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Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.

The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). In this research, a new comprehensive remote sensing ecological index (CRSEI) based on the quaternion Copula function is proposed to comprehensively characterize EEQ responded by integrating four eco-environment evaluation indexes. Additionally, the spatiotemporal variation of EEQ in Henan Province is evaluated using monthly CRSEI data from 2001 to 2020. The results show that: (1) The applicability and monitoring accuracy of CRSEI are better than that of RSEI, which can be used to assess the EEQ. (2) The EEQ of Henan Province declined between 2001 and 2010 but significantly improved and rebounded from 2011 to 2020. During this period, CRSEI values were higher in West and South Henan and lowest in central Henan, with West Henan consistently showing the highest values across all seasons. (3) The EEQ in Henan Province exhibited a tendency of deterioration from the central cities outward, followed by improvement from the outer areas back towards the central cities. In 2010, regions with poor EEQ made up 68.3% of the total area, whereas by 2020, regions with excellent EEQ accounted for 74% of the total area. (4) The EEQ was significantly negatively correlated with human activities, while it was positively correlated with precipitation. The research provides a reference and guidance for the scientific assessment of the regional eco-environment.

Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks.

In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations. To date, there have been few studies on how to enhance resolution under the framework of one-bit DOA estimation. This paper aims to improve the resolution constraints of one-bit DOA estimation. The log-det heuristic is applied to approximate and solve the atomic norm minimization problem. In particular, a reweighted binary atomic norm minimization with noise assumption constraints is proposed to achieve high-resolution and robust one-bit DOA estimation. Finally, the alternating direction method of multipliers algorithm is employed to solve the established optimization problem. Simulations are conducted to demonstrate that the proposed algorithm can effectively enhance the resolution.

“The article investigates the macro-cell corrosion behavior and corrosion resistance when the alloyed steel and the carbon steel are used together because the traditional carbon steel is difficult to meet the corrosion resistance and durability of the steel structure of the transmission line in the marine environment.” In this paper, a new type of Cr-alloyed corrosion-resistant steel (00Cr10MoV) is used to partially replace carbon structural steel in order to meet the actual needs of corrosion resistance and service life improvement of steel structures for offshore transmission lines. It is important to systematically study the macro-cell corrosion behavior of combinations of the same type of steel and dissimilar steel, induced by the chloride concentration difference in simulated concrete solutions, and employ electrochemical testing methods to scientifically evaluate the corrosion resistance of steel after macro-cell corrosion. The aim is to study and evaluate the macro-cell corrosion behavior of alloyed corrosion-resistant steel and to lay a foundation for its combined use with carbon steel in a chloride corrosion environment to improve the overall corrosion resistance and service life. Under the same concentration difference, the macro-cell corrosion of the alloyed steel combination is milder compared with the carbon steel combination. The corrosion current of the alloyed steel combination at 29 times the concentration difference is only 1/10 of the carbon steel combination. Moreover, at 29 times the concentration difference, the macro-cell corrosion potential of dissimilar steel is only 1/6 of the combined potential of carbon steel combination under the same concentration difference, and the corrosion current is only 1/10 of that of the carbon steel combination.

In space missions, heating films are crucial for uniformly heating onboard equipment for precise temperature control. This study develops an optimization method using surrogate models for lightweight anisotropic substrate thermal conductive heating films, meeting the requirements of uniform heating in thermal control for space applications. A feedforward neural network optimized by particle swarm optimization (PSO) was employed to create a surrogate model, mapping design parameters to the temperature uniformity of the heating film. This model served as the basis for applying the NSGA-II algorithm to quickly optimize both temperature uniformity and lightweight characteristics. In this study, the PSO-BP surrogate model was trained using heating film thermal simulation data, and the surrogate model demonstrated an accurate prediction of the mean square error (MSE) of the predicted temperature difference within 0.0168 s. The maximum temperature difference in the optimal model is 1.188 ℃, which is 30.5 times lower than before optimization, and the equivalent density is only increased by 3.9%. In summary, this optimization design method effectively captures the relationships among various parameters and optimization objectives. Its superior computational accuracy and design efficiency offer significant advantages in the design of devices such as heating films.

Rock bolting is a commonly used method for stabilizing the surrounding rock in coal-mine roadways. It involves installing rock bolts after drilling, which penetrate unstable rock layers, binding loose rocks together, enhancing the stability of the surrounding rock, and controlling its deformation. Although recent progress in drilling and anchoring equipment has significantly enhanced the efficiency of roof support in coal mines and improved safety measures, how to deal with drilling rigs’ misalignment with the through-hole center remains a big issue, which may potentially compromise the quality of drilling and consequently affect the effectiveness of bolt support or even result in failure. To address this challenge, this article presents a robotic teleoperation system alongside a hybrid visual servo control strategy. Addressing the demand for high precision and efficiency in aligning the drilling rigs with the center of the drilling hole, a hybrid control strategy is introduced combining position-based and image-based visual servo control. The former facilitates an effective approach to the target area, while the latter ensures high-precision alignment with the center of the drilling hole. The robot teleoperation system employs the binocular vision measurement system to accurately determine the position and orientation of the drilling-hole center, which serves as the designated target position for the drilling rig. Leveraging the displacement and angle sensor information installed on each joint of the manipulator, the system utilizes the kinematic model of the manipulator to compute the spatial position of the end-effector. It dynamically adjusts the spatial pose of the end-effector in real time, aligning it with the target position relative to its current location. Additionally, it utilizes monocular vision information to fine-tune the movement speed and direction of the end-effector, ensuring rapid and precise alignment with the target drilling-hole center. Experimental results demonstrate that this method can control the maximum alignment error within 7 mm, significantly enhancing the alignment accuracy compared to manual control. Compared with the manual control method, the average error of this method is reduced by 41.2%, and the average duration is reduced by 4.3 s. This study paves a new path for high-precision drilling and anchoring of tunnel roofs, thereby improving the quality and efficiency of roof support while mitigating the challenges associated with significant errors and compromised safety during manual control processes.

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results.

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

Reinforcement corrosion significantly impacts the service life of reinforced concrete structures. The present study investigates the circumferential and longitudinal non-uniformity of steel corrosion in concrete subjected to mechanical load. Results indicate that, in the case of steel corrosion in concrete subjected to mechanical load, the distribution of rust layer thickness around the perimeter of the steel bar is fitted well with a Gaussian distribution. As the corrosion rate gradually increases, the uniform coefficient is linearly proportional to the minimum thickness of the rust layer. With respect to the longitudinal non-uniformity of steel corrosion, load-induced transverse cracks have a significant impact on the non-uniformity of corrosion, leading to the formation of rust peaks near the locations of transverse cracks. In the vicinity of each rust peak, the corrosion rate of the steel bar follows a Gaussian distribution. With respect to the non-uniformity of corrosion along the longitudinal rebar, a Gumbel distribution is identified to fit well, both in the cases of the non-stressed section and the pure bending section, although with dissimilar non-uniform parameters. Crack coefficients (α and β) are introduced to describe the influence of transverse cracks on the longitudinal non-uniformity of steel corrosion.

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

Diluted magnetic semiconductors (DMSs) with tunable ferromagnetism are among the most promising materials for fabricating spintronic devices. Some DMS systems have sizeable magnetoresistances that can further extend their applications. Here, we report a new DMS Rb(Zn1−x−yLiyMnx)4As3 with a quasi-two-dimensional structure showing sizeable anisotropies in its ferromagnetism and transverse magnetoresistance (MR). With proper charge and spin doping, single crystals of the DMS display Curie temperatures up to 24 K. Analysis of the critical behavior via Arrott plots confirms the long-range ferromagnetic ordering in the Rb(Zn1−x−yLiyMnx)4As3 single crystals. We observed remarkable intrinsic MR effects in the single crystals (i.e., a positive MR of 85% at 0.4 T and a colossal negative MR of −93% at 7 T).

The grid-forming virtual synchronous generator (GFVSG) with large virtual inertia can provide a friendly grid-connected operational mode for power electronic converters, but it may also introduce the active power dynamic oscillation problems similar to traditional synchronous generators. In view of this, the dynamic equivalent circuit model of the GFVSG grid-tied active power-angle is established firstly, and, then, the understanding of the GFVSG active power oscillations under variable disturbances is revealed from the perspective of circuit energy flow in this paper. On this basis, an active power dynamic oscillation damping method based on an energy reshaping mechanism for the GFVSG is proposed, and a parameter design method using the second-order equivalent reduced-order control model is given. The MATLAB 2016a simulation as well as experimental test platforms of a 100 kV·A GFVSG grid-connected system are established, then, both the feasibility and effectiveness of the proposed active power dynamic oscillation damping method are verified by using the simulation and experimental comparison results.

The implementation of roof bolt support within a coal mine roadway has the capacity to bolster the stability of the encompassing rock strata and thereby mitigate the potential for accidents. To enhance the automation of support operations, this paper introduces a binocular vision positioning method for drilling holes, which relies on the adaptive adjustment of parameters. Through the establishment of a predictive model, the correlation between the radius of the target circular hole in the image and the shooting distance is ascertained. Based on the structural model of the anchor drilling robot and the related sensing data, the shooting distance range is defined. Exploiting the geometric constraints inherent to adjacent anchor holes, the precise identification of anchor holes is detected by a Hough transformer with an adaptive parameter-adjusted method. On this basis, the matching of the anchor hole contour is realized by using linear slope and geometric constraints, and the spatial coordinates of the anchor hole center in the camera coordinate system are determined based on the binocular vision positioning principle. The outcomes of the experiments reveal that the method attains a positioning accuracy of 95.2%, with an absolute error of around 1.52 mm. When compared with manual operation, this technique distinctly enhances drilling accuracy and augments support efficiency.

Nonalcoholic fatty liver disease (NAFLD), recently redefined as metabolic-dysfunction-associated fatty liver disease (MASLD), is liver-metabolism-associated steatohepatitis caused by nonalcoholic factors. NAFLD/MASLD is currently the most prevalent liver disease in the world, affecting one-fourth of the global population, and its prevalence increases with age. Current treatments are limited; one important reason hindering drug development is the insufficient understanding of the onset and pathogenesis of NAFLD/MASLD. C-reactive protein (CRP), a marker of inflammation, has been linked to NAFLD and aging in recent studies. As a conserved acute-phase protein, CRP is widely characterized for its host defense functions, but the link between CRP and NAFLD/MASLD remains unclear. Herein, we discuss the currently available evidence for the involvement of CRP in MASLD to identify areas where further research is needed. We hope this review can provide new insights into the development of aging-associated NAFLD biomarkers and suggest that modulation of CRP signaling is a potential therapeutic target.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max–min Kullback–Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max–min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide.

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.

(1) Background: Migraine is associated with comorbidities that are common in the general rural pediatric population. The purpose of this study is to evaluate the differences in the occurrence of comorbidities between rural children and adolescents with and without migraine. (2) Methods: A cross-sectional, secondary data analysis using electronic medical records of 1296 patients (53.8% females, aged 12.4 ± 3.2) was completed. Mann–Whitney U test was used to detect the difference in the number of comorbidities between the two groups. Chi-square test was used to identify the differences in the number of comorbidities, which were classified as low (0–1 comorbidities), medium (2–3 comorbidities), and high (4 or plus comorbidities) degree of comorbidities. (3) Results: Significant differences were found between those children and adolescents with migraine vs. those without for depression (p < 0.0001), anxiety (p < 0.0001), and Ehlers–Danlos Syndrome (EDS; p = 0.0309). A marginally significant difference was found between those children and adolescents with migraine (47.2%; n = 306) vs. those without (42.1%; n = 273) for unhealthy weight (p = 0.0652). Approximately 40% of the migraineurs had 2–3 comorbidities, whereas 32% of the non-migraineurs had 2–3 comorbidities (p = 0.0003). (4) Conclusions: Findings demonstrate the importance of identifying comorbidities associated with rural pediatric migraine in order to develop effective treatment strategies that optimize patient outcomes.

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations.

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation.

With the development of the concept of integrated flight testing, joint testing has become a key research trend. The use of a Logical Range can allow one to overcome the shortcomings of traditional test ranges, allowing for full use of the resources of each range when implementing joint flight testing. However, the Logical Range concept also has problems, such as those relating to the low reusability of test resources and insufficient ability to monitor the resource status in real-time. Considering such problems, a Logical Range framework based on digital twin technology is proposed in this paper. On the basis of this framework, a Logical Range system based on a digital twin for the real-time mapping of real physical behavior is constructed and the three key technologies used in the system are detailed. The feasibility of the framework is successfully verified through a flight test, demonstrating that the proposed framework can provide key support for the application of digital twin technology in the field of flight testing.

By addressing the imbalanced proportions of the data category samples in the velocity structure function of the LiDAR turbulence identification model, we propose a flight turbulence identification model utilizing both a conditional generative adversarial network (CGAN) and extreme gradient boosting (XGBoost). This model can fully learn small- and medium-sized turbulence samples, reduce the false alarm rate, improve robustness, and maintain model stability. Model training involves constructing a balanced dataset by generating samples that conform to the original data distribution via the CGAN. Subsequently, the XGBoost model is iteratively trained on the sample set to obtain the flight turbulence classification level. Experiments show that the turbulence recognition accuracy achieved on the CGAN-generated augmented sample set improves by 15%. Additionally, when incorporating LiDAR-obtained wind field data, the performance of the XGBoost model surpasses that of traditional classification algorithms such as K-nearest neighbours, support vector machines, and random forests by 14%, 8%, and 5%, respectively, affirming the excellence of the model for turbulence classification. Moreover, a comparative analysis conducted on a Zhongchuan Airport flight crew report showed that the model achieved a 78% turbulence identification accuracy, indicating enhanced recognition ability under data-imbalanced conditions. In conclusion, our CGAN/XGBoost model effectively addresses the proportion imbalance issue.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

Synchrotron-based soft X-ray tomography (SXT), providing three-dimensional morphology and quantitative distribution of linear absorption coefficient (LAC) of the imaged objects, is widely used in many fields to obtain ultra-structure images, especially in cellular imaging. Off-line fluorescence microscopies (FMs) are combined to identify the type of organelles and status of cells. However, deformation and displacement usually occur during the transfer and loading process, which decreases the precision of two-modal images’ registration. In this paper, we report on an on-line FM, at the SXT station (BL07W) of the National Synchrotron Radiation Laboratory (NSRL), which avoids deformation and displacement. Therefore, researchers can easily find the sample and take the useful data without tedious post-processing. Combining SXT with on-line FM, we achieved the identification and high-resolution imaging of an apoptotic cell. The experiments revealed that the LAC of the nucleus of the apoptotic cell was larger than that of a normal cell, which could be explained by nucleus pyknosis of the apoptotic cell.

It is generally believed that working memory (WM) is dysfunctional in depression. However, whether this impaired performance originates from impaired encoding, maintenance or both stages is still unclear. Here, we aimed to decompose the abnormal characteristics of encoding and maintenance in patients with recurrent major depressive disorder (MDD). Thirty patients and thirty-nine healthy controls completed a spatial working memory task where the encoding time and the retention time could vary under different load levels. Encoding performance was assessed by comparing accuracies between short and long encoding times, and maintenance performance was assessed by comparing accuracies between short and long retention times. The results show a lower performance in depression than the controls. However, while the decreased accuracy by long retention (vs. short retention) was increased by a short encoding time in the control group, the retention performance of the depression group did not further suffer from the short encoding time. The generally impaired encoding, together with limited maintenance of immunity against the constrained encoding time, suggests a common bias for fixed internal processing over external processing in recurrent MDD. The paradigm provided in this study can be a convenient and efficient clinical test for assessing the WM encoding and maintenance function.

The lakes in the arid or semi-arid regions of western China are more sensitive to climate changes, and lake levels are considered as a direct indicator of regional climate variability. In this study, we combined satellite altimetry data from ICESat-1 with a smaller footprint and higher accuracy (compared to radar altimetry) and CryoSat-2 with a higher resolution in the along-track direction to monitor lake levels in western China and their trends over a long time period from 2003 to 2021. Our satellite altimetry derived lake levels were well-validated by comparing them against in situ measurements for a lake and independent altimetry-derived product from the DAHITI database for the common lakes. Furthermore, the commonly used linear model was applied to our derived lake level time-series to estimate the overall change trends in 67 typical lake levels over western China. Our results showed that 55 (82%) of these lakes displayed an increasing tendency in water levels, and the remaining 12 (18%) lakes showed a decreasing trend. Overall, the mean water level changing rate in western China was +0.15 m/yr (−1.40 to +0.58 m/yr) during the studied time period. The spatial patterns of the lake level variations can be grouped into three subregions: lake level changes between 2003 and 2021 showed general rising lake levels for the central–northern TP (Tibetan Plateau) endorheic region and Xinjiang, but declining levels for the southern TP exorheic region. The seasonal characteristic of lake level changes showed a significant increase during the summer monsoon season, followed by decreases during the non-monsoon season. The precipitation variations play a leading role in the lake level changes in the context of warm and humid climate states. There were good correspondences between the monthly variations in the lake level and monthly mean precipitation. Additionally, the lake levels also showed a relationship with the air temperature change, in particular, the lake level increase showed a small degree of hysteresis behavior compared with the rising temperatures. Geographically, the precipitation increase in the westerlies regions led to widespread lake expansion in the central–northern TP and Xinjiang. Conversely, precipitation decrease in the Indian monsoon regions caused lake shrinkage in the exorheic region of the southern TP. This study helps us achieve a better understanding of the spatial-temporal patterns of lake level changes in the arid or semi-arid region of western China.

Atmospheric levels of atomic mercury pollution were measured using spectroscopic techniques in the city of Guangzhou, Guangdong Province, China. Assessments were mainly performed at ground level using a portable (vehicle mounted or hand carried) Zeeman modulation correlation spectrometer (Lumex RA-915M), and the results are given in easily comprehensible diagrams. Measurements were made with continuous recording in car traverses along major roads which cross the city, but also at selected spots, such as at a university campus with laboratory buildings. Further, pollution levels at different locations were recorded when walking through a major and a small hospital. While concentrations in the city in the range 3–10 ng/m3 were typical, and strongly dependent on the traffic situation, very high concentrations (up to 1300 ng/m3) were found at certain indoor hospital locations, again drawing attention to the fact that high mercury levels due to inadequate handling routines can remain undetected but could readily be eliminated by adequate measurements and subsequent sanitation.

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations.

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.

Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.

A hybrid energy-storage system (HESS), which fully utilizes the durability of energy-oriented storage devices and the rapidity of power-oriented storage devices, is an efficient solution to managing energy and power legitimately and symmetrically. Hence, research into these systems is drawing more attention with substantial findings. A battery–supercapacitor hybrid energy-storage system (BS-HESS) is widely adopted in the fields of renewable energy integration, smart- and micro-grids, energy integration systems, etc. Focusing on the BS-HESS, in this work we present a comprehensive survey including technologies of the battery management system (BMS), power conversion system (PCS), energy management system (EMS), predictive control techniques of the underlying system, application and cost-effective feasibility aspects, etc. This work reflects strong symmetry on different aspects of designing an HESS, and provides guidelines and design references for the research and application of an HESS.

Although pontine infarction is the most common subtype of posterior circulation stroke, there has been little research focusing on recurrent pontine infarction. Our study aimed to investigate the factors associated with site and mechanism of recurrent pontine infarction. Patients with acute isolated pontine infarction were enrolled and followed up for one year. Lesion topography was determined by diffusion-weighted imaging. Mechanisms were determined based on lesion topography and other vascular, cardiologic and laboratory results. A total of 562 patients with pontine infarction were included, with 67 patients experiencing recurrence during the follow-up period. Forty-one recurrences occurred at the same site as index pontine infarction (41/67, 61.2%). Results indicated that the mechanism of index pontine infarction was significantly associated with the recurrent sites (p = 0.041, OR 2.938, 95% CI 1.044–8.268), and also with the mechanisms of recurrence (p = 0.004, OR 6.056, 95% CI 1.774–20.679). Branch atheromatous disease-induced index pontine infarction was likely to recur at the same site and with the same mechanism. Moreover, if recurrence occurred at the same site, the mechanism was probably the same as that of the index stroke (p = 0.000). Our study may help physicians treat patients with pontine infarction by predicting the site and mechanism of recurrence.

Nephrotoxicity is a major side-effect of cisplatin in chemotherapy, which can occur acutely or progress into chronic kidney disease (CKD). The protein p53 plays an important role in acute kidney injury induced by cisplatin, but its involvement in CKD following cisplatin exposure is unclear. Here, we address this question by using experimental models of repeated low-dose cisplatin (RLDC) treatment. In mouse proximal tubular BUMPT cells, RLDC treatment induced p53 activation, apoptosis, and fibrotic changes, which were suppressed by pifithrin-α, a pharmacologic inhibitor of p53. In vivo, chronic kidney problems following RLDC treatment were ameliorated in proximal tubule-specific p53-knockout mice (PT-p53-KO mice). Compared with wild-type littermates, PT-p53-KO mice showed less renal damage (KIM-1 positive area: 0.97% vs. 2.5%), less tubular degeneration (LTL positive area: 15.97% vs. 10.54%), and increased proliferation (Ki67 positive area: 2.42% vs. 0.45%), resulting in better renal function after RLDC treatment. Together, these results indicate that p53 in proximal tubular cells contributes significantly to the development of chronic kidney problems following cisplatin chemotherapy.

The present study investigated the galvanic effect between tensile-stressed and non-stressed carbon steels, in addition to the influence of the tensile stress on the passivation and corrosion behavior of steel in a simulated concrete pore solution. Three different levels of tensile stress, ranging from elastic to plastic stress on the surface, were applied by adjusting the displacement of C-shape carbon steel rings. Different electrochemical measurements including the open circuit potential (OCP), the electrochemical impedance spectroscopy (EIS), the zero-resistance ammetry (ZRA), and the cyclic polarization were performed. Based on the results of EIS, the tensile stress degraded the resistance of the oxide film in moderate frequencies while enhancing the charge transfer resistance in low frequencies during passivation. As corrosion propagated, the stressed steel yielded a similar charge transfer resistance to or an even lower charge transfer resistance than the non-stressed steel, especially in the case of plastic tensile stress. The galvanic effect between the tensile-stressed and non-stressed steels increased the chloride threshold value of the tensile-stressed steel, although the susceptibility to pitting corrosion was exhibited after being corroded.

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

In order to solve multiple-attribute group decision-making (MAGDM) problems under a trapezoid intuitionistic fuzzy linguistic (TIFL) environment and the relationships between multiple input parameters needed, in this paper, we extend the Maclaurin symmetric mean (MSM) operators to TIFL numbers (TIFLNs). Some new aggregation operators are proposed, including the trapezoid intuitionistic fuzzy linguistic Maclaurin symmetric mean (TIFLMSM) operator, trapezoid intuitionistic fuzzy linguistic generalized Maclaurin symmetric mean (TIFLGMSM) operator, trapezoid intuitionistic fuzzy linguistic weighted Maclaurin symmetric mean (TIFLWMSM) operator and trapezoid intuitionistic fuzzy linguistic weighted generalized Maclaurin symmetric mean (TIFLWGMSM) operator. Next, based on the TIFLWMSM and TIFLWGMSM operators, two methods are presented to deal with MAGDM problems. Finally, there is a numerical example to verify the effectiveness and feasibility of the proposed approaches.

Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.

Marigold plants with symptoms of mosaic, crinkle, leaf curl and necrosis were observed and small RNA and ribo-depleted total RNA deep sequencing were conducted to identify the associated viruses. Broad bean wilt virus 2, cucumber mosaic virus, turnip mosaic virus, a new potyvirus tentatively named marigold mosaic virus (MMV) and a new partitivirus named as marigold cryptic virus (MCV) were finally identified. Complete genome sequence analysis showed MMV was 9811 nt in length, encoding a large polyprotein with highest aa sequence identity (57%) with the putative potyvirus polygonatumkingianum virus 1. Phylogenetic analysis with the definite potyviruses based on the polyprotein sequence showed MMV clustered closest to plum pox virus. The complete genome of MCV comprised of dsRNA1 (1583 bp) and dsRNA2 (1459 bp), encoding the RNA-dependent RNA polymerase (RdRp), and coat protein (CP), respectively. MCV RdRp shared the highest (75.7%) aa sequence identity with the unclassified partitivirus ambrosia cryptic virus 2, and 59.0%, 57.1%, 56.1%, 54.5% and 33.7% with the corresponding region of the definite delta-partitiviruses, pepper cryptic virus 2, beet cryptic virus 3, beet cryptic virus 2, pepper cryptic virus 1 and fig cryptic virus, respectively. Phylogenetic analysis based on the RdRp aa sequence showed MCV clustered into the delta-partitivirus group. These findings enriched our knowledge of viruses infecting marigold, but the association of the observed symptom and the identified viruses and the biological characterization of the new viruses should be further investigated.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

Measurements of the atmospheric concentration of polluting atomic mercury were performed using the differential absorption lidar (DIAL) technique in the Lintong district, about 35 km northeast of Xi’an, the capital of the Shaanxi province, China. Concentrations ranging from 2 to 13 ng/m3 were observed. As uniquely enabled by the lidar technique, representative average concentrations, integrated over a considerable volume, were recorded and put in relation to weather conditions, and vertical concentration profiles were measured. Considerable local non-uniformities were also observed, which may indicate the presence of localized hot-spots in the area, possibly related to ancient tombs.

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.

A series of polycrystalline samples of a new diluted magnetic semiconductor (DMS) (Ba,K)(Zn,Mn)2Sb2 has been synthesized and systematically studied. The parent phase is the so-called “Zintl compound” BaZn2Sb2, a week-degenerate semiconductor with a narrow band gap of 0.2 eV. In (Ba,K)(Zn,Mn)2Sb2, the charge is doped by (Ba,K) substitution while the spin is independently doped by (Zn,Mn) substitution. (Ba,K)(Zn,Mn)2Sb2 and analogue (Ba,K)(Zn,Mn)2As2 have comparable narrow band gaps, carrier and spin concentrations. However, the former establishes a short-range spin-glass order at a very low temperature (2Sb2 to be a touchstone for DMS theoretical models.

In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm.

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

We developed an efficient method for synthesis of substituted N-benzoylindole via Pd(II)-catalyzed C−H functionalization of substituted N-(2-allylphenyl)benzamide. The reaction showed a broad substrate scope (including N-acetyl and N-Ts substrates) and substituted indoles were obtained in good to excellent yields. The most distinctive feature of this method lies in the high selectivity for N-benzoylindole over benzoxazine, and this is the first example of Pd(II)-catalyzed synthesis of substituted N-benzoylindole. Notably, this new method was applied for the synthesis of key intermediate of indomethacin.