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There is a complex interaction between national land space and transportation. China is accelerating its strategy of building a strong transportation country and territorial spatial planning, which requires evaluating and strengthening the coordination relationship between transport and land space to promote development. Taking the Beijing–Tianjin–Hebei urban agglomeration in China as an example, this study analyze the high-speed transportation construction and land space development process, evaluates the coupling and coordination degree between high-speed transportation and land space, as well as the "development-protection" situation in the years 2000, 2010, and 2020 by means of the coupling and coordination model, spatial autocorrelation analysis, and GIS analysis. The result shows: (1) high-speed transportation construction and territorial space development have been active for a long time, and most of the cities have maintained a high level of increase in these two indicators. In terms of protection indicators, Tangshan and other coastal cities have been lagging behind for a long time without any improvement trend. (2) The coupling and coordination level between high-speed transportation and development is high, with slight weakening over time, with an average annual decrease of less than 0.09%. The change in the coupling coordination level with protection shows the wave dynamics of 'low-middle-low'. (3) In the future, high-speed transportation will have a significant impact on land space in the country for a long time. In this regard, it is suggested to: (1) promote the construction of high-speed transportation for network, systematic, and serial development; (2) accelerate the extension of the high-speed transportation advantages of the existing growth poles of Beijing and Tianjin; and (3) develop Shijiazhuang and Langfang as the new growth poles. This study will promote the integration of transportation and urban planning, which will contribute to the development of high-quality synergistic urban agglomerations around the world. [ABSTRACT FROM AUTHOR]

Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance. [ABSTRACT FROM AUTHOR]

In order to explore the strong heterogeneity of evaporite-carbonate symbolic system, based on core and logging data, this paper clarifies microfacies types of Arab Formation in B Oilfield of Abu Dhabi, and analyzes the reservoir characteristics of various microfacies and the main controlling factors of high-quality reservoirs. Twelve microfacies types (MF1--MF12) can be identified in the Arab Formation. Microfacies types and associations indicate that it is a sedimentary system of Sabha tidal flat-lagoon-barrier beach under the background of limited-evaporation. Microfacies control reservoir quality. MF2 and MF9--MF12 have thicker pore throats, better connectivity, and higher porosity and permeability, making them favorable microfacies types for reservoir development. MF2 and MF10 develop dolomite reservoirs, with the reservoir space mainly composed of intergranular pores, residual intergranular pores and intragranular dissolution pores. Grainstone reservoirs are developed in MF9, MF11 and MF12, with the reservoir space dominated by intergranular (dissolution) pores, mold pores and intragranular dissolution pores. The seismic change of relative sea level causes orderly stacking of sedimentary facies belts in longitudinal direction. The difference of microfacies types and diagenesis between different sedimentary facies belts and within the same sedimentary facies belt is the fundamental reason for the strong heterogeneity of the Arab Formation reservoir. The barrier beach and supratidal are favorable facies belts for the development of high-quality reservoirs. The primary intergranular pores of high-quality reservoirs in the barrier beach facies are well maintained and superimposed with significant early exposure and dissolution, resulting in the generation of secondary pores and the further improvement of pore structure. The development of high-quality reservoirs in the supratidal is controlled by early dolomitization and penecontemporaneous dissolution. Dolomitization improves pore structure, which is conducive to early pore preservation. The early dissolution of dispersed anhydrite produces a large number of secondary pores, significantly improving reservoir physical properties. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k- ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω , while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively. [ABSTRACT FROM AUTHOR]

This article discusses a study conducted to investigate the humoral immune response to the COVID-19 vaccine in patients with psoriasis who are receiving biologics and small molecule medications. The study found that patients treated with different medications had similar levels of neutralizing antibodies (NAbs) after receiving the vaccine. Additionally, the study showed that a 3-dose vaccine regimen was more effective in inducing NAbs production compared to a 2-dose regimen. However, some patients experienced a decrease in NAb titers after the booster-dose vaccination, possibly due to a longer interval between doses or poor compliance. The study acknowledges limitations such as a small sample size and the absence of healthy individuals as controls. [Extracted from the article]

BaTiO3-based ceramics with a positive temperature coefficient of resistivity (PTCR) and different alumina contents were prepared by the solid-state reaction technique. The experimental results show a peculiar 'W'-shaped trend. It is different from the 'U'-shaped trend which decrease first and then increase in the previous research. The room-temperature resistivity of PTCR materials with high alumina (1.6 ~ 5 at%) content decreased anomalously with an increase in the alumina additive. This is due to the generation of new donor defects during the formation of the new phase. The mechanism of alumina in BaTiO3-based ceramics was discussed from the point of defect chemistry and phases, thus explaining the 'W'-type behavior. This finding implies the possibility of significant improvement of the PTC effect of the material by introducing alumina. The addition of 2 at% alumina resulted in PTCR ceramics with a temperature coefficient as high as 35.46%/℃ and a PTCR jump as high as 4.7. [ABSTRACT FROM AUTHOR]