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Stridulation is used by male katydids to produce soundviathe rubbing together of their specialised forewings, either by sustained or interrupted sweeps of the file producing different tones and call structures. There are many species of Orthoptera that remain undescribed and their acoustic signals are unknown. This study aims to measure and quantify the mechanics of wing vibration, sound production and acoustic properties of the hearing system in a new genus of Pseudophyllinae with taxonomic descriptions of two new species. The calling behaviour and wing mechanics of males were measured using micro-scanning laser Doppler vibrometry, microscopy, and ultrasound sensitive equipment. The resonant properties of the acoustic pinnae of the ears were obtainedviaμ-CT scanning and 3D printed experimentation, and numerical modelling was used to validate the results. Analysis of sound recordings and wing vibrations revealed that the stridulatory areas of the right tegmen exhibit relatively narrow frequency responses and produce narrowband calls between 12 and 20 kHz. As in most Pseudophyllinae, only the right mirror is activated for sound production. The acoustic pinnae of all species were found to provide a broadband increased acoustic gain from ~40–120 kHz by up to 25 dB, peaking at almost 90 kHz which coincides with the echolocation frequency of sympatric bats. The new genus, namedSatizabalusn. gen., is here derived as a new polytypic genus from the existing genusGnathoclita, based on morphological and acoustic evidence from one described (S. sodalisn. comb.) and two new species (S. jorgevargasin. sp. andS. haucan. sp.). Unlike most Tettigoniidae,Satizabalusexhibits a particular form of sexual dimorphism whereby the heads and mandibles of the males are greatly enlarged compared to the females. We suggest thatSatizabalusis related to the genusTrichotettix, also found in cloud forests in Colombia, and not toGnathoclita. 

ABSTRACT Spatially resolved images of debris discs are necessary to determine disc morphological properties and the scattering phase function (SPF) thatantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris discs around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris discs in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disc forward modelling in order to obtain the highest-quality image reductions and constrain disc morphological properties of eight debris discs imaged by the Gemini Planet Imager at H-band with a consistent and uniformly applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended discs that may have resulted from substellar companion perturbations, along with a tentative positive trend in disc eccentricity with relative disc width. We also find that using a common SPF can achieve reasonable model fits for discs that are axisymmetric and asymmetric when fitting models to each side of the disc independently, suggesting that scattering behaviour from debris discs may be similar to Solar system dust. 

The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings. 

The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatiotemporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities. 

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-os between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of lockdown. Sectors that are worst hit are not the labor-intensive sectors such as Agriculture and Construction, but the ones with high valued jobs such as Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown. 

Abstract Bonding in the ground state of C $${}_{2}$$ 2 is still a matter of controversy, as reasonable arguments may be made for a dicarbon bond order of $$2$$ 2 , $$3$$ 3 , or $$4$$ 4 . Here we report on photoelectron spectra of the C $${}_{2}^{-}$$ 2 − anion, measured at a range of wavelengths using a high-resolution photoelectron imaging spectrometer, which reveal both the ground $${X}^{1}{\Sigma}_{\mathrm{g}}^{+}$$ X 1 Σ g + and first-excited $${a}^{3}{\Pi}_{{\mathrm{u}}}$$ a 3 Π u electronic states. These measurements yield electron angular anisotropies that identify the character of two orbitals: the diffuse detachment orbital of the anion and the highest occupied molecular orbital of the neutral. This work indicates that electron detachment occurs from predominantly $$s$$ s -like ( $$3{\sigma}_{\mathrm{g}}$$ 3 σ g ) and $$p$$ p -like ( $$1{\pi }_{{\mathrm{u}}}$$ 1 π u ) orbitals, respectively, which is inconsistent with the predictions required for the high bond-order models of strongly $$sp$$ s p -mixed orbitals. This result suggests that the dominant contribution to the dicarbon bonding involves a double-bonded configuration, with 2 $$\pi$$ π bonds and no accompanying $$\sigma$$ σ bond. 

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID- 19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines. 

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel agent-based modeling approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatio-temporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is signi ficantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56{110k infections, 3.2{5.4k hospitalizations, and 700{900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3{6 million fewer infections, 181{306k fewer hospitalizations, and 51{62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are signi ficant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.