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This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data.

The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable approach to research typhoons. High-frequency surface wave radar (HFSWR) as an over-the-horizon radar can achieve real-time monitoring of an extensive sea area and space. This paper derived the gravity wave perturbation spectrum by handling high-frequency surface wave radar data during typhoons. The gravity wave spectrum data were examined by applying the chaos examination approaches of the Lyapunov exponent and phase-space reconstruction to the gravity wave spectrum data after processing and extraction. The reconstructed phase space had a specific shape in a certain direction, with the maximum Lyapunov exponent greater than zero. The gravity wave spectrum data are suggested to have chaotic properties through two chaos examination approaches. This paper demonstrated that the gravity waves observed by a radar have chaotic properties through the measurement data of HFSWR. While the chaotic properties suggest that observed gravity wave data are predictable in the short term, they are unpredictable in the long term. Predicting gravity wave data is important for understanding the chaotic properties of the atmosphere and for future gravity wave prediction.

The aim of this study is to estimate the effects of some acoustic parameters on thermal lesions of atherosclerotic plaques in high-intensity focused ultrasound (HIFU) fields. A fluid-solid thermal coupling model is presented for describing the temperature elevation and thermal ablation of atherosclerotic plaque. A finite element approach is used to solve the coupling equations in cylindrical coordinates. The model considers the effect of the wall thickness of large arteries. The extent of the tissue lesion is determined by the accumulated thermal lesion with Arrhenius integral equation at each location. The results show the lesion size of atherosclerotic plaque is positively correlated to the excited frequency and acoustic output power with heating time. The computational model indicates HIFU may present a novel option for thermal ablation of atherosclerotic plaques with a completely non-invasive treatment paradigm.

Effectively learning and extracting the feature representations of 3D point clouds is an important yet challenging task. Most of existing works achieve reasonable performance in 3D vision tasks by modeling the relationships among points appropriately. However, the feature representations are only learned with a specific transform through these methods, which are easy to overlap and thus limit the representation ability of the model. To address these issues, we propose a novel Multi-Transform Learning framework for point clouds (PointMTL), which can extract diverse features from multiple mapping transform to obtain richer representations. Specifically, we build a module named Multi-Transform Encoder (MTE), which encodes and aggregates local features from multiple non-linear transforms. To further explore global context representations, a module named Global Spatial Fusion (GSF) is proposed to capture global information and selectively fuse with local representations. Moreover, to guarantee the richness and diversity of learned representations, we further propose a Spatial Independence Criterion (SIC) strategy to enlarge the differences between the transforms and reduce information redundancies. In contrast to previous works, our approach fully exploits representations from multiple transforms, thus having strong expressiveness and good robustness for point clouds related tasks. The experiments on three typical tasks (i.e., semantic segmentation on S3DIS and ScanNet, part segmentation on ShapeNet and shape classification on ModelNet40) demonstrates the effectiveness of our method.

This paper designs a parameter-dependent self-adaption integral sliding mode controller which converges the system in a finite time while focusing on an uncertain linear parameter varying (LPV) model of a variant aircraft that has a large-scale variation of the sweep angle and an extension. This design promotes the robustness and L2 performance of the aircraft's speed and altitude during the variant process. According to the longitudinal nonlinear model of a morphing aircraft obtained by the KANE method, the LPV model with the stretch and the sweep angle as the time-varying parameters is derived, and then, the equivalent linear time-invariant (LTI) system is obtained by the linear fractional representation (LFR). Furthermore, the state feedback LFR-H∞ controller has deduced from the linear matrix inequality (LMI) constraints, and then, the existing conditions of the integral sliding mode are obtained from the pole assignment. There are significant uncertainties and disturbances when the linear controller acts on the nonlinear system. Therefore, an adaptive algorithm is introduced to further improve the robustness of the integral sliding mode controller. Moreover, a parameter-dependent Lyapunov function analysis shows that the designed adaptive integral sliding mode control rate can converge the LPV system trajectories to the integral sliding mode surface in a finite time. The comparative simulation results of the nonlinear model of the morphing aircraft indicate the robustness and effectiveness of this approach. The method designed in this paper can be extended to general LPV systems.

Abstract Background Terrestrial ecosystems contain significant carbon storage, vital to the global carbon cycle and climate change. Alterations in human production activities and environmental factors affect the stability of carbon storage in soil. Carbon sequestration in plant phytoliths offers a sustainable method for long-term carbon stabilization. Carbon occluded in phytoliths (PhytOC) is a kind of carbon that can be stable and not decomposed for a long time, so it is crucial to conduct more in-depth research on it. Results We undertook a meta-analysis on PhytOC across global terrestrial ecosystems, analyzing 60 articles, encapsulating 534 observations. We observed notable differences in phytolith and PhytOC contents across various ecosystems. Bamboo forest ecosystems exhibited the highest vegetation phytolith and PhytOC content, while soil phytolith content was most prominent in bamboo forests and PhytOC content in croplands. Human activities, such as grassland grazing, had a lesser impact on soil PhytOC transport than actions like cutting and tillage in croplands and forests. Our study separated bamboo ecosystems, analyzing their PhytOC content and revealing an underestimation of their carbon sink capacity. Conclusions Notwithstanding our findings, phytoliths’ intricate environmental interactions warrant further exploration, crucial for refining ecosystem management and accurately estimating PhytOC stocks. This deepened understanding lays the foundation for studying phytoliths and the carbon sink dynamics.

Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer rates on soil greenhouse gas (GHG) emissions, vegetation and soil organic carbon (SOC) sinks, and to illustrate the differences in agroforestry systems’ carbon sinks through a one-year positioning test across 12 plots, applying different compound fertilizer application rates (0 (CK), 400 (A1), 800 (A2), and 1600 (A3) kg ha−1). The study demonstrated that, after fertilization, the total GHG emissions of A1 decreased by 4.41%, whereas A2 and A3 increased their total GHG emissions by 17.13% and 72.23%, respectively. The vegetation carbon sequestration of A1, A2, and A3 increased by 18.04%, 26.75%, and 28.65%, respectively, and the soil organic carbon sequestration rose by 32.57%, 42.27% and 43.29%, respectively. To sum up, in contrast with CK, the ecosystem carbon sequestration climbed by 54.41%, 51.67%, and 0.90%, respectively. Our study suggests that rational fertilization can improve the carbon sink of the ecosystem and effectively ameliorate climate change.

Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is known for its high capacity to sequester atmospheric carbon (C), which has a unique role to play in the fight against global warming. However, due to rising labor costs and falling bamboo prices, many Moso bamboo forests are shifting to an extensive management model without fertilization, resulting in gradual degradation of Moso bamboo forests. However, many Moso bamboo forests are being degraded due to rising labor costs and declining bamboo timber prices. To delineate the effect of degradation on soil microbial carbon sequestration, we instituted a rigorous analysis of Moso bamboo forests subjected to different degradation durations, namely: continuous management (CK), 5 years of degradation (D-5), and 10 years of degradation (D-10). Our inquiry encompassed soil strata at 0–20 cm and 20–40 cm, scrutinizing alterations in soil organic carbon(SOC), water-soluble carbon(WSOC), microbial carbon(MBC)and microbial residues. We discerned a positive correlation between degradation and augmented levels of SOC, WSOC, and MBC across both strata. Furthermore, degradation escalated concentrations of specific soil amino sugars and microbial residues. Intriguingly, extended degradation diminished the proportional contribution of microbial residuals to SOC, implying a possible decline in microbial activity longitudinally. These findings offer a detailed insight into microbial C processes within degraded bamboo ecosystems.

As a crucial forest resource in southern China and a significant economic forest species for forestry production, moso bamboo has a notable influence on carbon stocks across the entire bamboo forest ecosystem. Studying the impact of different management measures on carbon stocks in moso bamboo forests and soil carbon stocks can assist bamboo forest operators in incorporating the carbon sequestration capacity of bamboo into forest production and management decisions, which can contribute to achieving carbon sequestration, emission reduction, and sustainable development in the decision-making processes of forest production and management. In this study, we utilized a randomized block design to investigate the changes in moso bamboo forests’ carbon stocks and soil carbon stocks under different management measures across three intensities: high-intensity intensive management (HT), moderate-intensity intensive management (MT), and regular management (CK). Additionally, we employed meta-analysis methods to enhance the accuracy of our conclusions. The experimental results showed that MT increased the carbon storage in moso bamboo forests by 19.86%, which was significantly different from CK (p < 0.05), while there was no significant difference between the HT group and the MT and CK groups. For soil carbon stocks, in the 10–30 m and 0–50 m soil layers, HT decreased soil carbon storage by 29.89% and 22.38%, while MT increased soil carbon storage by 64.15% and 31.02%, respectively. Both HT and MT were significantly different from CK (p < 0.05). However, for the soil layers of 0–10 m and 30–50 m, there was no significant difference between the treatments within the experimental group. The results of the meta-analysis indicate that, compared to traditional regular management, intensive management, especially high-intensity intensive management, can significantly increase the carbon storage in bamboo forests (p < 0.05). However, it will significantly reduce soil carbon storage (p < 0.05). Moreover, a significant difference in soil carbon storage is observed only within the 0–20 cm soil layer group. Therefore, from the perspective of the long-term ecological benefits of bamboo forest management, the selection of management measures should prioritize reasonable and moderate-intensity intensive management. Additionally, adopting appropriate and moderate-intensity fertilization, ploughing, and other management methods is recommended to enhance the productivity of moso bamboo forests while concurrently protecting the natural environment and improving the carbon sequestration capacity of moso bamboo forests.

Purpose: The goal of this study was to evaluate the biomechanical effects such as sonoporation or permeability, produced by ultrasound-driven microbubbles (UDM) within microvessels with various parameters. Methods: In this study, a bubble-fluid-solid coupling system was established through combination of finite element method. The stress, strain and permeability of the vessel wall were theoretically simulated for different ultrasound frequencies, vessel radius and vessel thickness. Results: the bubble oscillation induces the vessel wall dilation and invagination under a pressure of 0.1 MPa. The stress distribution over the microvessel wall was heterogeneous and the maximum value of the midpoint on the inner vessel wall could reach 0.7 MPa as a frequency ranges from 1 to 3 MHz, and a vessel radius and an initial microbubble radius fall within the range of 3.5-13 µm and 1-4 µm, respectively. With the same conditions, the maximum shear stress was equal to 1.2 kPa and occurred at a distance of ±5 µm from the midpoint of 10 µm and the maximum value of permeability was 3.033 × 10-13. Conclusions: Results of the study revealed a strong dependence of biomechanical effects on the excitation frequency, initial bubble radius, and vessel radius. Numerical simulations could provide insight into understanding the mechanism behind bubble-vessel interactions by UDM, which may explore the potential for further improvements to medical applications. [ABSTRACT FROM AUTHOR]

Forests play a crucial role as the primary sink for greenhouse gases, and forest mortality significantly impacts the carbon sequestration capacity of forest ecosystems. A single type of forest mortality model has been developed, and its model variables are incomplete, leading to significant bias in mortality prediction. To address this limitation, this study harnessed data collected from 773 permanent plots situated in Zhejiang Province, China, spanning a period from 2009 to 2019. The primary objectives were to pinpoint the key variables influencing forest mortality and to construct forest mortality prediction models utilizing both traditional regression methods and machine learning techniques, ultimately aiming to provide a theoretical basis for forest management practices and future predictions. Four basic linear regression models were used in this study: Linear Regression (LR), Akaike Information Criterion (AIC) Stepwise Regression, Ridge Regression, and Lasso Regression. Four machine learning models, Gradient Boosting Regression (GBR), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), were used to model stand mortality. Mortality was used as the dependent variable, and environmental factors such as topographic factors, soil composition, stand characteristics, and climatic variables were used as independent variables. The findings unveiled that soil and stand-related factors exerted significant effects on the mortality rate, whereas terrain-related and climate factors did not exhibit statistical significance. The Random Forest model established by using stand age, tree height, ADBH, crown cover, humus layer thickness, and the biodiversity index has the highest fitting statistics such as R² and Mean Squared Error, indicating that it has a good fitting and prediction effect, which effectively predicts mortality at the stand level, and is a valuable tool for predicting changes in forest ecosystems, with practical value in estimating tree mortality to enhance forest management and planning.

Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems.

We first construct algebra automorphisms Ti (1 ≤ i ≤ r) of ℤ2-graded Hopf algebra Uq(osp(1|2r)), which are called Lusztig's symmetries. It is shown that they satisfy braid group relations. The algebraic automorphisms of Uq(osp(1|2r)) are also described. [ABSTRACT FROM AUTHOR]