A majority of Michigan general dentists treat patients with developmental disabilities. Addressing barriers like training and improved reimbursements might help in increasing the number of dentists willing to treat patients with developmental disabilities.

Animals can evade predators in multiple ways, one of the most effective of which is to avoid detection in the first place. We know much about the evolution of color patterns that match the visual background to avoid detection (i.e., crypsis), yet we know surprisingly less about the specific behaviors that have co‐evolved with these morphological traits to enhance or maintain crypsis. We here explore whether the match between body color and background in a seemingly well‐camouflaged tropical shore crab is a result of active background choice. Taking advantage of a coastal area in the Solomon Islands with variable sand color and a population of the pallid ghost crab Ocypode pallidula with varying carapace color, we experimentally tested whether individuals actively choose specific substrate that best matches their color patterns. We found that individuals taken from extreme sand colors chose substrate that maintained crypsis, with relatively darker crabs typically choosing dark sand and lighter crabs choosing light sand. Crabs of intermediate color pattern, in contrast, showed no clear preference for dark or light sand. Our results suggest that potential prey can actively choose specific backgrounds to enhance and maintain crypsis, providing insights into how behavior interacts with morphological traits to avoid predator detection.

Bidirectional Long Short Term Memory (Bidirectional LSTM) is a machine learning technique with the ability to capture data context by traversing backward data to forward data and vice versa. However, the characteristics of stock data with large fluctuations, high dimensions and non-linearity become a challenge in obtaining high stock price prediction accuracy values. The purpose of this study is to provide a solution to the problem of stock data characteristics with large fluctuations, high dimensions and non-linearity by combining the Complete Ensemble Empirical Mode Decomposition With Adaptive Noise method for exogenous features (XCEEMDAN), Bidirectional Long Short Term Memory (LSTM), and Splines. The predicted data will go through normalization and preprocessing using XCEEMDAN then the XCEEMDAN decomposition results are divided into high and low frequency signals. The bidirectional LSTM handles high frequency signals and the Spline model handles low frequency signals. The test is carried out by comparing the proposed XCEEMDAN-Bidirectional LSTM-Spline model with the XCEEMDAN-LSTM-Spline model using the same parameters and changing the noise seed randomly 50 times. The test results show that the proposed model has the smallest RMSE average value of0.787213833 while model which is compared only has the smallest RMSE average value of 0.807393567.

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