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Ginger, valued for its medicinal properties and economic significance, is vulnerable to environmental stressors such as intense light and high temperatures, which can hinder its growth and development. Class III peroxidases (PRXs) are plant-specific oxidoreductases essential for plant development, growth, and stress responses. Despite their importance, there is limited information available on the function of the class III peroxidase gene family in ginger (ZoPRX). In this study, 103 ZoPRX members within the ginger genome were identified, unevenly distributed across 11 chromosomes. The identified ZoPRX members were categorized into five subfamilies based on gene structures, protein motifs, and phylogenetic analysis. Gene duplication analysis revealed that ZoPRX has primarily undergone segmental duplication. Interspecies homology analysis between ginger and Arabidopsis thaliana, Oryza sativa, and Musa acuminata suggested most ZoPRXs in ginger originated after the divergence of dicotyledon and monocotyledon. Analysis of promoter cis-acting elements identified defense and stress response elements in 39 genes and hormone response elements in 95 genes, indicating their potential roles in responding to environmental stresses. Quantitative Real-Time PCR (qRT-PCR) analysis confirmed that the majority of ZoPRX members are responsive to high temperature and intense light stress. This study provides a comprehensive understanding of the PRX family in ginger, thereby laying the groundwork for future investigations into the functional role of ZoPRX genes under high-temperature and intense light-stress conditions. [ABSTRACT FROM AUTHOR]

Ginger (Zingiber officinale Roscoe) is a significant medicinal and culinary plant, with its growth influenced by various biotic and abiotic factors. The FWL gene, containing the PLAC8 motif, is prevalent in fungi, algae, higher plants, and animals. In plants, FWL primarily regulates fruit weight, cell division, and participates in heavy metal transport. However, the FWL family members in ginger have not been previously identified. This study identified 21 FWL members within the ginger genome, distributed across nine chromosomes. These 21 FWL genes were categorized into five subfamilies based on the phylogenetic analysis. Gene-structure and motif analyses revealed that ZoFWL has been conserved throughout evolution. Concurrently, the ZoFWL gene exhibits a homologous evolutionary relationship only with Musa acuminata. We identified three pairs of fragment-repeat events encompassing five genes, which likely represent the primary mechanism for amplification within the ZoFWL gene family. The promoter regions of the ZoFWL genes are enriched with numerous cis-acting elements implicated in plant growth, development, and responses to abiotic stress. These include elements responsive to low temperatures, anaerobic induction, MYB binding sites integral to defense and stress responses, and drought inducibility. Expression profiling revealed that the ZoFWL genes are responsive to a quartet of abiotic stressors, with ZoFWL18, in particular, demonstrating a pronounced response to osmotic, low-temperature, heat, and salinity stresses. This underscores the pivotal role of ZoFWLs in abiotic-stress responses. Our findings offer valuable insights into the potential of the ZoFWL gene family in modulating ginger rhizome development and the genes' response to abiotic stressors, laying a foundational framework for future research into ginger's resistance breeding. [ABSTRACT FROM AUTHOR]

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958). [ABSTRACT FROM AUTHOR]

The advantages of performing aerosol retrieval with multi-angle, multi-spectral photopolarimetric measurements over intensity-only measurements come from this technique's sensitivity to aerosols' microphysical properties, such as their particle size, shape, and complex refraction index. In this study, an extended LUT (Look Up Table) algorithm inherited from a previous work based on the assumption of surface reflectance spectral shape invariance is proposed and applied to PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) measurements to retrieve aerosols' optical properties including aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF). Case studies conducted over East China for different aerosol scenes are investigated. A comparison between the retrieved AOD regional distribution and the corresponding MODIS (Moderate-resolution Imaging Spectroradiometer) C6 AOD products shows similar spatial distributions in the Jing-Jin-Ji (Beijing–Tianjin–Hebei, China's mega city cluster) region. The PARASOL AOD retrievals were compared against the AOD measurements of seven AERONET (Aerosol Robotic Network) stations in China to evaluate the performance of the retrieval algorithm. In the fine-particle-dominated regions, lower RMSEs were found at Beijing and Hefei urban stations (0.16 and 0.18, respectively) compared to those at other fine-particle-dominated AERONET stations, which can be attributed to the assumption of surface reflectance spectral shape invariance that has significant advantages in separating the contribution of surface and aerosol scattering in urban areas. For the FMF validation, an RMSE of 0.23, a correlation of 0.57, and a bias of −0.01 were found. These results show that the algorithm performs reasonably in distinguishing the contribution of fine and coarse particles. [ABSTRACT FROM AUTHOR]

The thermal infrared data of Fengyun 4A (FY-4A) geostationary meteorological satellite can be used to retrieve hourly land surface temperature (LST). In this paper, seven candidate algorithms are compared and evaluated. The Ulivieri (1985) algorithm is determined to be optimal for the algorithm of FY-4A LST official products. The refined algorithm coefficients for distinguishing dry and moist atmosphere were established for daytime and nighttime, respectively. Then, FY-4A LST official products under clear-sky conditions are produced. The validation results show that: (1) Compared with in-situ measured LST data at the HeBi crop measurement network, the root mean square errors (RMSE) were 2.139 and 2.447 K. Compared with in-situ measured LST data at Naqu alpine meadow site of Tibet plateau, the RMSE was 2.86 K. (2) When compared with the MODIS LST product, the RMSE was 1.64, 2.17, 2.6, and 1.73 K in March, July, October, and December, respectively. By the bias long-time change at a single site, RMSE of the XLHT (city) and GZH (desert) sites were 2.735 and 2.97 K, respectively. Overall, the preferred algorithm exhibits good accuracy and meets the required accuracy of the FY-4A mission. [ABSTRACT FROM AUTHOR]

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage. [ABSTRACT FROM AUTHOR]

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol. [ABSTRACT FROM AUTHOR]

A novel communication and navigation fusion system (CNFS) based on cellular communication system is developed to realize high-accuracy localization. Because of the small signal coverage of every transmitter and the fluctuation of received signal and noise, the CNFS positioning receiver requires fast processing of signal and stable performance. As the first operation performed by receiver, signal acquisition with fast speed and stable detection performance should be realized. This paper proposed a novel fast acquisition method with fine detection performance using search space reduction (SSR) and DD-MAX/TC-CACFAR techniques. SSR method is utilized to reduce the size and number of search space in the cross ambiguity function (CAF) evaluation stage. DD-MAX/TC-CACFAR method is employed to realize fine and stable detection performance in signal detection stage. The expressions of acquisition time are then derived considering the size and number of search space and the detection performance. Theoretical performance analysis and Monte Carlo simulation, which contain the comparison with other acquisition method, are presented to demonstrate the effectiveness of proposed method. Simulation and analysis results show that the proposed method can realize fast acquisition with fine and stable detection performance. [ABSTRACT FROM AUTHOR]

Featured Application: User terminal positioning based on the 5G system or other ground-based wireless systems. Cellular communication systems support mobile phones positioning function for Enhanced-911 (E-911) location requirements, but the positioning accuracy is poor. The fifth-generation (5G) cellular communication system can use indoor distribution systems to provide accurate multiple time-difference-of-arrival (TDOA) and single time-of-arrival (TOA) measurements, which could significantly improve the indoor positioning ability. Unlike iterative localization algorithms for TDOA or TOA, the existing closed-form algorithms, such as the Chan-Ho algorithm, do not have convergence problems, but can only estimate position based on one kind of measurement. This paper proposes a closed-form localization algorithm for multiple TDOAs and single TOA measurements. The proposed algorithm estimates the final position result using three-step weighted least squares (WLSs). The first WLS provides an initial position for the last two steps. Then the algorithm uses two WLSs to estimate position based on heteroscedastic TDOA and TOA measurements. In addition, the geometric dilution of precision (GDOP) of the proposed hybrid TDOA and TOA positioning has been derived. The analysis of GDOP shows that the proposed hybrid positioning has lower GDOP than TDOA-only positioning, which means the proposed hybrid positioning has a higher accuracy limitation than TDOA-only positioning. The simulation shows that the proposed localization algorithm could have better performance than closed-form TDOA-only positioning methods, and the positioning accuracy could approximate Cramer-Rao lower bound (CRLB) when the TDOA measurement errors are small. [ABSTRACT FROM AUTHOR]

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable. [ABSTRACT FROM AUTHOR]

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies. [ABSTRACT FROM AUTHOR]

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced. [ABSTRACT FROM AUTHOR]

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system's human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users' perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user's perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user's perceptual imagery index, and thus the user's task operation experience was better. [ABSTRACT FROM AUTHOR]

A novel communication and navigation fusion system (CNFS) was developed to realized high accuracy positioning in constrained conditions. Communication and navigation fusion signal transmitted by base stations are in the same time and frequency band but are allocated different power levels. The positioning receiver of CNFS requires signal coverage of at least four base stations to realize positioning. The improvement of receiver sensitivity is an important way to expand signal coverage of base station. As an essential stage of signal processing in CNFS positioning receiver, signal acquisition requires a trade-off between improvement of acquisition frequency accuracy and reduction in computational load. A new acquisition algorithm called PMF-FC-BA-FFT method is proposed to acquire the carrier frequency accurately with lower computational load in a weak signal environment. The received signal is firstly filtered by partially matched filters (PMF) with local replica pseudorandom noise (PRN) sequences being coefficients to strip off the PRN code in the signal. Frequency compensation (FC) was performed to avoid the large attenuation in block accumulation (BA) and generate a series of signals with a small frequency offset step. Block accumulation was then executed. Finally, the acquisition detection was performed based on a series of fast Fourier transformation (FFT) outputs to obtain acquisition results with fine frequency estimation. Simulations and experimental tests results show that the proposed method can realize high accuracy frequency acquisition in a weak signal environment with fewer computational resources compared with existing acquisition methods. [ABSTRACT FROM AUTHOR]

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration. [ABSTRACT FROM AUTHOR]

Metamaterial-based absorbers have been extensively investigated in the terahertz (THz) range with ever increasing performances. In this paper, we propose an all-dielectric THz absorber based on doped silicon. The unit cell consists of a silicon cross resonator with an internal cross-shaped air cavity. Numerical results suggest that the proposed absorber can operate from THz to far-infrared regimes, having an average power absorption of ∼95% between 0.6 and 10 THz. Experimental results using THz time-domain spectroscopy show a good agreement with simulations. The underlying mechanisms for broadband absorption are attributed to the combined effects of multiple cavities modes formed by silicon resonators and bulk absorption in the doped silicon substrate, as confirmed by simulated field patterns and calculated diffraction efficiency. This ultra-wideband absorption is polarization insensitive and can operate across a wide range of the incident angle. The proposed absorber can be readily integrated into silicon-based photonic platforms and used for sensing, imaging, energy harvesting and wireless communications applications in the THz/IR range. [ABSTRACT FROM AUTHOR]

In order to improve the wear and corrosion resistance of an AZ91D magnesium alloy substrate, an Al0.5CoCrCuFeNi high-entropy alloy coating was successfully prepared on an AZ91D magnesium alloy surface by laser cladding using mixed elemental powders. Optical microscopy (OM), scanning electron microscopy (SEM), and X-ray diffraction were used to characterize the microstructure of the coating. The wear resistance and corrosion resistance of the coating were evaluated by dry sliding wear and potentiodynamic polarization curve test methods, respectively. The results show that the coating was composed of a simple FCC solid solution phase with a microhardness about 3.7 times higher than that of the AZ91D matrix and even higher than that of the same high-entropy alloy prepared by an arc melting method. The coating had better wear resistance than the AZ91D matrix, and the wear rate was about 2.5 times lower than that of the AZ91D matrix. Moreover, the main wear mechanisms of the coating and the AZ91D matrix were different. The former was abrasive wear and the latter was adhesive wear. The corrosion resistance of the coating was also better than that of the AZ91D matrix because the corrosion potential of the former was more positive and the corrosion current was smaller. [ABSTRACT FROM AUTHOR]

In order to improve the wear and corrosion resistance of an AZ91D magnesium alloy substrate, an Al 0.5 CoCrCuFeNi high-entropy alloy coating was successfully prepared on an AZ91D magnesium alloy surface by laser cladding using mixed elemental powders. Optical microscopy (OM), scanning electron microscopy (SEM), and X-ray diffraction were used to characterize the microstructure of the coating. The wear resistance and corrosion resistance of the coating were evaluated by dry sliding wear and potentiodynamic polarization curve test methods, respectively. The results show that the coating was composed of a simple FCC solid solution phase with a microhardness about 3.7 times higher than that of the AZ91D matrix and even higher than that of the same high-entropy alloy prepared by an arc melting method. The coating had better wear resistance than the AZ91D matrix, and the wear rate was about 2.5 times lower than that of the AZ91D matrix. Moreover, the main wear mechanisms of the coating and the AZ91D matrix were different. The former was abrasive wear and the latter was adhesive wear. The corrosion resistance of the coating was also better than that of the AZ91D matrix because the corrosion potential of the former was more positive and the corrosion current was smaller.