With the recent proliferation of CubeSats missions, there is a critical need for space-ready antennas with a desired set of characteristics. In this paper, an S-band wideband, circularly polarized (CP), slot antenna with a co-planar waveguide (CPW) feed is proposed for CubeSat applications. The antenna geometry is presented, and its performance is validated through extensive numerical electromagnetic simulation. The proposed antenna is shown to achieve fractional impedance bandwidth of 28.6?ntered around 3.2 GHz, axial ratio bandwidth (ARBW) of 13.7%, and 6-6.5 dBi gain all while being lower profile and constructed space-proven materials (i.e., Rogers RT/duriod 5880).
The COVID-19 pandemic has caused an increase in healthcare waste (HCW) generation. Mismanagement of waste, a result of archaic, paper-based systems, can pose environmental threats. In this paper, we propose a blockchain based system that tracks and records all steps of HCW management. Our solution records generation of HCW, transportation, and treatment and landfilling.
By 2050, the global population is estimated to increase to about 9.8 billion with over 70% living in the cities. The associated rise in the number of buildings has been considered to be one of the major contributors to energy consumption with its demands being mainly covered by burning fossil fuels. In the fight against climate change, buildings play an essential role as they account for around 40% of global energy consumption. Therefore, researching, designing, and adopting PV systems is essential to help transition towards more sustainable building and infrastructure sectors. A limited number of studies have evaluated the impact of uncertainty in operation patterns on the performance of RE systems. The goal of this research is to present a comprehensive framework to quantify the impact of uncertainty in building operation patterns on the techno-economic performance of PV systems.
In this complex world and rapidly shifting market environment, new product development (NPD) is a significant concern for companies regardless of industry. Notably, because of the continuous change in the demand and customers' needs and technology, the marketplace is considered to be dynamic. For this reason, companies are required to maintain their product and services by keeping them up-to-date and in line with the consumer desire. Even so, nowadays, organizations' willingness to deeply understand their customers is not enough the primary focus must be to address customer needs and requirements and achieve those requirements and exceed them. Consequently, this study aims to contribute and propose an advanced framework for developing a new product by Integrating the Theory of Inventive Problem Solving (TRIZ) into the existing Design for Six Sigma (DFSS) methodology.
An integrated approach based 2D seismic profiles and biostratigraphic data was preformed to provide preliminary results on the tectonic evolution of the sedimentary basins of Abu Dhabi. Three major stratigraphic sequences were identified; Lower Permian-Jurassic rifted-margin, intermediate Cretaceous passive-margin and upper Late Cretaceous-Tertiary active-margin sequences. The tectonic subsidence and uplift analysis by backstripping suggest two rifting phases followed by lithospheric flexure that resulted in formation of the UAE foreland basin. This study provides initial results of an ongoing research investigating tectono-stratigraphy of the basins underlying the oilfields of Abu Dhabi Emirate.
The Ferrar large igneous province was a volcanic event which resulted in the emplacement of a large amount of igneous rocks. It occurred during the Jurassic about 180 million years ago. The petrography of the rocks that were emplaced during this event remain largely understudied. This paper studies the petrography of the Catamaran core which originates from Tasmania, Australia. The core samples will undergo rigorous full rock sample and thin section analysis to determine the petrology. These analyses suggested that the samples were largely basalts. These basalts showcased features such as quartz amygdoles and calcareous lenses indicating hydrothermal alterations and impurities in the magmatic melt.
Loss of containment events are a significant concern for the upstream petroleum sector. The early detection of anthropogenic activities could limit the spread of contamination and immediately mitigate the spills. Microbes are ubiquitous and, as the relative abundance of key microbial community members shift with environmental perturbations (such as oil contamination), microbial communities are useful for environmental monitoring. However, the challenge lies in the lack of tools that could identify features of these microbial communities for this application. Machine learning (ML) approaches offer a promising means to address this challenge. The aim of this study is to establish a means for early oil contamination detection through the implementation of ML, specifically focusing on establishing which ML input (amplicon sequence variants or k-mer representation) data would best serve predictive ML framework for microbial feature detection to inform for oil contamination.
The application of Deep Learning (DL) in metagenomic studies is rapidly increasing due to its identification and predictive ability. This paper looks at utilizing a multilane capsule network (CapsNet) for early recognition of oil perturbation which could be used for environmental monitoring. Comparison between CapsNet and classic machine learning techniques (random forest, support vector machine, and multi-layer perceptron) would be conducted to establish which model best suits the aim of the study. Ideally, a tool that is cost-efficient, fast, and accurate is preferred as immediate identification of such perturbations is essential for rapid mitigation of spills to prevent further damage and harm. Thus, these are the aspects that would be evaluated when comparing different approaches.
