Global Monsoon Precipitation: Trends, Leading Modes, and Associated Drought and Heat Wave in the Northern Hemisphere

Global monsoon precipitation (GMP) brings the majority of water for the local agriculture and ecosystem. The Northern Hemisphere (NH) GMP shows an upward trend over the past decades, while the trend in the Southern Hemisphere (SH) GMP is weak and insignificant. The first three singular value decomposition modes between NH GMP and global SST during boreal summer reflect, in order, the Atlantic multidecadal oscillation (AMO), eastern Pacific (EP) El Niño, and central Pacific (CP) El Niño, when the AMO dominates the NH climate and contributes to the increased trend. However, the first three modes between SHGMP and global SST during boreal winter are revealed as EP El Niño, the AMO, and CP El Niño, when the EP El Niño becomes the most significant driver of the SHGMP, and the AMO-induced rainfall anomalies may cancel out each other within the SH global monsoon domain and thus result in a weak trend. The intensification of NH GMP is proposed to favor the occurrences of droughts and heat waves (HWs) in the midlatitudes through a monsoon–desert-like mechanism. That is, the diabatic heating associated with the monsoonal rainfall may drive large-scale circulation anomalies and trigger intensified subsidence in remote regions. The anomalous descending motions over the midlatitudes are usually accompanied by clear skies, which result in less precipitation and more downward solar radiation, and thus drier and hotter soil conditions that favor the occurrences of droughts and HWs. In comparison, the SH GMP may exert much smaller impacts on the NH extremes in spring and summer, probably because the winter signals associated with SHGMP cannot sufficiently persist into the following seasons

MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

A novel adaptive particle swarm optimization algorithm based high precision parameter identification and state estimation of lithium-ion battery.

Lithium-ion batteries are widely used in new energy vehicles, energy storage systems, aerospace and other fields because of their high energy density, long cycle life and high-cost performance. Accurate equivalent modeling, adaptive internal state characterization and accurate state of charge estimation are the cornerstones of expanding the application market of lithium-ion batteries. According to the highly nonlinear operating characteristics of lithium-ion batteries, the Thevenin equivalent model is used to characterize the operating characteristics of lithium-ion batteries, particle swarm optimization algorithm is used to process the measured data, and adaptive optimization strategy is added to improve the global search ability of particles, and the parameters of the model are identified innovatively. Combined with extended Kalman algorithm and Sage-Husa filtering algorithm, the state-of-charge estimation model of lithium ion battery is constructed. Aiming at the influence of fixed and inaccurate noise initial value in traditional Kalman filtering algorithm on SOC estimation results, Sage-Husa algorithm is used to adaptively correct system noise. The experimental results under HPPC condition show that the maximum error of the model is less than 1.5%. Simulation results of SOC estimation algorithm under two different operating conditions show that the maximum estimation error of adaptive extended Kalman algorithm is less than 0.05, which realizes high-precision lithium battery model parameter identification and high-precision state-of-charge estimation

Study of the Dynamic Strain-Induced Transformation Process of a Low-Carbon Steel: Experiment and Finite Element Simulation

The microstructures and mechanical properties of a low-carbon steel, hot-rolled by a six-pass dynamic strain-induced transformation (DSIT) process, with different start rolling temperatures, are studied by combining experiments and finite element simulations. The start rolling temperatures of the last three passes are about 10°C higher and 20°C lower than the Ar3 temperature, for Processes 1 and 2, respectively. The results show that as the rolling process proceeds, rolling forces increase, while slab temperatures decrease. Before starting Pass 4, the temperature of the slab center is higher than that of the slab surface. During Pass 4 to Pass 6, however, the temperatures of the slab center and surface are nearly identical but fluctuate remarkably due to the large reduction rate. The simulated maximum rolling force and start rolling temperature of each pass agree reasonably with the experimental measurements. It is found that the simulated start temperatures of the slab center in the last three passes are about 5~25°C higher than the Ar3 temperature for Process 1, and the DSIT condition is better satisfied for Process 2. As a result, Process 2 produces finer grain sizes and higher yield strengths than Process 1

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

Recovery and treatment of fracturing flowback fluids in the Sulige Gasfield, Ordos Basin

AbstractCentralized and group well deployment and factory-like fracturing techniques are adopted for low-permeability tight sandstone reservoirs in the Sulige Gasfield, Ordos Basin, so as to realize its efficient and economic development. However, environmental protection is faced with grim situations because fluid delivery rises abruptly on site in a short time due to centralized fracturing of the well group. Based on the characteristics of gas testing after fracturing in this gas field, a fracturing flowback fluid recovery and treatment method suitable for the Sulige Gasfield has been developed with the landform features of this area taken into account. Firstly, a high-efficiency well-to-well fracturing flowback fluid recovery and reutilization technique was developed with multi-effect surfactant polymer recoverable fracturing fluid system as the core, and in virtue of this technique, the treatment efficiency of conventional guar gum fracturing fluid system is increased. Secondly, for recovering and treating the end fluids on the well sites, a fine fracturing flowback fluid recovery and treatment technique has been worked out with “coagulation and precipitation, filtration and disinfection, and sludge dewatering” as the main part. Owing to the application of this method, the on-site water resource utilization ratio has been increased and environmental protection pressure concerned with fracturing operation has been relieved. In 2014, field tests were performed in 62 wells of 10 well groups, with 32980 m3 cumulative treated flowback fluid, 17160 m3 reutilization volume and reutilization ratio over 70%. Obviously, remarkable social and economical benefits are thus realized

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

Characteristics of rhizosphere and bulk soil microbial community of Chinese cabbage (Brassica campestris) grown in Karst area

Understanding the rhizosphere soil microbial community and its relationship with the bulk soil microbial community is critical for maintaining soil health and fertility and improving crop yields in Karst regions. The microbial communities in the rhizosphere and bulk soils of a Chinese cabbage (Brassica campestris) plantation in a Karst region, as well as their relationships with soil nutrients, were examined in this study using high-throughput sequencing technologies of 16S and ITS amplicons. The aim was to provide theoretical insights into the healthy cultivation of Chinese cabbage in a Karst area. The findings revealed that the rhizosphere soil showed higher contents of organic matter (OM), alkaline hydrolyzable nitrogen (AN), available phosphorus (AP), total phosphorus (TP), available potassium (AK), total potassium (TK), total nitrogen (TN), catalase (CA), urease (UR), sucrase (SU), and phosphatase (PHO), in comparison with bulk soil, while the pH value showed the opposite trend. The diversity of bacterial and fungal communities in the bulk soil was higher than that in the rhizosphere soil, and their compositions differed between the two types of soil. In the rhizosphere soil, Proteobacteria, Acidobacteriota, Actinobacteriota, and Bacteroidota were the dominant bacterial phyla, while Olpidiomycota, Ascomycota, Mortierellomycota, and Basidiomycota were the predominant fungal phyla. In contrast, the bulk soil was characterized by bacterial dominance of Proteobacteria, Acidobacteriota, Chloroflexi, and Actinobacteriota and fungal dominance of Ascomycota, Olpidiomycota, Mortierellomycota, and Basidiomycota. The fungal network was simpler than the bacterial network, and both networks exhibited less complexity in the rhizosphere soil compared with the bulk soil. Moreover, the rhizosphere soil harbored a higher proportion of beneficial Rhizobiales. The rhizosphere soil network was less complicated than the network in bulk soil by building a bacterial–fungal co-occurrence network. Furthermore, a network of relationships between soil properties and network keystone taxa revealed that the rhizosphere soil keystone taxa were more strongly correlated with soil properties than those in the bulk soil; despite its lower complexity, the rhizosphere soil contains a higher abundance of bacteria which are beneficial for cabbage growth compared with the bulk soil

Initial partial response and stable disease according to RECIST indicate similar survival for chemotherapeutical patients with advanced non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Stable disease (SD) has ambiguous clinical significance for patients according to the dominant Response Evaluation Criteria in Solid Tumours (RECIST). The primary aims of the study were: (1) to clarify the clinical significance of SD by comparing the progression-free survival (PFS) of response and SD patients with advanced non-small cell lung cancer (NSCLC) after the first two courses of the standard first-line platinum-based chemotherapy; (2) to explore the relationship between the percentage change in tumour size and PFS among initial SD patients, in order to provide some guidance for clinicians in deciding continuation/termination of the current treatment at a relative early time.</p> <p>Methods</p> <p>A total of 179 advanced NSCLC patients whose baseline CT image was available for review were included in the study. Another CT image was taken in the initial assessment after chemotherapy. A comparison of PFS between initial partial response (PR) and SD was used to determine whether significant differences exist. The relationship between the early percentage of change in tumour size of initial SD patients and their PFS was investigated. In addition, overall survival (OS), the secondary endpoint in this study, was investigated as well.</p> <p>Results</p> <p>Patients with initial PR are not significantly distinguished from those with initial SD when their PFS is concerned (median PFS 249 days [95% confidence interval, 187-310 days] versus 220 days [95% confidence interval, 191-248 days], p > 0.05). Their median OS was 364 days (95% confidence interval, 275-452 days) for the initial PR patients versus 350 days (95% confidence interval, 293-406 days) for the initial SD patients, which suggests no significant difference as well p > 0.05). In addition, all the initial SD patients enjoyed similar PFS and OS.</p> <p>Conclusions</p> <p>Initial PR and SD enjoy similar PFS and OS for patients with advanced NSCLC. Within the initial SD subgroup, different percentages of tumour shrinkage or increase undergo similar PFS and OS. RECIST remains a reliable norm in assessing the effectiveness of chemotherapy for patients with advanced NSCLC before functional assessment has been integrated into the criteria.</p