Alvaro García

Sequences, cycles and hiatuses in the Upper Albian-Cenomanian of the Iberian Ranges (Spain): a cyclostratigraphic approach

Abstract The Upper Albian-Cenomanian deposits in the Iberian Ranges are composed of a complex alternation of continental (sandstones), coastal (lime sandstones and marls) and marine (carbonates) facies. Facies are arranged mainly in transgressive and deepening-upward sequences and parasequences, but deepening-shallowing-upwards sequences and parasequences also exist. Depositional sequences and parasequences are bounded by subaerial erosive surfaces or ferruginous crusts, and by their correlative conformities. It is evident from the facies alternation that deposition reflects a cyclic process in which is superimposed several orders of cycles of relative sea-level rise and fall. The cycles are preserved as 3rd-order depositional sequences and 4th- and 5th-order discontinuity-bounded parasequences, the latter being the basic blocks of the field record. A cyclostratigraphic model for sedimentation shows that each of the 3rd-order cycles is composed of five 4th-order cycles. A 4th-order cycle is also composed of five 5th-order cycles. This 1:5:5 ratio reflects the maximum number of parasequences observed in the most complete and marine sections. The ratio remains unchanged throughout the basin, except by onlap caused by relative sea-level rises or truncations caused by relative sea-level falls. A detailed correlation of sections separated by more than 200 km shows that thickness differences are caused mainly by parasequence disappearance, not parasequence thinning. Cycles are mainly eustatic in origin, controlled by accommodation space. Increased accommodation generates thick parasequences of wider regional extension; they contain the more marine facies in any section. Only some sequences with a restricted regional distrubution are recorded if the accommodation is low. The average duration of 3rd-order cycles was 1.95 Ma (ranging from 1.33 to Ma), an average 4th-order cycle was 390 ka (266–533 ka) and a 5th-order cycle was 78 ka (53–106 ka).

Applying soft computing techniques to optimise a dental milling process

This study presents a novel soft computing procedure based on the application of artificial neural networks, genetic algorithms and identification systems, which makes it possible to optimise the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving both time and financial costs and/or energy. This novel intelligent procedure is based on the following phases. Firstly, a neural model extracts the internal structure and the relevant features of the data set representing the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques. This constitutes the model for the fitness function of the production process, using relevant features of the data set. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The proposed novel approach was tested under real dental milling processes using a high-precision machining centre with five axes, requiring high finishing precision of measures in micrometres with a large number of process factors to analyse. The results of the experiment, which validate the performance of the proposed approach, are presented in this study.

A bio-inspired computational high-precision dental milling system

A novel bio-inspired computational high-precision dental milling system is proposed in this interdisciplinar research. The system applies several bio-inspired models, based on unsupervised learning, that analyse and identify the most relevant features of high-precision dental-milling data sets and their internal structures. Finally, a supervised neural architecture and certain identification techniques are applied, in order to model and to optimize the high-precision process. This is done by empirically testing the model using a real data set taken from a dynamic high-precision machining centre with five axes.

Optimizing a dental milling process by means of soft computing techniques

A novel soft computing system to optimize a dental milling process is proposed. The model is based on the initial application of several statistical and projection methods as Principal Component Analysis and Cooperative Maximum Likelihood Hebbian Learning to analyze the structure of the data set and to identify the most relevant variables. Finally, a supervised neural model and identification techniques are applied, in order to model the process and optimize it. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

The Upper Cretaceous in the Tagus Basin (Central Spain): sequential analysis based on oil-well data and outcrop correlation

Abstract The logs from six oil wells drilled between 1960 and 1980 in the Upper Cretaceous succession of the Tagus Basin were analysed and correlated with surface outcrops, to estimate sedimentary environments and to detect cyclicity in sedimentation. Log interpretation has been carefully checked with outcrop data, because high peaks in gamma-ray logs, usually considered to represent open marine facies, may correspond either to open marine or to muddy coastal deposits. The former correspond to the maximum flooding surfaces of depositional sequences, whereas the latter correspond to sequence boundaries. Three second-order depositional megasequences have been recognized (MS-2, MS-3 and MS-4). The basal megasequence onlaps older rocks, grading upwards from continental to marine deposits. The megasequence MS-3 shows a basal marly transgressive interval and a thick carbonate pile at the top, grading to the SE to sabkha environments. The top megasequence is hardly recognized in surface outcrops, but in well logs it is a thick evaporite — claystone unit of mainly sabkha environments that grade westwards to coastal deposits. Sedimentation of megasequences MS-3 and MS-4 extended farther west than previously considered, covering areas considered as part of the exposed Hesperian Massif. These were areas of marine and coastal sedimentation where organic matter accumulation could be potentially high, and can be considered potential areas for oil or gas exploration.

Retreatment predictions in odontology by means of CBR systems

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

A hybrid system for dental milling parameters optimisation

This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a highprecision machining centre with five axes for dental milling purposes.

Air Regeneration of a Silicalite Column Loaded with Ethanol: Modelling and Energy Estimation

Concentration-thermal swing adsorption (CTSA) using an ethanol-selective adsorbent can be an attractive option to reduce the energy costs associated with the separation of ethanol from water. In this process, ethanol regeneration is the only step with significant energy consumption, in which the adsorbent column saturated with liquid ethanol is purged with a hot inert gas to recover ethanol by condensation. Despite many showing interest in applying CTSA, the method has received little attention in the open literature, and consequently little information is available about its energy requirement. In this work, we experimentally measured the regeneration dynamics of a heated silicalite column saturated with liquid ethanol by air purge using different column wall temperatures and purge gas flow rates. Using a theoretical model based on conservation equations, the mass and heat transfer kinetics in this process are adequately reproduced. The proposed model has been used to estimate the overall energy requirement of the ethanol vapourization/desorption and the subsequent condensation processes.

Modelling the Longevity of Dental Restorations by means of a CBR System

The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

Prediction of dental milling time-error by flexible neural trees and fuzzy rules

This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures --- evolutionary fuzzy rules and flexible neural trees --- for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.