Academic literature on the topic 'Konvalina'

Abstract Background: The use of continuous glucose monitoring (CGM) technology in the outpatient setting has been associated with both improved patient satisfaction1 and increased glucose monitoring2. It remains to be seen, how well this technology can be integrated during the transition from hospital discharge to outpatient settings. Here, we aim to assess the feasibility of introducing the FreeStyle Libre during the transition of care from inpatient to the outpatient environment in patients with Type 2 diabetes (T2D). We will assess CGM use as measured by the numbers of days used and frequency of daily scans. Methods: During the time period April and September 2020, 20 patients with T2D being discharged on multiple daily injections admitted to North Shore University Hospital were enrolled in this study. Exclusion criteria were those with adhesive allergy, CKD 4/5 or on dialysis and pregnant women. Participants were trained on how to use the FreeStyle Libre with the LibreLink mobile application. All patients received 2 Libre sensors at the time of discharge, one that was placed in the hospital and the other to be placed after 14 days. 1 participant died prior to discharge. Analyses included descriptive statistics, specifically categorical variables using frequencies and percentages while continuous variables using mean and standard deviation. Results: Among who used the mobile application, 10 were men and 9 were women. Majority of patients were Black (n=11, 57.9%) with a mean age of 52 years (range 31–76). The mean duration of diabetes was 9.7 years (range: 0 to 22) and mean Hemoglobin A1c of 11.2% (range: 5.5–15.5). 10 out of 19 persons used CGM for more than 2 weeks, while the remaining 9 utilized the CGM for less than 2 weeks. Mean average daily scans were 5 times per day (range: 1–12) with majority of the persons (n=15, 78.9%) scanning more frequently (3 or more times per day). The average glucose ranged from 62 to 268 mg/dl and the mean active CGM time was 52.05% (range 0–98). Mean glycemic variability was 29.17% (range: 14.5–56.7). Technical issues with the CGM included poor adhesion or issues connecting to the mobile application. Conclusion: Our study found that the initiation of CGM during the transition from hospital discharge to the outpatient setting is feasible and a useful tool. A limitation of this study was the inability for all people to use the mobile application due to incompatible phones or operating systems. References: 1. Beck RW, Riddlesworth TD, Ruedy K, et al. Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections. Ann Intern Med. 2017;167(6):365–374. doi:10.7326/M16-2855. 2. Shehav-Zaltzman G, Segal G, Konvalina N, Tirosh A. Remote Glucose Monitoring of Hospitalized, Quarantined Patients With Diabetes and COVID-19. Diabetes Care. 2020;43(7):e75-e76. doi:10.2337/dc20-0696.

Progression toward sophisticated machines with the capacity to act as partners in tactical and strategic situations means that human operators will increasingly rely on collaborative input from agent teammates (e.g., Masiello, 2013). However, plans to team autonomous agents with humans raise new questions regarding the effects that such teammates might have on important team psychological processes, such as team cognition and trust. Specifically, it is not known how modifications in team structure, such as changes in team size, influence team dynamics and psychological processes when the team includes an artificial agent, nor how trust established in such teams transfers to new environments, nor how measures that have been used to predict trust in humans generalize to agent teammates. Our current research examined these effects through the detection and analysis of an objective team phenomenon known as physio-behavioral coupling (PBC) using Multidimensional Recurrence Quantification Analysis (MdRQA; Wallot, Roepstorff, and Mønster, 2016) of shared physiological arousal during initial team formation and training. In particular, as shared physiological arousal measured in changing heart rhythms within human teams has been shown to be associated with measures of trust (Mitkidis, McGraw, Roepstorff, & Wallot, 2015) and concern for others (Konvalinka et al., 2011), we investigated how shared physiological arousal predicts willingness to trust an agent teammate in a novel task environment. We conducted an experiment consisting of collecting physio-behavioral data (i.e., heart rate) from teams of different sizes as they performed a series of collaborative, consensus building tasks. The independent variable was team size (teams of 2 or 3 human players, with an artificial agent teammate always present), and there were two separate team-oriented tasks: A first-round consensus-building wagering task, and a second-round task in which teams were able to make wagers on the expected performance of the agent teammate in a subsequent maze running task called Checkmate (Alarcon et al., 2017). We predicted that complimentary combinations of PBC (e.g., measures of overall similarity and stability in heart rate dynamics) obtained from MdRQA, along with self-reported measures of team and agent trust, would be positively related to future trusting behaviors in the agent teammate, and that increasing the number of teammates would result in higher order, more complex structure in the physio- behavioral data that would not be reducible to simpler patterns (e.g., Wallot et al., 2016). To this end, we predicted that measures of self-reported trust and multivariate PBC would be reducible to meaningful lower dimensional structures using principal components analysis (PCA), and that PBC calculated from the first task from the full team, but not from averages aggregated from subsets of the team, would significantly predict trusting behavior in the second task. Ninety-two participants (31 men and 61 women) recruited from the campus of a midwestern university in the U.S. took part in this study (19 dyads and 18 triads). Ages ranged from 18 to 42 ( M = 22, SD = 5.48). The experiment was a univariate (team size; two or three human teammates with an agent teammate always present) between-subjects design. Self-reported measures were collected from each team member before each of the two tasks and included items that measured: Team ability, team benevolence, team integrity, and team trust (adapted from Mayer & Davis, 1999); trust in human teammates (adapted from Naquin & Paulson, 2003); agent competence, cognitive trust in the agent, emotional trust in the agent, intention to delegate to the agent, and intention to adopt the agent as an aid (adapted from Komiak & Benbasat, 2006); and collective efficacy (adapted from Riggs & Knight, 1994). Factor analysis of the composite scales from aggregated survey data indicated the data loaded well onto factors that corresponded to trust in the team and trust in the agent teammate. Factor analysis of MdRQA from the full team and from the averaged lower order analyses showed that each had one component with an eigenvalue greater than what would be expected by chance. Results from analyses using logistic regression to predict Checkmate betting showed that self-reported measures of trust in the agent and MdRQA of full team PBC in the initial task significantly predicted subsequent trusting behavior in an agent teammate in Checkmate, but lower-order PBC estimated from averages of team subgroups did not. These results suggest that multivariate team-level coupling has predictive power in subsequent team outcomes that cannot be fully captured using data aggregated from subgroup averages, and that measures of PBC measured from human teammates is related to trust in an agent teammate. We note two important contributions of the present study. First, that PBC and subjective measures of trust were significant predictors of observed trusting behavior regardless of team size suggests that important team processes and outcomes are at least partially invariant to changes in team size, a promising outcome for the prospect of meaningfully scaling measures of PBC beyond the typical dyadic context. Second, we have shown that shared team- level arousal is a significant predictor of subsequent trusting behavior in an agent teammate in a novel task, demonstrating that these objective measures are extensible to trust in non-human partners.

Billey, Konvalinka, Petersen, Solfstra, and Tenner recently presented a method for counting parabolic double cosets in Coxeter groups, and used it to compute $p_n$, the number of parabolic double cosets in $S_n$, for $n\leq13$. In this paper, we derive a new formula for $p_n$ and an efficient polynomial time algorithm for evaluating this formula. We use these results to compute $p_n$ for $n\leq5000$ and to prove an asymptotic formula for $p_n$ that was conjectured by Billey et al.

The right-quantum algebra was introduced recently by Garoufalidis, Lê and Zeilberger in their quantum generalization of the MacMahon master theorem. A bijective proof of this identity due to Konvalinka and Pak, and also the recent proof of the right-quantum Sylvester's determinant identity, make heavy use of a bijection related to the first fundamental transformation on words introduced by Foata. This paper makes explicit the connection between this transformation and right-quantum linear algebra identities; we give a new, bijective proof of the right-quantum matrix inverse theorem, we show that similar techniques prove the right-quantum Jacobi ratio theorem, and we use the matrix inverse formula to find a generalization of the (right-quantum) MacMahon master theorem.

This study investigated whether a predictive tool developed by authors in the United States (Konvalina, Stephens and Wileman) could be used with University students in Australia (in particular the Australian Capital Territory) to predict their success in first year University computing courses. It also investigated the effect of demographic and past academic factors in conjunction with, and instead of the predictive test. The study examined differences in performance between male/female students, English as a Second Language (ESL)/non-ESL students and full-time/part-time students. It also examined the effect of all the above factors on the continuing success of students in the course. While significant differences in first-time performance were found between ESL and non- ESL students, no differences were found between the other pairings. No differences were found between any of the groups in the continuing success in the course. The KSW Test, while being an indicator of first year success, was not a strong enough model to be able to be used as a predictive tool. The demographic and previous academic data from students recently at High School, in particular, the Tertiary Entrance Score, level of mathematics studied, and previous computing study, were found to be more useful as an indicator of success in fust year, explaining 53% of the variation in h a 1 unit score. In addition, 67% of the variation in continuing success in their course was also explained by the Tertiary Entrance Score, ASAT verbal and ASAT quantitative scores.