Explore the vast range of titles available.

Betweenness centrality (BC) is widely used to identify critical nodes in a network by exploring the ability of all nodes to act as intermediaries for information exchange. However, one of its assumptions, i.e., the contributions of all shortest paths are equal, is inconsistent with variations in spatial interactions along these paths and has been questioned when applied to spatial networks. Hence, this paper proposes a spatial interaction incorporated betweenness centrality (SIBC) for spatial networks. SIBC weights the shortest path between each node pair according to the intensity of spatial interaction between them, emphasizing the combination of a network structure and spatial interactions. To test the rationality and validity of SIBC in identifying critical nodes and edges, two specific forms of SIBC are applied to the Shenzhen street network and China’s intercity network. The results demonstrate that SIBC is more significant than BC when we also focus on the network functionality rather than only on the network structure. Moreover, the good performance of SIBC in robustness analysis illustrates its application value in improving network efficiency. This study highlights the meaning of introducing spatial configuration into empirical models of complex networks.

This paper proposes a modified fast off‐grid L1‐SVD (M‐FOGL1SVD) method for direction of arrival (DOA) estimation. Unlike FOGL1SVD, after obtaining the positions of the nonzero rows of the signal sources, the off‐grid overcomplete basis matrix is used to update the signal sources, thus improving the estimation accuracy of it. In addition, to reduce the approximate error of the first‐order off‐grid model, a second‐order off‐grid model is introduced through a further Taylor expansion of the steering vector. Finally, the formula for solving the off‐grid gap under the novel model is derived. Extensive simulation results indicate that the proposed algorithm has better performance than FOGL1SVD in terms of DOA estimation precision.

In order to reduce the cost of scaffolding usage in engineering projects as much as possible under the premise of safety and rationality, while avoiding the risk of leakage caused by holes left in the building structure by the scaffolding, a type of inclined tensioned steel cantilevered scaffolding is proposed based on the stress characteristics of traditional cantilevered scaffolding. Considering the actual construction characteristics, a comprehensive approach involving theoretical analysis, on-site field tests, and finite element methods is adopted to clarify the full-process design method of the inclined tensioned steel cantilevered scaffolding. Additionally, recommendations are provided for the practical engineering design and application of the inclined tensioned steel cantilevered scaffolding. Finally, the inclined tensioned steel cantilevered scaffolding is applied in actual engineering. The research results indicate that the proposed full-process design method is feasible, and the inclined tensioned steel cantilevered scaffolding is more cost-effective than traditional cantilevered scaffolding.

With the atypical rise of infection (MPI) in 2023, prompt studies are needed to determine the current epidemic features and risk factors with emerging trends of MPI to furnish a framework for subsequent investigations. This multicentre, retrospective study was designed to analyse the epidemic patterns of MPI before and after the COVID-19 pandemic, as well as genotypes and the macrolide-resistance-associated mutations in sampled from paediatric patients in Southern China. Clinical data was collected from 1,33,674 patients admitted into investigational hospitals from 1 June 2017 to 30 November 2023. Metagenomic next-generation sequencing (mNGS) data were retrieved based on sequence positive samples from 299 paediatric patients for macrolide-resistance-associated mutations analysis. was used to compare categorical variables between different time frames. The monthly average cases of paediatric common respiratory infection diseases increased without enhanced public health measures after the pandemic, especially for influenza, respiratory syncytial virus infection, and MPI. The contribution of MPI to pneumoniae was similar to that in the outbreak in 2019. Compared to mNGS data between 2019-2022 and 2023, the severity of did not grow stronger despite higher rates of macrolide-resistance hypervariable sites, including loci 2063 and 2064, were detected in childhood samples of 2023. Our findings indicated that ongoing surveillance is necessary to understand the impact of post pandemic on transmission disruption during epidemic season and the severity of clinical outcomes in different scenarios.

Influenza-associated encephalitis/encephalopathy (IAE) is a severe neurological complication characterized by central nervous system dysfunction and structural damage following influenza virus infection. Predominantly affecting infants and young children, IAE exhibits its highest incidence in those under five years of age. Key clinical manifestations of IAE include acute seizures, sudden high fever, and impaired consciousness, frequently progressing to coma. Neuroimaging, particularly magnetic resonance imaging (MRI), often reveals multifocal brain lesions involving multiple brain regions, including the cerebellum, brainstem, and corpus callosum. The prognosis of IAE is poor, with a mortality rate reaching 30%. Current diagnosis relies heavily on clinical presentation and characteristic neuroimaging findings, as the precise pathogenesis of IAE remains elusive. While various research models, including cell lines, brain organoids, and animal models, have been developed to recapitulate IAE features, significant limitations persist in modeling the core clinical pathophysiology observed in pediatric patients, necessitating further model refinement. This review synthesizes the clinical spectrum of IAE, summarizes progress in understanding its pathogenesis, and critically evaluates existing research models. We aim to provide a foundation for utilizing experimental approaches to elucidate IAE mechanisms and identify potential therapeutic strategies.

Land use classification based on multi-modal data fusion has gained significant attention due to its potential to capture the complex characteristics of urban environments. However, effectively extracting and integrating discriminative features derived from heterogeneous geospatial data remain challenging. This study proposes an ensemble learning framework for land use classification by fusing cross-modal deep features from both physical and socioeconomic perspectives. Specifically, the framework utilizes the Masked Autoencoder (MAE) to extract global spatial dependencies from remote sensing imagery and applies long short-term memory (LSTM) networks to model spatial distribution patterns of points of interest (POIs) based on type co-occurrence. Furthermore, we employ inter-modal contrastive learning to enhance the representation of physical and socioeconomic features. To verify the superiority of the ensemble learning framework, we apply it to map the land use distribution of Bejing. By coupling various physical and socioeconomic features, the framework achieves an average accuracy of 84.33 %, surpassing several comparative baseline methods. Furthermore, the framework demonstrates comparable performance when applied to a Shenzhen dataset, confirming its robustness and generalizability. The findings highlight the importance of fully extracting and effectively integrating multi-source deep features in land use classification, providing a robust solution for urban planning and sustainable development.

The traditional research methods of the notch frequency phenomenon are mainly discussed by experimental observation or the semi-analytical finite element method. In this paper, the notch frequency characteristics of ultrasonic guided waves are simulated by the general finite element method. Firstly, the theoretical dispersion curve of the longitudinal mode in the axially loaded rod is derived by the acoustic elasticity theory, and the finite element simulation is carried out by ABAQUS/Explicit 6.14 to simulate the wave propagation in the seven-wire steel strand. In order to verify the model, laboratory experiments are carried out on three types of prestressed steel strands with diameters of 12.7 mm, 15.2 mm, and 17.8 mm, respectively. Each specimen is gradually loaded from 50 kN to 110 kN in increments of 30 kN. At each loading level, the ultrasonic signal is obtained, and the corresponding notch frequency is extracted from the spectrum. The experimental results confirm the accuracy of the model, and the maximum deviation between the predicted notch frequency and the measured value is 3%. The results show that the proposed method provides a robust and non-destructive means for structural health monitoring in civil engineering applications, and has the potential to be more widely used in complex waveguide structures.

Channel estimation is a challenging issue in millimeter-wave massive multiple-input-multiple-output (MIMO) communication systems due to the large number of antennas in the transceiver. Existing methods are usually based on phase shifters which may not be a simple circuit at mmWave band. In this paper, we construct a switch-based architecture for analog processors from the coarray point of view and then propose an atomic ℓ0-norm minimization problem. We then propose an efficient algorithm to solve this problem based on Wirtinger projection. Since the proposed method requires no angle discretization, it does not suffer from grid mismatch effect that greatly deteriorates the estimation performance of grid-based channel estimation methods. Compared to the atomic norm minimization (ANM) method, our method does not involve vectorization of the channel matrix and hence the dimensionality of the problem is much less than that of ANM. We show that our method is able to provide comparable estimation performance to ANM but with much less computational time. Extensive simulations are carried out to verify the effectiveness of our proposed method.

Acquired Hemophilia A (AHA), a rare bleeding disorder caused by autoimmune suppression of coagulation factor VIII(FVIII), has a low incidence in perinatal women but carries significant risks of severe hemorrhage. The lack of consensus guidelines and limited clinical data from developing countries underscore the importance of this case report. Consequently, this report informs clinical practice and expands the evidence base for the treatment of AHA. This case describes an extensive abdominal wall hematoma and hemorrhagic shock following a cesarean section in a patient. The condition was successfully managed through comprehensive interventions, including shock resuscitation, endovascular embolization, pharmacological hemostatic therapy, removal of circulating immune complexes, and immunosuppressive therapy. AHA usually causes acute bleeding, and early identification and removal of acquired inhibitors are the main treatment methods. In such cases, endovascular embolization is an effective procedure to control bleeding.

Here, we describe the characterization of tooth-germ organoids, three-dimensional (3D) constructs cultured in vitro with the potential to develop into living teeth. To date, the methods used to successfully create tooth organoids capable of forming functional teeth have been quite limited. Recently, hydrogel microparticles (HMP) have demonstrated utility in tissue repair and regeneration based on their useful characteristics, including their scaffolding ability, effective cell and drug delivery, their ability to mimic the natural tissue extracellular matrix, and their injectability. These outstanding properties led us to investigate the utility of using HMPs (average diameter: 158 ± 32 µm) derived from methacrylated gelatin (GelMA) (degree of substitution: 100%) to create tooth organoids. The tooth organoids were created by seeding human dental pulp stem cells (hDPSCs) and porcine dental epithelial cells (pDE) onto the HMPs, which provided an extensive surface area for the cells to effectively attach and proliferate. Interestingly, the cell-seeded HMPs cultured on low-attachment tissue culture plates with gentle rocking self-assembled into organoids, within which the cells maintained their viability and morphology throughout the incubation period. The self-assembled organoids reached a volume of ~50 mm3 within two weeks of the in vitro tissue culture. The co-cultured hDPSC-HMP and pDE-HMP structures effectively attached to each other without any externally applied forces. The presence of polarized, differentiated dental cells in these composite tooth-bud organoids demonstrated the potential of self-assembled dental cell HMPs to form tooth-bud organoid-like structures for potential applications in tooth regeneration strategies.