Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

Abstract: Structural analyses of tensegrity structures must account for geometrical nonlinearity. The dynamic relaxation method correctly models static behavior in most situations. However, the requirements for precision increase when these structures are actively controlled. This paper describes the use of neural networks to improve the accuracy of the dynamic relaxation method in order to correspond more closely to data measured from a full-scale laboratory structure. An additional investigation evaluates training the network during the service life for further increases in accuracy. Tests showed that artificial neural networks increased model accuracy when used with the dynamic relaxation method. Replacing the dynamic relaxation method completely by a neural network did not provide satisfactory results. First tests involving training the neural network online showed potential to adapt the model to changes during the service life of the structure. DOI: 10.1061/�ASCE�0733-9445�2003�129:5�672

A study of two stochastic search methods for structural control

Abstract: Many engineering tasks involve the search for good solutions among many possibilities. In most cases, tasks are too complex to be modeled completely and their solution spaces often contain local minima. Therefore, classical optimization techniques cannot, in general, be applied effectively. This paper studies two stochastic search methods, one well-established �simulated annealing � and one recently developed �probabilistic global search Lausanne�, applied to structural shape control. Search results are applied to control the quasistatic displacement of a tensegrity structure with multiple objectives and interdependent actuator effects. The best method depends on the accuracy related to requirements defined by the objective function and the maximum number of evaluations that are allowed

Bat responses to climate change: a systematic review

This is the final version. Available on open access from Wiley via the DOI in this recordUnderstanding how species respond to climate change is key to informing vulnerability assessments and designing effective conservation strategies, yet research efforts on wildlife responses to climate change fail to deliver a representative overview due to inherent biases. Bats are a species-rich, globally distributed group of organisms that are thought to be particularly sensitive to the effects of climate change because of their high surface-to-volume ratios and low reproductive rates. We systematically reviewed the literature on bat responses to climate change to provide an overview of the current state of knowledge, identify research gaps and biases and highlight future research needs. We found that studies are geographically biased towards Europe, North America and Australia, and temperate and Mediterranean biomes, thus missing a substantial proportion of bat diversity and thermal responses. Less than half of the published studies provide concrete evidence for bat responses to climate change. For over a third of studied bat species, response evidence is only based on predictive species distribution models. Consequently, the most frequently reported responses involve range shifts (57% of species) and changes in patterns of species diversity (26%). Bats showed a variety of responses, including both positive (e.g. range expansion and population increase) and negative responses (range contraction and population decrease), although responses to extreme events were always negative or neutral. Spatial responses varied in their outcome and across families, with almost all taxonomic groups featuring both range expansions and contractions, while demographic responses were strongly biased towards negative outcomes, particularly among Pteropodidae and Molossidae. The commonly used correlative modelling approaches can be applied to many species, but do not provide mechanistic insight into behavioural, physiological, phenological or genetic responses. There was a paucity of experimental studies (26%), and only a small proportion of the 396 bat species covered in the examined studies were studied using long-term and/or experimental approaches (11%), even though they are more informative about the effects of climate change. We emphasise the need for more empirical studies to unravel the multifaceted nature of bats' responses to climate change and the need for standardised study designs that will enable synthesis and meta-analysis of the literature. Finally, we stress the importance of overcoming geographic and taxonomic disparities through strengthening research capacity in the Global South to provide a more comprehensive view of terrestrial biodiversity responses to climate change.Natural Environment Research Council (NERC)MUR Rita Levi Montalcini programPortuguese Foundation for Science and TechnologySpanish Ministry of Science, Innovation and UniversitiesJunta de AndalucíaBulgarian National Science FundKaroll Knowledge Foundatio

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp