An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions.

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years' monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.

Rediscovery of Rubuspendulus Rusby (Rosaceae) and a new record for the flora of Ecuador and Peru.

We report the rediscovery of Rubuspendulus Rusby, "Mora India", described in 1933 from Colombia and not mentioned again until the present study. We also update its distribution with eight new localities in Colombia, seven in Ecuador and one in Peru, being a new record for the flora of the latter two countries. This is the first time that R.pendulus' stipules and flowers are found and detailed through a botanical description, illustrations and photographs. Rubuspendulus is morphologically differentiated from R.bogotensis Benth., R.mollifrons Focke, R.porphyromallos Focke and R.urticifolius Poir., with whom it was previously confused and we give a brief explanation on the type specimen status of R.mollifrons and R.porphyromallos.

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes.

Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification.

Two new species of Rubus L. (Rosaceae) from the western Andes of Ecuador.

Two new species of Rubus (Rosaceae) from the western Andes of northern Ecuador are described. Rubuslongistipularis D.A. Espinel-Ortiz & Romol. is a scandent or climbing shrub found in the mountain forests of Chocó Andino from northern Ecuador. Rubusmaquipucunensis D.A. Espinel-Ortiz & Romol. is a vine or climbing shrub found in the rainforests of Chocó Andino from Pichincha and Santo Domingo de los Tsáchilas. The species mentioned here are morphologically differentiated from all the Rubus species from Ecuador with a detailed botanical description, illustrations and photographs. We also report, for the first time, possible hybridisation between R.longistipularis and R.boliviensis Focke, as the samples reviewed showed mixed characteristics from both species.