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This chapter contains sections titled: Why Study Ethics through Worship? How Does the Liturgy Inform and Shape the Christian Life? Worship as a Series of Practices A Story Reference

The Schumpeterian process of 'creative destruction' is an essential ingredient of a dynamic economy. In many countries around the world, however, this process is weakened by pervasive regulation of product and factor markets. This book documents the regulatory obstacles faced by firms, particularly in developing countries, and assesses their implications for firm renewal and macroeconomic performance. Combining a variety of methodological approaches--analytical and empirical, micro and macroeconomic, single- and cross-country-- the book provides evidence that streamlining the regulatory framework would have a significant social pay-off, particularly in developing countries that are also burdened by weak governance. The book's chapters trace out analytically and empirically the links between microeconomic policies and distortions, on the one hand, and aggregate performance in terms of productivity, growth and volatility, on the other. The volume adds to a novel but increasingly influential literature that seeks to understand macroeconomic phenomena from a microeconomic perspective, and derive the relevant lessons for development policy. Such literature is still fairly scarce in the case of industrial countries, and virtually in its infancy for developing countries.

1. Invasive species are a major threat to global biodiversity and their introduction can have significant economic consequences. The invasive tunicate Didemnum vexillum is a notorious invader with significant negative impacts on cultured shellfish and natural benthic communities, including commercially important ones. 2. We conducted an expert survey, identifying the five most important transport vectors for D. vexillum along the west coast of North America. We determined the spatially explicit vector density for all vectors in order to identify introduction hotspots. Additionally, we developed an environmental niche model based on 46 occurrence points and nine environmental variables to identify areas suitable for D. vexillum. 3. Spatial distribution of the most important transport vectors (slow-moving vessels, aquaculture, fishing vessels, small vessels, and large commercial vessels) identified several hotspots with high vector densities. These proved to be a very good predictor of current D. vexillum occurrence in British Columbia (BC). Ecological niche modelling (Genetic Algorithm for Rule-set Prediction) predicted suitable environments in southern BC, parts of central BC and along the east coast of the Queen Charlotte Islands. Independent validation of the model based on the current distribution in BC indicated good predictive accuracy. Additional analytical steps confirmed that no environmental variable dominated the predictions and we identified ranges of environmental conditions predicted suitable by the model. 4. We identified areas of high establishment probability for D. vexillum by combining the vector model and environmental niche model. Parts of central BC, the west coast of Vancouver Island and the Strait of Georgia are areas where D. vexillum is most likely to establish. 5. Synthesis and applications. Spatially explicit predictions of the potential distribution of biological invaders are crucial for informing risk assessments, development of management strategies, and resource allocation. While most studies only focus on one step in the invasion process, we successfully combined the likelihood of introduction and establishment. Results from this study are informing the canadian risk assessment of invasive tunicates, guiding current monitoring efforts, and providing a basis for potential intervention/mediation measures.

Modeling ecological niches of species is a promising approach for predicting the geographic potential of invasive species in new environments. Argentine ants (Linepithema humile) rank among the most successful invasive species: native to South America, they have invaded broad areas worldwide. Despite their widespread success, little is known about what makes an area susceptible--or not--to invasion. Here, we use a genetic algorithm approach to ecological niche modeling based on high-resolution remote-sensing data to examine the roles of niche similarity and difference in predicting invasions by this species. Our comparisons support a picture of general conservatism of the species' ecological characteristics, in spite of distinct geographic and community contexts.

Ecological niche modeling provides a means for predicting the potential future distribution of a nonindigenous species based on environmental characteristics of the species' native range. We applied this method to the Chinese mitten crab (Eriocheir sinensis), a catadromous crustacean with a long history of invasion in Europe. We used genetic algorithm for rule-set prediction to predict the potential European distribution of mitten crab based on its distribution in 42 locations in its native Asia. The climatic variables, air temperature, number of days, amount of precipitation, and wetness index, contributed significantly to predictions of native distribution limits. Although the genetic algorithm for rule-set prediction model was developed for the native range, the species' extensive distribution in Europe (n =434) allowed independent validation of the predictions. Application of the model to Europe was successful, with 84% of occurrences in regions predicted to be suitable by >80% of the models and <4% of occurrences in areas predicted suitable by <50% of the models (mainly along the northern range). At the watershed scale, areas with established mitten crab populations had significantly higher habitat matching than sites that were not invaded. The independent validation of the Asian-based model by the European distribution revealed that predictions were highly accurate. The model also identified large areas of Europe, particularly along the Mediterranean coast, as vulnerable to future invasion. These predictions can be used to develop strategies to control the spread of mitten crab by preventing introductions into vulnerable areas.

Two Artificial Intelligence (AI) methods, Fuzzy Inference System (FIS) and Artificial Neural Network (ANN), are applied to Landslide Susceptibility Mapping (LSM), to compare complementary aspects of the potentials of the two methods and to extract physical relationships from data. An index is proposed in order to rank and filter the FIS rules, selecting a certain number of readable rules for further interpretation of the physical relationships among variables. The area of study is Rolante river basin, southern Brazil. Eleven attributes are generated from a Digital Elevation Model (DEM), and landslide scars from an extreme rainfall event are used. Average accuracy and area under Receiver Operating Characteristic curve (AUC) resulted, respectively, in 81.27% and 0.8886 for FIS, and 89.45% and 0.9409 for ANN. ANN provides a map with more amplitude of outputs and less area classified as high susceptibility. Among the 40 (10%) best-ranked FIS rules, 13 have high susceptibility output, while 27 have low; a cause is that low susceptibility areas are larger on the map. Slope is highly connected to susceptibility. Elevation, when high (plateau) or low (floodplain), inhibits high susceptibility. Six attributes show the same fuzzy set for the 18 best-ranked rules, meaning this fuzzy set is common on the map. Overall findings point out that ANN is best suited for LSM map generation, but, based on them, using FIS is important to help researchers understand more about AI models for LSM and about landslide phenomenon.

This study follows the previous study entitled ‘Rough set-based rule generation and Apriori-based rule generation from table data sets: A survey and a combination’, and this is the second study on ‘Rough set-based rule generation and Apriori-based rule generation from table data sets’. The theoretical aspects are described in the previous study, and here the aspects of application, an SQL-based environment for rule generation and decision support, are described. At first, the implementation of rule generator defined in the previous study is explained, then the application of the obtained rules to decision support is considered. Especially, the following two issues are focused on, (i) Rule generator from table data sets with uncertainty in SQL, (ii) The manipulation in decision support below: (ii-a) In the case that an obtained rule matches the condition, (ii-b) In the case that any obtained rule does not match the condition. The authors connect such cases with decision support and realised an effective decision support environment in SQL.

Aim Coyote (Canis latrans) distribution in Mexico and Central America has expanded recently reaching the Yucatan peninsula, Belize and Panama, probably promoted by deforestation of tropical areas. Historically, the southern distribution of coyotes prior to European settlement in America was described as reaching only as far south as central Mexico and that introduction of livestock favoured migration of coyotes to southern Mexico and Central America. However, coyote fossil records in Central America and Yucatan, as well as observational records of travellers during the sixteenth century suggest that the coyote's arrival to the region was earlier. Because of the uncertainty of past coyote distribution and the possible economic and ecological impacts due to recent range expansion, the objectives of this study were to confirm if paleontological and historical evidence support the hypothesis that the southernmost limit of coyote distribution before the arrival of European settlers was the centre of Mexico, to discuss the possible factors that have influenced historical shifts in coyote distribution, and to model the present distribution of the coyote in Mexico and Central America, determining the areas where they could invade in the near future. Location The research area comprises continental Mexico and the Central American Isthmus countries: Guatemala, Belize, El Salvador, Honduras, Nicaragua, Costa Rica and Panama. Methods The historical distribution (Pleistocene-Early Holocene, Pre-Columbian, sixteenth to nineteenth centuries and twentieth century) was established from coyote records obtained from museum collections and specialized literature. Present coyote distribution for Mexico and Central America was modelled using the Genetic Algorithms for Rule-set Prediction (GARP). Results Historical coyote records show that this species was distributed in southern Mexico and Central America during the Pleistocene-Early Holocene, the Pre-Columbian period, and during the arrival of Europeans in the sixteenth century. Coyote records indicate a continuous range expansion during the twentieth century. Historical advance and regression of tropical forests in southern Mexico and Central America produced by natural and human events such as climatic changes and variation in human densities could help us understand the historical coyote distribution. The modelled present-day coyote distribution included the north of Belize, the north of Panama, the north of the Yucatan Peninsula and a corridor on the Gulf costal plain of Campeche in Mexico. Also, the model predicted a region north of the Darien in southern Panama as appropriate for the presence of coyotes, although they have not been detected there so far. Main conclusion Coyote records in southern Mexico and Central America during the Pleistocene-Early Holocene, the Pre-Columbian period, and early arrival of European settlers to the area indicated that coyotes were probably already present there and did not recently disperse from the north of Mexico to the south due to livestock introduction. /// Objetivo La distribución del coyote (Canis latrans) se ha expandido recientemente en México y Centroamérica hasta alcanzar la península de Yucatán, Belice y Panamá, probablemente favorecida por la deforestación en regiones tropicales. Históricamente se ha creído que la distribución de esta especie a la llegada de los colonizadores europeos tenía su limite sur en el centro de México y que la introducción del ganado en el sur de México y Centroamérica favoreció la migración de los coyotes hacia estas áreas. Sin embargo, registros fósiles de coyotes en Centroamérica y Yucatán, así como registros visuales de esta especie por viajeros durante el siglo XVI, sugieren que la llegada de los coyotes a la región fue anterior a lo que previamente se creía. Debido a la controversia sobre la distribución anterior del coyote, así como por los posibles impactos económicos y ecológicos que los coyotes pueden provocar por su reciente expansión, los objetivos de este estudio fueron determinar si la evidencia paleontológica e histórica apoya la hipótesis de que la distribución de esta especie antes de la llegada de los colonizadores europeos llegaba hasta el centro de México, discutir los posibles factores que han influido sobre los cambios históricos en su distribución y modelar la distribución actual del coyote en México y Centroamérica, determinando las áreas que los coyotes pueden invadir en el futuro. Área de Estudio El estudio comprendió la porción continental de México y los países del Istmo Centroamericano: Guatemala, Belice, El Salvador, Honduras, Nicaragua, Costa Rica y Panamá. Métodos Se estableció la distribución histórica del coyote (Pleistoceno-Holoceno temprano, Precolombina, siglos XVI al XIX y siglo XX) a partir de registros de coyotes obtenidos de colecciones científicas de museos y literatura especializada. Se modeló la distribución actual del coyote en México y Centroamérica usando algoritmos genéticos (GARP). Resultados Los registros históricos de coyote indican que la especie estuvo distribuida en el sur de México y Centroamérica durante el Pleistoceno-Holoceno temprano, la etapa Precolombina y a la llegada de los europeos en el siglo XVI. Los registros indican un incremento continuo en el área de distribución del coyote durante el siglo XX. El avance y contracción de los bosques tropicales en el sur de México y Centroamérica debido a causas naturales y humanas, tales como cambios climáticos o variación de las densidades humanas, pueden ayudar a entender la distribución histórica del coyote. La distribución actual de esta especie obtenida a través del modelo, reconoce que puede estar presente al norte de la península de Yucatán y la llanura costera del Golfo de Campeche en México, y en Centroamérica, el norte de Belice y el norte de Panamá. Además, el modelo muestra que, aún cuando la especie no ha llegado a la región norte de Darién en el sur de Panamá, dicho lugar es apropiado para ser invadido por los coyotes. Conclusiones Principales Los registros de coyotes en el sur de México y Centroamérica en el Pleistoceno-Holoceno temprano, la época Precolombina y durante las primeras etapas de la Colonia Española indican que los coyotes probablemente ya estaban presentes en el área a la llegada de los colonizadores europeos, y no se dispersaron recientemente hacia el sur de México y Centroamérica desde el centro de México con la introducción de la ganadería en el área.

Controlling continuous data, such as Electroencephalographic (EEG) data, is challenging. The EEG data is recorded during the Concealed Information Test (CIT), performed with a 16-channel electrode. Some of the meta-heuristic algorithms that are already in use need to be improved so that they can handle the balance between the exploration and exploitation stages, deal with the problem of local optimums, and take continuous data. The Spider Monkey Optimization (SMO) algorithm is utilized as a candidate rule miner known as the SM-Candidate Rule Miner (SM-CRM) for categorizing EEG data into two groups, like guilty or innocent. These problems are solved by making an exhaustive optimal rule set that balances the SM-CRM’s sensitivity, accuracy, and specificity. These rules are applied to the entire EEG dataset, and ten cross-folder validations are performed to get the individual confusion matrix. The SM-CRM method was better than other well-known algorithms regarding average sensitivity, specificity, and classification accuracy. It achieved a decent mean rule length and mean rule set size. Compared to meta-heuristic algorithms like the genetic and bat, the proposed framework achieved a maximum accuracy of 97.66% .

The idea of setting up a European Banking Union, an essential element in the reinforcement of the Economic and Monetary Union (EMU), has become a concrete project and it has been outlined as such after the financial crisis started in 2007. The setting up of a Banking Union is a new project for the European Union and it comes as an answer to the financial crisis; this project has three major components: the Unique Mechanism of Surveillance (UMS), the Unique Mechanism of Resolution (UMR) and the European Deposit Guarantee Schemes. At the basis of these infrastructure elements there laid: the Single Rulebook\", the unique European regulation framework (made up of the capital requirements: \"CDR/CRR IV - CRD IV Directive\" and \"Regulation regarding capital requirements\" provided together with the standards and directions issued by the European Banking Authority \"ABE\") and a set of rules established for state subsidies. The large flow of important information which exists in the matter makes it necessary for us to deal with this topic in a consistent and complex argumentation7.