Connecticut College

Abstract Community-based learning (CBL) is a teaching method that develops critical and reflective thinking on social and civic issues by combining academic instruction with community work by partnering with local community groups. The most successful CBL courses are built around a partnership between all participants (community partners, faculty, and students). CBL provides students with practical experiences of their academic studies and an understanding of ethical community engagement. In 2024 KFF notes that there were approximately 1.2 million individuals residing in certified nursing home facilities. In the state of Connecticut, KFF estimates that there are approximately 20,000 residents, with the majority being older adults. The Connecticut Long-Term Care Ombudsman Program (CT-LTCOP) provides an essential service as advocates for individuals who reside in long-term care facilities (LTC) as the eight Regional Ombudsman work to resolve complaints and concerns of residents and offer assistance and support. Their critical work is aided by Volunteer Resident Advocates (VRAs) assigned to select buildings. Elder Abuse was a course offered in partnership with CT-LTCOP with the goal of integrating CBL into the classroom, resulting in students becoming certified Volunteer Resident Advocates at the completion of the course. The main goals for integrating CBL into the classroom were: (1) provide an opportunity for undergraduate students to connect concepts learned in the classroom to their work within the LTC community; and, (2) to develop a model for faculty on building an effective CBL curriculum that benefits the students and the community.

Wearable physiological sensors (WPS) can provide real-time, continuous data to make immediate safety decisions based on how workers respond under a given heat exposure. Estimated core temperature (TEST) calculated from WPS can provide meaningful data on heat illness, strain, and risk, but they must be validated against gold standard measures and during intermittent physical activity in the heat. Two TEST algorithm models (2013 and 2020) are commonly used to assess heat strain in warfighters using heart rate in WPS; however, these models may have limitations when applied to non-military populations. PURPOSE: To examine the validity of TEST (2013 and 2020) compared to a validated measure [rectal temperature (TREC)] during physical activity in the heat in physically active men. METHODS: Twelve healthy men [age, 32 ± 7 years; peak oxygen consumption (VO2peak), 48.19 ± 7.09 ml·kg−1·min−1] completed four experimental trials (~4.5 hours) consisting of cycles of intermittent exercise. WPS were placed at three different locations [chest strap (CS), armband (AB), and wristwatch (WW)] and each incorporated TEST 2013 and 2020. Bland-Altman plots and linear regression were utilized to measure mean difference (MD), 95% limits of agreement (LoA), and coefficient of determination (R2), respectively. TREC was used as the reference measurement. An acceptable MD was determined as ≤0.27 °C. Data were also stratified by TREC value (<38.0 °C, 38.0-38.5 °C, 38.5-39.0 °C, 39.0-39.5 °C, >39.5 °C). RESULTS: The lowest and only acceptable MD was noted in CS, AB, and WW devices that utilized TEST 2020 (0.21 °C, 0.19 °C, 0.27 °C, respectively). For all WPS, 95% of the data fell between MD of -0.54 and 1.22 °C (95% LoA). R2 for all WPS ranged from 0.32-0.39. Poor agreement (>0.27 °C) occurred above 38.5 °C across all WPS TEST 2020 (CS:0.53-0.73 °C, AB: 0.52- 0.74 °C, WW:0.67-0.97 °C). CONCLUSION: Overall, WPS utilizing TEST 2020 demonstrated acceptable MD, but exhibited moderate variability, reflected by R2 values. As TREC increased above 38.5 °C, the MD for all WPS TEST 2020 was greater than the acceptable MD, thus indicating that WPS utilized in this study may not be acceptable for hyperthermic individuals performing intermittent work in the heat. Supported by: This study was funded by Vigilife.

Although helmets protect against head injuries, many do not consider the effects of wearing a helmet, or protective cap, on thermoregulation during work in the heat. Purpose: To examine the influence of a protective cap on rectal temperature (T REC ), skin temperature (T SK ), and thermal gradient [core-to-skin ratio (CSR)] compared to a baseball cap during simulated work in the heat. We hypothesized that there would be significant differences in T REC , T SK , and CSR in the protective cap compared to a regular cap. Methods: Twelve physically-active men and women (age:26.5±3.5 years; VO 2 peak:45.8±7.4 ml·kg -1 ·min -1 ) were asked to complete threerandomized controlled trials in a hot, humid environment (35°C, 50% rH). The three experimental trials included: 1) black protective layer cap (DARK); 2) light grey protective layer cap (LIGHT); and 3) a baseball cap (CON). Each experimental trial included two cycles of: 15 min of walk/run intervals on a treadmill (2% incline) and 5 min of box lifting (BL). A 3-min rest break (RB) followed each cycle. Participants then ran at 65% velocity of VO 2 peak until their T REC reached 39.5°C (RTT). A Two-Way Repeated Measures Analysis of Variance (RMANOVA) examined changes in T REC , T SK , and CSR (n=8) between trials and over time until the first RB. Bonferroni post-hoc corrections were used when appropriate. An Analysis of Covariance (ANCOVA) examined the effect of cap type (DARK, LIGHT, or CON) on T REC prior to RTT with baseline T REC (n=6) as a co-variate. ANCOVA results were described by Partial Eta effect size (ηpa) and 95% confidence interval (CI). A One-Way ANOVA examined changes in the rate of rise of T REC between trials. Sex differences were not assessed. Results: There were no statistically significant differences in T REC [DARK: 37.99±0.38°C, LIGHT: 37.96±0.38°C, CON: 38.03±0.35°C], T SK [DARK: 35.28±1.17°C, LIGHT: 35.51±1.12°C, CON: 35.30±1.04°C] or CSR[DARK: 1.05±0.01, LIGHT: 1.06±0.01, CON: 1.07±0.01] between trials from the beginning of the experimental trial until the end of the first RB. There was no statistically significant effect of cap type on T REC prior to the start of RTT (p=0.273, ηpa=0.169, large effect, 95%CI=38.81-39.10). There were no statistically significant differences in the rate of rise of T REC between trials [DARK: 0.035±0.006, LIGHT: 0.036±0.006, CON: 0.034±0.009]. Conclusion: The addition of a protective layer in a cap (DARK or LIGHT) does not alter T REC or T SK responses during simulated work in the heat. Therefore, utilizing a cap that includes this protective layer as personal protective equipment may reduce the likelihood of head injuries without contributing to thermal strain. This study was funded by Delta Air Lines. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

In order for evolution to populate the planet with multiple species, two processes need to be at work. One is speciation, which involves the development of a new reproductively isolated species from an ancestral one. The other is that a reproductively isolated species can evolve to be more complex and potentially more capable over time. This second process is what we address in the research reported in this paper. One of the issues with developing individuals with more complex attributes is that those attributes typically take multiple mutations to be viable and before that point, the changes made by mutations are often detrimental. In this research, we use a simulated environment filled with simple agents and a genetic algorithm operating on these agents, each of which has its own set of chromosomes. We use this to test the plausibility of the species developing more complex structures (in this case sight) as the genetic algorithm population survives and evolves over 1000s of generations. In order to better simulate intermediate detrimental mutations, each mutation results in an increased metabolic cost for the agent, leading to higher energy expenditure with every movement or action taken. Tests were done with differing number of mutations required before sight was developed. The results showed that evolution of complex attributes are possible depending on the number of detrimental mutations required to make the attribute an advantage to the agent.

We study a variant of the offline Dial-a-Ride problem, where each request has a source and destination and the goal is to maximize the number of requests served within a specified time limit. We investigate this problem for the uniform metric space and show that the problem is NP-hard. We then present a 2/3 approximation algorithm called TWOCHAIN, which simply looks for pairs of requests that are “chained” together and serves those before serving requests that are not connected to any others. We also show that a natural generalization of this algorithm, k-chain, has an approximation ratio at most 7/9. We also analyze the longest-chain-first algorithm for the problem, characterizing graphs on which it is optimal, and showing that it has an approximation ratio no better than 5/6. Our experiments on all of these algorithms show that TWOCHAIN is a promising algorithm, performing nearly as well as more computationally intensive variants. We dedicate this article to the memory of Gerhard Woeginger, whose life and work greatly influenced our professional lives, as expanded upon in the Acknowledgments. Woeginger’s prolific research in scheduling, matching, bin-packing, TSP, and online algorithms in general, all served as important parts of the foundation on which our own scholarly pursuits were shaped and formed over the years. Woeginger also studied Dial-a-Ride (DARP) Problems, as DARP is a generalization both of scheduling problems and of TSP, which were two of his most active areas of research.

Our attention can sometimes be disrupted by salient but irrelevant objects in the environment. This distractor interference can be reduced when distractors appear frequently, allowing us to anticipate their presence. However, it remains unknown whether distractor frequency can be learned implicitly across distinct contexts. In other words, can we implicitly learn that in certain situations a distractor is more likely to appear, and use that knowledge to minimize the impact that the distractor has on our behavior? In two experiments, we explored this question by asking participants to find a unique shape target in displays that could contain a color singleton distractor. Forest or city backgrounds were presented on each trial, and unbeknownst to the participants, each image category was associated with a different distractor probability. We found that distractor interference was reduced when the image predicted a high rather than low probability of distractor presence on the upcoming trial, even though the location and (in Experiment 2) the color of the distractor was completely unpredictable. These effects appear to be driven by implicit rather explicit learning. We conclude that implicit learning of context-specific distractor probabilities can drive flexible strategies for the reduction of distractor interference.

Abstract We propose a novel and unified sampling scheme, called the accelerated group sequential sampling scheme, which incorporates four different types of sampling scheme: (i) the classic Anscombe–Chow–Robbins purely sequential sampling scheme; (ii) the accelerated sequential sampling scheme; (iii) the relatively new k-at-a-time group sequential sampling scheme; and (iv) the new k-at-a-time accelerated group sequential sampling scheme. The first-order and second-order properties of this unified sequential sampling scheme are fully investigated with two illustrations on minimum risk point estimation for the mean of a normal distribution and on bounded variance point estimation for the location parameter of a negative exponential distribution. We also provide extensive Monte Carlo simulation studies and real data analyses for each illustration.