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ABSTRACTUrban Green Space (UGS) plays a crucial socio-cultural role in improving the quality of life. Assessment of quantity, quality, and accessibility determines whether the UGS are important social benefits. The travel preference of urban residents is a good indicator for the social relevance of UGS. This study integrates remote sensing and geographic big data to quantitatively analyze and evaluate the social benefits of urban green space. We propose a Green Social Benefit Index (GSBI) based on remote sensing retrieval and classification of urban green space. The GSBI is combined with the data on population mobility to investigate the influence of urban green space on residents’ behavior. The results show that urban residents prefer to move around in places with a higher quantity and quality of urban green space. The magnitude of the influence of urban green spatial features on population heat difference shows that accessibility > quantity > quality. The social benefits of urban green space are higher in middle centers compared to urban centers and city borders. The relationship between urban mobility and urban green space may suggest how to improve the distribution of green space in urban areas. This study could be a reference for the government planning department.

Objective: To explore whether Bushen Huoxue Formula (BSHXF) improves the angiogenesis ability of transplanted endothelial progenitor cells (EPCs) in endplate and its potential mechanism in delaying intervertebral disc degeneration (IDD). Methods: BSHXF was analyzed via Ultra-High Performance Liquid Chromatography (UPLC). Rabbit axial compression lumbar IDD models were constructed and the effects of BSHXF, EPCs, and their combination in IDD were determined by MRI, histological evaluation, TUNEL, and immunofluorescence assays. Additionally, CCK-8 assay, flow cytometry, and tube formation assay were used to evaluate EPCs viability, proliferation, cell cycle and the angiogenesis ability of EPCs between groups. Results: BSHXF and transplanted EPCs both attenuate the process of IDD in the rabbit model assessed by MRI, HE staining and Masson staining. TUNEL-positive NP cells were significantly reduced in the BSHXF group, EPCs group, and EPC + BSHXF group compared to the model group (P

Abstract The fluctuations of the Pipe-to-Soil Potential (PSP) in buried pipes are influenced by the geoelectric field induced by changes in the Earth's magnetic field, which can accelerate pipeline corrosion. Monitoring data of PSP along China's West–East Gas Pipeline near the lake indicates obvious tidal changes. This study proposes that the periodic movement of the conductivity boundary of the seepage area near the lake shore caused by tides is the fundamental cause of the tidal changes in PSP. The movement of the boundary changes the range of the H polarization of the geoelectric field, resulting in periodic changes in the PSP of the pipeline outside the seepage area. To investigate this mechanism, a dynamic boundary model of conductivity in the seepage area of Sailimu Lake in Xinjiang was established, and the characteristics of PSP at different distances from the boundary were analyzed using electromagnetic field finite element method and DSTL pipeline model. The study compared the simulation results of the model with the PSP data of the cathodic protection station and PSP monitoring station near Sailimu Lake, during a geomagnetic disturbance on September 23–24, 2016. The results of the study, as well as the corrosion of the test strips along the lake, verified the validity of the boundary change mechanism and model of the seepage zone. This study provides a theoretical basis for corrosion protection of pipelines near the lake’s edge.

Objective: To establish a new animal model of intervertebral disc degeneration (IDD) by axial compression on lumbar spine of rabbits and to investigate the changes of microcirculation in bony endplates during the progress of IDD. Methods: 32 New Zealand white rabbits were equally divided into 4 groups as follows: Control group with no operation and compression, Sham operation group with apparatus placement only, 2-week compression group and 4-week compression group with the devices installed and compressed for their preset duration. All groups of rabbits underwent MRI, histological evaluation, disc height index measurement and Microfil contrast agent perfusions to examine the ratio of endplate microvascular channels. Results: The new animal model of IDD was successfully established after axial compression for 4 weeks. The MRI grades for the 4-week compression group was 4.63 ± 0.52 and significantly different to the sham operation group (P

This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.

Abstract Background This study was conducted to investigate the outcomes and complications of balloon kyphoplasty (KP) for the treatment of osteoporotic vertebral compression fracture (OVCF) in patients with rheumatoid arthritis (RA) and compare its radiological and clinical effects with OVCF patients without RA. Methods Ninety-eight patients in the RA group with 158 fractured vertebrae and 114 patients in the control group with 150 vertebrae were involved in this study. Changes in compression rate, local kyphotic angle, visual analog scale (VAS) and Oswestry disability index (ODI) scores, conditions of bone cement leakage, refracture of the operated vertebrae, and new adjacent vertebral fractures were examined after KP. In addition, patients in the RA group were divided into different groups according to the value of erythrocyte sedimentation rate (ESR), c-reactive protein (CRP), and whether they were glucocorticoid users or not to evaluate their influence on the outcomes of KP. Results KP procedure significantly improved the compression rate, local kyphotic angle, and VAS and ODI scores in both RA and control groups (p

In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.

The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red–green–blue (RGB), especially three-dimensional (3D) urban morphology information. In addition, the probabilistic methods ignore the potential semantic information of Point of Interest (POI) data. Therefore, we propose an “Image + Text” multimodal data fusion framework for UFZs recognition. To effectively utilize the information of Gaofen-7(GF-7) stereo images, we designed a semi-transfer UFZs recognition model. The transferred model uses the pre-trained model to extract the deep features from RGB images, and a small self-built convolutional network is designed to extract the features from RGB bands, near-infrared (NIR) band, and normalized digital surface model (nDSM) generated by GF-7. Latent Dirichlet allocation (LDA) is employed to extract POI semantic features. The fusion features of the deep features of the GF-7 image and the semantic features of POI are fed into a classifier to identify UFZs. The experimental results show that: (1) The highest overall accuracy of 88.17% and the highest kappa coefficient of 83.91% are obtained in the Beijing Fourth Ring District. (2) nDSM and NIR data improve the overall accuracy of UFZs identification. (3) POI data significantly enhance the recognition accuracy of UFZs, except for shantytowns. This UFZs identification is simple and easy to implement, which can provide a reference for related research. However, considering the availability of POI data distribution, other data with socioeconomic attributes should be considered, and other multimodal fusion strategies are worth exploring in the future.

Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.

The recently identified pathogenic Porcine circovirus type 3 (PCV3) may threaten to reduce the pig population dramatically worldwide. In our previous study, a PCV3-specific monoclonal antibody (mAb-1H11) was successfully applied in immune-histochemistry staining and ELISA, which specifically recognize PCV3 capsid protein in PCV3-positive pig tissues. In the present study, we expressed and purified the soluble sole capsid protein of PCV3. The purified capsid protein was capable of self-assembly into virus-like-particles (VLPs), which is validated by transmission electron microscopy and dynamic light scattering assays. Moreover, the epitope of mAb-1H11 was identified in the CD-loop region (a.a. 72-79) on the VLP surface, which is confirmed by PCV2-PCV3 epitope swapping assay. For the first time, we determined the cryo-EM structure of PCV3-VLP at 8.5 °A resolution that reveals the detailed structural information of PCV3-VLP. In our cryo-EM structure, PCV3-VLP is composed of 60 capsid protein subunits assembled with T = 1 icosahedral symmetry. Consistent to our bio-dot Western blot assay, the structural comparison between PCV3 and PCV2 revealed significant structural differences in the surface-exposed loops, including the CD-loop (a.a. 72-79) and the EF-loop (a.a. 109-131). Our work provides a structural framework for engineering future PCV3 vaccine and diagnosis kits development. [ABSTRACT FROM AUTHOR]