Search Results (4)

The development of reserve resources of cultivated land (RRCL) is a vital way of supplementing cultivated land in the northern arid and semi-arid regions of China. This study developed a suitability evaluation system for reserve resources of cultivated land from the nature–function–environment perspective. The evaluation considered the construction of water conservancy projects, establishing two scenarios of rain-fed and irrigated agriculture. The evaluation results for unused land were divided into four levels, including highly suitable, relatively suitable, moderately suitable and unsuitable, using the three-dimensional Rubik’s cube spatial classification method. The results show that: (1) Under the rain-fed agriculture scenario, the developable quantity and quality of unused land were poor. The potential area of reserve resources of cultivated land only accounted for 5% of the total area. (2) Under the irrigated agriculture scenario, the potential area of reserve resources of cultivated land increased to 21% of the total unused land. The areas were mainly distributed in Xinjiang, Inner Mongolia, and Gansu. The research considered the impact of water conservancy projects on the development and utilization of reserve resources of cultivated land. It can provide references for the development of arable land resources and territorial spatial planning in China. Full article

Background: The Sella Turcica is a critical structure from an orthodontic perspective, and its morphological characteristics can help in understanding craniofacial deformities. However, accurately extracting Sella Turcica shapes can be challenging due to the indistinct edges and indefinite boundaries present in X-ray images. This study aimed to develop and validate an automated Sella Morphology Network (SellaMorph-Net), a hybrid deep learning pipeline for segmenting Sella Turcica structure and extracting different morphological types; Methods: The SellaMorph-Net model proposed in this study combined attention-gating and recurrent residual convolutional layers (AGM and RrCL) to enhance the encoder’s abilities. The model’s output was then passed through a squeeze-and-excitation (SE) module to improve the network’s robustness. In addition, dropout layers were added to the end of each convolution block to prevent overfitting. A Zero-shot classifier was employed for multiple classifications, and the model’s output layer used five colour codes to represent different morphological types. The model’s performance was evaluated using various quantitative metrics, such as global accuracy and mean pixel-wise Intersection over Union (IoU) and dice coefficient, based on qualitative results; Results: The study collected 1653 radiographic images and categorised them into four classes based on the predefined shape of Sella Turcica. These classes were further divided into three subgroups based on the complexity of the Sella structures. The proposed SellaMorph-Net model achieved a global accuracy of 97.570, mean pixel-wise IoU scores of 0.7129, and a dice coefficient of 0.7324, significantly outperforming the VGG-19 and InceptionV3 models. The publicly available IEEE ISBI 2015 challenge dataset and our dataset were used to evaluate the test performance between the state-of-the-art and proposed models. The proposed model provided higher testing results, which were 0.7314 IoU and 0.7768 dice for our dataset and 0.7864 IoU and 0.8313 dice for the challenge dataset; Conclusions: The proposed hybrid SellaMorph-Net model provides an accurate and reliable pipeline for detecting morphological types of Sella Turcica using full lateral cephalometric images. Future work will focus on further improvement and utilisation of the developed model as a prognostic tool for predicting anomalies related to Sella structures. Full article

The development of reserve resources of cultivated land (RRCL) is a vital way of supplementing cultivated land, and plays a crucial role in ensuring food security. However, if we blindly pursue the quantity of development while ignoring the conservation of the ecosystem, we are likely to waste land resources and destroy the ecological environment. Therefore, it is necessary to address the urgent issue of preventing ecological risks resulting from the development of RRCL and to enhance the actual effect of supplementing cultivated land. Taking Linzhou City in Henan Province as an example, this paper first assessed the tillability of RRCL and estimated the functionality of ecosystem services. Then it projected the losses of ecosystem services incurred by RRCL development, based on which it determined the development priority. The following conclusions were drawn: (1) The total area of RRCL in Linzhou City amounts to 8845 hectares. (2) According to the research forecast, the total annual losses of ecosystem services incurred by RRCL development in Linzhou City include: water conservation of 776,200 m³, soil retention of 340.84 t, and carbon sequestration and oxygen release of 2311.12 t. Moreover, the total value of losses amounts to RMB 15.7754 million. (3) The ecological losses incurred by RRCL development vary with the different types of land. Overall, the ecological loss derived from reclaimable land is inferior to that of cultivable land, with the average value of ecological loss amounting to RMB 600 and RMB 5300 per hectare, respectively. The ecological loss from the development of class II land is: pond < garden land < artificial grassland < artificial forest land < natural grassland < bare land. Moreover, land development should be postponed when the quantity of ecological losses reaches level III or higher. (4) Corrections are made based on the ecological coefficient of the economic potential of RRCL development so as to determine the priority of development. The research findings indicate that the priority of development of eastern towns is higher than that of central and western towns in Linzhou City, with Donggang and Hengshui topping the priority list and Shibanyan, Lingyang, and Chengguan having the lowest priority. Full article

A random Nyström (R-Nyström) scheme for clutter subspace estimation is proposed in the context of polarimetric space-time adaptive processing (pSTAP). Unlike the standard Nyström scheme making use of only partial columns of the clutter plus noise covariance matrix (CNCM), R-Nyström exploits full CNCM information with a properly designed selection procedure under the newly developed random ridge cross leverage score (RRCLS) criterion. With R-Nyström, sup-ported by the complete CNCM columns, upgraded clutter subspace estimation can be achieved at the expense of an insignificant increase in computational complexity, in contrast to the standard Nyström. The R-Nyström-based pSTAP, termed pR-Nyström, is shown to be superior over the current eigendecomposition-free subspace pSTAP in the signal to clutter plus noise loss and computational complexity. The efficacy of R-Nyström/pR-Nyström is validated by the simulation results. Full article