A Study of the Relationship Between Alcoholism and Self-Esteem

The relationship between the length of sobriety in the twelve step program of Alcoholics Anonymous (AA) and self-esteem level was examined for 27 recovering alcoholics. Participants included L3 male and 14 female members of AA who volunteered to complete the Coopersmith Self-Esteem Inventory and a cover/survey letter that contained demographic questions. Where it was predicted that there would be no relationship between length of sobriety and self-esteem level, it was found that there was a positive relationship between the two for the total group and the female group. That is, where length of time sober in AA increased, the alcoholic\u27s self-esteem also increased. However, the prediction of no relationship was found to be true for the male group

Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review

The evaluation of the spatial and temporal distribution of pollutants is a crucial issue to assess the anthropogenic burden on the environment. Numerous chemometric approaches are available for data exploration and they have been applied for environmental health assessment purposes. Among the unsupervised methods, Self-Organizing Map (SOM) is an artificial neural network able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more interpretation ability is gained when the SOMbased model is merged with clustering algorithms. This review comprises: (i) a description of the algorithm operation principle with a focus on the key parameters used for the SOM initialization; (ii) a description of the SOM output features and how they can be used for data mining; (iii) a list of available software tools for performing calculations; (iv) an overview of the SOM application for obtaining spatial and temporal pollution patterns in the environmental compartments with focus on model training and result visualization; (v) advice on reporting SOM model details in a pape

Soil Contamination Interpretation by the Use of Monitoring Data Analysis

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm