Wireless Mesh Networks (WMN) are a solution with a reasonable cost/benefit ratio when provid-ing broadband internet access. The concept is being used not only in IEEE networks but also in other wireless networks such as cellular networks (4G/5G) and Low Power Wide Area Network (LPWAN). IEEE 802.11s is the facto standard and is well known for working with WMN. The standard defines Hybrid Wireless Mesh Protocol (HWMP) as a layer 2 routing protocol. The met-ric proposed by the protocol is Air Time Link (ALM), and the IEEE 802.11 standard proposes an equation for its calculation. This work proposes a new methodology for the calculation of ALM which has been developed in the following way: through several scripts, which simulate various WMN scenarios in NS-3, collect information in several databases, and then take the data to RStu-dio, where different statistical tests are developed, such as linear models (LM), generalized linear models (GLM), and other statistical tests for both the original and new model proposals. The result is a new methodology for the calculation of metrics, using statistical techniques in the selection and validation of model regressors, presenting an a priori tool to improve the per-formance of the WMS's.

Flaviviruses cause a significant amount of mortality and morbidity, especially in the area where they are endemic. A recent example is the outbreak of Zika virus though out the world. Development of antiviral drugs against different viral targets is as important as development of vaccine. During viral replication, the flavivirus genome is translated as a single polyprotein precursor, which must be cleaved into individual proteins by a complex of the viral protease, NS3, and its cofactor, NS2B. Flavivirus protease is the most attractive target for development of therapeutic antivirals because it is essential for processing of viral polyprotein precursor and generation of functional viral proteins. In this review, we have summarized recent development in drug discovery targeting NS3-NS2B protease of flaviviruses, especially Zika, dengue and West Nile virus.

The non-structural protein NS3/4A protease is a critical factor for hepatitis C virus (HCV) maturation that requires activation by NS4A. Synthetic peptide mutants of NS4A were found to inhibit NS3 function. The bridging from peptide inhibitors to heterocyclic peptidomimetics of NS4A has not been in consideration in literature, and therefore, we decided to explore this strategy to develop a new class of NS3 inhibitors. In this report, a structure-based design approach was used to convert the bound form of NS4A into 1H-imidazole-2,5-dicarboxamide derivatives as first generation peptidomimetics. This scaffold mimics the buried amino acid sequence Ile-25` to Arg-28` at the core of NS4A21`-33` needed to activate the NS3 protease. Some of the synthesized MOC compounds were able to compete with and displace NS4A21`-33` for binding to NS3. For instance, N5-(4-guanidinobutyl)-N2-(n-hexyl)-1H-imidazole-2,5-dicarboxamide (MOC-24) inhibited the binding of NS4A21`-33` with a competition IC50 of 1.9 ± 0.12 µM in a fluorescence anisotropy assay, stabilized the denaturation of NS3 by increasing the aggregation temperature by ΔTagg 0.6 ± 0.140 ℃. MOC-24 also inhibited NS3 protease activity in a fluorometric assay. Molecular dynamics simulations rationalized the structure-activity relationship (SAR) differences between the active MOC-24 and the inactive MOC-26. Our data shows that MOC compounds are possibly the first examples of NS4A peptidomimetics that demonstrated promising activities against NS3 proteins.

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the expansion of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for static and dynamic scenarios, regardless of the number of stations. Moreover, the performance difference is amplified as the number of stations increases, with DQN and DDPG showing a 27% increase in throughput with 50 stations compared to BEB. Furthermore, DQN and DDPG presented similar performances.