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Abstract ContextUnoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. ObjectivesThe review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. MethodsWe conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. ResultsWe summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. ConclusionWe observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics. 

As interest in programming as a major grows, instructors must accommodate more students in their programming courses. One particularly challenging aspect of this growth is providing quality assistance to students during in-class and out-of-class programming exercises. Prior work proposes using instructor dashboards to help instructors combat these challenges. Further, the introduction of ChatGPT represents an exciting avenue to assist instructors with programming exercises but needs a delivery method for this assistance. We propose a revision of a current instructor dashboard Assistant Dashboard Plus that extends an existing dashboard with two new features: (a) identifying students in difficulty so that instructors can effectively assist them, and (b) providing instructors with pedagogically relevant groupings of students’ exercise solutions with similar implementations so that instructors can provide overlapping code style feedback to students within the same group. For difficulty detection, it uses a state-of-the-art algorithm for which a visualization has not been created. For code clustering, it uses GPT. We present a first-pass implementation of this dashboard 

When irradiated with blue light in the presence of a Lewis base (L), [CpW(CO) 3 ] 2 undergoes metal–metal bond cleavage followed by a disproportionation reaction to form [CpW(CO) 3 L] + and [CpW(CO) 3 ] − . Here, we show that in the presence of pyridinium tetrafluoroborate, [CpW(CO) 3 ] − reacts further to form a metal hydride complex CpW(CO) 3 H. The rection was monitored through in situ photo 1 H NMR spectroscopy experiments and the mechanism of light-driven hydride formation was investigated by determining quantum yields of formation. Quantum yields of formation of CpW(CO) 3 H correlate with I −1/2 (I = photon flux on our sample tube), indicating that the net disproportionation of [CpW(CO) 3 ] 2 to form the hydride precursor [CpW(CO) 3 ] − occurs primarily through a radical chain mechanism. 

Fecal contamination is a significant source of water quality impairment globally. Aquatic ecosystems can provide an important ecosystem service of fecal contamination removal. Understanding the processes that regulate the removal of fecal contamination among river networks across flow conditions is critical. We applied a river network model, the Framework for Aquatic Modeling in the Earth System (FrAMES-Ecoli), to quantify removal of fecal indicator bacteria by river networks across flow conditions during summers in a series of New England watersheds of different characteristics. FrAMES-Ecoli simulates sources, transport, and riverine removal of Escherichia coli (E. coli). Aquatic E. coli removal was simulated in both the water column and the hyporheic zone, and is a function of hydraulic conditions, flow exchange rates with the hyporheic zone, and die-off in each compartment. We found that, at the river network scale during summers, removal by river networks can be high (19–99%) with variability controlled by hydrologic conditions, watershed size, and distribution of sources in the watershed. Hydrology controls much of the variability, with 68–99% of network scale inputs removed under base flow conditions and 19–85% removed during storm events. Removal by the water column alone could not explain the observed pattern in E. coli, suggesting that processes such as hyporheic removal must be considered. These results suggest that river network removal of fecal indicator bacteria should be taken into consideration in managing fecal contamination at critical downstream receiving waters. 

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’. 

The economic production and integration of nanomaterial-based wearable energy storage devices with mechanically-compliable form factors and reliable performance will usher in exciting opportunities in emerging technologies such as consumer electronics, pervasive computing, human–machine interface, robotics, and the Internet of Things. Despite the increased interests and efforts in nanotechnology-enabled flexible energy storage devices, reducing the manufacturing and integration costs while continuously improving the performance at the device and system level remains a major technological challenge. The inkjet printing process has emerged as a potential economic method for nanomanufacturing printed electronics, sensors, and energy devices. Nevertheless, there have been few reports reviewing the scalable nanomanufacturing of inkjet printed wearable energy storage devices. To fill this gap, here we review the recent advances in inkjet printed flexible energy storage technologies. We will provide an in-depth discussion focusing on the materials, manufacturing process integration, and performance issues in designing and implementing the inkjet printing of wearable energy storage devices. We have also compiled a comprehensive list of the reported device technologies with the corresponding processing factors and performance metrics. Finally, we will discuss the challenges and opportunities associated with related topics. The rapid and exciting progress achieved in many emerging and traditional disciplines is expected to lead to more theoretical and experimental advances that would ultimately enable the scalable nanomanufacturing of inkjet printed wearable energy storage devices.