Short-term effect of sawdust biochar and bovine manure on the physiological behavior of turnip (Brassica rapa L.) grown in open fields in the Algiers region

ArticleThis study was designed to determine the effect of different doses of biochar (B) 5.10, 20tha-1alone and mixed with manure (F) 10tha-1on turnips. The results showed that the OM (organic matter) rate had a maximum of 93.7% for (B20*F) and a minimum of 14.5% for (F); the CEC (cation exchange capacity) showed a maximum of 32.2% for (B10*F) and a minimum of 0.2% with (B5*F) compared to the control (T) and finally the pH to be increased with a maximum value of 11.2% for (B20*F) and a minimum value of 1.7% for (F) compared to (T) (≤0.01).For the chemical parameters of the turnip, the maximum nitrogen rate was 93.8% with (B10) and 2% for (B20). The highest value for phosphorus was recorded in (F) and a minimal value in (B5) (≤0.01).The potassium level was high 4.2% for the treatment (B20*F) with the lowest value of 4.4% for (B5) and (B10) compared to (T) (0.05).For the yield components, thefresh weight of the most important bulb was obtained with (F) with the value of 116.8% and minimum weight of 0.4% in the treatment (B5). The highest bulb length value was 36.8% in (F) and the lowest was 0.5% obtained with (B20*F). The bulb diameter was the largest in the treatment (F) and the smallest was 4.8% in (B20). Finally, the fresh weight of the leaves showed a maximum of 106.9% in (F) and an increase of 6% in (B20) compared to (T) (≤0.01)

Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider