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Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

For robust adaptive beamforming (RAB), the variable loading (VL) technique can provide a better trade-off between robustness and adaptivity than diagonal loading (DL). Despite its importance, few research efforts have explored the loading factor for VL to ensure robustness in various environments. Moreover, the performance of VL is restricted by the sample covariance matrix in snapshot deficiency situations. This paper proposes a modified variable loading (VL) method for robust adaptive beamforming, considering imprecise steering vector effects and finite sample size impairments. First, a novel subsampling method is used to construct the calibrated covariance matrix to improve the robustness of the VL in sample-starving scenarios. Then, a parameter-free method for the VL factor is proposed to further enhance the insensitivity to the steering vector mismatches of the antenna array. Simulation results verify the effectiveness and robustness of the proposed method as compared to the traditional VL and other widely used robust techniques.

Sponges are a vital source of pharmaceutically active secondary metabolites, of which the main structural types are alkaloids and terpenoids. Many of these compounds exhibit biological activities. Focusing specifically on diterpenoids, this article reviews the structures and biological activities of 228 diterpenes isolated from more than 33 genera of sponges from 2009 to 2022. The Spongia sponges produce the most diterpenoid molecules among all genera, accounting for 27%. Of the 228 molecules, 110 exhibit cytotoxic, antibacterial, antifungal, antiparasitic, anti-inflammatory, and antifouling activities, among others. The most prevalent activity is cytotoxicity, present in 54 molecules, which represent 24% of the diterpenes reported. These structurally and biologically diverse diterpenoids highlight the vast, yet largely untapped, potential of marine sponges in the discovery of new bioactive molecules for medicinal use.

Long-term continuous monoculture cultivation harms soil physicochemical and microbial communities in agricultural practices. However, little has been reported on the effect of continuous cropping of industrial hemp on bacterial community and diversity in the rhizosphere soil. Our study investigated the changes in physicochemical properties and bacterial communities of industrial hemp rhizosphere soils in different continuous cropping years. The results showed that continuous cropping would reduce soil pH and available phosphorus (AP), while electrical conductivity (EC), available nitrogen (AN), and available potassium (AK) would increase. Soil bacterial diversity and richness index decreased with continuous cropping years. At the same time, continuous cropping marked Acidobacteria, Bacteroidetes, and Gemmatimonadetes increase, and the Proteobacteria and Actinobacteria decreased. Moreover, we found that pH, AK, and AP were the critical factors associated with the changes in the abundance and structure of the bacterial community. Overall, our study first reported the effect of continuous cropping on the rhizosphere soil microflora of industrial hemp. The results can provide a theoretical basis for revealing the obstacle mechanism of continuous cropping of industrial hemp and contribute to the sustainable cultivation of industrial hemp in the future.

To further evaluate the effect of water stress on soil respiration (RS), reveal the influencing factors of daily and seasonal RS, and systematically evaluate and compare the sensibility of different machine learning algorithms (multiple nonlinear regression [MNR], support vector machine regression [SVR], backpropagation artificial neural network [BPNN]) to estimate RS from a maize field under water stress condition, the field experiments were conducted within a maize field in Inner Mongolia, China, during the entire 2019 growing season. Various levels of deficit irrigation were conducted in the vegetative, reproductive, and mature stages. Our research indicated that soil CO2 fluxes from 100% evapotranspiration treatment (Tr1) were significantly greater than various deficit irrigation treatments (Tr2, Tr3, Tr4) during each growth stage of summer maize. The cumulative soil CO2 fluxes of Tr2, Tr3, and Tr4 decreased 24.8%, 30.3%, and 43.7% compared with Tr1, respectively. We determined that the drivers affecting the daily RS were soil temperature at 5 cm depth (TS,5) and soil surface temperature (TSF), followed by water‐filled porosity (WFPS) at 5 cm depth, but no significant correlations were observed at 25 cm depths. TS,5 and TSF also performed similar correlation with seasonal RS with R greater than 0.753 among all water treatments, followed by chlorophyll content with R greater than 0.726. During the whole growing season, the BPNN model exhibited the best predicting result, and could explain the 60%–80% and 87.8% of the variations of RS at the daily and seasonal scales, with root mean square error of 48.7–100.9 mg m−2 h−1 and 91.5 mg m−2 h−1, respectively. The SVR and MNR models could estimate the 47.9%–57% and 39.9%–52.1% of the daily RS and 81.4% and 78.6% of the seasonal RS, respectively. Overall, our study indicated the machine learning algorithms could be successfully applied to estimate RS at daily and seasonal scales from a maize field under water stress condition.

The matching status of agricultural water and land resources is a prerequisite for grain production. The influence of gray water footprint has not been paid attention to in the study of agricultural water and land resources matching based on water footprint. To measure the matching status of agricultural water and land resources more comprehensively, the total water footprint (including blue, green and gray water footprint) and the cultivated land area was taken as the characterization parameters of water and land resources, respectively. The Gini coefficient model, and the agricultural water and land resources matching coefficient model were constructed to calculate the matching degree of agricultural water and land resources in a cold region (Heilongjiang Province) of China. Based on the amount of agricultural water consumption, the equivalent coefficient model was used to evaluate the degree of agricultural water and land resources shortage or to be developed. The result of agricultural water and land resources matching coefficient model showed that the matching degree of agricultural water and land resources in Heilongjiang Province is getting better year by year, which is consistent with the calculations determined from the Gini coefficient. The result of the equivalent coefficient method based on agricultural water consumption was consistent with the result of the Gini coefficient method based on total water footprint, which is verified that it is scientific and reasonable to take the total water footprint as the characterization parameter of water resource. The findings may provide implications for the spatial optimal allocation of regional agricultural water and land resources.

To evaluate the state of an agricultural development more comprehensively, a vulnerability assessment is introduced into agricultural water and land resources system, and it is expected that the vulnerability assessment can provide a basis for improving system structure and function and realizing sustainable development. In the study, 27 evaluation indicators are selected from the agricultural water and land resources system (AWLRS), socio-economic system and ecological structure system to construct the evaluation index system for agricultural water and land resource system vulnerability (AWLRSV). Seagull optimization algorithm (SOA) is used to calibrate the parameters of the random forest (RF) model. SOA-RF model is applied to measure the AWLRSV of Heilongjiang Province in China. The results show that the SOA-RF model has higher accuracy and stronger stability than the traditional RF model and DA-RF model. The value of AWLRSV in Heilongjiang Province presents a downward–upward–downward trend from 2008 to 2018. The vulnerability levels are mainly level II and III, and level III is mainly distributed northwest and southeast of Heilongjiang Province. The novelty of this paper is to regard the agricultural water and land resources system as a compound system, put forward the vulnerability assessment framework. The findings may provide reference for regional sustainable development from a new research perspective.

Radio frequency (RF) heating has the advantages of a much faster heating rate as well as the great potential for sterilization of food compared to traditional thermal sterilization. A new kettle was designed for sterilization experiments applying RF energy (27.12 MHz, 6 kW). In this research, beef sausages were pasteurized by RF heating alone, the dielectric properties (DPs) of which were determined, and heating uniformity and heating rate were evaluated under different conditions. The results indicate that the DPs of samples were significantly influenced (p < 0.01) by the temperature and frequency. The electrode gap, sample height and NaCl content had significant effects (p < 0.01) on the heating uniformity when using RF energy alone. The best heating uniformity was obtained under an electrode gap of 180 mm, a sample height of 80 mm and NaCl content of 3%. The cold points and hot spots were located at the edge of the upper section and geometric center of the sample, respectively. This study reveals the great potential in solid food for pasteurization using RF energy alone. Future studies should focus on sterilization applying RF energy and SW simultaneously using the newly designed kettle.

Railway turnouts are critical components of the rail track system, and their defects can lead to severe safety incidents and significant property damage. The irregular distribution and varying sizes of railway-turnout defects, combined with changing environmental lighting and complex backgrounds, pose challenges for traditional detection methods, often resulting in low accuracy and poor real-time performance. To address the issue of improving the detection performance of railway-turnout defects, this study proposes a high-precision recognition model, Faster-Hilo-BiFPN-DETR (FHB-DETR), based on the RT-DETR architecture. First, we designed the Faster CGLU module based on Faster Block, which optimizes the aggregation of local and global feature information through partial convolution and gating mechanisms. This approach reduces both computational load and parameter count while enhancing feature extraction capabilities. Second, we replaced the multi-head self-attention mechanism with Hilo attention, reducing parameter count and computational load, and improving real-time performance. In terms of feature fusion, we utilized BiFPN instead of CCFF to better capture subtle defect features and optimized the weighting of feature maps through a weighted mechanism. Experimental results show that compared to RT-DETR, FHB-DETR improved mAP50 by 3.5%, reduced parameter count by 25%, and decreased computational complexity by 6%, while maintaining a high frame rate, meeting real-time performance requirements.

Organic acids could improve the phosphorus (P) availability through enhancing the release of inorganic phosphorus (Pi) in the soil. However, the effects of organic acids on the Pi release are still poorly understood, especially from soils with different organic matter contents. Here, a biochemically produced humic acid and P fertiliser were added to the soil to modify the content of the soil organic matter (SOM) and soil P, respectively. And then the soil samples were incubated at 25 °C for 30 days. The release of Pi fractions (such as H2O-Pi, NaHCO3-Pi, NaOH-Pi, HCl-Pi, and Residual-P) from the soils with different organic matter contents in the presence of citric, oxalic, and malic acids was evaluated using a sequential chemical fractionation method. The results showed that the release of the NaHCO3-Pi, NaOH-Pi, and HCl-Pi fractions also showed a decreasing trend with an increasing content of soil organic matter, and more NaOH-Pi than the other Pi fractions was generally released in the presence of organic acids. Considering the types of organic acids, oxalic acid and malic acid most effectively and least effectively released Pi, respectively. The path analysis indicated that the NaOH-Pi release had the highest direct and indirect effects on the total inorganic P (TPi) release. NaOH-Pi was, therefore, the most effective source of Pi in the Mollisols.