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This work presents a methodology for distribution transformer rating selection and management. Measured or estimated daily load profiles are used to determine the transformer's loss of life. Loss of life values are obtained for a number of transformers and are stored together with the corresponding load profiles in a database of patterns. The expected loss of life for a transformer not included in the database is obtained by comparing the transformer's actual load profile to the ones stored in the database.

This article reviews the current situation in the Asian Elephant Elephas maximus European Association of Zoos and Aquaria Ex situ Programme (EEP). In recent years, developments in husbandry and gained knowledge about the reproductive biology of Asian elephants have contributed to increased breeding success and resulted in a mean of 15 births per year in the last 5 years. At the time of writing, the Asian elephant EEP population contains 307 individuals: 90.217 (♂♂.♀♀). Based on the life table for 1998–2018, most demographic parameters show healthy numbers [e.g. lambda (λ) = 1·025], while the population has retained 98·44% of the gene diversity. However, this EEP is also facing multiple challenges, such as the presence of subspecies, transport barriers between some EEP participants and the societal debate about the purpose of zoos. The growing number of male elephants in the EEP population appears to be the most immediate challenge. In the short term, the authors suggest that females could be managed to conceive for the first time at 8 years of age and adhere to an interbirth interval of 7 years. This would be an attempt to decrease the reproductive rate without compromising the future reproductive potential of the population. The authors also prescribe improving facilities for elephants to allow zoos to utilize a fission–fusion housing strategy, making it possible to house the increasing number of males appropriately over the longer term.

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Artificial Neural Networks (ANNs) have been successfully applied to the problem of forecasting future load values, especially in the short term framework (a few minutes to a few hours ahead). Traditional analytical models have shown difficulties when dealing with (i) the highly variable demand curve shapes, (ii) some independent variables that exhibit random behaviour, and (iii) the identification of variables that could explain relevant load variations, such as weather variables. Current available ANN applications to this problem are by far aimed at a systemwide level, where the load behaviour is more regular than at substation or even primary feeder levels.

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