Interpretation of the Concept of Religious Syncretism in Western European and Russian Researches

The article is devoted to religious syncretism, in particular, the history of its research in Western European and Russian science. Most foreign researchers give an ambiguous assessment of the mother concept of syncretism. However, when for Russian researchers syncretism is a universal term used to describe a group of processes and seems not to be problematic, in the Western European scientific community anthropologists and religious scholars have been actively discussing its application for almost fifty years: works devoted to religious syncretism, as a rule, are necessarily accompanied by a number of reservations, and in other cases researchers deliberately avoid it altogether, preferring to use synonymous concepts with a less complicated history. In this way the main objective this paper sets is to compare the visions of the Russian and the foreign academy on the term, map and draw the margins of the conceptual use that both Russian and European scholars share, shedding light on contexts and reasons. Positioning the concept within the field of its use, the text also notes the wide context of the referent phenomenon. Processes of spreading the boundaries of identity and religious self-determination are now taking place against the background of globalization. In this regard, the topic of religious syncretism not only has not lost its relevance, but, on the contrary, is increasingly at the center of religious studies. The author of the article covers the history of the emergence of the concept in religious studies. On the basis of comparative analysis, the traces of the coverage of this phenomenon in scientific publications of Western European and Russian researchers are shown. It is also revealed, how the connotations of the concept of religious syncretism have changed, with the key discrepancies and gaps in scientific approaches identified, and the main types of contexts in which it appears differentiated.

KEYWORD ANALYSIS WITH USING STATISTICAL METHODS

There are currently more than a billion websites worldwide. In so many websites, everyone wants to be visible to search engines through the keywords that people search for. The article deals with the process of creating keywords, through which we can identify the intention of the searcher. The process of creating keywords consists of several steps, namely the collection of keywords, subsequent cleaning of keywords, their categorization and the last step is the interpretation of keywords. The paper focuses mainly on the categorization of keywords, which we obtain through the use of statistical methods, which includes a method of visualizing relationships between keywords by determining the strength of the association between words called concept linking or term map.

Drought suppresses soil predators and promotes root herbivores in mesic, but not in xeric grasslands

Precipitation changes among years and locations along gradients of mean annual precipitation (MAP). The way those changes interact and affect populations of soil organisms from arid to moist environments remains unknown. Temporal and spatial changes in precipitation could lead to shifts in functional composition of soil communities that are involved in key aspects of ecosystem functioning such as ecosystem primary production and carbon cycling. We experimentally reduced and increased growing-season precipitation for 2 y in field plots at arid, semiarid, and mesic grasslands to investigate temporal and spatial precipitation controls on the abundance and community functional composition of soil nematodes, a hyper-abundant and functionally diverse metazoan in terrestrial ecosystems. We found that total nematode abundance decreased with greater growing-season precipitation following increases in the abundance of predaceous nematodes that consumed and limited the abundance of nematodes lower in the trophic structure, including root feeders. The magnitude of these nematode responses to temporal changes in precipitation increased along the spatial gradient of long-term MAP, and significant effects only occurred at the mesic site. Contrary to the temporal pattern, nematode abundance increased with greater long-term MAP along the spatial gradient from arid to mesic grasslands. The projected increase in the frequency of extreme dry years in mesic grasslands will therefore weaken predation pressure belowground and increase populations of root-feeding nematodes, potentially leading to higher levels of plant infestation and plant damage that would exacerbate the negative effect of drought on ecosystem primary production and C cycling.

CoStorm: a term map system to aid in a collaborative ideation process

The decisions made during the early stages of a design process have a huge impact on a product. Owing to the explosion of preliminary ideas, however, designers easily lose track of important ideas and significant information and end up being buried in a pile of plain words. Failing to locate an idea in the context of idea generation makes it difficult to generate new ideas or take optimized decisions. In this study, the authors propose the term map approach to provide a complete bird's eye view of all ideas, which is a higher-dimension graphical representation that helps in inspiring ideas and making decisions among design team members. A software application named CoStorm is developed. Through the case study of the cash-flattener module, which is a crucial component of an automated teller machine, this method is found to contribute in facilitating the ideation and decision-making progress.

Long-Term Ensemble Learning for Cross-Season Visual Place Classification

This paper addresses the problem of cross-season visual place classification (VPC) from the novel perspective of long-term map learning. Our goal is to enable transfer learning efficiently from one season to the next, at a small constant cost, and without wasting the robot’s available long-term-memory by memorizing very large amounts of training data. To achieve a good tradeoff between generalization and specialization abilities, we employ an ensemble of deep convolutional neural network (DCN) classifiers and consider the task of scheduling (when and which classifiers to retrain), given a previous season’s DCN classifiers as the sole prior knowledge. We present a unified framework for retraining scheduling and we discuss practical implementation strategies. Furthermore, we address the task of partitioning a robot’s workspace into places to define place classes in an unsupervised manner, as opposed to using uniform partitioning, so as to maximize VPC performance. Experiments using the publicly available NCLT dataset revealed that retraining scheduling of a DCN classifier ensemble is crucial in achieving a good balance between generalization and specialization. Additionally, it was found that the performance is significantly improved when using planned scheduling.