Role of Neural Network in Mobile Ad Hoc Networks for Mobility Prediction

The MANETs differ from traditional networks in a lot of aspects, such as high channel error rates, unusual channel features, frequent link breaks, and intense link layer contentions. These characteristics significantly reduce network connectivity, which affects overall network latency, network overhead, network throughput (i.e. the amount of data successfully transferred via a MANETs in a predetermined amount of time), and packet delivery ratio (PDR). For effective network resources preparation and organization in MANETs, the mobility forecast of MN and units is essential. This effectiveness would allow for better planning and higher overall quality - of - service, including reliable facility availability and efficient management of energy. In this research, we suggest to use ELMs, which are renowned for their ability to approximate anything, to model and forecast the mobility of each node in a MANET. Mobility-aware topology control methods and location-assisted routing both leverage mobility prediction in MANETs. It is assumed that each MN taking part in these protocols is aware of its current mobility data, including location, velocity, and movements direction angle. This approach predicts both the locations of future nodes and the distances between subsequent nodes. The interaction or relationship between the Cartesian longitude and latitude of the erratic nodes is better captured by ELMs than by multilayer perceptron’s, resulting in mobility prediction that is based on several conventional mobility models that is more precise and realistic

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security

An Efficient and Reliable Data Transmission Service using Network Coding Algorithms in Peer-to-Peer Network

Network coding is a progressive enhancement in natural network routing that increases throughput and reliability for unicast, multicast, and even broadcast transmissions. P2P networks are ideal for implementing network coding algorithms for two reasons: I. A P2P network's topology isn't predetermined. As a result, designing a topology that is compatible with the network coding algorithm is much easier. II. Every peer is an end host in this network.  As a result, instead of saving and sending the message, complex network coding operations, such as encoding and decoding, are now easier to perform. The objective of this work is to use the best features of network coding algorithms and properly apply them to P2P networks to create an efficient and reliable data transmission service. The goal of the network coding algorithm is to make better use of network resources and thus increase P2P network capacity. An encoding algorithm that enables an intermediate peer to produce output messages by encoding (that is, computing a function of the data it receives. The decoder's role is to obtain enough encoded packets so that the original information can be recovered. This research work has measured an amount of hypothetical and applied consequences in which the network coding procedure or a variation of it is used to improve performance parameters such as throughput and reliability in P2P network data transmission based on network coding. The comparison of data transmission between network routing and network coding algorithms was the main focus of this paper.  According to our simulations, the new network coding systems can reach 15% to 20% upper throughput than supplementary P2P network routing systems

Web3 Chain Authentication and Authorization Security Standard (CAA)

Web3 is the next evolution of the internet, which uses blockchains, cryptocurrencies, and NFTs to return ownership and authority to the consumers. The potential of Web3 is highlighted by the creation of decentralized applications (dApps), which are more secure, transparent, and tamper-proof than their centralized counterparts, allowing for new business models that were previously impossible on the traditional internet.Web3 also focuses on user privacy, where users have more control over their personal data and can choose to share only what they want. The emergence of Web3 represents an exciting new frontier in blockchain technology, and its focus on decentralization, user privacy, and trustless systems has the potential to transform the way we interact with the internet.Web3 authentication is required for enhanced security, increased privacy, and simplified user interface. Traditional login procedures and an authorization flow using web3 authentication work together seamlessly. However, there are several challenges associated with Web3, including scalability and regulatory issues. Chain Authentication and Authorization (CAA) is a multi-layer security mechanism that allows users to choose the security layer that suits them, just like a heavy iron chain, where the user and CAA developers act as blacksmith and form their security protocol that suits them. CAA is a solution to the challenges associated with Web3 authentication and authorization, and it focuses on creating a secure and decentralized authentication and authorization system that is scalable, flexible, and user-friendly

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

A 2<SUP>7-3</SUP> fractional factorial optimization of polybenzimidazole based membrane electrode assemblies for H<SUB>2</SUB>/O<SUB>2</SUB> fuel cells

We describe the usefulness of a statistical fractional factorial design to obtain consistent and reproducible behavior of a membrane-electrode-assembly (MEA) based on a phosphoric acid (PA) doped polybenzimidazole (PBI) membrane, which allows a H<SUB>2</SUB>/O<SUB>2</SUB> fuel cell to operate above 150°C. Different parameters involved during the MEA fabrication including the catalyst loading, amount of binder, processing conditions like temperature and compaction load and also the amount of carbon in the gas diffusion layers (GDL) have been systematically varied according to a 2<SUP>7-3</SUP> fractional factorial design and the data thus obtained have been analyzed using Yates's algorithm. The mean effects estimated in this way suggest the crucial role played by carbon loading in the gas diffusion layer, hot compaction temperature and the binder to catalyst ratio in the catalyst layer for enabling continuous performance. These statistically designed electrodes provide a maximum current density and power density of 1,800 mA cm<SUP>-2</SUP> and 280 mW cm<SUP>-2</SUP>, respectively, at 160°C using hydrogen and oxygen under ambient pressure

Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

Background Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data. Methods For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations. Findings We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5–MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand. Interpretation Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments. Funding Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)