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Cadmium (Cd) pollution has been rapidly increasing due to the global rise in industries. Cd not only harms the ecological environment but also endangers human health through the food chain and drinking water. Therefore, the remediation of Cd-polluted soil is an imminent issue. In this work, ryegrass and a strain of Cd-tolerant bacterium were used to investigate the impact of inoculated bacteria on the physiology and biochemistry of ryegrass and the Cd enrichment of ryegrass in soil contaminated with different concentrations of Cd (4 and 20 mg/kg). The results showed that chlorophyll content increased by 24.7% and 41.0%, while peroxidase activity decreased by 56.7% and 3.9%. In addition, ascorbic acid content increased by 16.7% and 6.3%, whereas glutathione content decreased by 54.2% and 6.9%. The total Cd concentration in ryegrass increased by 21.5% and 10.3%, and the soil’s residual Cd decreased by 86.0% and 44.1%. Thus, the inoculation of Cd-tolerant bacteria can improve the antioxidant stress ability of ryegrass in Cd-contaminated soil and change the soil’s Cd form. As a result, the Cd enrichment in under-ground and above-ground parts of ryegrass, as well as the biomass of ryegrass, is increased, and the ability of ryegrass to remediate Cd-contaminated soil is significantly improved.

Wetlands provide important ecosystem services, such as water conservation, biodiversity protection, and carbon sequestration. The Zoige alpine wetland is the largest high-altitude swamp in the world and plays a critical role in regional ecological balance and climate change. However, little is known about the fate of its soil organic carbon (SOC) storage. In this study, we estimated the degradation status of the wetland over the past 35 years and used machine learning to investigate the dynamics and driving factors of SOC at different soil depths of the Zoige wetland in 1985, 2000, and 2020. We also simulated the future SOC balance under different scenarios. The results showed that the area of Zoige wetland has degraded by 378.71 km2 in the past 35 years. Increased precipitation and solar radiation offset the adverse effects of global warming, making the soil act as a carbon sink in the past 35 years. The total SOC storage of the wetland soils in 1985, 2000, and 2020 was estimated to be 2.03 Pg, 2.05 Pg, and 2.21 Pg, respectively, with 46.95% of SOC distributed in the subsoil layers. Climate change was the most important driving factor controlling the SOC storage of the Zoige wetland, explaining 51.33% of the SOC changes in the soil. Temperature change was always the most important factor controlling wetland SOC, and precipitation had a greater impact on the topsoil. Under the temperature control targets of 1.5 °C and 2 °C, the SOC pool of the Zoige wetland will decrease by 60.21 Tg C and 69.19 Tg C, respectively. Under scenarios of a 10% and 20% increase in precipitation, the wetland soil will accumulate an additional 46.53 Tg C and 118.89 Tg C, respectively. The study results provide important references for the sustainable management of the Zoige wetland under the background of global climate change.

Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.

Plants are affected by changes in light and adaptation mechanisms can affect secondary metabolite synthesis. In this study, the physiological response and regulation of the coumarin biosynthetic pathway of Angelica dahurica to different light intensities (natural light (CK), shade rate 50% (L1), shade rate 70% (L2), and shade rate 90% (L3)) were examined. The chlorophyll content, level of the enzymes of the antioxidant system, extent of lipid peroxidation, and concentrations of the osmoregulatory solute levels were determined in potted plants. Root transcriptome under different light intensities was sequenced using high-throughput technology, and differentially expressed genes (DEGs) related to coumarin biosynthesis were analyzed by quantitative real-time PCR (qRT-PCR). With increasing shade, Chl a, Chl b, Chl a + b, and Chl a/b content increased, while the Chl a/b ratio decreased. The antioxidant enzyme system activity and extent of membrane lipid peroxidation increased. The soluble protein (SP) and proline (Pro) content decreased with the reduction in the light intensity, and soluble sugar (SS) content was found to be highest at 50% shade. The RNA-seq analysis showed that 9388 genes were differentially expressed in the L3 group (7561 were upregulated and 1827 were downregulated). In both the L1 and L2 groups, DEGs were significantly enriched in “Ribosome biosynthesis”; meanwhile, in the L3 group, the DEGs were significantly enriched in “Amino and ribonucleotide sugar metabolism” in KEGG metabolic pathway analysis. Additionally, 4CL (TRINITY_DN40230_c0_g2) and COMT (TRINITY_DN21272_c0_g1) of the phenylpropanoid metabolic pathway were significantly downregulated in the L3 group. In conclusion, A. dahurica grew best under 50% shade and the secondary-metabolite coumarin biosynthetic pathway was inhibited by 90% shade, affecting the yield and quality of medicinal compounds.

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B.

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.

Bolted joints are commonly used for assembling carbon fiber/resin composite structures. Since drilling may generate defects at hole edges which affect mechanical properties, it is of great engineering significance to develop proper repair methods to restore the mechanical properties of the defective parts. However, there are few studies on hole edge defects and their repair methods. Therefore, a novelty short fiber filling repair method was proposed to repair defective holes in this study. The mechanical properties of intact, defective and repaired countersunk composite joints were compared and investigated. Experimental tensile tests showed that defective joints had lower initial stiffness and failure loads compared to intact joints, while the mechanical properties were effectively restored after repair. Three-dimensional finite element models were also established to analyze the damage process of the joints. Results of numerical modelling were consistent with the experimental results. The simulations showed that changes in contact behaviors and local deformations caused by hole edge defects led to the low initial stiffness and stiffness transition point of the joint, while this phenomenon was reduced after repair. Additionally, despite different joint types, laminate failure mainly occurred around the hole and countersink.