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Effective video monitoring systems require optimization of camera and road network coverage, to exploit fully the hardware and software solutions in smart city traffic applications. Monitoring requirements have grown increasingly diverse as scenes are becoming increasingly complex, thereby transforming the camera and road network coverage optimization issue into a nonlinear, high-dimension, and multi-objective problem. Previous research on this topic however, has focused on a single, specific optimization objective, which may result in invalid optimization results in actual applications. To extend this research, we propose a multi-objective scheduling optimization algorithm for a camera network that addresses the problem of directional road network coverage. In this solution, we incorporate an expanding parameter of main optical axes into particle swarm optimization algorithm. Our new strategy divides the range of main optical axes of all the cameras to control the scheduling number, achieving collaborative optimization of multiple objectives. In a simulated camera and road network, an experiment was designed for evaluating the effectiveness of the proposed method, comparing the distribution of optimization results with the global and local optimal solutions of the true value. A second experiment compared the distribution, performance and running time of the optimization results with different values of expanding parameter of main optical axes. A third experiment compared the performance of the optimization solutions with different values of camera parameters. The results showed that the proposed method can adapt to user application preference, and is effective and robust to schedule and allocate monitoring resources in different scenarios. [ABSTRACT FROM AUTHOR]

Production of higher-fidelity digital elevation models is important; as such models are indispensable components of space data infrastructure. However, loss of terrain features is a constant problem for grid digital elevation models, although these models have already been defined in such a way that their distinct usage as data sources in terrain modeling processing is prohibited. Therefore, in this study, the novel concept-terrain semanteme is proposed to define local space terrain features, and a new process for generating grid digital elevation models based on this new concept is designed. A prototype system is programmed to test the proposed approach; the results indicate that terrain semanteme can be applied in the process of grid digital elevation model generation, and that usage of this new concept improves the digital elevation model fidelity. Moreover, the terrain semanteme technique can be applied for recovery of distorted digital elevation model regions containing terrain semantemes, with good recovery efficiency indicated by experiments. [ABSTRACT FROM AUTHOR]

Kv1.3 is a voltage-gated potassium channel expressed on T cells that plays an important role in T cell activation. Previous studies have shown that blocking Kv1.3 channels in human T cells during activation results in reduced calcium entry, cytokine production, and proliferation. The aim of the present study was to further explore the effects of Kv1.3 blockers on the response of different human T cell subsets under various stimulation conditions. Our studies show that, unlike the immune suppressor cyclosporine A, the inhibitory effect of Kv1.3 blockers was partial and stimulation strength dependent, with reduced inhibitory efficacy on T cells under strengthened anti-CD3/CD28 stimulations. T cell responses to allergens including house dust mites and ragweed were partially reduced by Kv1.3 blockers. The effect of Kv1.3 inhibition was dependent on T cell subsets, with stronger effects on CCR7- effector memory compared to CCR7+ central memory CD4 T cells. Calcium entry studies also revealed a population of CD4 T cells resistant to Kv1.3 blockade. Activation of CD4 T cells was accompanied with an increase in Kv1.3 currents but Kv1.3 transcripts were found to be reduced, suggesting a posttranscriptional mechanism in the regulation of Kv1.3 activities. In summary, Kv1.3 blockers inhibit T cell activation in a manner that is highly dependent on the T cell identity and stimulation strength, These findings suggest that Kv1.3 blockers inhibit T cells in a unique, conditional manner, further refining our understanding of the therapeutic potential of Kv1.3 blockers. [ABSTRACT FROM AUTHOR]