Premise of the study: Modeling the contemporary and future climate niche for rare plants is a major hurdle in conservation, yet such projections are necessary to prevent extinctions that may result from climate change...
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Premise of the study: Modeling the contemporary and future climate niche for rare plants is a major hurdle in conservation, yet such projections are necessary to prevent extinctions that may result from climate change. Methods: We used recently developed spline climatic models and modified randomforests statistical procedures to predict suitable habitats of three rare, endangered spruces of Mexico and a spruce of the southwestern USA. We used three general circulation models and two sets of carbon emission scenarios ( optimistic and pessimistic) for future climates. Key results: Our procedures predicted present occurrence perfectly. For the decades 2030, 2060, and 2090, the ranges of all taxa progressively decreased, to the point of transient disappearance for one species in the decade 2060 but reappearance in 2090. Contrary to intuition, habitat did not develop to the north for any of the Mexican taxa;rather, climate niches for two taxa re-materialized several hundred kilometers southward in the Trans-Mexican Volcanic Belt. The climate niche for a third Mexican taxon shrank drastically, and its two mitotypes responded differently, one of the first demonstrations of the importance of intraspecific genetic variation in climate niches. The climate niche of the U. S. species shrank northward and upward in elevation. Conclusion: The results are important for conservation of these species and are of general significance for conservation by assisted colonization. We conclude that our procedures for producing models and projecting the climate niches of Mexican spruces provide a way for handling other rare plants, which constitute the great bulk of the world's endangered and most vulnerable flora.
Supersecondary structure is a layer of structure between secondary structure and tertiary structure.β-hairpin is a super secondary structure *** theβ-βmotif,if two antiparallelβ-strands are connected by loop and t...
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Supersecondary structure is a layer of structure between secondary structure and tertiary structure.β-hairpin is a super secondary structure *** theβ-βmotif,if two antiparallelβ-strands are connected by loop and there are one or more hydrogen bonds between two adjacent strands,then the structure is called asβ-hairpin,otherwise
This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. ...
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This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and reliability of RF-based diagnosis method.
Supervised dive classification is a commonly used technique for categorising time-depth Profiles of diving vertebrates. Such analyses permit the description and quantification of dive behaviour and foraging tactics, a...
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Supervised dive classification is a commonly used technique for categorising time-depth Profiles of diving vertebrates. Such analyses permit the description and quantification of dive behaviour and foraging tactics, and highlight feeding locations. Ideally, classification functions should be validated, and this is commonly done visually. Visual classification is subjective, but it is currently one of the few measures available for validation. We develop several approaches to validate a supervised dive classification: (1) two people visually assigning dives and developing a dataset where both agree, and (2) use of dives from southern elephant seals identified as "drift dives" with characteristic velocity signatures. We classified the dives of three seals from their post-moult foraging trips and estimated the error associated with visual classification. We found classification error (disagreement) between classifiers up to 57%. We created a training dataset based on dives with agreement and applied this to a relatively new, automated classification method - the randomforests (RF) algorithm. A supervised function developed using this algorithm estimated a classification error of 5% on elephant seal dives;classification error on underrepresented dive classes ranged from 2 - 12%. Testing this classification function on independent data produced a low error (1.6%). RF function errors were lower than for visual classification, and errors were similar to or better than those estimated using discriminant functions. Swim velocity parameters were the most important predictors, but their absence did not reduce the randomforests function's effectiveness by much. Our results suggest that there is a temporal shift in diving behaviour as seals become more buoyant. We compared the temporal patterns in drift rate from the drift dives classified using the RF function with the drift dives validated via characteristic velocity signatures. This indicated that the classifications produc
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