Search

Nanoporous tungsten (NP–W) and molybdenum (NP–Mo) are of great interest in aerospace and nuclear fusion/fission reactor industrial sections. Molecular dynamics (MD) simulations are employed for understanding the influences of shock velocity and relative density on the shock responses of NP-W and NP-Mo with stochastic bicontinuous structural features. Thermodynamic simulations reveal that temperature changes exhibit relatively low sensitivity to variations in relative density for a given shock velocity. Conversely, pressure and shock wave velocity increase substantially with rising relative density. NP-W specimen demonstrates higher shock-induced pressures and temperatures compared to NP-Mo. The porous structure exhibits greater susceptibility to heat generation under shock loading than the bulk. Hugoniot relations reveal that the wave velocity of NP-Mo is slightly greater than that of NP-W. NP-Mo exhibits greater resistance to amorphization than NP-W at shock velocities below 2.0 km/s. Specifically, at up = 1.5 km/s and t = 50 ps, the amorphous conversion percentages of BCC atoms in NP-W/NP-Mo are 49.6%/47.0% (φ = 0.40), 58.5%/52.2% (φ = 0.50), and 68.1%/57.5% (φ = 0.60), respectively. This investigation provides a fundamental understanding of shock wave behavior exhibited by nanoporous refractory metals at atomic scales and will provide precious theoretical and design guidelines for potential industrial applications.

Abstract Hafnium oxide (HfO2)‐based devices have been extensively evaluated for high‐speed and low‐power memory applications. Here, the influence of aluminum (Al) and lanthanum (La) co‐doping HfO2 thin films on the ferroelectric characteristics of hafnium‐based devices is investigated. Among devices with different La/Al ratios, the Al and La co‐doped hafnium oxide (HfAlAO) device with 4.2% Al and 2.17% La exhibited the excellent remanent polarization and thermostability. Meanwhile, first principal analyses verified that hafnium‐based thin films with 4.2% Al and 2.17% La promoted the formation of the o‐phase against the paraelectric phase, providing theoretical support for supporting experimental results. Furthermore, a vertical ferroelectric HfO2 memory based on 3D macaroni architecture is reported. The devices show excellent ferroelectric characteristics of 22 µC cm−2 under 4.5 MV cm−1 and minimal coercive field of ≈1.6 V. In addition, the devices exhibit great memory performance, including the response speed of device can achieve 20 ns and endurance characteristic can achieve 1010 cycles.

Abstract In China, the exigency for precise wheat grain protein content (GPC) data rises with growing food consumption demands and global market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diverse geographical terrain in China exacerbates the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS. The results demonstrate that the GPC estimation model based on hierarchical linear model significantly outperformed other conventional models. The validation dataset exhibited an R2 of 0.45 and an RMSE of 0.96%. In cross-validation, the RMSE values ranged from 0.90% in Gansu to 1.32% in Anhui. For leave-one-year-out cross-validation, the RMSE values ranged from 0.77% to 1.11%. CNWheatGPC-500 offers valuable insights for enhancing wheat production, quality control, and agricultural decision-making.

Abstract Despite the success of PD-1 blockade in cancer therapy, how PD-1 initiates signaling remains unclear. Soluble PD-L1 is found in patient sera and can bind PD-1 but fails to suppress T cell function. Here, we show that PD-1 function is reduced when mechanical support on ligand is removed. Mechanistically, cells exert forces to PD-1 and prolong bond lifetime at forces 8pN (slip bond). Molecular dynamics of PD-1–PD-L2 complex suggests force may cause relative rotation and translation between the two molecules yielding distinct atomic contacts not observed in the crystal structure. Compared to wild-type, PD-1 mutants targeting the force-induced distinct interactions maintain the same binding affinity but suppressed/eliminated catch bond, lowered rupture force, and reduced inhibitory function. Our results uncover a mechanism for cells to probe the mechanical support of PD-1–PD-Ligand bonds using endogenous forces to regulate PD-1 signaling.

Air-to-air membrane-energy exchangers have been widely used in green buildings as passive energy-recovery technologies for energy conservation and carbon reduction. An air-to-air membrane-energy exchanger conserves building energy by forcing the indoor exhaust air and unconditioned fresh air to exchange heat and moisture. As a critical performance indicator, the energy-exchange efficiency is primarily determined by the core. To improve energy-saving performance, many studies have been conducted to analyze and enhance the efficiency of air-to-air membrane-energy exchanger cores by manufacturing, testing, modeling, and comparing different cores. However, few studies have investigated the uncertainty introduced by the manufacturing and testing processes (most related research only studied the uncertainty of the sensible efficiency), which means that some positive results of those controlled trials are possibly owing to the manufacturing tolerance or testing errors, rather than real technical progress. This study addresses this problem by quantifying the efficiency uncertainty caused by manufacturing and testing processes. Three cores were produced using the same manufacturing process and materials, followed by identical experimental tests on their sensible, latent, and enthalpy efficiencies. Comparison results suggest that even when all variables are controlled, the differential enthalpy efficiency of two “identical” cores still vary from 0 % to 2 %. Variance of repetitive testing results of a certain core can afford an enthalpy efficiency of 4 % owing to a non-ideal experimental procedure. When manufacturing tolerances and experimental errors are considered simultaneously, the differential sensible efficiency among multiple “identical” cores is within 0–5%, whereas latent efficiency data in winter shows a significant variation of 5–10 % for “identical” cores. The contributions and conclusions of this study are as follows. The efficiency uncertainty caused by manufacturing and testing processes is non-negligible, particularly in latent efficiency. This phenomenon could alert researchers and engineers in this field to conduct repetitive manufacturing and testing while trying to modify/test/compare air-to-air membrane-energy exchanger cores. Because this study is based on a limited number of cores, the quantitative results of this study should be considered a non-universal reference, and a systematic benchmark must be established in the future.

Abstract With the development of wearable devices and hafnium-based ferroelectrics (FE), there is an increasing demand for high-performance flexible ferroelectric memories. However, developing ferroelectric memories that simultaneously exhibit good flexibility and significant performance has proven challenging. Here, we developed a high-performance flexible field-effect transistor (FeFET) device with a thermal budget of less than 400 °C by integrating Zr-doped HfO2 (HZO) and ultra-thin indium tin oxide (ITO). The proposed FeFET has a large memory window (MW) of 2.78 V, a high current on/off ratio (ION/IOFF) of over 108, and high endurance up to 2×107 cycles. In addition, the FeFETs under different bending conditions exhibit excellent neuromorphic properties. The device exhibits excellent bending reliability over 5×105 pulse cycles at a bending radius of 5 mm. The efficient integration of hafnium-based ferroelectric materials with promising ultrathin channel materials (ITO) offers unique opportunities to enable high-performance back-end-of-line (BEOL) compatible wearable FeFETs for edge intelligence applications.

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is “from scientific rice to world rice”, is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development.

Clear cell renal cell carcinoma (ccRCC) represents a prevalent form of kidney cancer, and its poor prognosis is closely tied to the occurrence of metastasis. ROS can drive epithelial-mesenchymal transition (EMT), thereby promoting cancer metastasis. Astaxanthin (AXT), renowned for its exceptional antioxidant properties, has been shown in this study to suppress ccRCC metastasis by scavenging ROS. AXT dose-dependently reduced ROS levels and attenuated migratory and invasive capabilities of ccRCC cells. Additionally, ccRCC cells treated with AXT exhibited phenotypic characteristics indicating EMT inhibition, which was reflected by the increased E-cadherin and decreased vimentin. Furthermore, AXT-induced suppressed metastatic potential was also corroborated in mouse lung metastasis model. Notably, artificially elevating ROS levels reversed the AXT-induced reduction in migratory and invasive capacity. Taken together, these findings highlight the anti-metastatic function of AXT in ccRCC, suggesting its therapeutic potential in the management of ccRCC metastasis.

TRIM72 oligomerization via intermolecular disulfide bonds is important for cells to repair damaged membranes. Here, authors report the crystal structure of TRIM72 dimer and show that TRIM72 B-box dimerization facilitates TRIM72 oligomerization.

The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R2 of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R2 of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting.

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing energy consumption of 1.9 fJ/spike.

Leaf pigments are critical indicators of plant photosynthesis, stress, and physiological conditions. Inversion of radiative transfer models (RTMs) is a promising method for robustly retrieving leaf biochemical traits from canopy observations, and adding prior information has been effective in alleviating the “ill-posed” problem, a major challenge in model inversion. Canopy structure parameters, such as leaf area index (LAI) and average leaf inclination angle (ALA), can serve as prior information for leaf pigment retrieval. Using canopy spectra simulated from the PROSAIL model, we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll (Cab) and carotenoid (Car). The retrieval accuracies of the two pigments were increased by use of the priors of LAI (RMSE of Cab from 7.67 to 6.32 μg cm−2, Car from 2.41 to 2.28 μg cm−2) and ALA (RMSE of Cab from 7.67 to 5.72 μg cm−2, Car from 2.41 to 2.23 μg cm−2). However, this improvement deteriorated with an increase of additive and multiplicative uncertainties, and when 40% and 20% noise was added to LAI and ALA respectively, these priors ceased to increase retrieval accuracy. Validation using an experimental winter wheat dataset also showed that compared with Car, the estimation accuracy of Cab increased more or deteriorated less with uncertainty in prior canopy structure. This study demonstrates possible limitations of using prior information in RTM inversions for retrieval of leaf biochemistry, when large uncertainties are present.

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index (VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model (HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that (1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD. (2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively. (3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB, LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

Abstract HfAlO film‐based ferroelectric memory is a strong contender for the next‐generation nonvolatile memories. However, the remanent polarization intensity of HfAlO films is small compared to other Hf‐based ferroelectric films at low annealing temperatures. In order to further improve the remnant polarization of the device, the ferroelectric memory with metal‐ferroelectric‐metal structure using ZrO2 as the regulating layer (RL) is designed and fabricated. Experimental results show that the device with the ZrO2 regulating layer exhibits triple enhancement, which may be due to the fact that ZrO2 RL has an effect on the enhancement of the ferroelectric phase. In addition, the device with ZrO2 regulating layer exhibits a superior ON/OFF conductance ratio, endurance, and retention characteristics, demonstrating potential for application to memory. This work provides an effective way to improve the ferroelectricity in HfAlO films at low annealing temperatures.

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of “ different objects with the same spectra characteristics” between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.

Deploying feeder remote terminal units (FRTUs) in the distribution network and then integrating them into existing manual switches (MSs) can facilitate automatic locating of faulted sections and rapid isolation from faults to improve the power supply reliability. However, the fault management process, including fault location, fault isolation, and service restoration, is usually simplified in traditional FRTUs deployment methods. In particular, the fault isolation and service restoration operations are usually modeled individually without considering the FRTUs operation during the whole fault management process in these traditional methods. In this paper, the FRTUs in the potential service restoration paths for the customers are ruled out to avoid unnecessary power outage, and the effect of FRTUs operation on the fault management process is analyzed and discussed in branched distribution networks. Secondly, a quantification analysis model considering FRTUs operation is built to obtain the accurate interruption time of the distribution network. Thirdly, the quantification model is used to obtain the revenue generated by interruption cost reduction for the deployed FRTUs. Finally, an optimal deployment model of FRTUs in mixed-integer nonlinear format is constructed and solved efficiently by the software LocalSolver, which combines heuristic techniques and stochastic search algorithms. The new method is compared with the traditional FRTUs deployment method in the CSG 53-node test system, and the results show that the proposed method can achieve greater return on investment and higher power supply reliability.

Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.

Energy wheels and air cleaners play crucial roles in building air conditioning systems. The former is essential for conserving energy in air conditioning systems, while the latter is necessary for ensuring the quality of indoor air. Pressure drop is a crucial parameter for both energy wheels and air cleaners, and it is essential to conduct theoretical and experimental investigations to aid in their design. In this study, we focused on the study of pressure drop in a fiber–powder composite material which can be used for both total heat exchange and air purification. Experimental tests were initially conducted to examine the impact of different parameters on the pressure drop in the material. Subsequently, based on the special fiber–powder structure of the material, two pressure drop prediction methods with different prediction strategies were proposed. The two prediction strategies were compared by analyzing the prediction accuracy of the two methods. As tested by experimental data, for both methods, the absolute prediction error was less than ±6 Pa when the pressure drop was below 50 Pa, and the relative prediction error was less than ±8% for most data sets when the pressure drop was greater than 50 Pa. Moreover, the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of prediction for both methods were less than 4 Pa and 7% respectively. The test results show that although the prediction strategies are different, both prediction methods can obtain acceptable prediction results, and both methods are practical. This study is intended to serve as a valuable reference for the design of energy wheels and air cleaners.

Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1–5, labeled S1–S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage.

Abstract Background Long non-coding RNA (lncRNA) BM742401 is a tumor suppressor in gastric cancer and chronic lymphocytic leukemia. As the promoter and coding region of BM742401 are fully embedded in a CpG island, we hypothesized that BM742401 is a tumor suppressor lncRNA epigenetically silenced by promoter DNA methylation in multiple myeloma. Methods Methylation-specific PCR and quantitative bisulfite pyrosequencing were performed to detect the methylation of BM742401 in normal plasma cells, myeloma cell lines and primary myeloma samples. The expression of BM742401 was measured by qRT-PCR. The function of BM742401 in multiple myeloma cells was analyzed by lentivirus transduction followed by migration assay. Results BM742401 methylation was detected in 10 (66.7%) myeloma cell lines but not normal plasma cells, and inversely correlated with expression of BM742401. In primary samples, BM742401 methylation was detected in 3 (12.5%) monoclonal gammopathy of undetermined significance, 9 (15.8%) myeloma at diagnosis and 8 (17.0%) myeloma at relapse/progression. Moreover, BM742401 methylation at diagnosis was associated with inferior overall survival (median OS: 25 vs. 39 months; P = 0.0496). In myeloma cell line JJN-3, stable overexpression of BM742401 by lentivirus transduction resulted in reduced cell migration (P = 0.0001) but not impacting cell death or proliferation. Conclusions This is the first report of tumor-specific methylation-mediated silencing of BM742401 in myeloma, which is likely an early event in myelomagenesis with adverse impact on overall survival. Moreover, BM742401 is a tumor suppressor lncRNA by inhibiting myeloma cell migration, hence implicated in myeloma plasma cell homing, metastasis and disease progression.

To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. Classic optimal control methods for central chiller plants are mostly based on system performance models which takes much effort and cost to establish. In addition, inevitable model error could cause control risk to the applied system. To mitigate the model dependency of HVAC optimal control, reinforcement learning (RL) algorithms have been drawing attention in the HVAC control domain due to its model-free feature. Currently, the RL-based optimization of central chiller plants faces several challenges: (1) existing model-free control methods based on RL typically adopt single-agent scheme, which brings high training cost and long training period when optimizing multiple controllable variables for large-scaled systems; (2) multi-agent scheme could overcome the former problem, but it also requires a proper coordination mechanism to harmonize the potential conflicts among all involved RL agents; (3) previous agent coordination frameworks (identified by distributed control or decentralized control) are mainly designed for model-based control methods instead of model-free controllers. To tackle the problems above, this article proposes a multi-agent, model-free optimal control approach for central chiller plants. This approach utilizes game theory and the RL algorithm SARSA for agent coordination and learning, respectively. A data-driven system model is set up using measured field data of a real HVAC system for simulation. The simulation case study results suggest that the energy saving performance (both short- and long-term) of the proposed approach (over 10% in a cooling season compared to the rule-based baseline controller) is close to the classic multi-agent reinforcement learning (MARL) algorithm WoLF-PHC; moreover, the proposed approach’s nature of few pending parameters makes it more feasible and robust for engineering practices than the WoLF-PHC algorithm.

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield. Biomass is also a key trait in increasing grain yield by crop breeding. The aims of this study were (i) to identify the best vegetation indices for estimating maize biomass, (ii) to investigate the relationship between biomass and leaf area index (LAI) at several growth stages, and (iii) to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network (DNN) algorithm. The results showed that biomass was associated with all vegetation indices. The three-band water index (TBWI) was the best vegetation index for estimating biomass and the corresponding R2, RMSE, and RRMSE were 0.76, 2.84 t ha−1, and 38.22% respectively. LAI was highly correlated with biomass (R2 = 0.89, RMSE = 2.27 t ha−1, and RRMSE = 30.55%). Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm (R2 = 0.83, RMSE = 1.96 t ha−1, and RRMSE = 26.43%). Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices (R2 = 0.91, RMSE = 1.49 t ha−1, and RRMSE = 20.05%). Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass. Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices (R2 = 0.87, RMSE = 1.84 t ha−1, and RRMSE = 24.76%). The DNN algorithm was effective in improving the estimation accuracy of biomass. It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region. Keywords: Biomass estimation, Maize, Vegetation indices, Deep neural network algorithm, LAI

Abstract Background With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. Results In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). Conclusions This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.

The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology.

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes.

Crop production is one of the major interactions between humans and the natural environment, in the process, carbon is translocated cyclically inside the ecosystem. Data assimilation algorithm has advantages in mechanism and robustness in yield estimation, however, the computational efficiency is still a major obstacle for widespread application. To address the issue, a novel hybrid method based on the combination of the Crop Biomass Algorithm of Wheat (CBA-Wheat) to the Simple Algorithm For Yield (SAFY) model and the transfer learning method was proposed in this paper, which enables winter wheat yield estimation with acceptable accuracy and calculation efficiency. The transfer learning techniques learn the knowledge from the SAFY model and then use the knowledge to predict wheat yield. The main results showed that: (1) The comparison using CBA-Wheat between measured AGB and predicted AGB all reveal a good correlation with R2 of 0.83 and RMSE of 1.91 t ha−1, respectively; (2) The performance of yield prediction was as follows: transfer learning method (R2 of 0.64, RMSE of 1.05 t ha−1) and data assimilation (R2 of 0.64, RMSE of 1.01 t ha−1). At the farm scale, the two yield estimation models are still similar in performance with RMSE of 1.33 t ha−1 for data assimilation and 1.13 t ha−1 for transfer learning; (3) The time consumption of transfer learning with complete simulation data set is significantly lower than that of the other two yield estimation tests. The number of pixels to be simulated was about 16,000, and the computational efficiency of the data assimilation algorithm and transfer learning without complete simulation datasets. The transfer learning model shows great potential in improving the efficiency of production estimation.

Crop yields are important for food security and people’s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield.

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok’s scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R2 from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R2 > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information.

Model-free reinforcement learning (RL) techniques are currently drawing attention in the control of heating, ventilation, and air-conditioning (HVAC) systems due to their minor pre-conditions and fast online optimization. The simultaneous optimal control of multiple HVAC appliances is a high-dimensional optimization problem, which single-agent RL schemes can barely handle. Hence, it is necessary to investigate how to address high-dimensional control problems with multiple agents. To realize this, different multi-agent reinforcement learning (MARL) mechanisms are available. This study intends to compare and evaluate three MARL mechanisms: Division, Multiplication, and Interaction. For comparison, quantitative simulations are conducted based on a virtual environment established using measured data of a real condenser water system. The system operation simulation results indicate that (1) Multiplication is not effective for high-dimensional RL-based control problems in HVAC systems due to its low learning speed and high training cost; (2) the performance of Division is close to that of the Interaction mechanism during the initial stage, while Division’s neglect of agent mutual inference limits its performance upper bound; (3) compared to the other two, Interaction is more suitable for multi-equipment HVAC control problems given its performance in both short-term (10% annual energy conservation compared to baseline) and long-term scenarios (over 11% energy conservation).

In China, the most common grain crop is maize (Zea mays). The increasing pressure to meet the food demands of its growing population has pushed Chinese maize farmers toward an excessive use of chemical fertilizers, a practice which ultimately leads to a massive waste of resources and widespread environmental pollution. As a result, increasing the yield and improving the nitrogen (N) use efficiency of maize has become a critical issue for agriculture in China. This study, which analyzes the combined data from a simulation carried out using the Decision Support System for Agrotechnology Transfer (DSSAT), a field experiment, and a household survey, explored the effectiveness of several approaches aimed at narrowing the maize yield gap and improving the N utilization efficiency in the Huang-Huai-Hai Plain (HHHP), the most important area for the production of summer maize in China. The various approaches we studied deploy different methods for the integrated management of N fertilizer input and the planting density. The study produced the following results: (1) For the simulated and actual maize yields, the root mean square error (RMSE), the normalized root mean squared errors (NRMSE) and the index of agreement (d) were 1,171 (kg ha–1), 12% and 0.84, respectively. These results show that the model is viable for the experiment included in the study; (2) The potential yield was 15.58 t ha–1, and the yields achieved by the super-high-yield cultivation pattern (SH), the optimized nutrient and density management pattern (ONM), the simulated farmer’s practice cultivation pattern (FP) and actual farmer’s practice (AFP) were 11.43, 11.06, 10.33, and 7.95 t ha–1, respectively. The yield gaps associated with the different yield levels were large; (3) For summer maize, the high yield and a high N partial factor productivity (NPFP) was found when applying a planting density of 9 plants m–2 and an N application amount of 246 kg ha–1. These results suggest that the maximum yield that can actually be achieved by optimizing the N application and planting density is less than 73% of the potential yield. This implies in turn that in order to further narrow the observed yield gaps, other factors, such as irrigation, sowing dates and pest control need to be considered.

The accuracy of nitrogen (N) diagnosis is essential to improve N use efficiency. The standard critical N concentration (standard Nc) dilution curves, an expression of the dynamics of N uptake and dry matter accumulation in plants, are widely used to diagnose the N status of crops. Several standard Nc dilution curves were proposed and validated for several crops, based on experiments involving different N fertilizer treatments. However, standard Nc dilution curves are affected by crop water status, e.g., resulting from differences in irrigation management. This paper aimed at developing a N diagnostic model under the coupling effect of irrigation and fertilizer managements. For this purpose, Nc dilution curves were developed under different irrigation rates. Additionally, plant water content (PWC), leaf water content (LWC), leaf area index (LAI), equivalent water thickness (EWT), and leaf area duration (LAD) were introduced into the model, to construct a modified Nc (mNc) dilution curve. The mNc dilution curves were designed using the principle of hierarchical linear model (HLM), introducing aboveground dry biomass (AGB) as the first layer of information, whereas the second layer of information included the different agronomic variables (PWC, LWC, LAI, EWT, and LAD). The results showed that parameters “a” and “b” of the standard Nc dilution curves ranged from 5.17 to 6.52 and −0.69 to −0.38 respectively. Parameter “a” was easily affected by different management conditions. The performance of standard Nc dilution models obtained by the cross-validation method was worse than that of mNc dilution models. The Nc dilution curve based on 4 years of data was described by the negative power equation Nc = 5.05 × AGB–0.47, with R2 and nRMSE of 0.63 and 0.21, respectively. The mNc dilution curve considers different treatments and was represented by the equation mNc = a×AGB−b, where a = 2.09 × PWC + 3.24, b = −0.02 × LAI + 0.51, with R2 and nRMSE of 0.79 and 0.13, respectively. For winter wheat, C3 crop, there would be a few problems in using standard Nc dilution methods to guide field management, however, this study provides a reliable method for constructing mNc dilution curves under different water and N fertilizer management. Due to the significant differences in hereditary, CO2 fixation efficiency and N metabolism pathways for C3 and C4 crops, the construction of mNc dilution curve suitable for different N response mechanisms will be conducive to the sustainable N management in crop plants.

Abstract Background Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. Results In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. Conclusions These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

To explore the effect and mechanism of bivalent ion doping on yttrium iron garnet (YIG), Zn-YIG (Zn-doped YIG) nanoparticles with a size of 60~70 nm were prepared by the sol-gel method. It was proven that Zn ion doping resulted in lattice expansion and internal stress due to crystallite size shrinkage. A Raman spectroscopic analysis proved the influence of Zn doping on the crystal structure and peak intensity by analyzing Raman vibration modes. The characteristics and chemical mechanism of mass loss and phase evolution in each temperature region were explored through TG-DSC measurements. Moreover, it was revealed that the antiferromagnetic coupling, pinning mechanisms and particle aggregation lead to coercivity, exhibiting different variation trends. A saturation magnetization (Ms) curve variation mechanism was further revealed, which was due to the thermal effects, super-exchange effect, and coupling effect between sub-lattices. Meanwhile, the influence of the thermal effect on Ms and its mechanism were explored by spin theory, and it was proven that it was mainly caused by the random arrangement of magnetic moments and thermal vibration. These results provide theoretical support for the wider application of YIG devices in microwave and high-temperature fields.

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

In the face of the increasingly severe situation of air pollution, the research on evaluation technology and method of high-efficiency air pollution control equipment in China is being advanced. This paper discusses the background and significance of the research on high efficiency air cleaner evaluation techniques and methods, and introduces the General Ideas and principles of the research on high efficiency air cleaner evaluation techniques and methods, the technical methods of high efficiency air cleaner evaluation are established, including the evaluation object and scope of application, basic requirements of high efficiency air cleaner qualitative evaluation, quantitative evaluation requirements of key evaluation indicators, testing, calculation and evaluation methods, etc., the application of the research results of air cleaner will facilitate the production and consumption of air cleaner products.

With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model’s influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov–Smirnov statistic of 0.030 and lowest Anderson–Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov–Smirnov statistic of 0.122 and Anderson–Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

Phenotyping under field environmental conditions is often considered as a bottleneck in crop breeding. Unmanned aerial vehicle high throughput phenotypic platform (UAV-HTPP) mounted with multi-sensors offers an efficiency, non-invasive, flexible and low-cost solution in large-scale breeding programs compared to ground investigation, especially where measurements are time-sensitive. This study was conducted at the research station of the Xiao Tangshan National Precision Agriculture Research Center of China. Using the UAV-HTPP, RGB and multispectral images were acquired during four critical growth stages of maize. We present a method of extracting plant height (PH) at the plot scale using UAV-HTPP based on the spatial structure of the maize canopy. The core steps of this method are segmentation and spatial Kriging interpolation based on multiple neighboring maximum pixels from multiple plants in a plot. Then, the relationships between the PH extracted from imagery collected using UAV-HTPP and the ground truth were examined. We developed a semi-automated pipeline for extracting, analyzing and evaluating multiple phenotypic traits: canopy cover (CC), normalized vegetation index (NDVI), PH, average growth rate of plant height (AGRPH), and contribution rate of plant height (CRPH). For these traits, we identify genotypic differences and analyze and evaluate dynamics and development trends during different maize growth stages. Furthermore, we introduce a time series data clustering analysis method into breeding programs as a tool to obtain a novel representative trait: typical curve. We classified and named nine types of typical curves of these traits based on curve morphological features. We found that typical curves can detect differences in the genetic background of traits. For the best results, the recognition rate of an NDVI typical curve is 59%, far less than the 82.3% of the CRPH typical curve. Our study provides evidence that the PH trait is among the most heritable and the NDVI trait is among the most easily affected by the external environment in maize.

Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) to estimate LAI and biomass with combined OSVIs and RPPs (the product of OSVIs and RPPs (COSVI-RPPs)), (3) to use multiple stepwise regression (MSR) and partial least squares regression (PLSR) to test and compare the estimations of LAI and biomass in winter wheat, respectively. The results showed that LAI and biomass were highly correlated with several OSVIs (the enhanced vegetation index (EVI) and modified triangular vegetation index 2 (MTVI2)) and RPPs (the radar vegetation index (RVI) and double-bounce eigenvalue relative difference (DERD)). The product of MTVI2 and DERD (R2 = 0.67 and RMSE = 0.68, p < 0.01) and that of MTVI2 and RVI (R2 = 0. 68 and RMSE = 0.65, p < 0.01) were strongly related to LAI, and the product of the optimized soil adjusted vegetation index (OSAVI) and DERD (R2 = 0.79 and RMSE = 148.65 g/m2, p < 0.01) and that of EVI and RVI (R2 = 0. 80 and RMSE = 146.33 g/m2, p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.