Parametric Design, Manufacturing and Simulation of On-Demand Fixed Wing UAVs

AIAA Scitech 2021 Conference Paper.As the market for Unmanned Aerial Vehicles (UAVs) continues to expand, an unfulfilled need has been identified for tailor-made solutions leveraging an end-to-end process for the design and manufacture of the vehicle. The use of computer aided design combined with new manufacturing techniques allows small UAVs to be parametrically sized and quickly prototyped and deployed. This parametrization technique can be used throughout the entire design process to create optimized, attritable, on-demand solutions that can be adapted to evolving customer requirements. High-level requirements are mapped to quantitative design constraints and an automated process uses these constraints to design and manufacture a vehicle within a specified amount of time. The proposed framework is demonstrated with the generation of a fixed wing UAV solution for the detection and tracking of wildlife in remote areas. National Parks seek to prevent illegal poaching but often lack either the resources to monitor endangered animals, or the budget to purchase UAVs specially designed for wildlife tracking. First, mission requirements are identified and define a design space from which an optimal design point is selected. This design point sizes a UAV model, which is then optimized to minimize manufacturing time with the objective to yield a ready-to-fly solution within 48 hours. A flight simulation of the mission is then performed to ensure that the vehicle will fly as designed. Structural limitations of the UAV are accounted for and linked to parameters of the flight control algorithm to ensure that the UAV can safely fly its mission

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

Applications and Techniques for Fast Machine Learning in Science.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

The parrotfish–coral relationship: refuting the ubiquity of a prevailing paradigm

It has become almost paradigmatic in the coral reef literature that fishing-induced reductions of parrotfish abundance cause benthic phase shifts from coral to macroalgal dominance. This study examined the alternatives of top-down control of the benthos by parrotfish density and bottom-up control of parrotfish density by the benthos at four Philippine islands in a long-term (7.5-30 years) "natural experiment". No-take marine reserves (NTMRs) demonstrated that fishing reduced parrotfish density significantly at two islands (Sumilon, Mantigue), but not significantly at two other islands (Apo, Selinog). There was no evidence that cover of hard coral decreased, nor macroalgal cover increased, in fished areas relative to NTMRs, no evidence that parrotfish density affected hard coral cover significantly, and thus no evidence of top-down, fishing-induced benthic phase shifts at all four islands. There was, however, compelling evidence that benthos (cover of dead substrata and hard coral) exerted strong bottom-up control on parrotfish density. This bottom-up control was demonstrated most clearly by major environmental disturbances (e.g. typhoons, coral bleaching) that changed benthic habitat and, subsequently, parrotfish density. As hard coral cover declined (and cover of dead substratum increased), parrotfish density increased and vice versa. This response occurred in both major parrotfish feeding guilds (scrapers and excavators). This long-term study on heavily fished coral reefs suggests that the benthos drives the parrotfish, not the other way around. The paradigm of fishing-induced benthic phase shifts may not apply to all coral reefs at all times. Multiple drivers of benthic change on coral reefs should always be considered

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

Analytical methods for lignocellulosic biomass structural polysaccharides

The use of lignocellulosic biomass has been postulated as a potential pathway toward diminishing global dependence on nonrenewable sources of chemicals and fuels. Before a specific feedstock can be selected for biochemical conversion into biofuels and bio-based chemicals, it must first be characterized to evaluate the chemical composition of the cell walls. Polysaccharides, specifically cellulose and hemicellulose, are often the focal point of these appraisals, since these constituents are the dominant substrates converted into monomeric sugars like glucose and xylose. These monosaccharides can be transformed, using microorganisms like yeast, into substances such as ethanol. Plant species containing abundant polysaccharides are highly desirable, as higher quantities of sugars should translate into larger end-product yields. Given the vast pool of potential feedstocks, qualitative and quantitative analytical methods are needed to assess cell wall polysaccharides. Many of these tools, such as wet chemical and chromatographic techniques, have been ubiquitously used for some time. Shortcomings in these analyses, however, prevent their usage in screening large sample sets for quintessential, high-yield, fuel-producing traits. This chapter briefly summarizes how analytical spectroscopy can lessen some of these limitations and how it has been utilized for polysaccharide analysis