Revisiting a central prediction of the Gill Oxygen Limitation Theory: Gill area index and growth performance

The Gill Oxygen Limitation Theory (GOLT) posits that a mismatch in oxygen supply and demand stemming from geometric constraints on gill surface area limits metabolic rate and energy available for biological processes. This theory has been suggested to explain numerous phenomena observed with warming yet is based upon a relationship among maximum size, growth, and gill surface area established over 40 years ago. However, the metric used in this relationship to characterize gill surface area, gill area index, fails to capture the known variability in the scaling of gill surface area and is biased by the sizes at which gills were measured. Here, we revisit a central prediction of the GOLT, asking four key questions that examine limitations in the original relationship. We find that gill area index does indeed explain variation in growth performance across 132 species of fish and this relationship is strikingly similar to the original relationship across 42 species. Yet, we argue that gill area index is not an adequate measure of gill surface area because (1) gill surface area has a non-linear relationship with size and, thus, changes ontogenetically as an individual grows over time and (2) because it is based on mean estimates of both gill surface area and body mass. Indeed, we show that the value of gill area index for a given species is variable depending on how it is calculated. We therefore suggest a pathway forward for assessing whether gill surface area is an important factor in explaining variation in growth performance.     

基于激光雷达与深度相机融合的SLAM算法

针对单一传感器地图构建时存在环境表征不充分，无法为移动机器人自主导航提供完整环境地图等问题，本文通过将激光雷达与深度相机获取的环境信息进行互补融合，构建出更完整精确的栅格地图。首先，对传统ORB-SLAM2算法进行增强，使其具备稠密点云地图构建、八叉树地图构建以及栅格地图构建等功能。其次，为验证增强后ORB-SLAM2算法的性能，在fr1＿desk1数据集和真实场景下进行测试，数据显示增强后ORB-SLAM2算法绝对位姿误差降低52.2%,相机跟踪轨迹增长14.7%,定位更加精准。然后，D435i型深度相机采用增强型ORB-SLAM2算法，激光雷达采用的Gmapping-Slam算法，按照贝叶斯估计的规则进行互补融合构建全局栅格地图。最后，搭建实验平台进行验证，并分别与深度相机和激光雷达2个传感器建图效果进行对比。实验结果表明，本文融合算法对周围障碍物的识别能力更强，可获取更完整的环境信息，地图构建更加清晰精确，满足移动机器人导航与路径规划的需要。     

基于贝叶斯与元学习的氨氮浓度预测模型优化

针对小样本数据下氨氮浓度模型预测精度不高、收敛速度较慢的问题，采用长短期记忆网络（long short-term memory，LSTM）建立氨氮浓度预测模型，并利用贝叶斯优化算法和元学习机制对模型进行优化。其中贝叶斯优化算法用来优化预测模型的超参数，同时给出模型参数的初始值，再使用Meta-LSTM算法学习模型梯度并允许优化器之间进行参数共享和更新，最终实现对氨氮浓度预测模型的优化。将该方法与LSTM、GRU和RNN模型进行对比试验，结果显示，研究所建模型对氨氮浓度预测的均方根误差、平均绝对误差和均方误差分别为0.027 6、0.023 9和0.000 76，均优于其他预测模型。表明基于贝叶斯和元学习的氨氮浓度预测模型对小样本数据建模有效，可以实现网络快速收敛，精度满足水产养殖中氨氮浓度预测需求。