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Soil conservation service (SC) is defined as the ability of terrestrial ecosystems to control soil erosion and protect soil function. A long-term and high-resolution estimation of SC is urgent for ecological assessment and land management on a large scale. Here, a 300-m resolution Chinese soil conservation dataset (CSCD) from 1992 to 2019, for the first time, is established based on the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE modelling was conducted based on five key parameters, including the rainfall erosivity (interpolation of daily rainfall), land cover management (provincial data), conservation practices (weighted by terrain and crop types), topography (30 m), and soil properties (250 m). The dataset agrees with previous measurements in all basins (R 2  > 0.5) and other regional simulations. Compared with current studies, the dataset has long-term, large-scale, and relatively high-resolution characteristics. This dataset will serve as a base to open out the mechanism of SC variations in China and could help assess the ecological effects of land management policies.

To address issues of food authenticity, such as fraud and origin tracing, it is essential to employ methods in food fingerprinting that are efficient, economical, and easy to use. This review highlights the capabilities of vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, as non-invasive tools for food authentication. These methods offer rapid, cost-effective, and environmentally friendly analysis across diverse food matrices. This review further discusses recent advances such as hyperspectral imaging, portable devices, and data fusion strategies that integrate chemometrics and artificial intelligence. Despite their promise, challenges remain, including limited sensitivity for certain compounds, spectral overlaps, fluorescence interference in Raman spectroscopy, and the need for standardized validation protocols. Looking forward, trends such as the miniaturization of devices, real-time monitoring, and AI-enhanced spectral interpretation are expected to significantly advance the field of food authentication.

Mitochondria, as vital organelles, play a central role in subcellular research and biomedical innovation. Although functional nucleic acid (FNA) nanostructures have witnessed remarkable progress across numerous biological applications, strategies specifically tailored to target mitochondria for molecular imaging and therapeutic interventions remain scarce. This review delves into the latest advancements in leveraging FNA nanostructures for mitochondria-specific imaging and cancer therapy. Initially, we explore the creation of FNA-based biosensors localized to mitochondria, enabling the real-time detection and visualization of critical molecules essential for mitochondrial function. Subsequently, we examine developments in FNA nanostructures aimed at mitochondrial-targeted cancer treatments, including modular FNA nanodevices for the precise delivery of therapeutic agents and programmable FNA nanostructures for disrupting mitochondrial processes. Emphasis is placed on elucidating the chemical principles underlying the design of mitochondrial-specific FNA nanotechnology for diverse biomedical uses. Lastly, we address the unresolved challenges and outline prospective directions, with the goal of advancing the field and encouraging the creation of sophisticated FNA tools for both academic inquiry and clinical applications centered on mitochondria.

Wide-bandgap perovskite solar cells (WBG-PSCs) are critical for developing perovskite/silicon tandem solar cells. The defect-rich surface of WBG-PSCs will lead to severe interfacial carrier loss and phase segregation, deteriorating the device’s performance. Herein, we develop a surface reconstruction method by removing the defect-rich crystal surface by nano-polishing and then passivating the newly exposed high-crystallinity surface. This method can refresh the perovskite/electron-transporter interface and release the residual lattice strain, improving the charge collection and inhibiting the ion migration of WBG perovskites. As a result, we can achieve certified efficiencies of 23.67% and 21.70% for opaque and semi-transparent PSCs via a 1.67-eV perovskite absorber. Moreover, we achieve four-terminal perovskite/silicon tandem solar cells with a certified efficiency of 33.10% on an aperture area of one square centimeter. The defect-rich surface of wide-bandgap perovskite solar cells leads to severe interfacial carrier loss and phase segregation. Here, the authors reconstruct the surface through nano-polishing followed by passivation, achieving certified efficiency of 33.1% for perovskite/silicon tandem solar cells.

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment.

Ku-band drive amplification and a 7-bit true-time-delay (TTD) function were realized as a part of a LTCC-based T/R module to increase integration. The 8-channel T/R module was fabricated and its key characteristics were measured, including a 3-bit (1/2/4 λ) TTD, 4-bit (0.25/0.5/1/2 λ) TTD, receive gain, noise figure and output power. The 8-channel T/R module can be further adopted to increase bandwidth and scanning angle of phased arrays without beam squint.

Advanced receiver autonomous integrity monitoring (ARAIM), based on multi-constellation and dual-frequency, can provide vertical navigation in terminal approaches. The baseline ARAIM receiver algorithm is based on the multiple hypothesis solution separation (MHSS). The fault modes determination scheme, proposed in the ARAIM baseline algorithm, is a sequential structure and a core processing which can be summarized in three steps: calculating the maximum number of simultaneous faults, forming all subsets, and filtering the subsets. The fault modes determined by the maximum number of simultaneous faults are sufficient, but not necessary. A set of redundant fault modes is included, which reduces the ARAIM performance and increases the computation burden. The continuity risk is not considered in the filtering. We propose a new fault modes determination scheme based on feedback structure with probability accumulation. The number of fault modes in the baseline algorithm is defined subject to a given integrity risk requirement. In the proposed algorithm, the fault modes are directly determined subject to this parameter. The fault modes are accumulated in descending order of fault probability. The probability of not monitored risk obtained in probability accumulation is closer to the integrity risk requirement compared to the baseline method, and the number of fault modes is reduced noticeably. The continuity detection is added to the feedback structure to find the severe integrity risk in time. The fault modes, determined in the proposed scheme, are sufficient and necessary. The performance evaluation results under a GPS-Galileo dual-constellation situation for localizer precision vertical 200 (LPV-200) requirements show that the proposed scheme can improve the availability of ARAIM from 84.92 to 92.25%. An enhanced effective monitor threshold (EMT) mainly contributes to this improvement. Furthermore, the proposed scheme also shows positive superiority in calculation load. The reduction in fault modes can directly contribute to the alleviation of the receiver calculation burden, which saves nearly 40% computational time on a PC-based software-defined receiver.

An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements.

Background To evaluate the antimicrobial and microbicidel activity of B. radicata fermentation broth, the broth was purified by DEAE-cellulose and sephadex LC-20 column. The compounds were submitted to spectral analyses (HPLC, FT-IR, 1D and 2D NMR etc.). Results The purified compounds were identified as the Griseococcin(s) which were naphthoquinone derivatives, the Chemical formula and MW of Griseococcin (1) was determined as C 37 O 10 H 43 N and 661 Da. only Griseococcin (1) has good antimicrobial activity among the Griseococcin(s). The zone of inhibition (ZOI), minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) or minimum fungicidal concentration (MFC) of Griseococcin (1) were used to investigate the antimicrobial activity. Antifungal activity of Griseococcin (1) was significant, especially for main pathogenic fungus Trichophyton rubrum and Trichophyton mentagrophytes, MFC/MIC of Griseococcin (1) was 1, while MFC/MIC of postive control was greater than 4, the fungicidal effect of Griseococcin (1) was better than that of positive control. Conclusions In this paper, the secondary metabolite compound Griseococcin (1) from B. radicata was purified. The purified compound can restrain main pathogens ( T. rubrum and T. mentagrophytes ) leading to tinea pedis. The antifungal activity of Griseococcin (1) was similar to that of the positive control and the fungicidal effect of Griseococcin (1) was better than that of positive control, it might be suitable for pharmaceutical industries.