slightly amplifying the second peak; (b) When replacing leaf spring suspension with air suspension, the upper-layer cargo exhibited significantly larger vibration reductions (14% vertical, 28% pitch) than the lower-layer cargo under identical cargo parameters. The roll angle should be controlled to prevent the cargo overturning when equipping air suspensions; (c) Under light loading conditions, the vertical vibration response in upper-layer cargo is amplified. This amplification can be effectively suppressed through two complementary approaches, i.e., employing low-stiffness cushion materials and reducing orifice resistance through tunable orifices, which collectively attenuate characteristic peaks in the frequency-domain response and comprehensively mitigate the vertical vibration of cargo. These findings provide guidance for designing transportation schemes for cargo in air suspension vehicles to enhance cargo safety. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

Real-time and accurate monitoring of rice nitrogen status is essential for precision nitrogen management. Although unmanned aerial vehicle (UAV)-based spectral sensors have been widely used, existing estimation models that rely solely on crop phenotypes still suffer from limited accuracy and stability. In this study, the UAV vegetation indices (VIs), meteorological parameters (M) and fertilization (F) data were incorporated as input variables to establish rice N nutrition index (NNI) estimation models using three machine learning (ML) algorithms (adaptive boosting (AB), partial least squares (PLSR) and random forest (RF). The results showed that the models’ predictive accuracy ranked as follows based on input variable combinations: VI + M + F > VI + F > VI + M > VI. Among the three ML models, the RF algorithm demonstrated the best performance and achieved validation R² values ranging from 0.94 to 0.95 across all growth stages. Both meteorology and fertilization factors benefited the model, with their incorporation greatly improving model accuracy. This demonstrated the potential to enhance the diagnosis of seasonal rice nitrogen status and provide guidance for in-season site-specific N management through consumer-grade UAV imagery and machine learning. Full article

With the rapid development of internet technologies, enhancing security protection for patient information during its transmission has become increasingly important. Compared with traditional image encryption methods, chaotic image encryption schemes leveraging sensitivity to initial conditions and pseudo-randomness demonstrate superior suitability for high-security-demand scenarios like medical image encryption. In this paper, a novel 3D fractional-order memristive Hopfield neural network (FMHNN) chaotic model with a minimum number of neurons is proposed and applied in medical image encryption. The chaotic characteristics of the proposed FMHNN model are systematically verified through various dynamical analysis methods. The parameter-dependent dynamical behaviors of the proposed FMHNN model are further investigated using Lyapunov exponent spectra, bifurcation diagrams, and spectral entropy analysis. Furthermore, the chaotic behaviors of the proposed FMHNN model are successfully implemented on FPGA hardware, with oscilloscope observations showing excellent agreement with numerical simulations. Finally, a medical image encryption scheme based on the proposed FMHNN model is designed, and comprehensive security analyses are conducted to validate its security for medical image encryption. The analytical results demonstrate that the designed encryption scheme based on the FMHNN model achieves high-level security performance, making it particularly suitable for protecting sensitive medical image transmission. Full article

Due to the slender geometry and low-amplitude vibrations of stayed cables, existing vision-based methods often fail to accurately identify their full-field dynamic parameters, especially the higher-order modes. This paper proposes a novel holographic vision-based method to accurately identify the high-order full-field dynamic parameters and estimate the tension of the stayed cables. Particularly, a full-field optical flow tracking algorithm is proposed to obtain the full-field dynamic displacement information of the stayed cable by tracking the changes in the optical flow field of the continuous motion signal spectral components of holographic feature points. Frequency-domain analysis is applied to extract the natural frequencies and damping ratios, and the vibration frequency method is used to estimate the tension. Additionally, an Eulerian-based amplification algorithm—holographic feature point video magnification (HFPVM)—is proposed for enhancing weak visual motion signals of the stayed cables, so that the morphological motion information of the stayed cables can be visualized. The effectiveness of the proposed method has been validated through experiments on the stayed cable models. Compared with the results obtained using contact sensors, the proposed holographic vision-based method can accurately identify the first five natural frequencies with overall errors below 5% and a maximum deviation of 6.86% in cable tension estimation. The first three normalized holographic mode shapes and dynamic displacement vectors are successfully identified, with the MAC value reaching up to 99.51%. This entirely non-contact vision-based method offers a convenient and low-cost approach for cable tension estimation, and this is also the first study to propose a comprehensive, visual, and quantifiable strategy for periodic or long-term monitoring of cable-supported structures, highlighting its strong potential in practical applications. Full article