Poster 85: Subacromial Impingement Syndrome as a Consequence of a Long Thoracic Neuropathy: A Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147003/1/pmr2s43a.pd

Poster 101 Pediatric Mononeuritis Multiplex Secondary to a Nonsystemic Vasculitis: A Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147008/1/pmr2s203a.pd

Poster 350: Neurosarcoidosis Presenting as a Central Cord Syndrome: A Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146922/1/pmr2s256a.pd

Assessing Graphical Robot Aids for Interactive Co-working

The shift towards more collaborative working between humans and robots increases the need for improved interfaces. Alongside robust measures to ensure safety and task performance, humans need to gain the confidence in robot co-operators to enable true collaboration. This research investigates how graphical signage can support human–robot co-working, with the intention of increased productivity. Participants are required to co-work with a KUKA iiwa lightweight manipulator on a manufacturing task. The three conditions in the experiment differ in the signage presented to the participants – signage relevant to the task, irrelevant to the task, or no signage. A change between three conditions is expected in anxiety and negative attitudes towards robots; error rate; response time; and participants’ complacency, suggested by facial expressions. In addition to understanding how graphical languages can support human–robot co-working, this study provides a basis for further collaborative research to explore human–robot co-working in more detail

The Impact of Different Human-Machine Interface Feedback Modalities on Older Participants’ User Experience of CAVs in a Simulator Environment

Rapidly developing Autonomous Vehicle (AV) technology has potential to provide solutions to some of the aging population challenges, such as social isolation resulting from an inability to be independently mobile. However for AVs success, users’ acceptance is essential. Fifteen participants (M 70 years) participated in an autonomous driving simulator trial with voice-based CAV status feedback in a decision-making scenario – whether to pick up a friend on the way. The within-subject conditions/journeys were: Audio feedback (Audio)/Pick-Up; Audio/No-Pick-Up; No-Audio/Pick-Up. Additionally, the effect of feedback during different external journey conditions was also considered, resulting in two between-subjects conditions – day and night travel. Participants physiological, cognitive and affective measures show greater situational awareness and workload ratings in the No-Audio/Pick-Up condition with increased Post-trial trust rating and overall higher positive affect. These results indicate that the greatest concentration was required in the no-sound condition, suggesting that sound/multimodal feedback improved ease of operation and journey experience

Prosocial personality traits differentially predict egalitarianism, generosity, and reciprocity in economic games

Recent research has highlighted the role of prosocial personality traits—agreeableness and honesty-humility—in egalitarian distributions of wealth in the dictator game. Expanding on these findings, we ran two studies to examine individual differences in two other forms of prosociality—generosity and reciprocity—with respect to two major models of personality, the Big Five and the HEXACO. Participants (combined N = 560) completed a series of economic games in which allocations in the dictator game were compared with those in the generosity game, a non-constant-sum wealth distribution task where proposers with fixed payoffs selected the size of their partner’s payoff (“generosity”). We further examined positive and negative reciprocity by manipulating a partner’s previous move (“reciprocity”). Results showed clear evidence of both generosity and positive reciprocity in social preferences, with allocations to a partner greater in the generosity game than in the dictator game, and greater still when a player had been previously assisted by their partner. There was also a consistent interaction with gender, whereby men were more generous when this was costless and women were more egalitarian overall. Furthermore, these distinct forms of prosociality were differentially predicted by personality traits, in line with the core features of these traits and the theoretical distinctions between them. HEXACO honesty-humility predicted dictator, but not generosity allocations, while traits capturing tendencies towards irritability and anger predicted lower generosity, but not dictator allocations. In contrast, the politeness—but not compassion—aspect of Big Five agreeableness was uniquely and broadly associated with prosociality across all games. These findings support the discriminant validity between related prosocial constructs, and have important implications for understanding the motives and mechanisms taking place within economic games

Language-free graphical signage improves human performance and reduces anxiety when working collaboratively with robots

As robots become more ubiquitous, and their capabilities extend, novice users will require intuitive instructional information related to their use. This is particularly important in the manufacturing sector, which is set to be transformed under Industry 4.0 by the deployment of collaborative robots in support of traditionally low-skilled, manual roles. In the first study of its kind, this paper reports how static graphical signage can improve performance and reduce anxiety in participants physically collaborating with a semi-autonomous robot. Three groups of 30 participants collaborated with a robot to perform a manufacturing-type process using graphical information that was relevant to the task, irrelevant, or absent. The results reveal that the group exposed to relevant signage was significantly more accurate in undertaking the task. Furthermore, their anxiety towards robots significantly decreased as a function of increasing accuracy. Finally, participants exposed to graphical signage showed positive emotional valence in response to successful trials. At a time when workers are concerned about the threat posed by robots to jobs, and with advances in technology requiring upskilling of the workforce, it is important to provide intuitive and supportive information to users. Whilst increasingly sophisticated technical solutions are being sought to improve communication and confidence in human-robot co-working, our findings demonstrate how simple signage can still be used as an effective tool to reduce user anxiety and increase task performance

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

The Making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.publishedVersio