Teleoperator systems for manned space missions

The development of remote mechanical systems to augment man's capabilities in our manned space effort is considered. A teleoperator system extends man's innate intelligence and sensory capabilities to distant hostile and hazardous environments through a manipulator-equipped spacecraft and an RF link. Examined are space teleoperator system applications in the space station/space shuttle program, which is where the most immediate need exists and the potential return is greatest

Group polarization, influence, and domination in online interaction networks: A case study of the 2022 Brazilian elections

In this work, we investigate the evolution of polarization, influence, and domination in online interaction networks. Twitter data collected before and during the 2022 Brazilian elections is used as a case study. From a theoretical perspective, we develop a methodology called d-modularity that allows discovering the contribution of specific groups to network polarization using the well-known modularity measure. While the overall network modularity (somewhat unexpectedly) decreased, the proposed group-oriented approach allows concluding that the contribution of the right-leaning community to this modularity increased, remaining very high during the analyzed period. Our methodology is general enough to be used in any situation when the contribution of specific groups to overall network modularity and polarization is needed to investigate. Moreover, using the concept of partial domination, we are able to compare the reach of sets of influential profiles from different groups and their ability to accomplish coordinated communication inside their groups and across segments of the entire network during some specific time window. We show that in the whole network, the left-leaning high-influential information spreaders dominated, reaching a substantial fraction of users with fewer spreaders. However, when comparing domination inside the groups, the results are inverse. Right-leaning spreaders dominate their communities using few nodes, showing as the most capable of accomplishing coordinated communication. The results bring evidence of extreme isolation and the ease of accomplishing coordinated communication that characterized right-leaning communities during the 2022 Brazilian elections

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Evaluation of a Fotonovela to Increase Depression Knowledge and Reduce Stigma Among Hispanic Adults

Fotonovelas—small booklets that portray a dramatic story using photographs and captions— represent a powerful health education tool for low-literacy and ethnic minority audiences. This study evaluated the effectiveness of a depression fotonovela in increasing depression knowledge, decreasing stigma, increasing self-efficacy to recognize depression, and increasing intentions to seek treatment, relative to a text pamphlet. Hispanic adults attending a community adult school (N = 157, 47.5 % female, mean age = 35.8 years, 84 % immigrants, 63 % with less than high school education) were randomly assigned to read the fotonovela or a low-literacy text pamphlet about depression. They completed surveys before reading the material, immediately after reading the material, and 1 month later. The fotonovela and text pamphlet both produced significant improvements in depression knowledge and self-efficacy to identify depression, but the fotonovela produced significantly larger reductions in antidepressant stigma and mental health care stigma. The fotonovela also was more likely to be passed on to family or friends after the study, potentially increasing its reach throughout the community. Results indicate that fotonovelas can be useful for improving health literacy among underserved populations, which could reduce health disparities

Sick-listed employees with severe medically unexplained physical symptoms: burden or routine for the occupational health physician? A cross sectional study

Background: The two primary objectives of this study were to the assess consultation load of occupational health physicians (OHPs), and their difficulties and needs with regard to their sickness certification tasks in sick-listed employees with severe medical unexplained physical symptoms (MUPS). Third objective was to determine which disease-, patient-, doctor- and practice-related factors are associated with the difficulties and needs of the OHPs. Methods: In this cross-sectional study, 43 participating OHPs from 5 group practices assessed 489 sick-listed employees with and without severe MUPS. The OHPs filled in a questionnaire about difficulties concerning sickness certification tasks, consultation time, their needs with regard to consultation with or referral to a psychiatrist or psychologist, and communication with GPs. The OHPs also completed a questionnaire about their personal characteristics. Results: OHPs only experienced task difficulties in employees with severe MUPS in relation to their communication with the treating physician. This only occured in cases in which the OHP attributed the physical symptoms to somatoform causes. If they attributed the physical symptoms to mental causes, the OHPs reported a need to consultate a psychiatrist about the diagnosis and treatment. Conclusions: OHPs experience few difficulties with their sickness certification tasks and consultation load concerning employees with severe MUPS. However, they encounter problems if the diagnostic uncertainties of the treating physician interfere with the return to work process. OHPs have a need for psychiatric expertise whenever they are uncertain about the psychiatric causes of a delayed return to work process. We recommend further training programs for OHPs. They should also have more opportunity for consultation and referral to a psychiatrist, and their communication with treating physicians should be improved

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725

The Contribution of High Levels of Somatic Symptom Severity to Sickness Absence Duration, Disability and Discharge

Introduction: The primary objectives were to compare the duration of sickness absence in employees with high levels of somatic symptom severity (HLSSS) with employees with lower levels of somatic symptom severity, and to establish the long-term outcomes concerning return to work (RTW), disability and discharge. Secondary objective was to evaluate determinants of the duration of sickness absence in employees with HLSSS. Methods: 489 sick-listed employees registered with five Occupational Health Physician (OHP) group practices were included in this study. We measured their baseline scores for somatic symptoms severity, depressive disorders, anxiety disorders, health anxiety, distress and functional impairment. The OHPs filled in a questionnaire on their diagnosis. A prospective 2-year follow-up was carried out to assess the long-term outcomes concerning sickness absence, and retrospective information was gathered with regard to sickness absence during the 12 months before the employees were sick-listed. Results: The median duration of sickness absence was 78 days longer for employees with HLSSS. They more often remained disabled and were discharged more often, especially due to problems in the relationship between the employer and the employee. HLSSS, health anxiety and older age contributed to a longer duration of sickness absence of employees. Conclusion: High levels of somatic symptom severity are a determinant of prolonged sickness absence, enduring disabilities and health-related job loss. Occupational health physicians should identify employees who are at risk and adhere to guidelines for medically unexplained somatic symptoms