Updating the principles of the liturgical movement for a new liturgical movement

The author is a Benedictine monk of L’abbaye Sainte-Madeleine du Barroux in France. The original paper (in French) was delivered in July 2001 at a Liturgy Conference held at the Abbey of Notre Dame, Fontgombault, France,
in the presence of the then Joseph Cardinal Ratzinger when Prefect of the Congregation for the Doctrine of the Faith and was later published in the English translated proceedings of the conference edited by Dr Alcuin Reid, Looking Again at the Question of the Liturgy with Cardinal Ratzinger:
Proceedings of the July 2001 Fontgombault Liturgical Conference (St Michael’s Abbey, Farnborough, 2003). The paper here published is revised and translated from the original French especially for The Priest by Professor David Birch (Deakin University, Melbourne) at the invitation of the Editor, with the cooperation of Dom Charbel. The full set of footnotes is available in Reid (Ed.) (2003). Where passages are quoted from Magisterial texts, the Vatican website English translation is given rather than a translation of the French version used in the original paper. Sub-headings are due to the Editor.

All things bright and beautiful

The famous hymn played on a piano.

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.