A Changing World of Workplace Conflict Resolution and Employee Voice: An Australian Perspective

The authors contribute to dispute resolution theory and provide new insights on such important issues as employee voice, workplace disputes and employees’ intentions to quit. They conducted and analyzed a survey of managers in Australian workplaces. They apply Budd and Colvin’s (2008) path-finding dispute resolution framework to examine two research questions: first, is there a relationship between the resolution of disputes and employee voice as measured by employee perceptions of influence over decision-making? Second, is there a relationship between the resolution of workplace disputes and employees’ intentions to quit? These are important questions in view of the high costs of workplace conflict and employee turnover. The authors find that employee voice facilitates successful dispute resolution. Further, employee voice has the additional benefit of directly reducing employee turnover intentions, above and beyond its indirect effect by helping to resolve conflicts at work

A family of repulsive neutral conductor geometries via abstract vector spaces

Recently it was shown that it is possible for a neutral, isolated conductor to repel a point charge (or, a point dipole). Here we prove this fact using general properties of vectors and operators in an inner-product space. We find that a family of neutral, isolated conducting surface geometries, whose shape lies somewhere between a hemispherical bowl and an ovoid, will repel a point charge. In addition, we find another family of surfaces (with a different shape) that will repel a point dipole. The latter geometry can lead to Casimir repulsion.Comment: 6 pages, 4 figure

A Scalable Information Theoretic Approach to Distributed Robot Coordination

This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots.This work is sponsored by the Department of the Air Force under Air Force contract number FA8721-05-C-0002. The opinions, interpretations, recommendations, and conclusions are those of the authors and are not necessarily endorsed by the United States Government. This work is also supported in part by ARO grant number W911NF-05-1-0219, ONR grant number N00014-09-1-1051, NSF grant number EFRI-0735953, ARL grant number W911NF-08-2-0004, MIT Lincoln Laboratory, the European Commission, and the Boeing Company

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Associations Between Imprinted Gene Expression in the Placenta, Human Fetal Growth and Preeclampsia

Genomic imprinting is essential for normal placental and fetal growth. One theory to explain the evolution of imprinting is the kinship theory (KT), which predicts that genes that are paternally expressed will promote fetal growth, whereas maternally expressed genes will suppress growth. We investigated the expression of imprinted genes using microarray measurements of expression in term placentae. Correlations between birthweight and the expression levels of imprinted genes were more significant than for non-imprinted genes, but did not tend to be positive for paternally expressed genes and negative for maternally expressed genes.  Imprinted genes were more dysregulated in preeclampsia (a disorder associated with placental insufficiency) than randomly selected genes, and we observed an excess of patterns of dysregulation in preeclampsia that would be expected to reduce nutrient allocation to the fetus, given the predictions of the KT. However, we found no evidence of coordinated regulation among these imprinted genes. A few imprinted genes have previously been shown to be associated with fetal growth and preeclampsia, and our results indicate that this is true for a broader set of imprinted genes

Consent-GPT: is it ethical to delegate procedural consent to conversational AI?

Obtaining informed consent from patients prior to a medical or surgical procedure is a fundamental part of safe and ethical clinical practice. Currently, it is routine for a significant part of the consent process to be delegated to members of the clinical team not performing the procedure (eg, junior doctors). However, it is common for consent-taking delegates to lack sufficient time and clinical knowledge to adequately promote patient autonomy and informed decision-making. Such problems might be addressed in a number of ways. One possible solution to this clinical dilemma is through the use of conversational artificial intelligence using large language models (LLMs). There is considerable interest in the potential benefits of such models in medicine. For delegated procedural consent, LLM could improve patients’ access to the relevant procedural information and therefore enhance informed decision-making. In this paper, we first outline a hypothetical example of delegation of consent to LLMs prior to surgery. We then discuss existing clinical guidelines for consent delegation and some of the ways in which current practice may fail to meet the ethical purposes of informed consent. We outline and discuss the ethical implications of delegating consent to LLMs in medicine concluding that at least in certain clinical situations, the benefits of LLMs potentially far outweigh those of current practices

Measurement of Spin-orbit Misalignment and Nodal Precession for the Planet around Pre-main-sequence Star PTFO 8-8695 from Gravity Darkening

PTFO 8-8695b represents the first transiting exoplanet candidate orbiting a pre-main-sequence star (van Eyken et al. 2012, ApJ, 755, 42). We find that the unusual lightcurve shapes of PTFO 8-8695 can be explained by transits of a planet across an oblate, gravity-darkened stellar disk. We develop a theoretical framework for understanding precession of a planetary orbit's ascending node for the case when the stellar rotational angular momentum and the planetary orbital angular momentum are comparable in magnitude. We then implement those ideas to simultaneously and self-consistently fit two separate lightcurves observed in 2009 December and 2010 December. Our two self-consistent fits yield Mp = 3.0 M_Jup and Mp = 3.6 M_Jup for assumed stellar masses of M* = 0.34 M_☉ and M* = 0.44 M_☉ respectively. The two fits have precession periods of 293 days and 581 days. These mass determinations (consistent with previous upper limits) along with the strength of the gravity-darkened precessing model together validate PTFO 8-8695b as just the second hot Jupiter known to orbit an M-dwarf. Our fits show a high degree of spin-orbit misalignment in the PTFO 8-8695 system: 69° ± 2° or 73°.1 ± 0°.5, in the two cases. The large misalignment is consistent with the hypothesis that planets become hot Jupiters with random orbital plane alignments early in a system's lifetime. We predict that as a result of the highly misaligned, precessing system, the transits should disappear for months at a time over the course of the system's precession period. The precessing, gravity-darkened model also predicts other observable effects: changing orbit inclination that could be detected by radial velocity observations, changing stellar inclination that would manifest as varying vsin i, changing projected spin-orbit alignment that could be seen by the Rossiter–McLaughlin effect, changing transit shapes over the course of the precession, and differing lightcurves as a function of wavelength. Our measured planet radii of 1.64 R_Jup and 1.68 R_Jup in each case are consistent with a young, hydrogen-dominated planet that results from a "hot-start" formation mechanism

Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks

This paper presents a decentralized control strategy for positioning and orienting multiple robotic cameras to collectively monitor an environment. The cameras may have various degrees of mobility from six degrees of freedom, to one degree of freedom. The control strategy is proven to locally minimize a novel metric representing information loss over the environment. It can accommodate groups of cameras with heterogeneous degrees of mobility (e.g., some that only translate and some that only rotate), and is adaptive to robotic cameras being added or deleted from the group, and to changing environmental conditions. The robotic cameras share information for their controllers over a wireless network using a specially designed multihop networking algorithm. The control strategy is demonstrated in repeated experiments with three flying quadrotor robots indoors, and with five flying quadrotor robots outdoors. Simulation results for more complex scenarios are also presented.United States. Army Research Office. Multidisciplinary University Research Initiative. Scalable (Grant number W911NF-05-1-0219)United States. Office of Naval Research. Multidisciplinary University Research Initiative. Smarts (Grant number N000140911051)National Science Foundation (U.S.). (Grant number EFRI-0735953)Lincoln LaboratoryBoeing CompanyUnited States. Dept. of the Air Force (Contract FA8721-05-C-0002