642 research outputs found
Rethinking Peer Review: Expanding the Boundaries for Community-Engaged Scholarship
Peer review in the academic arena is the evaluation of a scholar or a scholarly work by peers— typically, qualified members of the scholar’s discipline or profession with similar or greater competence, expertise, or rank. Peer review serves as a mechanism of self-regulation within a field or an institution in order to assure quality and may be applied to a product of scholarship, to scholars and their bodies of work, or to programs and organizations. Special considerations arise when peer review is undertaken in the context of community-engaged scholarship (CES), since CES generally involves partners outside the academy, and the typical concerns of peer review (such as rigorous methods, participant risks and benefits, and significance of findings for the field) are complemented by equivalent and sometimes greater concerns for the quality of the engagement process, community- level ethical considerations, and benefit to the community. This article, authored by some of the founding members of the Working Group on Rethinking Peer Review, explores these issues and invites readers to contribute to this discussion by considering questions about the appropriateness of conventional peer review mechanisms and who should be considered “peers” in reviewing products of CES and the work of community-engaged scholars. The Working Group hopes others will initiate discussions within their own institutions, professional associations, journals, and other settings to debate the notion of peer review and determine if expanded concepts are feasible. Through these various activities, the authors hope to begin seeing changes in the peer review process that embrace community expertise and enhance the quality and impact of CES.
Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments
In an efficient and flexible human-robot collaborative work environment, a
robot team member must be able to recognize both explicit requests and implied
actions from human users. Identifying "what to do" in such cases requires an
agent to have the ability to construct associations between objects, their
actions, and the effect of actions on the environment. In this regard, semantic
memory is being introduced to understand the explicit cues and their
relationships with available objects and required skills to make "tea" and
"sandwich". We have extended our previous hierarchical robot control
architecture to add the capability to execute the most appropriate task based
on both feedback from the user and the environmental context. To validate this
system, two types of skills were implemented in the hierarchical task tree: 1)
Tea making skills and 2) Sandwich making skills. During the conversation
between the robot and the human, the robot was able to determine the hidden
context using ontology and began to act accordingly. For instance, if the
person says "I am thirsty" or "It is cold outside" the robot will start to
perform the tea-making skill. In contrast, if the person says, "I am hungry" or
"I need something to eat", the robot will make the sandwich. A humanoid robot
Baxter was used for this experiment. We tested three scenarios with objects at
different positions on the table for each skill. We observed that in all cases,
the robot used only objects that were relevant to the skill.Comment: To Appear In International Conference on Intelligent Robots and
Systems (IROS), Detroit, MI, USA, Oct 202
Not Immune to Mental Health Problems: The Prevalence of Depression and Anxiety in Student-Athletes
Estimation of Gait Kinematics and Kinetics from Inertial Sensor Data Using Optimal Control of Musculoskeletal Models
Inertial sensing enables field studies of human movement and ambulant assessment of patients. However, the challenge is to obtain a comprehensive analysis from low-quality data and sparse measurements. In this paper, we present a method to estimate gait kinematics and kinetics directly from raw inertial sensor data performing a single dynamic optimization. We formulated an optimal control problem to track accelerometer and gyroscope data with a planar musculoskeletal model. In addition, we minimized muscular effort to ensure a unique solution and to prevent the model from tracking noisy measurements too closely. For evaluation, we recorded data of ten subjects walking and running at six different speeds using seven inertial measurement units (IMUs). Results were compared to a conventional analysis using optical motion capture and a force plate. High correlations were achieved for gait kinematics (rho \u3e= 0.93) and kinetics (rho \u3e= 0.90). In contrast to existing IMU processing methods, a dynamically consistent simulation was obtained and we were able to estimate running kinetics. Besides kinematics and kinetics, further metrics such as muscle activations and metabolic cost can be directly obtained from simulated model movements. In summary, the method is insensitive to sensor noise and drift and provides a detailed analysis solely based on inertial sensor data
A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
Cloud computing is a concept introduced in the information technology era,
with the main components being the grid, distributed, and valuable computing.
The cloud is being developed continuously and, naturally, comes up with many
challenges, one of which is scheduling. A schedule or timeline is a mechanism
used to optimize the time for performing a duty or set of duties. A scheduling
process is accountable for choosing the best resources for performing a duty.
The main goal of a scheduling algorithm is to improve the efficiency and
quality of the service while at the same time ensuring the acceptability and
effectiveness of the targets. The task scheduling problem is one of the most
important NP-hard issues in the cloud domain and, so far, many techniques have
been proposed as solutions, including using genetic algorithms (GAs), particle
swarm optimization, (PSO), and ant colony optimization (ACO). To address this
problem, in this paper, one of the collective intelligence algorithms, called
the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The
performance of the proposed algorithm has been compared with that of GAs, PSO,
continuous ACO, and the basic SSA. The results show that our algorithm has
generally higher performance than the other algorithms. For example, compared
to the basic SSA, the proposed method has an average reduction of approximately
21% in makespan.Comment: 15 pages, 6 figures, 2023 IFIP International Internet of Things
Conference. Dallas-Fort Worth Metroplex, Texas, US
A Pilot Study with a Novel Setup for Collaborative Play of the Humanoid Robot KASPAR with children with autism
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.This article describes a pilot study in which a novel experimental setup, involving an autonomous humanoid robot, KASPAR, participating in a collaborative, dyadic video game, was implemented and tested with children with autism, all of whom had impairments in playing socially and communicating with others. The children alternated between playing the collaborative video game with a neurotypical adult and playing the same game with the humanoid robot, being exposed to each condition twice. The equipment and experimental setup were designed to observe whether the children would engage in more collaborative behaviours while playing the video game and interacting with the adult than performing the same activities with the humanoid robot. The article describes the development of the experimental setup and its first evaluation in a small-scale exploratory pilot study. The purpose of the study was to gain experience with the operational limits of the robot as well as the dyadic video game, to determine what changes should be made to the systems, and to gain experience with analyzing the data from this study in order to conduct a more extensive evaluation in the future. Based on our observations of the childrens’ experiences in playing the cooperative game, we determined that while the children enjoyed both playing the game and interacting with the robot, the game should be made simpler to play as well as more explicitly collaborative in its mechanics. Also, the robot should be more explicit in its speech as well as more structured in its interactions. Results show that the children found the activity to be more entertaining, appeared more engaged in playing, and displayed better collaborative behaviours with their partners (For the purposes of this article, ‘partner’ refers to the human/robotic agent which interacts with the children with autism. We are not using the term’s other meanings that refer to specific relationships or emotional involvement between two individuals.) in the second sessions of playing with human adults than during their first sessions. One way of explaining these findings is that the children’s intermediary play session with the humanoid robot impacted their subsequent play session with the human adult. However, another longer and more thorough study would have to be conducted in order to better re-interpret these findings. Furthermore, although the children with autism were more interested in and entertained by the robotic partner, the children showed more examples of collaborative play and cooperation while playing with the human adult.Peer reviewe
Maternal age effect and severe germ-line bottleneck in the inheritance of human mitochondrial DNA
The manifestation of mitochondrial DNA (mtDNA) diseases depends on the frequency of heteroplasmy (the presence of several alleles in an individual), yet its transmission across generations cannot be readily predicted owing to a lack of data on the size of the mtDNA bottleneck during oogenesis. For deleterious heteroplasmies, a severe bottleneck may abruptly transform a benign (low) frequency in a mother into a disease-causing (high) frequency in her child. Here we present a high-resolution study of heteroplasmy transmission conducted on blood and buccal mtDNA of 39 healthy mother–child pairs of European ancestry (a total of 156 samples, each sequenced at ∼20,000× per site). On average, each individual carried one heteroplasmy, and one in eight individuals carried a disease-associated heteroplasmy, with minor allele frequency ≥1%. We observed frequent drastic heteroplasmy frequency shifts between generations and estimated the effective size of the germ-line mtDNA bottleneck at only ∼30–35 (interquartile range from 9 to 141). Accounting for heteroplasmies, we estimated the mtDNA germ-line mutation rate at 1.3 × 10−8 (interquartile range from 4.2 × 10−9 to 4.1 × 10−8) mutations per site per year, an order of magnitude higher than for nuclear DNA. Notably, we found a positive association between the number of heteroplasmies in a child and maternal age at fertilization, likely attributable to oocyte aging. This study also took advantage of droplet digital PCR (ddPCR) to validate heteroplasmies and confirm a de novo mutation. Our results can be used to predict the transmission of disease-causing mtDNA variants and illuminate evolutionary dynamics of the mitochondrial genome
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