Agent-based resource management for grid computing

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capability. An ideal grid environment should provide access to the available resources in a seamless manner. Resource management is an important infrastructural component of a grid computing environment. The overall aim of resource management is to efficiently schedule applications that need to utilise the available resources in the grid environment. Such goals within the high performance community will rely on accurate performance prediction capabilities. An existing toolkit, known as PACE (Performance Analysis and Characterisation Environment), is used to provide quantitative data concerning the performance of sophisticated applications running on high performance resources. In this thesis an ASCI (Accelerated Strategic Computing Initiative) kernel application, Sweep3D, is used to illustrate the PACE performance prediction capabilities. The validation results show that a reasonable accuracy can be obtained, cross-platform comparisons can be easily undertaken, and the process benefits from a rapid evaluation time. While extremely well-suited for managing a locally distributed multi-computer, the PACE functions do not map well onto a wide-area environment, where heterogeneity, multiple administrative domains, and communication irregularities dramatically complicate the job of resource management. Scalability and adaptability are two key challenges that must be addressed. In this thesis, an A4 (Agile Architecture and Autonomous Agents) methodology is introduced for the development of large-scale distributed software systems with highly dynamic behaviours. An agent is considered to be both a service provider and a service requestor. Agents are organised into a hierarchy with service advertisement and discovery capabilities. There are four main performance metrics for an A4 system: service discovery speed, agent system efficiency, workload balancing, and discovery success rate. Coupling the A4 methodology with PACE functions, results in an Agent-based Resource Management System (ARMS), which is implemented for grid computing. The PACE functions supply accurate performance information (e. g. execution time) as input to a local resource scheduler on the fly. At a meta-level, agents advertise their service information and cooperate with each other to discover available resources for grid-enabled applications. A Performance Monitor and Advisor (PMA) is also developed in ARMS to optimise the performance of the agent behaviours. The PMA is capable of performance modelling and simulation about the agents in ARMS and can be used to improve overall system performance. The PMA can monitor agent behaviours in ARMS and reconfigure them with optimised strategies, which include the use of ACTs (Agent Capability Tables), limited service lifetime, limited scope for service advertisement and discovery, agent mobility and service distribution, etc. The main contribution of this work is that it provides a methodology and prototype implementation of a grid Resource Management System (RMS). The system includes a number of original features that cannot be found in existing research solutions

Progressively Dual Prior Guided Few-shot Semantic Segmentation

Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples. Currently, few-shot segmentation methods mainly focus on leveraging foreground information without fully utilizing the rich background information, which could result in wrong activation of foreground-like background regions with the inadaptability to dramatic scene changes of support-query image pairs. Meanwhile, the lack of detail mining mechanism could cause coarse parsing results without some semantic components or edge areas since prototypes have limited ability to cope with large object appearance variance. To tackle these problems, we propose a progressively dual prior guided few-shot semantic segmentation network. Specifically, a dual prior mask generation (DPMG) module is firstly designed to suppress the wrong activation in foreground-background comparison manner by regarding background as assisted refinement information. With dual prior masks refining the location of foreground area, we further propose a progressive semantic detail enrichment (PSDE) module which forces the parsing model to capture the hidden semantic details by iteratively erasing the high-confidence foreground region and activating details in the rest region with a hierarchical structure. The collaboration of DPMG and PSDE formulates a novel few-shot segmentation network that can be learned in an end-to-end manner. Comprehensive experiments on PASCAL-5i and MS COCO powerfully demonstrate that our proposed algorithm achieves the great performance

MicroRNA-519a promotes proliferation and inhibits apoptosis of hepatocellular carcinoma cells by targeting FOXF2

AbstractRecent studies report that microRNA-519a (miR-519a) is a novel oncomir, which facilitates the onset and progression of human cancers. However, the clinical significance of miR-519a and its functional role and underlying mechanisms in hepatocellular carcinoma (HCC) are poorly investigated. In the present study, elevated expression of miR-519a was observed in HCC tissues compared with adjacent non-tumor tissues. The increased level of miR-519a expression was significantly correlated with adverse clinical features of HCC including hepatitis B virus (HBV) infection, large tumor size, cirrhosis and advanced tumor-node-metastasis tumor stage. Furthermore, high expression of miR-519a was prominently associated with a poorer 5-year overall survival and recurrence-free survival of HCC patients. Gain- and loss-of function experiments showed that miR-519a overexpression enhanced proliferation and reduced apoptosis of Huh7 cells. By contrast, miR-519a knockdown inhibited SMMC-7721 cell proliferation and induced apoptosis. Importantly, up-regulation of miR-519a reduced the expression of FOXF2 mRNA and protein in Huh7 cells, while down-regulation of miR-519a resulted in increased expression of FOXF2 in SMMC-7721 cells. An inverse correlation between mRNA levels of miR-519a and FOXF2 was observed in HCC tissues. Thus, Forkhead box F2 (FOXF2) was identified as a downstream target of miR-519a in HCC. Mechanistically, the effects of miR-519a knockdown on SMMC-7721 cells were abrogated by FOXF2 repression. In conclusion, miR-519a is a novel prognostic predictor for HCC patients and it may potentiate proliferation and inhibits apoptosis of HCC cells by targeting FOXF2

The determinants of public acceptance of telemedicine apps: an innovation diffusion perspective

With the rapid advancement of information technology, telemedicine apps have gradually become an indispensable tool for providing patients with more convenient, efficient, and accessible healthcare services. However, the successful implementation of these apps largely depends on widespread acceptance among the public. To thoroughly investigate the factors influencing the public’s acceptance of these apps and the relationships between these factors, this study developed a theoretical model based on the Diffusion of Innovation theory and the Theory of Perceived Value. To validate this model, we conducted a survey of 387 residents in Beijing, China, and employed structural equation modeling to analyze the collected data. The research findings indicate that attributes of innovation diffusion, including relative advantage, compatibility, complexity, trialability, and observability, significantly and positively influence the public’s perceived value. Particularly noteworthy is that perceived value partially mediates the relationship between innovation attributes and public acceptance, emphasizing the crucial role of perceived value in the public decision-making process. This study employed a theory-driven approach to elucidate the acceptance of telemedicine apps and offers fresh insights into the existing literature. By integrating the research paradigms of innovation diffusion and customer perceived value, we provide a coherent explanation of how individual cognitive processes lead to acceptance behavior. In summary, this research enriches the existing theoretical studies on the acceptance of telemedicine apps and holds positive implications for healthcare practice