Grid information service (GIS) provides data about distributed resources within a Grid. A comprehensive performance investigation of a GIS can aid in detecting potential bottlenecks, advice in deployment, and help improve future system development. Targeting the proposed DHT-based GIS architecture with data caching, we designed a set of experiments addressing performance issues and conducted tests against the GIS deployed in a small scaled testbed. By analyzing the performance data collected from the experiments, we obtained a quantitative view of the behaviour of the GIS. It will help further the optimization and enhancement of the GIS architecture.
(1) Background: The nursing of the elderly has received more and more attention, especially the nursing of urination and defecation for the elderly. (2) Purpose: Design an excretion nursing equipment that can accurately identify and deal with urine and stool. (3) Methods: In this paper, based on the analysis of the requirements of excretion nursing equipment, a split mechanical design method and a modular control method are used to design the equipment. The Dempster–Shafer (D-S) evidence theory is used in the identification of urine and stool. (4) Results: The excretion nursing equipment designed in this paper works well according to functional test, and the success rate of stool and urine identification method using D-S evidence theory is 20% higher than that of traditional methods, reaching 90%. (5) Conclusions: The urine and stool recognition and detection algorithm based on the D-S evidence theory used in this paper can improve the recognition accuracy of traditional detection methods, and the designed excretion nursing equipment can realize the function of excretion care for patients.
Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However, training these two stages separately leads to significant data bias in the inference phase, thus affecting the quality of reconstructed results. We introduce a unified 3D generation framework, named Ouroboros3D, which integrates diffusion-based multi-view image generation and 3D reconstruction into a recursive diffusion process. In our framework, these two modules are jointly trained through a self-conditioning mechanism, allowing them to adapt to each other's characteristics for robust inference. During the multi-view denoising process, the multi-view diffusion model uses the 3D-aware maps rendered by the reconstruction module at the previous timestep as additional conditions. The recursive diffusion framework with 3D-aware feedback unites the entire process and improves geometric consistency.Experiments show that our framework outperforms separation of these two stages and existing methods that combine them at the inference phase. Project page: https://costwen.github.io/Ouroboros3D/
Recent advancements in controllable human image generation have led to zero-shot generation using structural signals (e.g., pose, depth) or facial appearance. Yet, generating human images conditioned on multiple parts of human appearance remains challenging. Addressing this, we introduce Parts2Whole, a novel framework designed for generating customized portraits from multiple reference images, including pose images and various aspects of human appearance. To achieve this, we first develop a semantic-aware appearance encoder to retain details of different human parts, which processes each image based on its textual label to a series of multi-scale feature maps rather than one image token, preserving the image dimension. Second, our framework supports multi-image conditioned generation through a shared self-attention mechanism that operates across reference and target features during the diffusion process. We enhance the vanilla attention mechanism by incorporating mask information from the reference human images, allowing for the precise selection of any part. Extensive experiments demonstrate the superiority of our approach over existing alternatives, offering advanced capabilities for multi-part controllable human image customization. See our project page at https://huanngzh.github.io/Parts2Whole/.
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Abstract Background Dual specificity phosphatase 22 (DUSP22), also named as Jun N‐terminal kinase pathway associated phosphatase recently, is reported to be closely engaged in immune and inflammation regulation. This study aimed to investigate the interaction between synovium DUSP22 and serum DUSP22 levels and to explore their correlation with rheumatoid arthritis (RA) risk, inflammation, and disease activity. Methods Synovium and serum samples from 42 RA patients with knee involvement underwent arthroscopy, and 20 knee trauma patients were collected. Besides, serum samples from 40 healthy controls were also obtained. Synovium DUSP22 expression was detected by reverse transcription quantitative polymerase chain reaction, while serum DUSP22 level was detected by enzyme‐linked immunosorbent assay. Results Synovium DUSP22 level was greatly decreased in RA patients compared to trauma controls ( p < 0.001), and it was negatively correlated with tender joint count (TJC) ( r = −0.318, p = 0.040), C‐reactive protein (CRP) ( r = −0.330, p = 0.033), and Lysholm score ( r = −0.423, p = 0.005) in RA patients. Serum DUSP22 level was lowest in RA patients, followed by trauma controls, then highest in healthy controls ( p < 0.001). Serum DUSP22 level was negatively associated with TJC ( r = −0.438, p = 0.004), swollen joint count (SJC) ( r = −0.372, p = 0.015), CRP ( r = −0.391, p = 0.011), and disease activity score in 28 joints (DAS28 ESR ) score ( r = −0.406, p = 0.008), and it increased after treatment ( p = 0.001) in RA patients. In addition, serum DUSP22 level positively related to synovium DUSP22 level in RA patients ( r = 0.394, p = 0.010). Conclusion Synovium and serum DUSP22 are intercorrelated and insufficiently expressed in RA patients; meanwhile, their deficiency correlates with increased systemic inflammation, disease activity, and joint dysfunction.
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