Small Unmanned Aircraft Systems Research Articles

Enhancing safety feedback to the design of small, unmanned aircraft by joint assessment of impact area and human fatality.

Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product in case of a crashing UAS, where is the size of the crash impact area on the ground, and is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.

Blue Unmanned Aircraft Systems Explained: The Current Drone Market, Flight Regulations, and Debunking Common Misconceptions

The unmanned aircraft system (UAS) sector is a rapidly evolving domain characterized by fast technological advancement, diverse use cases, and an intricate regulatory environment. As a result, understanding and navigating the UAS industry presents a complex challenge for operators, manufacturers, regulatory authorities, and data users alike. This study provides a comprehensive analysis of the UAS market in the United States, with a particular focus on the Defense Innovation Unit’s (DIU’s) Blue Small UAS (sUAS) initiative. This paper provides an overview of the current UAS market in the U.S., accounting for 49 manufacturers from 11 countries. The study offers a breakdown of market shares and identifies the significant players, primarily the U.S. and China, where 53% and 18% of all manufacturers reside, respectively. It also discusses the impact of the newly introduced Remote ID regulations and the compliance among UAS manufacturers with these regulations. Moreover, the study covers the Blue sUAS list, addressing its origins and purpose, as well as the often-misunderstood implications for the broader UAS market. The study clarifies that Blue-listed UAS are not exclusively superior or the only options for purchase by the U.S. Department of Defense (DoD) or other U.S. government (USG) agencies. Moreover, it highlights that the Blue sUAS list’s suitability for military applications does not automatically render it optimal for civilian use, especially in infrastructure applications. This research aims to deepen understanding and stimulate informed discussion around the U.S. UAS market, regulatory compliance, the Blue sUAS list, and the broader implications for drone usage in both military and civilian contexts.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400–1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green–red range (550–650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models – Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) – under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers

Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

Experimental Assessment of Chance-Constrained Motion Planning for Small Uncrewed Aircraft

This work extends the experimental evaluation of chance-constrained motion planning algorithms to fielded fixed-wing small uncrewed aircraft systems (sUAS). Despite advances in planning algorithms, certain challenges remain to producing trajectories for nonholonomic mobile robotic systems such as sUAS. These challenges include nonlinear dynamics which create a complex mapping from inputs to outputs, initialization uncertainty in online motion planning due to compute time of planners and latency in data transfer, environmental uncertainty that has non-Gaussian impact on the robot’s trajectory, and system uncertainty that arises from incomplete models of complex systems. Small UAS often have proprietary components such as commercial, off-the-shelf autopilots which prevent motion planning models from accurately capturing system behavior in all regions of the state space. These challenges can be addressed by leveraging probabilistic motion planners that accurately represent and reason over dynamics and uncertainty. Chance-constrained motion planning offers a method of reasoning over uncertainty as feasibility constraints, and Monte Carlo sampling within a motion planning algorithm offers a method of representing complex uncertainty that may not have a closed form representation. This work extends the chance-constrained motion planning problem to reason over constraint on the trajectory and constraints on the state which ensure that the system model remains valid. Dynamical and systems models are formulated to extend to a broad class of fixed-wing UAS through the experimental derivation of input distributions and system parameters. Uncertainty is quantified using a data-driven approach to modeling input distributions, and Monte Carlo uncertainty sampling deployed within a Rapidly-exploring Random Tree motion planning algorithm is used to plan a trajectory containing a representation of uncertainty. The motion planning algorithm’s ability to accurately reason over layered chance constraints is evaluated experimentally in 61 fielded missions. The results show that when the flight conditions fall within the domain of the uncertainty distributions, the motion planning system is able to accurately reason over the chance constraints.

Short-Term Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System Receivers in Unmanned Aircraft Systems

HighlightsAn evaluation of relative GNSS receiver accuracy was performed using a UAS-based GNSS accuracy testing system.RTK receivers displayed minimal mean error and consistent standard deviations across deployment strategies and error directions.Non-RTK receivers exhibited significantly greater mean error and variability across deployment strategies, particularly in elevation error.UAS deployment strategy did not have a meaningful impact on RTK receiver positioning performance.Abstract. Global navigation satellite system (GNSS) receivers commonly integrated into small unmanned aircraft systems (UAS) generally function in standard or differential fix configurations, providing horizontal and vertical accuracies of approximately ±5 meters and ±15 meters, respectively. The accuracy of GNSS positioning is an important factor with widespread implications, affecting domains such as precision agriculture, meteorology, and photogrammetry. In the context of atmospheric observations, spatial accuracy plays a critical role, particularly in applications related to barometric pressure and precipitable water vapor. Similarly, UAS-based photogrammetry applications rely on high geospatial precision for tasks including topographic mapping and environmental monitoring. As such, this study aimed to improve our understanding of GNSS positioning accuracy in UAS-based observations. The main objectives included (1) deploying a UAS-based GNSS accuracy testing system and (2) evaluating the static and dynamic short-term accuracy of L1 and L1/L2 GNSS receivers in RTK and non-RTK fix modes. Results indicated significant differences across receiver configurations and deployment strategies. RTK receivers displayed minimal mean error and consistent standard deviations, while non-RTK receivers exhibited greater mean error and variability, especially in elevation. Though the study did not conclusively confirm a consistent reduction in accuracy due to UAS deployment, findings suggest that RTK receivers substantially enhance accuracy by reducing position measurement error by two orders of magnitude (1–6 cm for RTK; 50–315 cm for non-RTK), thereby mitigating measurement variability attributable to timing or deployment strategy. In conclusion, this research contributed insights into GNSS accuracy for UAS-based observations and underscored the importance of considering receiver configurations and deployment strategies for position measurement during atmospheric and photogrammetric observations. Keywords: Global navigation satellite systems (GNSS), Meteorology, Photogrammetry, Positioning accuracy, Precision agriculture, Real-time kinematic (RTK), Unmanned aircraft systems (UAS).

Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies

Highlights The response of dicamba-susceptible soybean to three simulated rates and growth stages has been studied. Soybean physiological data, such as plant height and phototoxicity, was collected at the selected growth stage. Aerial images of the experiment field using UAV were collected on specific days after treatment. Soybean yield, biological response, and the correlation of vegetative indices in predicting soybean yield were achieved. Abstract. Many dicotyledonous weeds can be selectively controlled by dicamba application during the growing season. However, vapor or spray drift from dicamba application could cause significant yield loss on non-target species such as soybean [Glycine max (L.) Merrill] that do not contain the dicamba resistance trait. In this study, the impact of simulated dicamba drift was investigated on dicamba-susceptible soybeans during different growth stages. A field experiment was conducted employing small unmanned aircraft systems (sUAS) and multispectral image sensors to assess the dose-response relationship between dicamba drift and soybean yield. The experiment followed a randomized complete block design, with main plots representing three distinct soybean growth stages (third trifoliate, sixth trifoliate, and early reproductive stage) and sub-plots encompassing various simulated dicamba drift rates. The analysis, involving non-linear regression and normalized difference vegetation index (NDVI) regression, revealed that visible soybean injuries were observed at minimal dicamba rates. However, significant yield loss occurred during the V6 growth stage at a dicamba rate of 98 g.ai ha-1, emphasizing the critical growth stage sensitivity. Moreover, a moderate yet meaningful relationship (R2 = 0.46) was established between sUAS multispectral NDVI data and soybean yield, highlighting the potential for sUAS with multispectral sensors to predict soybean yield losses. These results contribute valuable insights into the impact of dicamba drift on soybeans and the efficacy of remote sensing technology in assessing yield outcomes. Keywords: Dicamba-drift injury, Multispectral image, Precision agriculture, Soybean yield, sUAS, Yield prediction.

A Method for Evaluating Global Navigation Satellite System Position Accuracy in Small Unmanned Aircraft Systems

Highlights A novel UAS-based GNSS testing system was developed to assess both horizontal and vertical accuracy components. Recommendations are provided for further standard development related to UAS GNSS testing protocols. Future research can utilize this framework to enhance understanding of UAS GNSS accuracy. Abstract. Current standards for Global Navigation Satellite Systems (GNSS) testing are primarily designed for assessing horizontal positioning accuracy in ground-based applications. However, there is a notable absence of guidelines specifically addressing vertical accuracy assessment and applications that extend beyond ground-based scenarios, including those involving small unmanned aircraft systems (UAS). This effort aimed to develop a UAS-based GNSS testing system, tailored for the evaluation of GNSS receiver performance in dynamic low-altitude scenarios. The main objectives included (1) designing a GNSS testing system for UAS applications and (2) validating the system through field testing. The system development consisted of GNSS receiver configuration, data acquisition system design, processing procedures, and UAS payload and base station component design. The UAS payload featured multiple GNSS receivers operating in different modes, and data were collected using a custom Python script on a Raspberry Pi computer. For in-field validation, dynamic flight testing with vertical profiles was conducted. A robotic tracking total station (TTS) was used as the reference instrument for measuring the location of the UAS payload. The findings underscore the system's capabilities, primarily emphasizing its functionality rather than conducting an extensive assessment of the individual GNSS receiver performance. The UAS-based testing system that was developed was capable of satisfying most of the criteria for existing GNSS test standards. The insights derived from this process offer recommendations for standardizing UAS-based GNSS testing methods, with a particular focus on mission design, battery management, data processing, and environmental adaptability. Keywords: Dynamic Error, Global Navigation Satellite Systems (GNSS), Positioning Accuracy, Tracking Total Station (TTS), Unmanned Aircraft Systems (UAS).

The conservation impact of botanical drones: Documenting and collecting rare plants from vertical cliffs and other hard‐to‐reach areas

Abstract A high percentage of island floras are at risk of extinction and have been reduced to relic populations, often in remote hard‐to‐reach areas. Uncrewed aircraft systems (UAS aka drones) are now being utilized to assist in the survey and collection of rare plants in inaccessible areas or vertical cliff habitats. Here, we test the application of this technology for conservation of 23 plant taxa in three oceanic island hotspots: Hawaiʻi, Madeira and the Republic of Palau. We collect high‐resolution imagery using a small UAS to document the distribution and abundance of vascular flowering plants. Location information is then used to map and assess plant populations. Depending on the terrain, collections are completed using either traditional rope techniques or newly developed remote drone‐based collection methods. Over the course of 6 years, we have greatly expanded our knowledge of rare and endangered species, while increasing survey efficiency and staff safety. Most importantly, this work has had a large impact on the conservation of critically endangered plants. Although using drones for botanical conservation comes with limits and challenges, we see great potential in the continued employment of these techniques wherever plants are growing on cliffs or in other hard‐to‐reach areas.

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms

Construction of interpolated source from dynamic flights (UAV/AAM Special Issue)

A previous comparison of far-field predictions from equivalent acoustic sources generated from dynamic flight measurements of small unmanned aircraft systems demonstrated that a single pass over an acoustic array was insufficient to accurately predict far-field acoustic levels. In effort to reduce the uncertainty in the current study, measurements of multiple passes of dynamic flight within finite engine speed/airspeed bins were combined. From these measurements, equivalent acoustic sources were decomposed using spherical harmonics fitted to levels depropagated on a spherical surface. Two source descriptions, one at a low speed and one at high speed, represent end points on the noise-power-distance curve, which was similar to a technique demonstrated in a previous study of static measurements. Interpolation of spherical harmonic coefficients between the two sources permitted the determination of intermediate speed conditions. The resulting equivalent acoustic sources removed more than 10 dB of uncertainty from the dynamic flight definitions.

Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading

Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.

Leveraging machine learning and similarity judgements to identify perceptually relevant acoustic features of small, unmanned aircraft system signal similarity

Auditory signals can be described quantitatively by a set of measurable acoustic features (i.e., zero crossing rate, attack slope, etc.) or qualitatively with adjectives, such as whooping, thunderous, melodic, or in comparative terms such as different, louder, etc. Listeners can rate the similarity of signals and assign qualitative descriptions relatively easily; however, most lack the ability to articulate the quantitative basis of these judgments. Because the qualitative differences in signals typically correlate to a measurable difference in the acoustic features, signal similarity ratings can be used to recover the acoustic features that define signal similarity. In the present study, subjects were given combinations of small, unmanned aircraft systems (SUAS) signals consisting of either two different SUASs or SUAS and non-SUAS and asked to rate similarity on a scale from non-similar to highly similar. Using these similarity ratings along with acoustic difference features, machine learning algorithms were trained to predict human responses. These algorithms predict the position of a withheld UAS signal within the similarity feature-space. Crucial prediction acoustic difference features are extracted from the algorithms via feature importance and sensitivity analysis techniques. The extracted acoustic difference features may be inferred as prominent information impacting the human perception of signal similarity.

Cancer Care During the COVID-19 Pandemic: The African Narrative and Prospects.

The COVID-19 pandemic has had a significant impact on healthcare services globally. Whilst it has been particularly disruptive for cancer care in low-resource settings, a few African countries have been able to adapt strategies to enable continued delivery of medical care to persons with cancer. This study seeks to highlight how much effect the coronavirus pandemic has had on oncological care in Africa and to indicate the way forward. For this narrative review, PubMed and Google Scholar were used to search for literature addressing the effect of the coronavirus pandemic on the care of patients with cancer in Africa with ensuing coping strategies. Selection criteria were manuscripts published since the onset of the pandemic in 2019 and written in the English language with Africa being the focus. In total, 52 research papers involving up to 21 African nations were found and reviewed. Across the board, the COVID-19 pandemic resulted in the deferral of oncological screening programs and a halt in immunization activities routinely scheduled for preventable cancers. It caused a colossal shortage in the availability of appropriately trained medical personnel, reduced frequency and duration of outpatient consultations, and a delay in cancer investigations and diagnosis. It also stirred up the substandard modification of chemotherapy regimens and radiotherapy due to the scarcity of anticancer medications and radioisotopes and engendered the cancellation of cancer surgical procedures. Palliative care for patients with locally advanced and metastatic disease was in many cases interrupted and cancer research activities were abruptly deferred. Ultimately, these led to poor patient outcomes and increased cancer-related fatalities. However, a few African countries - Rwanda, Ghana, and Tunisia - have continued to adapt telemedicine, small unmanned aircraft systems (sUAS), and home therapy to facilitate cancer care. To date, there is a paucity of data concerning the successesand cost-effectiveness of these relatively new methods recently adapted to cater to the medical needs of cancer patients in Africa. The pandemic has presented the African communityan opportunity to advance her healthcare systems, especially as it pertains to the delivery of medical care to persons with cancer. The need of the hour is to study further the alternative cancer care delivery systems initiated during the pandemic in order to determine their sustainability in Africa at large.

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft Systems (UAS) Industry

Industry groups engage in venue shifting when they seek to overturn or alter restrictive regulations imposed by one political venue through another. A critical step in this process is resolving uncertainties surrounding the preference of the targeted venue and the nature of the relevant policy proposal. While existing studies emphasize a long-term trial-and-error process of policy learning, we focus on nascent industries and argue that ventures seek other information sources to resolve these uncertainties quickly. In particular, nascent industry groups are likely to perceive that the targeted venue will support their policy proposal if the targeted venue is ideologically distant from the venue that has enacted the restrictive regulations, if the targeted venue has recently supported other nascent industry groups’ similar policy proposals, or if the industry groups themselves are more exposed to industry peers’ success in promoting the same policy proposal in other jurisdictions. Under these conditions, the industry groups invest more to influence the targeted venue in response to restrictive regulations enacted by other venues. We find support for our theory by examining how from 2013 to 2019 the small unmanned aircraft systems industry trade associations in the United States lobbied state governments to nullify local regulations.