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Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study algorithmic fairness in kidney placement, these methods replace true outcomes with surgeons’ decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons’ decisions disregards expert stakeholders’ biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) that predicts a kidney’s acceptance rate in a given kidneymatch pair. The survey is launched on Prolific, a crowdsourcing platform, and public opinions are collected from 85 anonymous crowd participants. A novel social fairness preference learning algorithm is proposed based on minimizing social feedback regret computed using a novel logit-based fairness feedback model. The proposed model and learning algorithm are both validated using simulation experiments as well as Prolific data. Public preferences towards group fairness notions in the context of kidney placement have been estimated and discussed in detail. The specific ARP tested in the Prolific survey has been deemed fair by the participants. 

Quantifying disorder in physical systems can provide unique opportunities to engineer-specific properties. Here, we apply a methodology based on the approach pioneered by Bragg and Williams for metal alloys to quantify the disorder characterizing polymer fibers including polyaniline (PANI), polyaniline-polycaprolactone (PANI-PCL), and polyvinylidene difluoride (PVDF). Both PANI and PVDF possess electrical properties such as conductivity and piezoelectric response that find a wide range of applications in energy storage and tissue engineering. On the other hand, the mechanical properties of polymer fibers can be tuned by varying the concentration of PANI and PCL during synthesis. Here, we demonstrate that it is possible to control the amount of disorder characterizing a fiber, which provides a route to engineering desired values for specific material properties. The resulting measure of disorder is shown to have a direct relationship to Young’s modulus, band gap, and specific capacitance values. 

Abstract The design, principles of operation, calibration, and data analysis approaches of the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on the NASA Ionospheric Connection (ICON) satellite have been documented prior to the ICON launch. Here we update and expand on the MIGHTI wind data analysis and discuss the on-orbit instrument performance. In particular, we show typical raw data and we describe key processing steps, including the correction of a “signal-intensity dependent phase shift,” which is necessitated by unexpected detector behavior. We describe a new zero-wind calibration approach that is preferred over the originally planned approach due to its higher precision. Similar to the original approach, the new approach is independent of any a priori data. A detailed update on the wind uncertainties is provided and compared to the mission requirements, showing that MIGHTI has met the ICON mission requirements. While MIGHTI observations are not required to produce absolute airglow brightness profiles, we describe a relative brightness profile product, which is included in the published data. We briefly review the spatial resolution of the MIGHTI wind data in addition to the data coverage and data gaps that occurred during the nominal mission. Finally, we include comparisons of the MIGHTI wind data with ground-based Fabry-Perot interferometer observations and meteor radar observations, updating previous studies with more recent data, again showing good agreement. The data processing steps covered in this work and all the derived wind data correspond to the MIGHTI data release Version 5 (v05). 

Abstract Radar and sounding rocket observations of plasma irregularities in theF‐region ionosphere acquired on 19 June 2019 during NASA experiment Too WINDY on Kwajalein Atoll are presented. The experiment was conducted near local midnight during a period of low solar flux and quiet geomagnetic conditions. Plasma density irregularities were seen by the rocket and also in the incoherent scatter radar data to emerge and persist mainly in the topside. Density irregularities in the bottomside remained very small by comparison throughout the observations. Zonal plasma drifts measured by the rocket were highly structured in the topside. Patches of coherent scatter entrained in the large‐scale topside density irregularities appeared to propagate slowly westward in a narrow flow channel detected by the rocket. Broadband ELF emissions were also detected in the topside. Some of the characteristics of the topside irregularities are typical of postsunset equatorialF‐region irregularities observed frequently by coherent scatter radars, and some of the common features in the coherent scatter database are reviewed. Two scenarios that have been proposed to account for postmidnight spreadFare tested computationally. One involves unseasonably large background zonal electric fields, and the other involves forcing from below by neutral waves and turbulence. Neither scenario appears to be able to account for the Too WINDY observations and the preponderance of topside irregularities without bottomside precursors in particular. 

In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled sensory analysis and fusion. 

Most of the world's crops depend on pollinators, so declines in both managed and wild bees raise concerns about food security. However, the degree to which insect pollination is actually limiting current crop production is poorly understood, as is the role of wild species (as opposed to managed honeybees) in pollinating crops, particularly in intensive production areas. We established a nationwide study to assess the extent of pollinator limitation in seven crops at 131 locations situated across major crop-producing areas of the USA. We found that five out of seven crops showed evidence of pollinator limitation. Wild bees and honeybees provided comparable amounts of pollination for most crops, even in agriculturally intensive regions. We estimated the nationwide annual production value of wild pollinators to the seven crops we studied at over $1.5 billion; the value of wild bee pollination of all pollinator-dependent crops would be much greater. Our findings show that pollinator declines could translate directly into decreased yields or production for most of the crops studied, and that wild species contribute substantially to pollination of most study crops in major crop-producing regions.