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When teaming with humans, the reliability of intelligent agents may sporadically change due to failure or environmental constraints. Alternatively, an agent may be more reliable than a human because their performance is less likely to degrade (e.g., due to fatigue). Research often investigates human-agent interactions under little to no time constraints, such as discrete decision-making tasks where the automation is relegated to the role of an assistant. This paper conducts a quantitative investigation towards varying reliability in human-agent teams in a time-pressured continuous pursuit task, and it interconnects individual differences, perceptual factors, and task performance through structural equation modeling. Results indicate that reducing reliability may generate a more effective agent imperceptibly different from a fully reliable agent, while contributing to overall team performance. The mediation analysis shows replication of factors studied in the trust and situation awareness literature while providing new insights: agents with an active stake in the task (i.e., success is dependent on team performance) offset loss of situation awareness, differing from the usual notion of overtrust. We conclude with generalizing implications from an abstract pursuit task, and we highlight challenges when conducting research in time-pressured continuous domains.

Traditional event exploration techniques in safety operations such as RCA and FMEA focus on developing explanations of specific events and in so doing, risk producing over-simplified highly linearized models of organizational behavior which are largely divorced from the realities of the work. Techniques such as FRAM, STAMP, and AcciMap, produce highly accurate models of system behavior but are often prohibitively resource intensive. These difficulties have made the types of inquiry which can inform Safety-II-style analysis difficult and expensive. In a review of four recent studies, we discuss implementation of the Systemic Contributors and Adaptations Diagramming (SCAD) framework, demonstrating its value in rapidly identifying useful cases, engaging a wider range of organizational roles, and arriving at useful insights more quickly and with reduced resource burden. Rather than focus on a specific event, SCAD explores the distribution of pressures across the system and the associated adaptive behaviors which employees undertake to address those pressures.

An increasing number of children have complex care needs (CCN) that impact their health and cause limitations in their lives. More of these youth are transitioning from paediatric to adult healthcare due to complex conditions being increasingly associated with survival into adulthood. Typically, the transition process is plagued by barriers, which can lead to adverse health consequences. There is an increased need for transitional care interventions when moving from paediatric to adult healthcare. To date, literature associated with this process for youth with CCN and their families has not been systematically examined.The objective of this scoping review is to map the range of programmes in the literature that support youth with CCN and their families as they transition from paediatric to adult healthcare.The review was conducted in accordance with the Joanna Briggs Institute's methodology for scoping reviews. A search, last run in April 2021, located published articles in PubMed, CINAHL, ERIC, PsycINFO and Social Work Abstracts databases.The search yielded 1523 citations, of which 47 articles met the eligibility criteria. A summary of the article characteristics, programme characteristics and programme barriers and enablers is provided. Overall, articles reported on a variety of programmes that focused on supporting youth with various conditions, beginning in the early or late teenage years. Financial support and lack of training for care providers were the most common transition program barriers, whereas a dedicated transition coordinator, collaborative care, transition tools and interpersonal support were the most common enablers. The most common patient-level outcome reported was satisfaction.This review consolidates available information about interventions designed to support youth with CCN transitioning from paediatric to adult healthcare. The results will help to inform further research, as well as transition policy and practice advancement.

Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL) in a pursuit-evasion (a.k.a predator-prey pursuit) game, which shares task goals with target acquisition, and we create different adversarial scenarios by replacing RL-trained pursuers' policies with two distinct (non-RL) analytical strategies. Using heatmaps of agents' positions (state-space variable) over time, we are able to categorize an RL-trained evader's behaviors. The novelty of our approach entails the creation of an influential feature set that reveals underlying data regularities, which allow us to classify an agent's behavior. This classification may aid in catching the (enemy) targets by enabling us to identify and predict their behaviors, and when extended to pursuers, this approach towards identifying teammates' behavior may allow agents to coordinate more effectively., This article appeared in the news at: https://www.army.mil/article/258408/u_s_army_scientists_invent_a_method_to_characterize_ai_behavior

Background Different trials showed that patients treated with blood transfusion (BT) have unfavorable prognosis regarding outcomes and mortality. Nevertheless, outcomes in patients treated with BT hospitalized in a contemporary tertiary intensive cardiac care unit (ICCU) is limited. Objective To determine mortality rate in patients treated with BT in a contemporary tertiary ICCU. Methods A prospective single center study to evaluate short- and long-term mortality rate of patients treated with BT between January 2020 and December 2021 in a tertiary ICCU. Outcomes During the study period 2132 consecutive patients were admitted with a mean age of 66.9±16 and 650 (30.5%) of them were women. Of the total population, 108 (5%) patients were treated with BT (BT-group) during their hospitalization. Patients were followed-up for 2 years with a median [IQR] follow up time of 82.5 [12.25–240.50] days in BT-group vs. 138.5 [34–325, 75] days in the non-BT (NBT) group. Mean age was 73.81±14 years in BT-group vs. 66.59±16 years in NBT-group, p Conclusion Even in contemporary tertiary ICCU, with advanced technology, equipment and delivery of care, BT remains a strong predictor for short- and long-term mortality. Funding Acknowledgement Type of funding sources: None.

Introduction Transcatheter aortic valve implantation (TAVI) is becoming the preferred treatment for elderly patients with severe aortic stenosis. Clinical outcomes of these patients according to different age groups and especially in the very old population are under reported. Purpose To describe clinical characteristics at baseline, according to different age groups, and to compare clinical outcomes in the very old population with other patients undergoing the procedure. Methods Data was extracted from the Israeli National TAVI Registry. Clinical outcomes in very old patients (above the age of 90 years) were compared with younger patients. Multivariable analysis for 30-day and 1-year mortality were performed. Procedural complication was defined as one or more of the following: need for permanent pacemaker, major vascular complication, severe acute kidney injury and major stroke. Results A total of 5,936 patients who underwent TAVI from 2008 to 2021 were analyzed (age 81.6±7.1 years, 50.6% female). Patients older than 90 years of age had more comorbidities than the younger population (STS score for mortality 6.4%±3.8% vs. 4.1%±4.8%, p Conclusions Although older patients undergoing TAVI had a higher risk profile, short- and long-term survival of these patients were associated significantly more with procedural complications than with patient age. It seems that extreme patient age alone should not be viewed as a consideration for not performing valve replacement, as long as the risk for procedural complications does not seem to be severely elevated. Funding Acknowledgement Type of funding sources: None.

Freezing of gait (FoG), a paroxysmal gait disturbance commonly experienced by patients with Parkinson’s disease (PD), is characterized by sudden episodes of inability to generate effective forward stepping. Recent studies have shown an increase in beta frequency of local-field potentials in the basal-ganglia during FoG, however, comprehensive research on the synchronization between different brain locations and frequency bands in PD patients is scarce. Here, by developing tools based on network science and non-linear dynamics, we analyze synchronization networks of electroencephalography (EEG) brain waves of three PD patient groups with different FoG severity. We find higher EEG amplitude synchronization (stronger network links) between different brain locations as PD and FoG severity increase. These results are consistent across frequency bands (theta, alpha, beta, gamma) and independent of the specific motor task (walking, still standing, hand tapping) suggesting that an increase in severity of PD and FoG is associated with stronger EEG networks over a broad range of brain frequencies. This observation of a direct relationship of PD/FoG severity with overall EEG synchronization together with our proposed EEG synchronization network approach may be used for evaluating FoG propensity and help to gain further insight into PD and the pathophysiology leading to FoG., Asher et al analyzed synchronization networks of electroencephalography brain waves from three Parkinson’s disease (PD) patient groups with different freezing of gait (FoG) severity using tools based on network science and non-linear dynamics. Their findings suggest that there is a potential relationship between PD/FoG severity and overall EEG synchronization and their approach could be used to help gain further insight into the pathophysiology leading to FoG.

This systematic review will evaluate the effectiveness of nutrition care interventions delivered by general practitioners versus usual care or no care on dietary and health outcomes in adults with diet-related chronic conditions or risk states.General practitioners are usually the first contacts in the health care system for patients with diet-related chronic conditions. While there is some evidence that general practitioners can be effective in delivering nutrition care for a number of outcomes, to inform future care, an update of the evidence is required as well as an examination of which components are associated with positive outcomes.Published studies will be included if they report on adults with or at risk of diet-related chronic conditions; one-on-one nutrition care interventions individually delivered by general practitioners during primary care consultations; usual or no care as comparators; dietary and/or health outcomes with a minimum three-month follow-up; and randomized controlled trials. Included studies will be available in, or able to be translated into, English and will have no date restrictions.The databases to be searched will include CINAHL, Embase, MEDLINE, and ProQuest Nursing and Allied Health. Following deduplication, two reviewers will independently screen the titles and abstracts in Covidence, followed by the full texts of potentially relevant studies. Disagreements will be resolved through discussion or with a third reviewer. Included studies will be critically appraised and data will be extracted using a modified JBI tool. Findings will be reported in tables and narrative synthesis, and pooled with statistical meta-analysis, where possible.PROSPERO CRD42021289011.

Little is known about Canadian dietitians' perceptions and experiences of interprofessional collaboration. To address this knowledge gap, a cross-sectional online survey was administered to registered dietitians in Canada. Quantitative data was analyzed in SPSS using descriptive and inferential statistics, while thematic analysis was used for open-text responses. A total of

PurposeMeat consumption has a variety of implications in society. While various types of meat-restricted diets exist to address this, not enough is known about how the average meat consumer views different avenues to lessening their intake. In response, this study aims to assess US omnivores’ impressions of three meat-restricted diets.Design/methodology/approachAn online survey was administered to a cross-sectional, census-balanced sample from Nielsen of 30,000+ US adults. Omnivores (N = 928) were randomized into one of the three conditions where they were asked about their perceptions of a vegetarian diet, a reduced-meat diet or a chicken-free diet as individuals not currently following that dietary pattern.FindingsThe findings showed that omnivores had a more favorable perception of a reduced-meat diet on a greater number of study variables as compared to the vegetarian or chicken-free diets. The research also demonstrated that a majority of omnivores were in the precontemplation stage of cognitive change, suggesting that most American omnivores are not actively demonstrating a readiness to alter their meat consumption in the ways presented.Originality/valueThis research is the first to examine the comparative trends around these three diets among omnivores. It also speaks to how the desirability of meat restriction varies by type of approach, i.e. elimination or reduction, and if the latter, what type. The findings may be of relevance for efforts to reduce global meat consumption for ethical, health, or environmental reasons.

The recent recognition of the importance of the microbiome to the host’s health and well-being, has yielded efforts to develop therapies that aim to shift the microbiome from a disease-associated steady-state to a healthier one. Direct manipulation techniques of the species’ assemblage are currently available, e.g., using probiotics or narrow-spectrum antibiotics to introduce or eliminate specific taxa. However, predicting the species’ abundances at the new steady-state remains a challenge, mainly due to the difficulties of deciphering the delicate underlying network of ecological interactions or constructing a predictive model for such complex ecosystem. Here, we propose a model-free method to predict the species’ abundances at the new steady state based on their presence/absence configuration by utilizing a multi-dimensional k-nearest-neighbors (kNN) regression algorithm. By analyzing data from numeric simulations of ecological dynamics, we show that our predictions, which consider the presence/absence of all species holistically, outperform both the null model that uses the statistics of each species independently and a predictive neural network model. We analyze real metagenomic data of human-associated microbial communities and found that by relying on a small number of ‘neighboring’ samples, i.e., samples with similar species assemblage, the kNN predicts the species abundance better than the whole-cohort average. By studying both real metagenomic and simulated data, we show that the predictability of our method is tightly related to the dissimilarity-overlap relationship of the training data. Our results demonstrate how model-free methods can prove useful in predicting microbial communities and may facilitate the development of microbial-based therapies.

The distribution of liquid droplets emerging from an evaporator tube bundle is characterized for refrigerant R-134a with a triangular tube arrangement with a pitch of 1.167. The purpose of this research was to improve understanding of the droplet ejection process to aid in design of evaporators typically used in larger chiller systems. A laser and camera system captured images of the evaporator headspace at varying conditions. Conventional shadowgraphy techniques were applied to recognize and match droplets for velocity calculations. The evaporator conditions varied with bundle mass fluxes of 20.3 and 40.7 kg s−1m−2, top-rows heat fluxes of 15.8 and 31.5 kWm−2, and outlet saturation temperatures of 4.4 and 12.8 °C. Conditions ranged from flooded to dryout of the top rows. Droplet number, size distribution, velocity, and liquid volume fraction are presented in the headspace above the bundle. A method to numerically duplicate the droplet loading in the headspace using CFD with a Lagrangian discrete-phase model is also presented and verified, providing a powerful design tool. Liquid distribution in the headspace is found to be a strong function of all varied properties, particularly mass flux, liquid level, and saturation temperature.

Clean meat (grown from animal cells rather than rearing animals) has the potential to address many concerns associated with meat production. However, research suggests that the perceived unnaturalness of clean meat could be a barrier to consumer acceptance. This study investigated the efficacy of different messages designed to address consumers' concerns about clean meat naturalness. In an experimental design, participants read one of four messages: clean meat is natural, conventional meat is unnatural, naturalness is not important, or highlighting benefits of clean meat without addressing naturalness. The results indicated that arguing that conventional meat is unnatural resulted in a significant increase in some measures of acceptance compared to other messages. Arguing that clean meat is natural and challenging the appeal to nature were less persuasive, and challenging the appeal to nature resulted in some measures of acceptance being lower than not addressing naturalness. We discuss these results in the context of existing naturalness research and give recommendations for further research.

When oceanic rainfall occurs, it creates a vertical salinity profile that is fresher at the surface. This freshwater lens is mixed downward by turbulent diffusion, dissipating over a few hours until the upper layer (1–5 m depth) becomes well mixed. Thus, there will be a transient bias between the in situ bulk salinity and the satellite-measured sea surface salinity (SSS) (representative of the first centimeter of the ocean depth). Based on measurements of Aquarius (AQ) SSS under rainy conditions, a model called rain impact model (RIM) was developed to assess the SSS variations due to the accumulation of rainfall prior to the time of the AQ observation. RIM uses ocean surface salinities from hybrid coordinate ocean model and the NOAA global precipitation product, climate prediction center morphing, to estimate changes in the near-surface salinity profile. Also, the RIM analysis has been applied to soil moisture and ocean salinity with similar results observed. The Soil Moisture Active Passive (SMAP) satellite carries an L -band radiometer, which measures SSS over a swath of 1000 km at 40-km resolution. SMAP can extend AQ salinity data record with improved temporal/spatial sampling. This paper describes RIM that simulates the effects of rain accumulation on SMAP SSS, showing good correlation between the model and the observed SSS values. Given the better resolution of SMAP, the goal of this paper is to continue the previous analysis of AQ to better understand the effects of the instantaneous and accumulated rain on the salinity measurements.

Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks. Technologically advancing adversaries pose substantial risks to a friendly nation's interests and resources. Superior resources alone are not enough to defeat adversaries in modern complex environments because adversaries create standoff in multiple domains against predictable military doctrine-based maneuvers. Therefore, as part of a defense strategy, friendly forces must use strategic maneuvers and disruption to gain superiority in complex multi-faceted domains such as multi-domain operations (MDO). One promising avenue for implementing strategic maneuver and disruption to gain superiority over adversaries is through coordination of MAS in future military operations. In this paper, we present overviews of prominent works in the RL domain with their strengths and weaknesses for overcoming the challenges associated with performing autonomous strategic maneuver and disruption in military contexts., 23 pages, 3 figures, 60 references, Review Paper

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks., This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_ai

In adversarial multi-agent paradigms, it is often difficult to describe what is learned by reinforcement-learning agents and to measure the robustness of the learned policies. To achieve a better view, we explore the effect of manipulating agent capabilities and policies in a predator-prey pursuit task. In these experiments, we trained a single prey using multi-agent reinforcement learning with three slower predators, then tested the prey against three faster predators using fixed “interceptor” strategies (head to the closest possible intersection with the prey assuming the prey maintains its current velocity) instead of their learned policies. While the prey’s performance was impressive under these novel conditions, it varied widely. Initial locations and velocities (randomized during training and testing) were limited in explaining differences in prey’s performance across test conditions. Nevertheless, visual inspection indicates that more successful prey quickly begin a circling pattern, whereas less successful prey often become cornered and double back into predator collisions. To quantify this behavior, we computed windowed entropy measures of the prey’s angle relative to the arena origin to show when an agent transitioned in and out of this unsuccessful behavior. Ultimately, these transitions suggest that circling is triggered by coming close to the center of the arena. By varying agents’ capabilities and predator policies upon evaluation, we achieve a more comprehensive view of the prey’s learned policy, and we suggest that these windowed entropy measures, along with correlations between entropy and performance, result in a quantification of the learned policy.