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Grassland ecosystems are a significant part of the global ecosystem and support the livelihoods of millions of people. The Inner Mongolia grassland is the largest temperate grassland in the world, and an important ecological barrier for China, but due to human activities and climate change it has been faced with an ecological crisis in recent years. In this study, a modified Carnegie-Ames-Stanford approach (CASA) model based on the Google Earth Engine platform was used to determine the net primary production (NPP) in the Inner Mongolia grassland from 2000 to 2020. The results show that the average annual NPP of the Inner Mongolia grassland is 278.63 g C/m2, and 83.22% of the total area has shown an increasing trend during the study period. We also analyzed the impact of land-use/cover change (LUCC) and climatic factors on NPP. We found that: (1) the total area of grassland increased from 2000 to 2010 and then decreased from 2010 to 2020. During the whole study period, although the grassland area increased slightly by 4728.69 km2 because of LUCC, the overall effect of LUCC on grassland NPP was negative, with a loss of 17.63 Tg C compared to an increase of 16.38 Tg C. (2) The main meteorological factor affecting the NPP of the Inner Mongolia grassland is precipitation, followed by sunshine duration and temperature. About 97.06% of the grassland shows a positive correlation between NPP and precipitation. (3) The results for NPP and its changing trends are not completely consistent in the long- and short-term study periods. Considering the instability of grassland growth, it is necessary to take the periodic variation of precipitation into account when studying NPP. These results could provide basic information for policy formulation and scientific research into the ecological environment management of grassland areas in the future.

In recent years, ship target detection in synthetic aperture radar (SAR) images has significantly progressed due to the rapid development of deep learning (DL). However, since only the spatial feature information of ship targets is utilized, the current DL-based SAR ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale, rotations, or complex backgrounds. To address these issues, in this paper, a novel deep-learning network for SAR ship rotation detection, called a morphology and topology-based feature alignment network, is proposed which can better exploit the morphological features and inherent topological structure information. This network consists of the following three main steps: First, deformable convolution is introduced to improve the representational ability for irregularly shaped ship targets, and subsequently, a morphology and topology feature pyramid network is developed to extract inherent topological structure information. Second, based on the aforementioned features, a rotation alignment feature head is devised for fine-grained processing as well as aligning and distinguishing the features; to enable regression prediction of rotated bounding boxes; and to adopt a parameter-sharing mechanism to improve detection efficiency. Therefore, utilizing morphological and inherent topological structural information enables a superior detection performance to be achieved. Finally, we evaluate the effectiveness of the proposed method using the rotated ship detection dataset in SAR images (RSDD-SAR). Our method outperforms other DL-based algorithms with fewer parameters. The overall average precision is 90.84% and recall is 92.21%. In inshore and offshore scenarios, our method performs well for the detection of multi-scale and rotation-varying ship targets, with its average precision reaching 66.87% and 95.72%, respectively.

Membrane-type acoustic metamaterials (MAMs) are the focus of the current research due to their lightweight, small size, and good low-frequency sound insulation performance. However, there exists difficulties for extensive application because of the narrow sound insulation band. In order to achieve broadband sound isolation under the premise of lightweight, a novel MAM with asymmetric rings is firstly proposed in this paper. The sound transmission loss (STL) of this MAM is calculated by an analytical method and is verified by the finite element model. The different properties of the membrane when it is loaded with one, two, or four mass blocks are analyzed. The comparison with the traditional MAM proves the superior performance of this novel MAM. Moreover, by discussing the influence of the eccentricity and distribution position of the masses on the results, the tunability of the sound insulation performance of this MAM is proven. Finally, the Isight platform is used to optimize the MAM to further improve the broadband sound insulation performance: the average STL of the MAM is improved by 15.7%, the bandwidth above 30 dB is improved by 11.5%, and the mass density is reduced by 30.01%.

The sound insulation capacity of traditional sound insulation boards is limited by the law of mass, and any improvement in sound insulation capacity contradicts the demand for light weight. In order to overcome this shortcoming, a lightweight particle reinforced gradient honeycomb sandwich panel is proposed to achieve lightweight sound insulation. The sound insulation of the particle reinforced honeycomb sandwich panel is calculated based on the transfer matrix method. The accuracy of the theory is verified through finite element simulation, and the influence of material and structural parameters on the sound insulation performance of the sandwich panel is analyzed. The results show that the sound insulation of the honeycomb sandwich panel can be significantly improved by adding reinforcement particles to the aluminum matrix, and the sound insulation also increases as the particle mass fraction of the reinforcement increases. In addition, the valley value of the sound insulation curve moves towards the low frequency direction, which indicates that the sound insulation performance of the sandwich plate at low frequencies is effectively improved.

The present studies were conducted to evaluate key serum proteins and other components that mediate anchorage-independent growth (3-D growth) of LNCaP prostate cancer cells as spheroids. The cells were cultured on ultra-low attachment plates in the absence and presence of fetuin-A and with or without extracellular vesicles. The data show that fetuin-A (alpha 2HS glycoprotein) is the serum protein that mediates 3-D growth in these cells. It does so by sequestering extracellular vesicles of various sizes on the surfaces of rounded cells that grow as spheroids. These vesicles in turn transmit growth signals such as the activation of AKT and MAP kinases in a pattern that differs from the activation of these key growth signaling pathways in adherent and spread cells growing in 2-D. In the process of orchestrating the movement and disposition of extracellular vesicles on these cells, fetuin-A is readily internalized in adhered and spread cells but remains on the surfaces of non-adherent cells. Taken together, our studies suggest the presence of distinct signaling domains or scaffolding platforms on the surfaces of prostate tumor cells growing in 3-D compared to 2-D.

Membrane-type acoustic metamaterials (MAMs) have recently received widespread attention due to their good low-frequency sound-transmission-loss (STL) performance. A fast prediction method for the STL of rectangular membranes loaded with masses of arbitrary shapes and surface density values is proposed as a semi-analytical model for calculating the STL of membrane-type acoustic metamaterials. Through conformal mapping theory, the mass blocks of arbitrary shapes were transformed into regular shapes, which simplified the calculation model of acoustic propagation loss prediction, and the prediction results were verified by finite element simulations. The results show that the change in mass surface density was closely related to the size and frequency distribution of STL. The influence of the mass center on the STL and characteristic frequency of the film metamaterial is discussed.

The existing sandwich structure of the aircraft cabin demonstrates a good sound insulation effect in medium and high frequency bands, but poor in the low frequency band. Therefore, we propose an infinite new lightweight broadband noise control structure and study its sound transmission loss (STL). The structure is an orthogonally rib-stiffened honeycomb double sandwich structure with periodic arrays of shunted piezoelectric patches, and demonstrates lighter mass and better strength than the existing sandwich structure. The structure is equivalent according to Hoff’s equal stiffness theory and the effective medium (EM) method. Using the virtual work principle for a periodic element, two infinite sets of coupled equations are obtained. They are solved by truncating them in a finite range until the solution converges. The correctness and validity of the model are verified by using simulation results and theoretical predictions. Eventually, a further study is performed on the factors influencing the STL. All the results demonstrate that the STL in low-frequency can be improved by the structure, and the sound insulation bandwidth is significantly broadened by adding shunted piezoelectric patches. The structure can provide a new idea for the design of broadband sound insulation.

In this paper, a trench-assisted low-bending-loss large-mode-field-area multi-core fiber with air hole is proposed, which can achieve dual-mode transmission. The influence of structural parameters on fiber performance is analyzed systematically, and the structure of the trench, with a lower refractive index than the cladding, is also analyzed and optimized. By adjusting the structural parameters, the effective mode field area of the fundamental mode can reach 2003.24 um2 at 1550 nm, and when the bending radius is 1 cm, the bending loss is 2.57 × 10−3 dB/m. The practical implementation of the proposed fiber is feasible using existing fabrication technology and is applicable to the transmission of large-capacity optical communication systems and high-power lasers.

Although the bubble contacting a uniformly superaerophilic surface has caused concern due to its application potential in various engineering equipment, such as mineral flotation, very little is known about the mechanism of how the bubble spreads on a surface with anisotropic superaerophilicity. To unveil this mystery, we experimentally studied the anisotropic behavior of a bubble (2 mm in diameter) spreading on the superaerophilic straight trajectories (SALTs) of different widths (0.5 mm−5 mm) in water using a high-speed shadowgraphy system. The 1−3 bounces mostly happened as the bubble approached the SALTs before its spreading. It is first observed that the bubble would be split into two highly symmetrical sub-bubbles with similar migration velocity in opposite directions during the anisotropic spreading. Two essential mechanisms are found to be responsible for the anisotropic spreading on the narrow SALTs (W ≤ 2 mm with two subregimes) and the wide SALTs (W ≥ 3 mm with four subregimes). Considering the combined effect of the surface tension effect of SALT and Laplace pressure, a novel model has been developed to predict the contact size r(t) as a function of time. The nice agreement between this model and our experiments reconfirms that the surface tension effect and Laplace pressure prevail over the hydrostatic pressure.