To Vaccinate or Not? The Role Played by Uncertainty Communication on Public Understanding and Behavior Regarding COVID-19

Communication regarding COVID-19 vaccines requires evidence-based strategies. We present findings from a quantitative survey measuring participants’ understanding, trust, and decision-making in response to information conveying low or high uncertainty regarding the vaccine. Communication conveying high uncertainty led to lower self-assessed understanding but higher actual understanding of possible outcomes. Communication conveying low uncertainty increased vaccine acceptance by those who previously opposed vaccines. This indicates that communicating uncertainty may have different effects over time and that adjusting messaging depending on audiences’ prior vaccine attitudes might be important. These findings support the need for further investigation of how uncertainty communication influences vaccine acceptance.

Mean–variance relationship and uncertainty

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots

The application of robots to replace manual work in live-line working scenes can effectively guarantee the safety of personnel. To improve the operation efficiency and reduce the difficulties in operating a live-line working robot, this paper proposes a multi-DOF robot motion planning method based on RRT and extended algorithms. The planning results of traditional RRT and extended algorithms are random, and obtaining sub-optimal results requires a lot of calculations. In this study, a sparse offline tree filling the planning space are generated offline through the growing–withering method. In the process of expanding the tree, by removing small branches, the tree can fully wiring in the planning space with a small number of nodes. Optimize wiring through a large number of offline calculations, which can improve the progressive optimality of the algorithm. Through dynamic sampling and pruning, the growth of trees in undesired areas is reduced and undesired planning results are avoided. Based on the offline tree, this article introduces the method of online motion planning. Experiments show that this method can quickly complete the robot motion planning and obtain efficient and low-uncertainty paths.

A Workflow to Derive Rock Mechanics Correlations and Stress Profile by the Integration of Dynamic and Static Formation Evaluation Data

Rock mechanics utilizes empirical formulas which are based on studies of certain environments. The shortcoming of such criteria is having estimations of rock physical properties with high uncertainty and not field/formation specific. The objective of this paper is to apply a core-log integration to convert dynamic mechanical properties captured from formation evaluation logs and calibrate them with core static data to generate a continuous profile of data with low uncertainty and generate correlations applicable to the specific physical environment. To obtain proper rock mechanical correlations, building a mechanical earth model (MEM) calibrated with core data and stimulation data is essential. Multiple wells drilled in a certain sandstone field with rock mechanical physical tests are analyzed. Multi-arm caliber data is also put in use to establish knowledge about in-situ stress directions. The procedure starts with gathering and filtering acoustic slowness & shear, formation pressure, density, and oriented multi-arm caliper logs. Next, calibration of dynamic to core static mechanical data collected in the lab is established. The geomechanical analysis includes an understanding of the state of stresses in a chosen reservoir along with rock elastic and failure properties. The complied data is then integrated using different workflows to develop Mechanical Earth Model (MEM). The intended rock mechanics correlations include elastic constants (Young's Modulus and Poisson's ratio), and rock failure parameters. Once Mechanical Earth Model (MEM) is established, dynamic logging data and core static data are correlated to produce key rock mechanics elements that are field and formation specific. The correlations include Young's Modulus, Poisson's Ratio, Unconfined Compressive Strength (UCS) correlation, and Friction Angle (FANG) correlation. A range of each rock mechanic element is also highlighted for the specific environment showcasing the limits expected for collapse and fracture. Ultimately, stress profile is generated with low uncertainty highlighting magnitudes of maximum and minimum horizontal stresses along with the given interval.

Promoting COVID-19 Vaccination: The Interplay of Message Framing, Psychological Uncertainty, and Public Agency as a Message Source

The study examines how framing, psychological uncertainty, and agency type influence campaign effectiveness in promoting coronavirus disease 2019 (COVID-19) vaccines. A 2 (gain vs. loss frame) × 2 (high vs. low uncertainty) × 2 (national vs. local agency) between-subjects experiment was conducted among Houston residents ( N = 382). Findings revealed that a loss frame was more effective among participants primed with high uncertainty through a thought-listing task; however, it was less persuasive under conditions of low uncertainty due to increased psychological reactance. Moreover, there was an interaction effect between uncertainty and agency type on vaccine beliefs. The study contributes to the framing literature by identifying psychological uncertainty as a moderator and provides useful suggestions for vaccine message design.

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

This paper presented an regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF's geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method's ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method's robustness and efficacy.

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

This paper presented an regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF's geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method's ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method's robustness and efficacy.

Uncertainty-induced instantaneous speed and acceleration of a levitated particle

Levitating nanoparticles trapped in optical potentials at low pressure open the experimental investigation of nonlinear ballistic phenomena. With engineered non-linear potentials and fast optical detection, the observation of autonomous transient mechanical effects, such as instantaneous speed and acceleration stimulated purely by initial position uncertainty, are now achievable. By using parameters of current low pressure experiments, we simulate and analyse such uncertainty-induced particle ballistics in a cubic optical potential demonstrating their evolution, faster than their standard deviations, justifying the feasibility of the experimental verification. We predict, the maxima of instantaneous speed and acceleration distributions shift alongside the potential force, while the maximum of position distribution moves opposite to it. We report that cryogenic cooling is not necessary in order to observe the transient effects, while a low uncertainty in initial particle speed is required, via cooling or post-selection, to not mask the effects. These results stimulate the discussion for both attractive stochastic thermodynamics, and extension of recently explored quantum regime.

Chronic Wasting Disease Transmission Risk Assessment for Farmed Cervids in Minnesota and Wisconsin

CWD (chronic wasting disease) has emerged as one of the most important diseases of cervids and continues to adversely affect farmed and wild cervid populations, despite control and preventive measures. This study aims to use the current scientific understanding of CWD transmission and knowledge of farmed cervid operations to conduct a qualitative risk assessment for CWD transmission to cervid farms and, applying this risk assessment, systematically describe the CWD transmission risks experienced by CWD-positive farmed cervid operations in Minnesota and Wisconsin. A systematic review of literature related to CWD transmission informed our criteria to stratify CWD transmission risks to cervid operations into high-risk low uncertainty, moderate-risk high uncertainty, and negligible-risk low uncertainty categories. Case data from 34 CWD-positive farmed cervid operations in Minnesota and Wisconsin from 2002 to January 2019 were categorized by transmission risks exposure and evaluated for trends. The majority of case farms recorded high transmission risks (56%), which were likely sources of CWD, but many (44%) had only moderate or negligible transmission risks, including most of the herds (62%) detected since 2012. The presence of CWD-positive cervid farms with only moderate or low CWD transmission risks necessitates further investigation of these risks to inform effective control measures.