Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods.

Mordenite from Spain and its application as pozzolana

In Spain, natural zeolites have been only found in the volcanic areas of Canary Islands (San Gil, M.M., 1959). However, several years ago the study of the South Eastern region of continental Spain (Cabo de Gata) revealed the presence of a deposit of mordenite being industrially commercialized as bentonite. The deposit San José-Los Escullos is located in the concession registered as Los Murcianos, managed by Bentonitas Especiales, S.A. (BENESA) who was extracting bentonite while ignoring the presence of the zeolite. The name San José-Los Escullos refers to the discovery of mordenite in this deposit. The deposit is located in a volcanic area, constituted by pyroxenic andesites, breccias, tuffs, pyroclasts with andesitic and dacitic composition, largely altered by the hydrothermal solutions. The recent discovery of this zeolite with mordenite content up to 97%, has changed the initial vision about that deposit, and new possible uses of this mineral in the pozzolanic cement industry are foreseen. This work intends to give preliminary data on the characterization of this zeolite according to X-ray diffraction (XRD), ICP analyses, and scanning electron microscopy (SEM), as well as to emphasize the pozzolanic properties of this material, using both chemical and mechanical tests.

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.