A Platform for Antenna Optimization with Numerical Electromagnetics Code Incorporated with Genetic Algorithms

This thesis investigation presents a unique incorporation of the Method of Moments (MoM) with a Genetic Algorithm (GA). A GA is used in accord with the Numerical Electromagnetics Code, Version 4 (NEC4) to create and optimize typical wire antenna designs, including single elements and arrays. Design parameters for the antenna are defined and encoded into a chromosome composed of a series of numbers. The cost function associated with the specific antenna of interest is what quantifies improvement and, eventually, optimization. This cost function is created and used by the GA to evaluate the performance of a population of antenna designs. The most successful designs of each generation are kept and altered through crossover and mutation. Through the course of generations, convergence upon a best design is attained. The Yagi-Uda and the Log Periodic Dipole Array (LPDA) antennas are the focus of this study. The objectives for each antenna are to maximize the main power gain while minimizing the Voltage Standing Wave Ratio (VSWR) and the antenna\u27s length. Results for the Yagi-Uda exceed previous designs by as much as 40 dB in the main lobe while maintaining respectable length and VSWR values. The improvements made in the LPDA antenna were not as drastic, finding a nominal increase in power gain while truncating original allowance in the length by more than half, along with nominal VSWR values that were close to the ideal value of one. The percentage of bandwidth covered for the frequencies of interest are 8.11% for the Yagi-Uda and 10.7% for the LPDA. GA performance is evaluated and, based on previous results, implemented with real-numbered chromosomes as opposed to the classic binary encoding. This methodology is very robust and is improved upon in this research, all while using a novel approach with an optimization program platform called iSIGHT, developed by Engineous Software

Estimating the health effects of COVID-19-related immunisation disruptions in 112 countries during 2020-30: a modelling study.

BACKGROUND: There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered. METHODS: For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO-UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up. FINDINGS: We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248-134 941) during calendar years 2020-30, largely due to measles. For years of vaccination 2020-30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52-2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249-40 241 202) to 36 410 559 deaths averted (33 515 397-39 241 799). We estimated that catch-up activities could avert 78·9% (40·4-151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037-60 223] of 25 356 [9859-75 073]). INTERPRETATION: Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption. FUNDING: The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation. TRANSLATIONS: For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section