Miracle-dz/UniFAR-FTUs huggingface dataset:
Polysubstance Overdose Deaths in the Fentanyl Era: A Latent Class Analysis

Objectives: To elucidate the main latent classes of substances detected among overdose decedents, and latent class associations with age, sex, race, and jurisdiction of death in Maryland. Methods: We used toxicology data from the Office of the Chief Medical Examiner of Maryland for all decedents. We analyzed all cases of drug overdose deaths that occurred from 2016 to 2018 (N = 6566) using latent class analysis and regression. Results: Drug overdose deaths were concentrated in 2 of 24 counties in Maryland (Baltimore City and County). Fentanyl was involved in 71% of all drug overdose deaths, and the majority (76%) of these deaths included multiple substances. Three latent classes emerged: (1) fentanyl/heroin/cocaine (64%); (2) fentanyl/alcohol (18%); and (3) prescription drugs including opioids, benzodiazepines and antidepressants (18.0%). The fentanyl/heroin/cocaine class members were significantly younger (<30 years), female and White compared to the fentanyl/alcohol class, but more male and non-White than the prescription drugs class (all P < 0.05). Deaths in Baltimore City/County were more likely than in other locations to involve fentanyl/alcohol (P < 0.05). Conclusions: The majority of fentanyl-involved overdose deaths in Maryland involved multiple substances, and several demographic and geographic differences in these patterns emerged. Geographically-targeted interventions that are tailored to reduce the harms associated with polysubstance use (including cocaine, alcohol, and prescription drugs) for different demographic groups are warranted.
An Estimate of the Interatomic Distances in Monohaloacetylene Radical Cations from Photoelectron-spectroscopic Data†

A convenient method is given for the semiquantitative determination of those changes ΔSi in internal coordinates, which describe the structure of the radical cation M+(Ψ) relative to the neutral molecule M in its electronic ground state. The changes ΔSi correspond to those associated with the relaxation process which follows the ionisation M + hv → M+(Ψ) + e, M+ being in the electronic state Ψ. The method, based on the procedure originally developped by Smith & Warsop [3], uses the spacing and relative intensity of the vibrational fine-structure components of the bands in the photoelectron spectrum of M. From these data are calculated the changes ΔQk in normal coordinates, which are then transformed into the ΔSi by applying the L-matrix. The changes ΔRCX, ΔRCC and ΔRCH which describe the structure of the radical cations of the four monohaloacetylenes I(X) (X = F, Cl, Br, I) in their 2IIΩ(1) and 2IIΩ(2) states have been determined. It is shown that the results are in agreement with theoretical expectation and that they confirm the band assignement proposed in a previous communication [2].
Queen loss increases worker survival in leaf-cutting ants under paraquat-induced oxidative stress

Longevity is traded off with fecundity in most solitary species, but the two traits are positively linked in social insects. In ants, the most fecund individuals (queens and kings) live longer than the non-reproductive individuals, the workers. In many species, workers may become fertile following queen loss, and recent evidence suggests that worker fecundity extends worker lifespan. We postulated that this effect is in part owing to improved resilience to oxidative stress, and tested this hypothesis in three Myrmicine ants: Temnothorax rugatulus, and the leaf-cutting ants Atta colombica and Acromyrmex echinatior. We removed the queen from colonies to induce worker reproduction and subjected workers to oxidative stress. Oxidative stress drastically reduced survival, but this effect was less pronounced in leaf-cutting ant workers from queenless nests. We also found that, irrespective of oxidative stress, outside workers died earlier than inside workers did, likely because they were older. Since At. colombica workers cannot produce fertile offspring, our results indicate that direct reproduction is not necessary to extend the lives of queenless workers. Our findings suggest that workers are less resilient to oxidative stress in the presence of the queen, and raise questions on the proximate and ultimate mechanisms underlying socially mediated variation in worker lifespan. This article is part of the theme issue ‘Ageing and sociality: why, when and how does sociality change ageing patterns? ’
The Impact of the Variation of CT Scanner on the Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) using Radiomic Models

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner model) and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on the computed tomography (CT)-derived radiomic features to predict association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired with two different scanner manufacturers. We demonstrate scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments revealed the effect of scanner manufacturers on the robustness of the radiomic features, and the extent of this dependency is reflected on the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reduce the impact of imaging parameters on radiomic features and consequently on the machine learning models.
Conducting global mental health research: lessons learned from Kenya

Abstract Mental health disorders are prevalent among youth and adolescents in low- and middle-income countries, and access to evidence-based treatments is poor. Although there is a great need for high-quality research to serve young people in low- and middle-income countries, there is limited guidance available for researchers who wish to conduct such work. Here, we describe our process of conducting school-based youth mental health work in Kenya over the last several years. We focus on five key lessons we learned that could guide future global mental health work with youth: (a) reducing stigma with strengths-focused interventions, (b) expanding access by working in schools, (c) generating buy-in from local stakeholders, (d) adapting the intervention via multicultural collaboration, and (e) applying insights from low- and middle-income countries to serve young people in high-income countries. We conclude by discussing how these lessons, and those shared by other teams, can be applied to help reduce the treatment gap for young people around the world.
The Effect of Fuel Type and Aftertreatment Method on Ultrafine Particle Emissions from a Heavy-Duty Diesel Engine

Two potential strategies for reducing diesel emissions are exhaust aftertreatment and the use of reformulated or alternative fuels. Little is yet known about the impact on ultrafine particle emissions of combining exhaust aftertreatment with such increasingly common fuels. This paper reports ultrafine particle size distribution measurements for a study in which the impact of such fuels on emissions from a heavy duty diesel engine employing different aftertreatment configurations was evaluated. Eight different fuels were tested: Canadian No. 1 and No. 2 diesel; low sulfur diesel fuel; two different ultra low sulfur diesel fuels (< 30 ppm S); Fischer-Tropsch diesel fuel; 20% biodiesel blended with ultra low sulfur diesel fuel; and PuriNOx™. The fuels were tested in combination with four exhaust configurations: engine out, diesel oxidation catalyst (DOC), continuously regenerating diesel particle filter (CRDPF), and engine gas recirculation with CRDPF (EGR-DPF). In general, aftertreatment configuration was found to have a greater impact on ultrafine particle size distributions than fuel composition, and the effects of aftertreatment tended to be uniform across the entire particle size distribution. Steady state tests revealed complex behavior based on fuel type, particularly for PuriNOx. This behavior included bimodal particle size distributions with modes as low as 8–10 nm for some fuels. Unlike previous results for gravimetric PM from this study, no significant correlation for ultrafine emissions was found for fuel properties such as sulfur level.
Performance Limits for a Class of Irreversible Internal Combustion Engines

The performance of a class of irreversible internal combustion engines with finite rate heat exchange with the environment and nonzero entropy generation due to combustion chemical reactions in the cylinder is studied in this paper by using finite time thermodynamics. It is assumed that the heat transfer between the working fluid in the cylinder and the environment obey linear phenomenological law [q ∞ Δ(T 1 )] in the irreversible thermodynamics, and the combustion chemical reactions in the cylinder obey a general rate equation of reactions. The upper bounds ofpower output and efficiency of the internal combustion engines are derived by applying optimal control theory. For the special examples with one chemical reaction and some linearly independent chemical reactions, the average optimal control problems are transformed into nonlinear programming problems, and the Kuhn-Tucker conditions corresponding to the optimal solutions are found. Analytical solutions of minimum entropy generation for the two cases are provided. The results obtained herein are compared with those obtained with different rate equation of reactions and Newton's heat transfer law ([q ∝ Δ(T)]). The methods used and the results obtained in this paper can provide some theoretical guidelines for the optimal design and operation of practical internal combustion engines.
Current status and solutions for gender equity in anaesthesia research

Despite increasing numbers of women entering anaesthesia, they remain persistently under‐represented within academic anaesthesia and research. Gender discordance is seen across multiple aspects of research, including authorship, editorship, peer review, grant receipt, speaking and leading. Women are also under‐represented at higher faculty ranks and in department chair positions. These inequities are further magnified for women with intersectional identities, such as those who identify as Black, indigenous and women of colour. Several barriers to participation in research have been identified to date, including a disproportionate amount of family responsibilities, a disproportionate burden of clinical service, gender bias, sexual harassment and the gender pay gap. Several strategies to improve gender equity have been proposed. Increasing access to formal mentorship of women in academic medicine is frequently cited and has been used by healthcare institutions and medical societies. Senior faculty and leaders must also be conscious of including women in sponsorship and networking opportunities. Institutions should provide support for parents of all genders, including supportive parental leave policies and flexible work models. Women should also be materially supported to attend formal educational conferences targeted for women, aimed at improving networking, peer support and professional development. Finally, leaders must display a clear intolerance for sexual harassment and discrimination to drive culture change. Peers and leaders alike, of all genders, can act as upstanders and speak up on behalf of targets of discrimination, both in the moment or after the fact. Gender inequities have persisted for far too long and can no longer be ignored. Diversifying the anaesthesia research community is essential to the future of the field.
Robot Vitals and Robot Health: An Intuitive Approach to Quantifying and Communicating Predicted Robot Performance Degradation in Human-Robot Teams

In this work we introduce the concept of Robot Vitals and propose a framework for systematically quantifying the performance degradation experienced by a robot. A performance indicator or parameter can be called a Robot Vital if it can be consistently correlated with a robot's failure, faulty behaviour or malfunction. Robot Health can be quantified as the entropy of observing a set of vitals. Robot vitals and Robot health are intuitive ways to quantify a robot's ability to function autonomously. Robots programmed with multiple levels of autonomy (LOA) do not scale well when a human is in charge of regulating the LOAs. Artificial agents can use robot vitals to assist operators with LOA switches that fix field-repairable non-terminal performance degradation in mobile robots. Robot health can also be used to aid a tele-operator's judgement and promote explainability (e.g. via visual cues), thereby reducing operator workload while promoting trust and engagement with the system. In multi-robot systems, agents can use robot health to prioritise robots most in need of tele-operator attention. The vitals proposed in this paper are: rate of change of signal strength; sliding window average of difference between expected robot velocity and actual velocity; robot acceleration; rate of increase in area coverage and localisation error.
Experimental and Numerical Simulation of Spent Catalyst Separation in an Active Pulsing Air Classifier

Research on recycling spent catalyst is at the forefront of preventing environmental pollution and recycling resources. An active pulsing air classifier is applied to recover the valuable concentrate from spent catalyst. A multi-factor separation experiment was designed using Box-Behnken module of Design-Expert software, the results show that the separation efficiency ranged from 96.26 to 99.29%. When the airflow velocity is 8.06 m/s, pulsing frequency is 3.55 Hz, feed frequency is 35 Hz, and thus 99.29% separation efficiency can be achieved. A sharp separation is achieved with a wider performance range of operation conditions. A DEM-CFD model is developed to study the gas-solid flow in a lab-scale active pulsing air classifier. In the model, the motion of particles is obtained using the Discrete Element Method facilitated with the concept of “particle-particle,” while the gas flow by Computational Fluid Dynamics based on the local averaged Navier-Stokes equations. The simulated results agree reasonably well with the experimental observation. It will provide a cost-effective way to study the gas-solid flow in the larger-scale active pulsing air classifier with different geometrical, operational and material conditions.
Who Wants to Grant Robots Rights?

The robot rights debate has thus far proceeded without any reliable data concerning the public opinion about robots and the rights they should have. We have administered an online survey (n = 439) that investigates layman’s attitudes toward granting particular rights to robots. Furthermore, we have asked them the reasons for their willingness to grant them those rights. Finally, we have administered general perceptions of robots regarding appearance, capacities, and traits. Results show that rights can be divided in sociopolitical and robot dimensions. Reasons can be distinguished along cognition and compassion dimensions. People generally have a positive view about robot interaction capacities. We found that people are more willing to grant basic robot rights such as access to energy and the right to update to robots than sociopolitical rights such as voting rights and the right to own property. Attitudes toward granting rights to robots depend on the cognitive and affective capacities people believe robots possess or will possess in the future. Our results suggest that the robot rights debate stands to benefit greatly from a common understanding of the capacity potentials of future robots.
Anthropomorphize me!: Effects of Robot Gender on Listeners' Perception of the Social Robot NAO in a Storytelling Use Case

Social robots have started taking on storytelling, an age-old human tradition. However, the narrator's voice is central in storytelling and a robot cannot match the capabilities of human voice modulation, which might affect the perception of the robot by the listener. Using a robot and gendered voice as a medium for storytelling, we take a first step towards identifying effects of narrators' voice on anthropomorphism. We examine the robot's perceived anthropomorphism and the influence of its voice (female, male, or neutral) on recipients' attitude towards the robotic storyteller concerning gender and cross-gender effects. In addition, transportation indicating the quality of storytelling is investigated. We found no significant results, neither for attitudes toward the robot nor for transportation. Our gender-based voice manipulation did not affect the storytelling process. A lack of anthropomorphism of the robot may explain these findings and should be investigated in further studies.
Vapor−Liquid Equilibria and Critical Behavior of Heavy n-Alkanes Using Transferable Parameters from the Soft-SAFT Equation of State

A new set of molecular transferable parameters for the n-alkane series is proposed. n-Alkanes are modeled as homonuclear chainlike molecules formed by tangentially bonded Lennard-Jones segments of equal diameter and the same dispersive energy. Phase equilibria calculations of heavy pure members of the series, up to n-octatetracontane (n-C48H98), and of ethane/n-decane and ethane/n-eicosane mixtures are performed with the soft-SAFT (statistical associating fluid theory) equation of state. This SAFT-type equation explicitly accounts for repulsive and dispersive forces in the reference term through a Lennard-Jones interaction potential, and it has been proven to accurately describe the phase behavior of light n-alkanes. Using the new set of parameters, the soft-SAFT equation is able to accurately predict the phase behavior of pure heavy n-alkanes. The dependence of the critical properties of pure n-alkanes with the carbon number is also predicted to be in quantitative agreement with experimental data, valida.
TSUNDERE Interaction: Behavior Modification by the Integrated Interaction of Cold and Kind Actions

In order to reinforce behavior modification, we first applied a user's favorite avatar with TSUNDERE type to operant conditioning, an approach that reinforces desired behaviors by providing a combination of punishment and reward. Then, we considered TSUNDERE Interaction, an interaction using TSUN (cold action) as punishment, DERE (kind action) as reward, and TSUN/DERE action by that avatar. Furthermore, we use a 3D virtual avatar superimposed on the robot using augmented reality technology as an avatar, and use the user's state of concentration to provide appropriate feedback to the user. In this paper, we discuss the core concepts we have implemented to develop the system based on their idea as a means of behavior modification.
Exploring Non-verbal Gaze Behavior in Groups Mediated by an Adaptive Robot

In this study, non-verbal behavior in diversely-skilled groups was observed while participating in a collaborative educational game with a humanoid robot. Research has indicated that a mediating robot gaze can equalize the verbal contributions from each differently skilled participant, promoting inclusion and learning. The experiment results were further analyzed, extending to non-verbal effects. The initial results from two experiments under different robot gaze behavior indicate that modifications in the robot gaze can lead to different gaze behavior in participants. It was observed that a gaze mediating behavior in the robot led to increased gaze change frequency among participants as well as more time spent mirroring the robot's gaze. These initial results show promise in how a robot can balance attention in a collaborative learning environment.
Developing a Data-Driven Categorical Taxonomy of Emotional Expressions in Real World Human Robot Interactions

Emotions are reactions that can be expressed through a variety of social signals. For example, anger can be expressed through a scowl, narrowed eyes, a long stare, or many other expressions. This complexity is problematic when attempting to recognize a human's expression in a human-robot interaction: categorical emotion models used in HRI typically use only a few prototypical classes, and do not cover the wide array of expressions in the wild. We propose a data-driven method towards increasing the number of known emotion classes present in human-robot interactions, to 28 classes or more. The method includes the use of automatic segmentation of video streams into short (<10s) videos, and annotation using the large set of widely-understood emojis as categories. In this work, we showcase our initial results using a large in-the-wild HRI dataset (UE-HRI), with 61 clips randomly sampled from the dataset, labeled with 28 different emojis. In particular, our results showed that the "skeptical" emoji was a common expression in our dataset, which is not often considered in typical emotion taxonomies. This is the first step in developing a rich taxonomy of emotional expressions that can be used in the future as labels for training machine learning models, towards more accurate perception of humans by robots.
Exploring Web-Based VR for Participatory Robot Design

With the pandemic preventing access to universities and consequently limiting in-person user studies, it is imperative to explore other mediums for conducting user studies for human-robot interaction. Virtual reality (VR) presents a novel and promising research platform that can potentially offer a creative and accessible environment for HRI studies. Despite access to VR being limited given its hardware requirements (e.g. need for headsets), web-based VR offers universal access to VR utilities through web browsers. In this paper, we present a participatory design pilot study, aimed at exploring the use of co-design of a robot using web-based VR. Results seem to show that web-based VR environments are engaging and accessible research platforms to gather environment and interaction data in HRI.
Introducing SMRTT: A Structural Equation Model of Multimodal Real-Time Trust

Advances in autonomous technology have led to an increased interest in human-autonomy interactions. Generally, the success of these interactions is measured by the joint performance of the AI and the human operator. This performance depends, in part, on the operator having appropriate, or calibrated, trust of the autonomy. Optimizing the performance of human-autonomy teams therefore partly relies on the modeling and measuring of human trust. Theories and models have been developed on the factors influencing human trust in order to properly measure it. However, these models often rely on self-report rather than more objective, and real-time behavioral and physiological data. This paper seeks to build off of theoretical frameworks of trust by adding objective data to create a model capable of finer grain temporal measures of trust. Presented herein is SMRTT: SEM of Multimodal Real Time Trust. SMRTT leverages Structured Equation Modeling (SEM) techniques to arrive at a real time model of trust. Variables and factors from previous studies and existing theories are used to create components of SMRTT. The value of adding physiological data to the models to create real-time monitoring is discussed along with future plans to validate this model.