Explore the vast range of titles available.

The extraction of individual trees from three‐dimensional (3D) forest point clouds plays a pivotal role in forest inventory updates, forest resource management, growth and yield estimation of trees, etc. Existing end‐to‐end deep learning‐based methods for extracting individual trees typically rely on extracting instance‐sensitive features and clustering techniques. In this paper, inspired by the Segment Anything Model (SAM) and prompt‐driven paradigm, we propose a novel approach to forest point cloud instance segmentation, called the 3D Promptable Segmentation Network (3DPS‐Net). This network generates object masks using prompt points, thereby enabling the extraction of individual trees. We have designed two testing modes: prompt testing and automated testing. Prompt testing allows for interactive manual tree segmentation, providing a flexible and controllable analysis tool, while automated testing supports the autonomous identification and segmentation of individual trees within the input dataset. Through accuracy evaluation experiments conducted on two forest point cloud datasets (the ForestSemantic dataset and the FOR‐instance dataset), we have demonstrated the integrity and reliability of the individual tree instances predicted by 3DPS‐Net. Our proposed network exhibits robust performance with overstory trees. It requires only 1.5 seconds to predict a 3D individual tree mask from a prompt point, indicating significant potential for real‐time processing applications. This innovative approach introduces a new methodology for individual tree extraction using deep learning, thereby contributing to the advancement and future progress of this field.

The integrity of a point cloud frequently suffers from discontinuous material surfaces or coarse sensor resolutions. Existing methods focus on reconstructing the overall structure, but salient points or small irregular surfaces are difficult to be predicted. Toward this issue, we propose a new end-to-end neural network for point cloud completion. To avoid non-uniform point density, the regular voxel centers are selected as reference points. The encoder and decoder are designed with Patchify, transformers, and multilayer perceptrons. An implicit classifier is incorporated in the decoder to mark the valid voxels that are allowed for diffusion after removing vacant grids from completion. With newly designed loss function, the classifier is trained to learn the contours, which helps to identify the grids that are difficult to be judged for diffusion. The effectiveness of the proposed model is validated in the experiments on the indoor ShapeNet dataset, the outdoor KITTI dataset, and the airbone laser dataset by competing with state-of-the-art methods, which show that our method can predict more accurate point coordinates with rich details and uniform point distributions.

Background Current studies have reported a high association between the Hashimoto’s thyroiditis (HT) and papillary thyroid cancer (PTC). However, studies on the characteristics of central lymph node metastasis (CLNM) in PTC patients with HT are scarce. Therefore, this study aims to evaluate the risk factors of CLNM in PTC and HT patients. Methods We retrospectively collected clinical data from 933 PTC patients with HT who underwent thyroid surgery. Of these, 653 patients were categorized into a training cohort for constructing the nomogram, and 280 patients formed a validation cohort to verify the performance of the model. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses were used to select risk factors. A nomogram model for predicting CLNM was developed and internally validated. We subsequently evaluated thyroid function within 3 years after surgery and estimated the prevalence and incidence of postoperative complications between the CLNM ( +) and CLNM (-) groups. Results LASSO regression revealed that 19 nonzero variables were associated with CLNM. Multivariate logistic regression analysis revealed that younger, patients of low body mass index (BMI), drinking, intranodular hyperechoic (IH) status, diameter ≥ 1 cm, multifocality, extrathyroidal extension (ETE), enlarged central lymph nodes (ECLNs) and lateral lymph node metastasis (LLNM) were at higher risk of CLNM ( P  < 0.05). A nomogram to predict CLNM in PTC patients with HT was constructed and internally validated on the basis of risk factors. The areas under the ROC curve (AUCs) of nomogram were 0.768 (95% CI, 0.723–0.812) and 0.773 (95% CI, 0.705–0.841) in training and validation groups, respectively. Moreover, the nomogram data showed a good discrimination and calibration ability to training and validation groups. Postoperative follow-up revealed that TGAb levels and the incidence of hypothyroidism were significantly greater in CLNM ( +) group than CLNM (-) group, respectively. Conclusions Our nomogram schedule developed and validated with a comprehensive set of preoperative risk factors showed a high benefit in predicting CLNM in PTC patients with HT. Postoperative follow-up of thyroid function allow to clarify the trend, as well as prevalence and incidence of common thyroid complications.

This article mainly studies first-order coherence related to the robustness of the triplex MASs consensus models with partial complete graph structures; the performance index is studied through algebraic graph theory. The topologies of the novel triplex networks are generated by graph operations and the approach of graph spectra is applied to calculate the first-order network coherence. The coherence asymptotic behaviours of the three cases of the partial complete structures are analysed and compared. We find that under the condition that the number of nodes in partial complete substructures n tends to infinity, the coherence asymptotic behaviour of the two sorts of non-isomorphic three-layered networks will be increased by r−12(r+1), which is irrelevant to the peripheral vertices number p; when p tends to infinity, adding star copies to the original triplex topologies will reverse the original size relationship of the coherence under consideration of the triplex networks. Finally, the coherence of the three-layered networks with the same sorts of parameters, but non-isomorphic graphs, are simulated to verify the results.

The western mining regions of China, known for shallow-buried and high-intensity mining activities, face significant ecological threats due to damage to loose strata and the surface. The evolution of fissures within the loose layer is a critical issue for surface ecological environment protection in coal mining areas. The study employed field measurements, mechanical experiments, numerical simulations, and theoretical analysis, using the ‘triaxial consolidation without drainage’ experiment to assess the physical and mechanical properties of various strata in the loose layer. Additionally, the PFC2D numerical simulation software was employed to construct a numerical model that elucidates the damage mechanisms and reveals the evolution of loose layer fissures and the development of ground cracks. The research findings indicate that during shallow-buried high-intensity mining loose layer fissures undergo a dynamic evolution process characterized by “vertical extension-continuous penetration-lateral expansion”. As the working face advances, these fissures eventually propagate to the surface, forming ground cracks. The strong force chains within the overlying rock (or soil) layers develop in the form of an “inverted catenary arch”. As the arch foot and the middle of the arch overlap, fissures propagate along these strong force chains to the surface, resulting in ground cracks. The study elucidates the surface damage patterns in shallow-buried, high-intensity mining, offering theoretical insights for harmonizing coal mining safety with ecological conservation in fragile regions.

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

In this paper, a diffusive and delayed virus dynamics model with Crowley-Martin incidence function and CTL immune response is investigated. By constructing the Lyapunov functionals, the threshold conditions on the global stability of the infection-free, immune-free and interior equilibria are established if the space is assumed to be homogeneous. We show that the infection-free equilibrium is globally asymptotically stable if the basic reproductive number R 0 ≤ 1 ; the immune-free equilibrium is globally asymptotically stable if the immune reproduction number and the basic reproduction number satisfy R 1 ≤ 1 < R 0 ; the interior equilibrium is globally asymptotically stable if R 1 > 1 .

Development zones have been an important spatial approach to promoting economic development since China’s reform and opening-up. They have also been major contributors to the processes of China’s industrialization and urbanization. Along with improvements in the worldwide industrial division of labor and the gradual implementation of China’s development zones’ Go Global strategy, it is necessary for Africa, a hot spot of global industrialization in recent years, to learn from China’s development zone model. By attracting China’s capital, technology and enterprises to Africa via Sino-African co-built development zones, a pattern of high complementarity and mutual development between China and Africa can be formed which does favor further improvement of the global industrial division of labor. In order to study the applicability and prospect of China’s development zone model in Africa as per the above-mentioned international situation, this paper first sorts out the development course of China’s development zones and discusses their roles in China’s industrialization and urbanization. Subsequently, this paper analyzes the status quo of industrial development in Africa as a whole and the differences in industrial development between China and Africa, aiming to justify the timing of industrial transfer from China to Africa by constructing Sino-African co-built development zones. Lastly, this paper analyzes the current situation of six Sino-African co-built development zones by focusing on their operation modes, industry types and investment promotion models. In the authors’ view, Sino-African co-built development zones can function as a new window of China-African cooperation, a new carrier of African industrialization, and a new engine of global industrial restructuring. China should adhere to the general principles of ‘Sino-African Integration, Multi-Cooperation, Mutual Benefit, Scientific Location, Systematic Planning, Cluster Growth and Open Development’ in the planning and construction of development zones in Africa, effectively promoting Africa as the very important part of the global industry system.

Cholestatic liver injury is associated with intrahepatic biliary fibrosis, which can progress to cirrhosis. Resident hepatic progenitor cells (HPCs) expressing Prominin‐1 (Prom1 or CD133) become activated and participate in the expansion of cholangiocytes known as the ductular reaction. Previously, we demonstrated that in biliary atresia, Prom1(+) HPCs are present within developing fibrosis and that null mutation of Prom1 significantly abrogates fibrogenesis. Here, we hypothesized that these activated Prom1‐expressing HPCs promote fibrogenesis in cholestatic liver injury. Using Prom1CreERT2‐nLacZ/+;Rosa26Lsl‐GFP/+ mice, we traced the fate of Prom1‐expressing HPCs in the growth of the neonatal and adult livers and in biliary fibrosis induced by bile duct ligation (BDL). Prom1‐expressing cell lineage labeling with Green Fluorescent Protein (GFP) on postnatal day 1 exhibited an expanded population as well as bipotent differentiation potential toward both hepatocytes and cholangiocytes at postnatal day 35. However, in the adult liver, they lost hepatocyte differentiation potential. Upon cholestatic liver injury, adult Prom1‐expressing HPCs gave rise to both PROM1(+) and PROM1(‐) cholangiocytes contributing to ductular reaction without hepatocyte or myofibroblast differentiation. RNA‐sequencing analysis of GFP(+) Prom1‐expressing HPC lineage revealed a persistent cholangiocyte phenotype and evidence of Transforming Growth Factor‐β pathway activation. When Prom1‐expressing cells were ablated with induced Diphtheria toxin in Prom1CreERT‐nLacZ/+;Rosa26DTA/+ mice, we observed a decrease in ductular reactions and biliary fibrosis typically present in BDL as well as decreased expression of numerous fibrogenic gene markers. Our data indicate that Prom1‐expressing HPCs promote biliary fibrosis associated with activation of myofibroblasts in cholestatic liver injury. In this study, we demonstrate that during cholestatic liver injury following bile duct ligation that cholangiocytes derived from Prominin‐1‐expressing hepatic progenitor cells promote biliary fibrosis; ablation of Prom1‐expressing cells decreases fibrosis.

Despite the growing attention on uncontrolled and unprofitable urban sprawling in many African countries, few pragmatic solutions have been raised or effectively implemented. While uncontrolled and unprofitable urban expansions happened primarily due to poor land use management and dysfunctional land market, the cost of land management enforcement and reform is high. This paper suggests that the recently re-emerging special economic zones (SEZs) in Africa could be a practical way of using government intervention to reduce uncontrolled urban expansion and optimize urban land use. By evaluating the spatial impacts of two SEZs on their host cities in Ethiopia and Zambia, this paper demonstrates that SEZs could notably change urban expansion in terms of its speed, direction, and spatial structure. By using SEZs as an experimental area for land policy reform, the government can also effectively unlock a profitable urban development model with the functional primary and secondary land market. However, the diverging results in Ethiopia and Zambia also show that the optimizing effect can be significant only when the government is participatory and can fulfil its public function, including delivering proper planning in advance, lunching land policy reform, and even executing compulsory land acquisition for public interests.