**Introduction:** The well-being of academics at universities, including students, staff, and faculty is a topic of increasing concern. The lack of well-being may be attributed to the current academic context which does not consistently provide cues that affirm social inclusion to all members of the academic population (Estrada et al., 2018). The lack of cues that affirm the dignity of others has consequences and undermines a basic need that people are more likely to survive and experience well-being when feeling socially connected (Baumeister & Leary, 1995; Dunbar & Barrett, 2007). Further, literature shows that social connection can reduce experiences of stress in pressured environments such as academia (Holt-Lunstad et al., 2015; USPHS, 2023; Valtorta et al., 2016). This study examines the role of kindness (defined as actions that affirm dignity and social inclusion) in promoting connectivity and well-being among academics. 
**Methods:** The Social Influence of Kindness Study was conducted through a one-time anonymous 10-minute online survey via Qualtrics. Participants were recruited using the snowball method via email and social media based on interest in taking a 10-minute survey about Kindness and Dignity during Spring 2020. For this study, 182 participants were selected for analysis based on their occupation designation of studying or working within an academic institution. Each survey included a written reflective micro-narrative in response to the prompt "recall an experience of kindness" and consisted of measures of Kindness Given, Kindness Received, Institutional Identity, Stress, and Well-Being. 
**Results:** We found engaging in acts of kindness, as measured by two novel psychometric rating scales Kindness Received (alpha=.927) and Kindness Given (alpha=.859), was significantly associated with increased institutional identity, well-being, and reduced stress. Our structural equation model (SEM) further validated the correlation results with receiving kindness. Interestingly through the SEM we found conducting acts of kindness had a significant negative relationship with institutional identity and reduced stress. Qualitative analysis of micronarratives on kindness showed that feeling safe and being acknowledged are the most common ways people describe experiencing kindness, an affirmation of their dignity. 
**Conclusions and Discussion:** This study shows that receiving kindness among academics has the potential to improve institutional identity, well-being, and reduce stress. Further, the results show that receiving kindness and giving kindness can be reliably measured amongst students and faculty in academia. We provide evidence that reflecting on experiences of kindness promotes positivity, with the potential to encourage people to conduct further acts of kindness. Together these findings provide knowledge that can inform workshops and interventions to improve social connection and inclusion amongst diverse academics. This study contributes to broader research highlighting positive contextual factors that promote the persistence of diverse students and faculty in academia. 
**Acknowledgments:** The project was funded by NIH grant #R01GM138700

The Influence of Kindness on Academic Well-Being - Poster

The Center for Energy and Environmental Sustainability (CEES) was established in October 2010 with $5M in funding from NSF. CEES Phase II award from NSF started on October 1, 2019. CEES personnel include eight faculty members, 1 program coordinator, and 4 research staff members. CEES is addressing the challenging interdisciplinary problems and is paving the way for scientific and technological breakthroughs in energy engineering to meet short- and long-term national needs. The Center’s research mission is accomplished through 3 distinct research thrust areas and strong education and outreach programs. The research areas include Bio-Energy (Energy crop cultivation and gasoline/diesel-like hydrocarbon fuel production from energy crops), Offshore Wind Energy (Massive offshore power generation and risk analysis with big data), and Environmental Sustainability (Produced water treatment and environmental impact evaluation with life cycle assessment). The Center’s education and outreach activities are carried through the Energy Engineering and Research Learning Initiative (EERLI), which includes a myriad of activities that engage K-12 students through graduate students. The researchers, including our students, have published 1 book, 185 peer-reviewed publications and have given more than 220 presentations at regional, national, and international conferences. CEES faculty and staff are also supported by other leveraged funding of about $12.82 M from NSF, USDA, Tran-SET, and industrial partners. CEES has supported more than 220 students, including 73 graduate, and 145 undergraduate students. The Center also established state-of-the-art research laboratories with high-quality equipment using leveraged funds.

Center for Energy & Environmental Sustainability (CEES)

To contribute to the diversity of the next generation of NIH funded biomedical researchers, ensuring that undergraduate student participants enter and succeed in graduate school is one of the goals of the BUILDing SCHOLARS center at the University of Texas at El Paso. To accomplish this goal, the center implemented the Johnson and Bozeman Asset Bundles model. Using the Student Hallmarks of Success developed by the NIH Diversity Program Consortium as guidance, and mapping each program activity to the model, academic metrics in combination with results from a comprehensive evaluation plan for the first three cohorts of participants will be presented.<

Developing the Biomedical Researchers of the Future at a Hispanic Serving Institution

"**Introduction.** Mentoring has been demonstrated to increase science identity, retention and academic performance. Students that develop science identity and sense of belonging are more likely to persist in STEM areas. Mentoring can also provide psychosocial support by normalizing struggles and reinforcing identity in the field. Peer-mentoring is a form of mentorship that involves a more experienced student guiding a less experienced student through social, emotional and/or academic support. The benefits of peer-mentoring have been observed to help Latina/o students find role models, access emotional and academic support, and career help, thereby increasing integration into their university. Peer-mentoring has also been credited as one of the factors which lead to the success of Black undergraduate women students graduating with STEM degrees at Historically Black Colleges and Universities (HBCUs). For Latinas and Black in STEM, peer mentoring has been shown to support  student integration and persistence, and increase academic performance and degree achievement. 

**Goal of Research:** We conducted a 1-year pilot intervention that had the goal of providing female Latina and Black STEM undergraduates, usually underrepresented (URM) in STEM, with a social, professional and academic support structure by inviting them to join a peer-mentoring program. Here we present the findings  

**Methods.** First-year science students attending a 4-year public college were invited to join a peer-mentoring program. Peer-mentors were recruited from upper level cohorts, with junior and senior standing in cell and molecular biology, toxicology, and forensic science majors. A total of 20 first-year female students joined the program in September and 8 upper-level female students joined as mentors. All participants were Latinas and/or Black. Participants formed small groups of on average three first-year students that met with a mentor monthly during the semester. In these meetings that lasted between 30-60 minutes, the mentors and mentees shared ideas, resources, discussed strategies and shared concerns. The whole cohort met three times during the semester, and participated in seminars in which invited speakers shared their professional experience and the work they were currently doing. The invited speakers were also female scientists from underrepresented backgrounds in STEM. We assessed the student’s experiences using a 6-question survey at the end of each mentor-mentee meeting, that evaluated the implementation of the intervention and identified strengths and areas in need of improvement. The survey was voluntary and anonymous. The survey contained Likert-scale items and one opened-ended question. We use Leximancer software to conduct a thematic analysis and a relational (or semantic) analysis of the interview data. The software provides word frequency counts and co-occurrence counts of concepts present in participants' responses.  

**Results.** A total of 28 students participated in this intervention, all of which are URM in STEM (either African American or Hispanic). In the fall semester only 14 students joined the program, but 6 additional students requested to participate in the spring semester for a total of 20 participants. We originally had 8 mentors, but only 7 mentors completed the program. Groups were formed in the fall, and mentor-mentee sets continue to work together through the school year. New participants joined groups that already existed. In the quantitative part of the survey students were asked about their experience at each meeting. 58% and 67% responded that they ‘strongly agree’ to the statements  “this was a productive session” and “I look forward to my next mentoring session”. The qualitative assessment of the mentees’ responses showed that  ‘careers’, ‘internships’ and ‘time’ were among the most frequent terms used in the responses. Compound concepts showed ‘careers & goals’ and ‘internship & study’ to have the highest prominence in the meeting category, followed by “time & study’ and ‘helpful & goals’. These highlight the areas of most concern to the students attending. The concept map showed a large component in which terms like ‘improved’, ‘mentor’, ‘discussed’, ‘needs’, ‘helpful’ and ‘opportunities’ were the most frequent. The analysis identified top strengths of the intervention including 1)The mentorship program is comprehensive, covering a wide range of topics from academics to personal development, 2) Mentors are approachable and friendly, fostering a comfortable environment for mentees, and 3) The program provides a platform for mentees to discuss their goals and aspirations. In addition, some concerns were also highlighted by the software including, 1) The program may not be able to accommodate all the topics and concerns that mentees want to discuss due to time constraints, 2) The program may not be able to provide sufficient guidance and support for mentees who join late or miss meetings, and 3) The program may not be able to maintain its quality and effectiveness if it becomes too flexible and unstructured. This initial analysis provides suggestions on how to implement peer-mentoring programs and which aspects to keep in mind during their organization. 

**Conclusions and Discussions.** Our intervention adds to current evidence supporting the need for programs that aid first-year students in facing challenges associated with the college transition. It particularly highlights the importance of these support structures for URM females in STEM majors.  

**Acknowledgments.** This research is funded by the Andrew Mellon Foundation in support of  the Black, Race, Ethnic Studies Initiative (BRESI) at the City University of New York (CUNY)"

Peer-mentoring intervention supports undergraduate Latinas and African-American students in STEM

NRMNet: Where We’ve Been and Where We are Going

Mitigating Unconscious Bias: The Development of an Unconscious Bias Course for a National Network

The present study investigated the degree to which linguistic alignment is affected by communication medium and conversation type. Taking advantage of the changes instigated by the COVID-19 pandemic where nearly everyone was forced to engage over videoconferencing (VC) platforms or to meet face-to-face (FF) with public health constraints, we asked participants to conduct three conversations in one of three communication media. Here we analyze conversations from 23 dyads: 8 dyads who conversed FF, 8 who conversed in a laboratory VC set-up and 7 who conversed in a remote VC set-up. Every dyad had an affiliative, an argumentative, and a task-based cooperative conversation. Results showed differences in lexical and syntactic alignment between conversation types but not communication media. Interestingly, we also found interaction effects. These results point to changes in alignment based on communication constraints and provide support for the interpersonal synergies approach to conversation.


Language in the Time of COVID: Sensitivity of Linguistic Alignment to Conversation Type and Communication Modality

Bats have evolved highly advanced biosonar systems that combine information encoding in the physical domain by virtue of complex ear motions with neural extraction of information. In order to emulate this behavior, prior research has approximated the dynamics of bat pinna using fixed deformation patterns, e.g., by virtue of a lever deforming a silicone replica of a bat pinna. However, these systems could capture little of the known and suspected variability in the pinna motions that may allow bats to adapt to different sensing situations in their natural environments. As a first step towards exploring the use of variability in the acoustic characteristics of the bat pinnae, we have designed a deep-regression network that can predict the
beampattern of deformed pinnae. To enable this, we have rigged a 3D tomographic reconstruction model of a hipposiderid bat’s (Hipposideros pratti) pinna with three tendons that were used to test 10 actuator states each. All resulting shape deformations were then exported into a frequency-domain BEM model which predicted the ultrasonic beampatterns as function of the actuator states. Finally, we have reproduced the BEM beampattern prediction with two deep neural network architectures: a deep radial basis function neural network and a multilayer perceptron neural network. Both architectures were able to reproduce the BEM results. Since inference is computationally much less costly than BEM simulations, these deep-learning models for the link between pinna actuation and acoustic properties can be used for finding ways to harness the variability in the bats’ pinna shapes using expensive training methods such as reinforcement learning.

Deep-Learning Models of the Dynamic Periphery of Bat Biosonar

There is growing interest in the use of machine learning (ML) techniques to extract and classify signals of interest from passive sonar data. As with all machine learning applications a large body of data is required to train the ML model. Whilst a large body of open source acoustic data is available from a variety of sources and for different applications this data is often unlabelled, or labelled only for the task at hand. Marine mammal data recordings for example may be labelled with a species of interest but other sounds, such as vessels and other marine life, may not be labelled. For an effective classification model to be developed all sources in the recording must be labelled.
One solution to this problem is to manually label each recording, however the time required to create sufficient labels using this method can be prohibitive. This work follows a different approach, developing an algorithm to auto label large datasets using semi-supervised and active learning approaches. These make use of a small labelled dataset and minimal expert user interaction to create a large pseudo-labelled training dataset. Testing of pseudo-labelling has shown that it is possible to use this technique to create large training sets, however, Accuracy remains low. The introduction of active learning increases this Accuracy by guiding the model training.



Automating the Labelling of Underwater Audio Recordings

Synthetic data generation is crucial to performative and predictive autonomy technology research. The efficient generation of robust synthetic data requires 3D Computer-Aided Design (CAD) models that correctly represent the target but sufficiently small as to promote agile computation. "Watertight" CAD models - models with no gaps or surface flaws - are necessary for computer simulations, generation of synthetic datasets for machine learning, and accurate synthetic data analysis. This is joint work with Dr. Igor Ternovskiy (AFRL/RYAT) and Ms. Elizabeth Sudkamp (AFRL/RYAP).

A Facet to Watertight CAD Converter

The future success of the Navy will depend, in part, on artificial intelligence. In practice, many artificially intelligent algorithms, and in particular deep learning models, rely on continual learning to maintain performance in dynamic environments. The software requires adaptation to maintain its initial level of performance in unseen situations. However, if not monitored properly, continual learning may lead to several issues including catastrophic forgetting in which a trained model forgets previously learned tasks when being retrained on new data. The authors created a new framework for safely performing continual learning with the goal of pairing this safety framework with a deep learning computer vision algorithm to allow for safe and high-performing automatic deck tracking on carriers and amphibious assault ships. The safety framework includes several features, such as an ensemble of convolutional neural networks to perform image classification, a manager to record confidences and determine the best answer from the ensemble, a model of the environment to predict when the system may fail to meet minimum performance metrics, a performance monitor to log system and domain performance and check against requirements, and a retraining component to update the ensemble and manager to maintain performance. The authors validated the proposed method using extensive simulation studies based on dynamic image classification. The authors showed the safety framework could probabilistically detect out of distribution data. The results also show the framework can detect when the system is no longer performing safely and can significantly extend the image classifier’s working envelope. Future follow-on work will include increasing the fidelity of the images to mimic views from the Integrated Launch and Recovery Television.

Assurance for Continual Learning Systems

For general-sum, n-player, strategic games with transferable utility, the COCO value provides a computable method to both 1) quantify the strategic value of a player; and 2) make cooperation rational through side payments. We give a simple formula to compute the COCO value in normal-form games. Next, we provide two methods to generalize the COCO values to stochastic (or Markov) games, and show that one of them may be computed using generalized Q-learning algorithms. Finally, an empirical validation is performed on stochastic grid-games with three or more players.

Underline digital video library

The Influence of Kindness on Academic Well-Being - Poster

Center for Energy & Environmental Sustainability (CEES)

Developing the Biomedical Researchers of the Future at a Hispanic Serving Institution

Peer-mentoring intervention supports undergraduate Latinas and African-American students in STEM

NRMNet: Where We’ve Been and Where We are Going

Mitigating Unconscious Bias: The Development of an Unconscious Bias Course for a National Network

Language in the Time of COVID: Sensitivity of Linguistic Alignment to Conversation Type and Communication Modality

Deep-Learning Models of the Dynamic Periphery of Bat Biosonar

Automating the Labelling of Underwater Audio Recordings

A Facet to Watertight CAD Converter

Assurance for Continual Learning Systems

N-Player COCO-Q Reinforcement Learning

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