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Significance Pediatric SARS-CoV-2 infections, though generally mild, are associated with substantial morbidity and contribute to transmission dynamics. No SARS-CoV-2 vaccines are available for young children. Bovine/human parainfluenza virus 3 (B/HPIV3) vectors for intranasal immunization of children were evaluated previously in phase 1/2 studies and were well-tolerated in children as young as 2 mo of age. This manuscript describes a B/HPIV3 vector expressing a prefusion-stabilized version of the SARS-CoV-2 S protein (S-2P), and shows that a single intranasal dose is highly immunogenic and protective against SARS-CoV-2 challenge in the hamster model, the most robust SARS-CoV-2 challenge model available. Based on these results, B/HPIV3/S-2P represents a promising vaccine candidate for clinical evaluation as a pediatric vaccine for intranasal immunization against HPIV3 and SARS-CoV-2., Single-dose vaccines with the ability to restrict SARS-CoV-2 replication in the respiratory tract are needed for all age groups, aiding efforts toward control of COVID-19. We developed a live intranasal vector vaccine for infants and children against COVID-19 based on replication-competent chimeric bovine/human parainfluenza virus type 3 (B/HPIV3) that express the native (S) or prefusion-stabilized (S-2P) SARS-CoV-2 S spike protein, the major protective and neutralization antigen of SARS-CoV-2. B/HPIV3/S and B/HPIV3/S-2P replicated as efficiently as B/HPIV3 in vitro and stably expressed SARS-CoV-2 S. Prefusion stabilization increased S expression by B/HPIV3 in vitro. In hamsters, a single intranasal dose of B/HPIV3/S-2P induced significantly higher titers compared to B/HPIV3/S of serum SARS-CoV-2–neutralizing antibodies (12-fold higher), serum IgA and IgG to SARS-CoV-2 S protein (5-fold and 13-fold), and IgG to the receptor binding domain (10-fold). Antibodies exhibited broad neutralizing activity against SARS-CoV-2 of lineages A, B.1.1.7, and B.1.351. Four weeks after immunization, hamsters were challenged intranasally with 104.5 50% tissue-culture infectious-dose (TCID50) of SARS-CoV-2. In B/HPIV3 empty vector-immunized hamsters, SARS-CoV-2 replicated to mean titers of 106.6 TCID50/g in lungs and 107 TCID50/g in nasal tissues and induced moderate weight loss. In B/HPIV3/S-immunized hamsters, SARS-CoV-2 challenge virus was reduced 20-fold in nasal tissues and undetectable in lungs. In B/HPIV3/S-2P–immunized hamsters, infectious challenge virus was undetectable in nasal tissues and lungs; B/HPIV3/S and B/HPIV3/S-2P completely protected against weight loss after SARS-CoV-2 challenge. B/HPIV3/S-2P is a promising vaccine candidate to protect infants and young children against HPIV3 and SARS-CoV-2.

The HIV-mediated decline in circulating CD4 T cells correlates with increased risk of active tuberculosis (TB)1–4. However, HIV/Mycobacterium tuberculosis (Mtb) co-infected individuals also have an increased incidence of TB prior to loss of CD4 T cells in blood3,5, raising the possibility that HIV co-infection leads to disruption of CD4 T cell responses at the site of lung infection before they are observed systemically. Here we used a rhesus macaque model of SIV/Mtb co-infection to study the early effects of acute SIV infection on CD4 T cells in pulmonary Mtb granulomas. Two weeks after SIV co-infection CD4 T cells were dramatically depleted from granulomas, before significant bacterial outgrowth, disease reactivation as measured by PET-CT imaging, or CD4 T cell loss in blood, airways, and lymph nodes. Mtb-specific CD4 T cells, CCR5-expressing, in granulomas were preferentially depleted by SIV infection. Moreover, CD4 T cells were preferentially depleted from the granuloma core and lymphocyte cuff relative to B cell-rich regions, and live imaging of granuloma explants showed that SIV co-infection reduced T cell motility. Thus, Mtb-specific CD4 T cells in pulmonary granulomas may be decimated before many patients even experience the first symptoms of acute HIV infection.

Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid.

Human-robot interaction requires communication, however what form this communication should take to facilitate effective team performance is still undetermined. One notion is that effective human-agent communications can be achieved by combining transparent information-sharing techniques with specific communication patterns. This study examines how transparency and a robot’s communication patterns interact to affect human performance in a human-robot teaming task. Participants’ performance in a target identification task was affected by the robot’s communication pattern. Participants missed identifying more targets when they worked with a bidirectionally communicating robot than when they were working with a unidirectionally communicating one. Furthermore, working with a bidirectionally communicating robot led to fewer correct identifications than working with a unidirectionally communicating robot, but only when the robot provided less transparency information. The implications these findings have for future robot interface designs are discussed.

The effect of task switching on performance has been examined in many different fields and contexts. Sudden changes in task load can significantly impair performance, which can have detrimental consequences in dismounted military operations. As technology is advancing, robots are sought to take on the role of a teammate to the human soldier in the field. Robot-to-human communication modality may need to switch when mayhem occurs in military missions. Modality switching has been associated with performance decrements, although these effects are largely unknown in military human-robot teaming situations. The present study examined the cost associated with switching task demand and robot-to-human communication modality type on performance in a simulated cordon-and-search mission. The results showed that switches in task load affected threat detection performance. Auditory reporting increased performance more than visual reporting in low-after-high task load epochs. Performance with auditory reports was also higher in high-after-low demand blocks than low-after-high. The effect of switching needs to be taken into account for high-stakes human-robot interactions.

Analog, full-scope, full-scale simulators with the fidelity to simulate all of the physical and underlying thermodynamics in the real system are representative of training simulators used by current operating nuclear power plants. However, digital simulators are becoming desirable to researchers and utility companies alike due to their increased accessibility and the capability of integrating new system upgrades. The present study compared operators’ workload response in a given operating procedure using an analog, full-scope/scale simulator and a digital, part-task simulator. Subjective measures (NASA-TLX, MRQ, ISA) and physiological measures (electrocardiography) were used to profile workload response. The results suggested the feasibility of using digital simulators for research purposes with potential future implications for training.

This paper presents the analyses of data collected from four previous studies to compare the sensitivity of multiple physiological and subjective workload measures in detecting the workload changes induced by common nuclear power plant (NPP) main control room tasks in three types using three simulators. Analyses of effect sizes were used to quantify the magnitude of response or rating changes in the workload metrics. The results suggest that the majority of the workload measures utilized in the Human Performance Test Facility (HPTF) studies show practically relevant sensitivity to the workload changes induced by the experimental manipulations in the simulated NPP operations.

The tidal energy industry is developing rapidly worldwide. However, it is solar and wind power that saturate the media and make up the majority of sustainable energy installations. Why is there seemingly so little activity regarding tidal energy? This research investigates challenges for tidal energy, especially targeting public perception in two countries in different hemispheres: Australia and Canada. To investigate this, a survey was distributed to people living in both countries. Scientific and academic literature was also reviewed to give insight into how the future might look regarding marine renewable energy (MRE) and offshore renewable energy such as tidal energy. Looking at previous research done in the fields of sustainability and social science and conducting a survey on public opinion, this thesis identifies some of the obstacles and challenges that the tidal energy industry faces: construction costs, technical challenges, lack of research and knowledge and social and cultural factors. With the two surveys, this study concluded that both residents in Australia (based on 64 respondents) and Canada (40 respondents) are not confident in distinguishing the difference between tidal energy and wave energy, pointing towards a gap in knowledge about tidal energy. The thesis identified communication and possibly media coverage as a solution to obstacles for the advancement of tidal energy arising from public opposition. Respondents from Australia showed a greater interest in more media coverage of tidal energy than the respondents from Canada. Both groups of respondents were concerned with their energy being green and both groups perceived tidal energy as green. The biggest concerns were the same for the two groups: disruption and injury to wildlife and impact on nearby land. The thesis also presents an evaluation of the potential for the tidal energy industry to overcome these issues and what possible solutions look like. As a multidisciplinary study, concerned with the technological, economical and human dimensions of tidal energy and its industry, it offers a multifaceted overview of various issues at hand and attempts to compile knowledge from various fields into a comprehensive look at the sustainability and viability of tidal energy.

Multimodal communication between humans and autonomous robots is essential to enhance effectiveness of human–robot team performance in complex, novel environments, such as in military intelligence, surveillance, and reconnaissance operations in urban settings. It is imperative that a systematic approach be taken to evaluate the factors that each modality contributes to the user’s ability to perform successfully and safely. This paper addresses the effects of unidirectional speech and gesture methods of communication on perceived workload, usability preferences, and expectations of robot behavior while commanding a robot teammate to perform a spatial-navigation task. Each type of communication was performed alone or simultaneously. Results reveal that although the speech-alone condition elicited the lowest level of perceived workload, the usability preference and expectations of robot behavior after interacting through each communication condition was the same. Further, workload ratings between the gesture and speech-gesture conditions were similar indicating systems that employ gesture communication could also support speech communication with little to no additional subjectively perceived cognitive burden on the user. Findings also reveal that workload alone should not be used as a sole determining factor of communication preference during system and task evaluation and design. Additionally, perceived workload did not seem to negatively impact the level of expectations regarding the robot’s behavior. Recommendations for future human–robot communication evaluation are provided.

In the dismounted military field, robots and unmanned vehicles are increasingly used as force multipliers and teammates. As such, a fluent human-robot interaction (HRI) becomes vital and is stimulated by fitting the robot and its interface to the human teammate’s capabilities. This is where individual differences of the human needs to be considered, such as those found in spatial ability. In HRI, information presented to the human teammate requires mental manipulation and interpretation to inform subsequent human actions, which relies on spatial ability. In order to generalize findings to the armed forces and to inform future design requirements, factors pertaining to construct operationalization, measurement, and task type need to be examined. The aim of the present literature review is to investigate spatial ability findings in military HRI.

We present AutonoVi-Sim, a novel high-fidelity simulation platform for autonomous driving data generation and driving strategy testing. AutonoVi-Sim is a collection of high-level extensible modules which allows the rapid development and testing of vehicle configurations and facilitates construction of complex traffic scenarios. Autonovi-Sim supports multiple vehicles with unique steering or acceleration limits, as well as unique tire parameters and dynamics profiles. Engineers can specify the specific vehicle sensor systems and vary time of day and weather conditions to generate robust data and gain insight into how conditions affect the performance of a particular algorithm. In addition, AutonoVi-Sim supports navigation for non-vehicle traffic participants such as cyclists and pedestrians, allowing engineers to specify routes for these actors, or to create scripted scenarios which place the vehicle in dangerous reactive situations. Autonovi-Sim facilitates training of deep-learning algorithms by enabling data export from the vehicle's sensors, including camera data, LIDAR, relative positions of traffic participants, and detection and classification results. Thus, AutonoVi-Sim allows for the rapid prototyping, development and testing of autonomous driving algorithms under varying vehicle, road, traffic, and weather conditions. In this paper, we detail the simulator and provide specific performance and data benchmarks.

A field assessment of multimodal communication (MMC) was conducted as part of a program integration demonstration to support and enable bi-directional communication between a dismounted Soldier and a robot teammate. In particular, the assessment was focused on utilizing auditory and visual/gesture based communications. The task involved commanding a robot using semantically-based MMC. Initial participant data indicates a positive experience with the multimodal interface (MMI) prototype. The results of the experiment inform recommendations for multimodal designers regarding perceived usability and functionality of the currently implemented MMI.

The state-of-the-art in robotics is advancing to support the warfighters’ ability to project force and increase their reach across a variety of future missions. Seamless integration of robots with the warfighter will require advancing interfaces from teleoperation to collaboration. The current approach to meeting this requirement is to include human-to-human communication capabilities in tomorrow’s robots using multimodal communication. Though advanced, today’s robots do not yet come close to supporting teaming in dismounted military operations, and therefore simulation is required for developers to assess multimodal interfaces in complex multi-tasking scenarios. This paper describes existing and future simulations to support assessment of multimodal human-robot interaction in dismounted soldier-robot teams.

This study focuses on the increase of situation awareness (SA) in human-agent mixed-initiative mission planning. Complex agent behavior and the failure to comprehend the agent’s proposed actions might result in a loss of SA and a decrease in trust. This study adopted the transparency strategies proposed by the SA-based Agent Transparency (SAT) model to improve the human operator’s perception, comprehension, and projection of the agent’s interventions. The concept was applied to prototype of a planning associate for multi-vehicle mission planning. A human-in-the-loop experiment revealed a higher SA and performance. Subjective trust measures could not verify a change in operator’s trust in the agent. The results and the potential for further research are discussed.

This work describes and validates a concept of transparent behavior for adaptive automation in the field of military helicopter missions. The adaptive automation is implemented as a cognitive agent, to serve as an artificial co-pilot. It dynamically adjusts its level of assistance by choosing from different workload-adapted strategies of assistive intervention. However, adaptive interventions may entail a possible drawback. It might be difficult for the human operator to build up a sufficient and stable mental model of the interaction. For the purpose of creating transparent behavior, this contribution provides an approach for the agent to communicate in a more human-like fashion. To quantify the impacts of the additional transparency information the artificial agent communicated, we conducted a human-in-the-loop experiment. The results revealed an enhancement of situation awareness and an increase of perceivable intelligence and other human-like characteristics of the cognitive agent.

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively., 9 pages, 6 figures

Predicting decision making may be essential for personnel selection. The present study aimed to predict sustained decision making using measures of subjective state and physiological response to a short task battery. Volunteers completed a short battery of decision tasks, followed by a dynamic task simulating business decision making. Subjective stress state and cerebral blood flow velocity (CBFV) responses to each task suggested that sustained decision making may induce stress and fatigue. Some positive associations were found between CBFV responses to the short battery and long task performance. The right hemisphere CBFV responses taken during the long task correlated highly with long task performance. These findings suggest that hemodynamic response to a verbal task, like the short battery, may provide an effective means for predicting subsequent decision making effectiveness. The findings also suggest that CBFV may access multiple resources required for sustained decision making, localized in left and right hemispheres.

Research into human-system interaction, specifically focusing on workload, has intensified in the nuclear domain. Past research on workload in the Nuclear Power Plant (NPP) domain has attempted to use both subjective and physiological measures of workload, yet the sensitivity of the workload measures used in past experiments is unknown. This initial experiment will guide future research in the NPP domain by identifying whether the NASA-TLX, EEG, and ECG are sensitive to detecting workload changes in common NPP Main Control Room (MCR) tasks. Results suggest the three workload measures did not reveal expected differences between task types in the NPP MCR context.

Nuclear Power Plant (NPP) operators complete multiple types of tasks within Emergency Operating Procedures (EOPs). Due to the potential serious consequences of committing an error, it is important to determine if the workload (WL) demands operators encounter are at acceptable levels. This study investigates whether there are workload differences are distinct between task types and if there is a difference between each task type over multiple sessions in a simulated environment. Previous research supports that EEG, ECG, and the NASA-TLX are sensitive to changes in WL. The present preliminary experiment sought to investigate WL changes for experienced participants over a number of sessions and task types. During each session, participants completed tasks derived from a combination of EOPs and subject matter expert input that consisted of checking, detection, and response implementation task types. WL changes were measured through EEG, ECG, and NASA-TLX responses. The results indicate that WL differences were found among the different task types, but not sessions. The implications for these findings are discussed in detail.

Various psychophysiological indices of mental workload exhibit sensitivity to task demand factors, but the psychometrics of indices has been neglected. In particular, the extent to which different metrics converge on a common latent factor is unclear. In the present study, 150 participants performed in four task scenarios based on a simulation of unmanned vehicle operation. Scenarios required threat detection and/or change detection. Both single- and dual-task scenarios were used. Workload metrics were derived from the electroencephalogram (EEG), electrocardiogram (ECG), transcranial Doppler sonography (TCD), functional Near Infra-Red (fNIR) and eyetracking. Subjective workload was also assessed. Several metrics were appropriately sensitive to the differing levels of task load presented by the four scenarios. However, factor analysis identified multiple factors, each of which was associated with a single response system only, with no general factor. Caution should be used in assessing workload in the individual operator.

Robots that collaborate with humans must be equipped with interfaces that support deeper and richer interaction. Such interfaces may involve the understanding and production of speech. This calls for an understanding of speech and natural language in various contexts. The present study investigates the preferred words and phrases used in giving directions to a robot teammate in an intelligence and surveillance reconnaissance (ISR) mission. Results indicate that participants may have had a perceptual mental model that influenced choice of words or phrases. Recommendations for future research include examining the factors that affect development of schemas when interacting with robots.

For effective human-robot teaming, robots must gain the appropriate social-cognitive mechanisms that allow them to function naturally and intuitively in social interactions with humans. However, there is a lack of consensus on social cognition broadly, and how to design such mechanisms for embodied robotic systems. To this end, recommendations are advanced that are drawn from HRI, psychology, robotics, neuroscience and philosophy as well as theories of embodied cognition, dual process theory, ecological psychology, and dynamical systems. These interdisciplinary and multi-theoretic recommendations are meant to serve as integrative and foundational guidelines for the design of robots with effective social-cognitive mechanisms.

Robots are following an evolutionary path similar to computers. One obstacle for this evolution is language interaction between humans and robots. Traditionally, human-to-robot language has been one way in the form of commands from human to robot. One path of robot-to-human language is tactile. Tactile communication affords the user a hands-free method of interaction. Hands-free tactile communication is an asset for military ground personnel allowing the hands, eyes, and ears to be fully utilized for other tasks. Speech and language are traditionally associated with the left cerebral hemisphere. Since tactile communication does not include the traditional auditory pathway of verbal language, there is potential for employing a more right cerebral hemisphere spatial pathway. The present study looks at the right and left cerebral hemispheric activity measured by an electroencephalogram of participants using a vibrotactile belt for robot to human communication.

In addition to efforts to increase the intelligence and perception capabilities of robots to enable collaboration with human counterparts, there is also a focus towards improving interaction mechanics. Multimodal communication is one such tool under investigation due to its dynamic ability to select explicit and implicit communication modalities with the aim of facilitating robust exchanges of information. Although there is extensive research in the domain of explicit communication using auditory, visual, and tactile interfaces, investigations into systems that incorporate implicit methods, or actually adapt and select appropriate modalities for reporting data from a robot to human is limited. Furthermore, a missing piece is identifying how and when to trigger these changes. A novel strategy to accomplish adaptation is through identification of teammate’s physiological state. From the literature, one can find examples of researchers using high fidelity systems to measure physiological response and predict user workload, but many of these technologies are prohibitively expensive or not suitable for use in many domains of interest for human robot interaction such as dismounted infantry operations. Recent advancements in wearable consumer technologies, specifically fitness trackers supporting integration with third party software, are making it possible for incorporation of low cost systems in a variety of novel applications. A logical extension of these applications being physiological state measurement to drive adaptive automation in the form of multimodal interfaces. This paper describes the results of a study to assess the feasibility of using data from a wearable fitness tracker in an adaptive multimodal interface for squad-level human-robot interaction.

Advances in the technology of autonomous systems calls for an examination of the factors that confer resilience on the human-machine system. We identify challenges for teaming between human operators and autonomous systems associated with cognitive demands, trust and operator self-regulation. Solutions to these challenges partly require designing systems for effective signaling of capabilities and “intent” to the human operator. They also require selection and training of operators to team with systems that may simulate intelligent, social behaviors, as well as diagnostic monitoring of operator neurocognitive status. Implementing such solutions supports resilience at a systems level, so that machine and human can compensate for each other's limitations in challenging circumstances.

The drive to progress dismounted Soldier-robot teaming is toward more autonomous systems with effective bi-directional Soldier-robot dialogue, which in turn requires a strong understanding of interface design factors that impact Soldier-robot communication. This experiment tested effects of various exogenous orienting visual display cues on simulation-based reconnaissance and communication performance, perceived workload, and usability preference. A 2 × 2 design provided four exogenous orienting visual display designs, two for navigation route selection and two for building identification. Participants’ tasks included signal detection and response to visual prompts within a tactical multimodal interface (MMI). Within the novice non-military sample, results reveal that all display designs elicited low perceived workload, were highly accepted in terms of usability preference, and did not have an effect on task performance regarding responses to robot assistance requests. Results suggest inclusion of other factors, such as individual differences (experience, ability, motivation) to enhance a predictive model of task performance.

This paper discusses two studies testing the effects of agent transparency in joint cognitive systems involving supervisory control and decision-making. Specifically, we examine the impact of agent transparency on operator performance (decision accuracy), response time, perceived workload, perceived usability of the agent, and operator trust in the agent. Transparency has a positive impact on operator performance, usability, and trust, yet the depiction of uncertainty has potentially negative effects on usability and trust. Guidelines and considerations for displaying transparency in joint cognitive systems are discussed.

In order for humans to safely work alongside robots in the field, the human-robot (HR) interface, which enables bi-directional communication between human and robot, should be able to quickly and concisely express the robot's intentions and needs. While the robot operates mostly in autonomous mode, the human should be able to intervene to effectively guide the robot in complex, risky and/or highly uncertain scenarios. Using smart glasses such as Google Glass∗, we seek to develop an HR interface that aids in reducing interaction time and distractions during interaction with the robot.