The impact of hurricane-related flooding on infectious diseases in the US is not well understood. Using geocoded electronic health records for 62,762 veterans living in North Carolina counties impacted by Hurricane Matthew coupled with flood maps, we explore the impact of hurricane and flood exposure on infectious outcomes in outpatient settings and emergency departments as well as antimicrobial prescribing. Declines in outpatient visits and antimicrobial prescribing are observed in weeks 0-2 following the hurricane as compared with the baseline period and the year prior, while increases in antimicrobial prescribing are observed 3+ weeks following the hurricane. Taken together, hurricane and flood exposure appear to have had minor impacts on infectious outcomes in North Carolina veterans, not resulting in large increases in infections or antimicrobial prescribing

Aerodynamic characterisation from flight testing is an integral subroutine for evaluating a new flight vehicle’s aerodynamic performance, stability and controllability. The estimation of aerodynamic parameters from flight test data has extensively been explored, in the past, using estimation methods such as the equation error method, output error method and filter error method. However, in the current era, non-gradient-based estimation techniques are gaining attention from researchers due to their inherent data-driven optimisation capability to find the global best solution. In this paper, a novel non-gradient-based estimation method is proposed for the aerodynamic characterisation of unmanned aerial vehicles from flight data, which relies on the maximum likelihood method augmented with particle swarm optimisation. Flight data sets of a wing-alone unmanned aerial vehicle are used to demonstrate the capabilities of the proposed method in estimating aerodynamic derivatives. Estimates from the proposed method are corroborated with the wind tunnel test and output error method results. It has been observed that simulated flight vehicle responses using estimated parameters are in good agreement with measured data in most of the manoeuvers considered. Confidence in the estimates of linear and nonlinear aerodynamic parameters is well established with the lower limit of Cramer-Rao bounds, which are minimal. The proposed method also demonstrates good predictability of the quasi-steady stall aerodynamic model by estimating stall characteristic parameters such as aerofoil static stall characteristics parameter, hysteresis time constant and breakpoint. The overall performance of the proposed estimation method is on par with the output error method and is validated with the proof-of-match exercise.

From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

We present the most sensitive and detailed view of the neutral hydrogen ( ${\rm H\small I}$ ) emission associated with the Small Magellanic Cloud (SMC), through the combination of data from the Australian Square Kilometre Array Pathfinder (ASKAP) and Parkes (Murriyang), as part of the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) pilot survey. These GASKAP-HI pilot observations, for the first time, reveal ${\rm H\small I}$ in the SMC on similar physical scales as other important tracers of the interstellar medium, such as molecular gas and dust. The resultant image cube possesses an rms noise level of 1.1 K ( $1.6\,\mathrm{mJy\ beam}^{-1}$ ) $\mathrm{per}\ 0.98\,\mathrm{km\ s}^{-1}$ spectral channel with an angular resolution of $30^{\prime\prime}$ ( ${\sim}10\,\mathrm{pc}$ ). We discuss the calibration scheme and the custom imaging pipeline that utilises a joint deconvolution approach, efficiently distributed across a computing cluster, to accurately recover the emission extending across the entire ${\sim}25\,\mathrm{deg}^2$ field-of-view. We provide an overview of the data products and characterise several aspects including the noise properties as a function of angular resolution and the represented spatial scales by deriving the global transfer function over the full spectral range. A preliminary spatial power spectrum analysis on individual spectral channels reveals that the power law nature of the density distribution extends down to scales of 10 pc. We highlight the scientific potential of these data by comparing the properties of an outflowing high-velocity cloud with previous ASKAP+Parkes ${\rm H\small I}$ test observations.

Background: Clinical guidelines recommend MAP maintenance at 85-90 mmHg to optimize spinal cord perfusion post-SCI. Recently, there has been increased interest in spinal cord perfusion pressure as a surrogate marker for spinal cord blood flow. The study aims to determine the congruency of subdural and intramedullary spinal cord pressure measurements at the site of SCI, both rostral and caudal to the epicenter of injury. Methods: Seven Yucatan pigs underwent a T5 to L1 laminectomy with intramedullary (IM) and subdural (SD) pressure sensors placed 2 mm rostral and 2 mm caudal to the epicenter of SCI. A T10 contusion SCI was performed followed by an 8-hour period of monitoring. Axial ultrasound images were captured at the epicenter of injury pre-SCI, post-SCI, and hourly thereafter. Results: Pigs with pre-SCI cord to dural sac ratio (CDSR) of >0.8 exhibited greater occlusion of the subdural space post-SCI with a positive correlation between IM and SD pressure rostral to the injury and a negative correlation caudal to the epicenter. Pigs with pre-SCI CDSR <0.8 exhibited no correlation between IM and SD pressure. Conclusions: Congruency of IM and SD pressure is dependent on compartmentalization of the spinal cord occurring secondary to swelling that occludes the subdural space.

In this era of spatially resolved observations of planet-forming disks with Atacama Large Millimeter Array (ALMA) and large ground-based telescopes such as the Very Large Telescope (VLT), Keck, and Subaru, we still lack statistically relevant information on the quantity and composition of the material that is building the planets, such as the total disk gas mass, the ice content of dust, and the state of water in planetesimals. SPace Infrared telescope for Cosmology and Astrophysics (SPICA) is an infrared space mission concept developed jointly by Japan Aerospace Exploration Agency (JAXA) and European Space Agency (ESA) to address these questions. The key unique capabilities of SPICA that enable this research are (1) the wide spectral coverage $10{-}220\,\mu\mathrm{m}$ , (2) the high line detection sensitivity of $(1{-}2) \times 10^{-19}\,\mathrm{W\,m}^{-2}$ with $R \sim 2\,000{-}5\,000$ in the far-IR (SAFARI), and $10^{-20}\,\mathrm{W\,m}^{-2}$ with $R \sim 29\,000$ in the mid-IR (SPICA Mid-infrared Instrument (SMI), spectrally resolving line profiles), (3) the high far-IR continuum sensitivity of 0.45 mJy (SAFARI), and (4) the observing efficiency for point source surveys. This paper details how mid- to far-IR infrared spectra will be unique in measuring the gas masses and water/ice content of disks and how these quantities evolve during the planet-forming period. These observations will clarify the crucial transition when disks exhaust their primordial gas and further planet formation requires secondary gas produced from planetesimals. The high spectral resolution mid-IR is also unique for determining the location of the snowline dividing the rocky and icy mass reservoirs within the disk and how the divide evolves during the build-up of planetary systems. Infrared spectroscopy (mid- to far-IR) of key solid-state bands is crucial for assessing whether extensive radial mixing, which is part of our Solar System history, is a general process occurring in most planetary systems and whether extrasolar planetesimals are similar to our Solar System comets/asteroids. We demonstrate that the SPICA mission concept would allow us to achieve the above ambitious science goals through large surveys of several hundred disks within $\sim\!2.5$ months of observing time.

We present the data and initial results from the first pilot survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers $270 \,\mathrm{deg}^2$ of an area covered by the Dark Energy Survey, reaching a depth of 25–30 $\mu\mathrm{Jy\ beam}^{-1}$ rms at a spatial resolution of $\sim$ 11–18 arcsec, resulting in a catalogue of $\sim$ 220 000 sources, of which $\sim$ 180 000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.

In view of the increasing complexity of both cardiovascular implantable electronic devices (CIEDs) and patients in the current era, practice guidelines, by necessity, have become increasingly specific. This document is an expert consensus statement that has been developed to update and further delineate indications and management of CIEDs in pediatric patients, defined as ≤21 years of age, and is intended to focus primarily on the indications for CIEDs in the setting of specific disease categories. The document also highlights variations between previously published adult and pediatric CIED recommendations and provides rationale for underlying important differences. The document addresses some of the deterrents to CIED access in low- and middle-income countries and strategies to circumvent them. The document sections were divided up and drafted by the writing committee members according to their expertise. The recommendations represent the consensus opinion of the entire writing committee, graded by class of recommendation and level of evidence. Several questions addressed in this document either do not lend themselves to clinical trials or are rare disease entities, and in these instances recommendations are based on consensus expert opinion. Furthermore, specific recommendations, even when supported by substantial data, do not replace the need for clinical judgment and patient-specific decision-making. The recommendations were opened for public comment to Pediatric and Congenital Electrophysiology Society (PACES) members and underwent external review by the scientific and clinical document committee of the Heart Rhythm Society (HRS), the science advisory and coordinating committee of the American Heart Association (AHA), the American College of Cardiology (ACC), and the Association for European Paediatric and Congenital Cardiology (AEPC). The document received endorsement by all the collaborators and the Asia Pacific Heart Rhythm Society (APHRS), the Indian Heart Rhythm Society (IHRS), and the Latin American Heart Rhythm Society (LAHRS). This document is expected to provide support for clinicians and patients to allow for appropriate CIED use, appropriate CIED management, and appropriate CIED follow-up in pediatric patients.

We summarize a series of numerical experiments of collisional dynamics in dense stellar systems such as globular clusters (GCs) and in weakly collisional plasmas using a novel simulation technique, the so-calledMulti-particle collision (MPC) method, alternative to Fokker-Planck and Monte Carlo approaches. MPC is related to particle-mesh approaches for the computation of self consistent long-range fields, ensuring that simulation time scales with N log N in the number of particles, as opposed to N2 for direct N-body. The collisional relaxation effects are modelled by computing particle interactions based on a collision operator approach that ensures rigorous conservation of energy and momenta and depends only on particles velocities and cell-based integrated quantities.