No abstract

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics’ potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

We present a simple link-state routing protocol called DLL-XL that provides packet routing at the link layer for ad-hoc 802.11s-based networks. DLL-XL can be considered as a deployment of the Approximate Link state algorithm XL proposed by Levchenko et al. By building upon the XL algorithm, the DLL-XL routing protocol achieves routing efficiency by suppressing flooding of link-cost changes when appropriate to tradeoff between message flooding overhead and path costs. The correctness of DLL-XL is inherited from the formal correctness of the XL algorithm. We evaluate the performance of DLL-XL by simulation using the ns-2 network simulator.

No abstract

Nowadays, the penetration of wind and solar sources is relatively high in Micro Grid. Wind speed and solar radiation forecasting hardly gives an exact value and leads to the values in intervals. Therefore forecasted output powers of these sources are also in the intervals. The constraint on power balance in Micro Grid has the right-hand-side uncertainty, in the interval. So for Micro-Grid in grid connection mode, the presence of the distributed generations based on wind and solar energy sources makes optimal dispatching problems of distributed generations become an uncertainty problem. The optimal solutions for the lower and upper ends of this interval are the best and the worst optimal solution. This paper proposes to treat the above problem as the optimal problem with two objectives: reach the best and the worst solution. The principle of fuzzy set and the Particle Swarm Optimization algorithm will be applied for solving the multi-objective problem. The final optimal value will belong to an interval. Meanwhile, the output power of the swing generator varies to respond to the uncertainty of wind and solar source power. An example of a low-voltage MG with three distributed generators is considered with two cases: connecting to the utility grid via the circuit breaker and via power controller.