A revised list of seaweeds from the Red Sea (1756–2020)

A taxonomic list of macro marine algae (seaweeds) described in the literature for the Red Sea during the years 1756–2020 is presented. The list was prepared using existing published studies, local monitoring reports, as well as “grey” or unpublished lists of seaweeds for the area. Altogether, we examined more than 300 publications and compiled more than 900 taxonomic names, of which 576 correspond to valid species, whilst 355 names were considered synonyms for these species. The phylum Chlorophyta (green seaweeds) was represented by 37 currently accepted genera and 133 species (including 74 species synonyms). The phylum Ochrophyta (Phaeophyceae only; brown seaweeds) was represented by 52 genera, 157 species and 99 synonyms; and the phylum Rhodophyta (red seaweeds) by 130 genera, 286 species and 182 synonyms. The brown seaweed Sargassum appears to be a particularly biodiverse genus in the area represented by 58 species and 26 synonyms. Our study shows the inconsistency and lack of long-term taxonomic studies and recent molecular investigations of seaweeds from nearly the whole Red Sea.

Modelling COVID-19 contagion: risk assessment and targeted mitigation policies

We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies.

CLUSTER BASED ROUTING PROTOCOLS IN WSN SENSOR

Over the past few years we can observe the WSNs or Wireless Sensor Network applications in various fields increasing immensely. The energy efficiency, network lifetime and clustering process prime goal is the working network's optimization is the focus of many of the routing algorithm. Keeping in mind the network homogeneity, for network performance reinforcement we suggest instead of single path to use multiple paths. For WSNs, Reinforcement Intelligence Routing Protocol (RIRP)[1]. In the multihop wireless sensor networks an efficient and effective method for security improvements local monitoring has worked well [2]. But taking in consideration the power consumption in the current practice of local monitoring is costly. For ensuring long-lived operations in the sensor network reinforcement intelligent routing protocol is critical [3]. For ensuring both the aspects improvement in security and long-lived operations, the development of mechanism that is effective and incorporated with the Reinforcement protocol is an open problem. With the help of local monitoring to solve this issue, section of the traffic going in and out of its neighbors is supervised by each node to keep a check on any suspicious behavior like unlikely long delays in packet forwarding [4]. To integrate the existing reinforcement protocol of the network and without any niggardly in the consumption of energy in the local monitoring with the help of a protocol [5]. In comparison to other protocols in this protocol the region of instability starts later. At a constant rate the nodes of the RIRP or Reinforcement Intelligent Routing Protocol dies. Few problems such as cluster head selection process, network lifetime and network stability are evaluated and worked in the technique proposed here [6]. To reduce the overload consumption as much as possible the nodes switches in between the active and sleep mode.