Bayesian reinforcement learning models reveal how great-tailed grackles improve their behavioral flexibility in serial reversal learning experiments by Lukas, Dieter, McCune, Kelsey, Blaisdell, Aaron, Johnson-Ulrich, Zoe, MacPherson, Maggie, Seitz, Benjamin, Sevchik, August, Logan, Corina

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Introducing reservoir sustainability indexing to investigate reservoir operations and piloting it at the basin of Lake Urmia with sparse data by Sina Sadeghfam, Navid Fahmfam, Rahman Khatibi, Brian Mark Crookston, Meysam Vadiati, Marjan Moazamnia

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Filters Effect on Image Quality of 177Lu-DOTATATE SPECT Images for Neuro-Endocrine Tumors Scanning by Hussein Dakhil, Mohammad Reza Deevband, Mahasti Amoui, Ahmad Mostaar, Mahdi Ghorbani, Mohsen Khosroabadi, Mohammad Ali Ghodsirad

“…Results: In all cases, the application of different filters increased Contrast to Noise Ratio (CNR) (9.1%, 1.8%, 10.9%, 61.8%, 23.6%, 29.1%, and 58.2% for Wiener, Butterworth, Parzen, Metz, Ramp, Shepp-Logan, and Hamming filters, respectively). …”
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Development and Evaluation of an AI-based Exergame Training System for Ice-Hockey Players: a Randomized Controlled Trial by Nicole Sieber, Simon Walser, Thomas Weber, Raphael Gubler, Hannes Badertscher, Patrick Eggenberger

“…The three Victoria Stroop Test conditions showed no significant or meaningful time x group interaction effects (p≥0.05). For the relative CMJ peak power [W/kg body weight], a statistically significant time x group interaction with medium effect size was evident (F(1,31)=3.10, p=0.044 one-tailed, r=0.301) and similarly, analyses showed a medium, however non-significant effect for CMJ jump height [cm] (F(1,31)=2.80, p=0.052 one-tailed, r=0.288). …”
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