First measurement of the helicity asymmetry E in eta photoproduction on the proton

Results are presented for the first measurement of the double-polarization helicity asymmetry E for the eta photoproduction reaction gamma p - \u3e eta p. Data were obtained using the FROzen Spin Target (FROST) with the CLAS spectrometer in Hall B at Jefferson Lab, covering a range of center-of-mass energy W from threshold to 2.15 GeV and a large range in center-of-mass polar angle. As an initial application of these data, the results have been incorporated into the Julich-Bonn model to examine the case for the existence of a narrow N* resonance between 1.66 and 1.70 GeV. The addition of these data to the world database results in marked changes in the predictions for the Eobservable from that model. Further comparison with several theoretical approaches indicates these data will significantly enhance our understanding of nucleon resonances. (C) 2016 Published by Elsevier B.V

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today's economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking (how to assess the conformance of the schedule and the actual process execution) and performance improvement (how to improve the operational process performance). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%

МЕТОД ОПРЕДЕЛЕНИЯ РАСПОЛОЖЕНИЯ ИСТОЧНИКА КОЛЕБАНИЙ НАПРЯЖЕНИЯ В ЭЛЕКТРИЧЕСКОЙ СЕТИ

Purpose. The purpose of work is development of a method of definition of the location of a source of fluctuations of voltage. Methodology. The reasons of emergence of fluctuations of voltage at an arrangement of a source both in power lines, and in the consumer's networks, are connected with changes of consumption and active and reactive capacities. As criterion for definition of the location of a source of fluctuations of voltage we choose change of size of the active power received by reception substation on equivalent communication with system. The source of fluctuations of voltage is external for the consumer if emergence of fluctuations of voltage leads to the coordinated changes of tension and consumed in the area of active power that corresponds to a condition of the positive regulating effect of active loading on voltage (1). The source of fluctuations of voltage is internal for the consumer if emergence of fluctuations of voltage leads to counter changes of tension and consumed in the area of active power that resembles a condition of the negative regulating effect of active loading on voltage superficially (6). Results. The method of definition of the location of a source of fluctuations of voltage in an electric network which, works by the principle of an assessment of correlation of change of power and tension in a power supply network is developed. The method allows to consider shift between extrema of curves of change of voltage of U(t) and power of Pload(t). Originality. The method of definition of an arrangement of a source of fluctuations of voltage is developed. Practical value. The answer to this question where the source of fluctuations of voltage (in the territory of the consumer is located or in an external network) confirmed with the determined calculation, can form a basis of the expert opinion for the solution of legal disputes at an assessment of the damages caused by poor quality of electric energy.Предложен метод корреляции колебаний мощности и напряжения, который позволяет определять место расположения источника колебаний напряжения в системе электроснабжения.Запропоновано метод кореляції коливань потужності і напруги, який дозволяє визначати місце розташування джерела коливань напруги в системі електропостачання

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs

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