A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable

Recently, several algorithms have been proposed to solve the problem of target coverage in wireless sensor networks (WSNs). A conventional assumption is that sensors have a single power level (i.e., fixed sensing range); however, in real applications, sensors might have multiple power levels, which determines different sensing ranges and, consequently, different power consumptions. Accordingly, one of the most important problems in WSNs is to monitor all the targets in a specific area and, at the same time, maximize the network lifetime in a network in which sensors have multiple power levels. To solve the problem, this paper proposes a learning-automata based algorithm equipped with a pruning rule. The proposed algorithm attempts to select a number of sensor nodes with minimum energy consumption to monitor all the targets in the network. To investigate the efficiency of the proposed algorithm, several simulations were conducted, and the obtained results were compared with those of two greedy-based algorithms. The results showed that, compared to the greedy-based algorithms, the proposed learning automata-based algorithm was more successful in prolonging the network lifetime and constructing higher number of cover sets

Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime

Molecular detection of multidrug-resistant Pseudomonas aeruginosa of different avian sources with pathogenicity testing and in vitro evaluation of antibacterial efficacy of silver nanoparticles against multidrug-resistant P. aeruginosa

ABSTRACT: Pseudomonas aeruginosa (P. aeruginosa) is a serious zoonotic pathogen threaten the poultry industry causing severe economic losses therefor, this study aimed to isolation, phenotypic, molecular identification of P. aeruginosa from different avian sources (chickens, turkey, pigeons, table eggs, and dead in shell chicken embryos), from different Egyptian governorates (Giza, Qalubia, Beheira, El-Minya, and Al-Sharqia) with applying of antibiotic sensitivity test on all P. aeruginosa isolates. Highly resistant isolates (n = 49) were subjected to molecular identification of P. aeruginosa with detection of resistant genes including carbapenemase-encoding genes blaKPC, blaOXA-48, and blaNDM. On the base of molecular results, a highly resistant P. aeruginosa strain was tested for its pathogenicity on day old specific pathogen free (SPF) chicks. Also, in vitro experiment was adopted to evaluate the efficacy of silver nanoparticles (Ag-NPs) against highly antibiotic-resistant P. aeruginosa strains. The overall isolation percentage was from all examined samples were 36.2% (571/1,576) representing 45.2% (532/1,176) from different birds' tissues and 39/400 (9.7%) from total egg samples. Some of isolated strains showed multidrug resistance (MDR) against kanamycin, amoxicillin, amoxicillin-clavulanic acid, neomycin, chloramphenicol, vancomycin, cefotaxime clavulanic acid, lincomycin-spectinomycin, co-trimoxazole, cefoxitin, gentamycin, and doxycycline. These MDR strains were also molecularly positive for ESBL and carbapenemase-encoding genes. MDR strain showed high pathogenicity with histopathological alterations in different organs in challenged birds. Main histopathological lesions were necrosis of hepatocytes, renal tubular epithelium, and heart muscle bundles. The MDR strain showed in vitro sensitivity to Ag-NPs. In conclusion, MDR P. aeruginosa is a serious pathogen causing high morbidity, mortality, and pathological tissue alterations. Ag NPs revealed a promising in vitro antimicrobial sensitivity against MDR P. aeruginosa and further in vivo studies were recommended

Incidence, household transmission, and neutralizing antibody seroprevalence of Coronavirus Disease 2019 in Egypt: Results of a community-based cohort.

SARS-CoV-2 virus is transmitted in closed settings to people in contact with COVID-19 patients such as healthcare workers and household contacts. However, household person-to-person transmission studies are limited. Households participating in an ongoing cohort study of influenza incidence and prevalence in rural Egypt were followed. Baseline enrollment was done from August 2015 to March 2017. The study protocol was amended in April 2020 to allow COVID-19 incidence and seroprevalence studies. A total of 290 households including 1598 participants were enrolled and followed from April to October 2020 in four study sites. When a participant showed respiratory illness symptoms, a serum sample and a nasal and an oropharyngeal swab were obtained. Swabs were tested by RT-PCR for SARS-CoV-2 infection. If positive, the subject was followed and swabs collected on days three, six, nine, and 14 after the first swab day and a serum sample obtained on day 14. All subjects residing with the index case were swabbed following the same sampling schedule. Sera were collected from cohort participants in October 2020 to assess seroprevalence. Swabs were tested by RT-PCR. Sera were tested by Microneutralization Assay to measure the neutralizing antibody titer. Incidence of COVID-19, household secondary attack rate, and seroprevalence in the cohort were determined. The incidence of COVID-19 was 6.9% and the household secondary attack rate was 89.8%. Transmission within households occurred within two-days of confirming the index case. Infections were asymptomatic or mild with symptoms resolving within 10 days. The majority developed a neutralizing antibody titer by day 14 post onset. The overall seroprevalence among cohort participants was 34.8%. These results suggest that within-household transmission is high in Egypt. Asymptomatic or mild illness is common. Most infections seroconvert and have a durable neutralizing antibody titer