This chapter covers how to create JIRA plugins for use with
workflows. Conditions, validators and post-functions are each described in
detail, with both nonconfigurable and configurable examples. All the
source code for the examples in this chapter is available from https://marketplace.atlassian.com/41293. The standard JIRA
conditions, validators, and post-functions plugins are also useful
examples and can be found in the file system-workflow-plugin.xml.
Conditions control whether a transition appears in a users list of
available workflow actions. For instance, the default JIRA workflow only
shows the Start Progress transition to the current
assignee of an issue. Other users dont see the transition as a choice in
their view of the issues actions.
Conditions are configured with the Add link on
the Conditions tab of a workflow transition, as
described at http://confluence.atlassian.com/display/JIRA/Configuring+Workflow#ConfiguringWorkflow-Addingacondition.
You can also combine multiple conditions using AND and OR operations, but
It will be argued in this chapter that there is no single, all-purpose psychoanalytic life history to be told, for the account of that life keeps changing during the course of analysis. This continuous change occurs not only because the history gets to be told more insightfully, that is, from the psychoanalytic point of view told more completely, more consistently, and with a greater sense of relevance regarding the variables that are crucial in analysis, such as the varieties of sexual, aggressive, and defensive activity during different phases of development. If it were just this kind of change, one could still end up with a single coherent history, as Michael Sherwood, for example, assumes to be the case in his important book The Logic of Explanation in Psychoanalysis (1969). But it is not just this kind of change. The historical account also changes whenever the major questions change; for in the context established by each such question, different aspects of events and people and conflictual compromised activity come to the fore in distinctive ways. One sees that this is so when new, surprising, and long repressed or neglected details of the life history are told with special significance as different analytic questions are pursued in depth. One also sees how remembering is so largely a function of the context established by one or another question.
By modern-day definition, hope means to wish for something, without the certainty that it will be fulfilled. It is an unsure optimism. The Bible, however, gives a different meaning to the word hope. In biblical terms, hope is an indication of certainty; a strong and confident expectation. It is not just wishful thinking; it is a sure belief that what you hope for will come to pass.
I throw around the word hope on a daily basis, in the modern sense of the word: I hope it doesn’t rain; I hope the stain comes out of my new dress; I hope I get a refund on my taxes. All of these are things that I desire to happen, but I have no confident expectation that they will happen (especially the tax refund!). If these wishes do not come true, it’s really no big deal. Hoping for life on that other hand—well that’s a different matter. It is here that I employ the biblical definition of the word hope.
Jesus said, “Therefore I tell you, whatever you ask in prayer, believe that you have received it, and it will be yours” (Matthew 11:24). In this quote, Jesus assures us that when we have faith, and truly believe that we will receive, our prayers are certain to be answered. The power of prayer, then, lies not in the asking but in the belief that what you ask for will be granted. That is hope. In that sense, cancer gave me something to really hope for. I did not “wish” for a full recovery, I “hoped,” with certainty, that my prayers would be answered.
Use of Artificial Neural Networks for Completing Data
Series Streamflow Hydrologic
Sérgio Luis Yoneda; Rogério Andrade Flauzino; Lucas Assis de Moraes; Marcel Ayres de Araújo
Department of Electrical and Computing Engineering, University of São Paulo – USP, São Carlos, Brazil
Abstract—Knowing the hydrological behavior of hydroelectric reservoirs affluent rivers is one of the main tools for managing the production of electricity. To identify the streamflow values of a river is extremely important for planning the operation and expansion of hydroelectric systems. However, in many cases there are insufficient or incomplete data for the specific location of a particular river. Concerning this situation, the research exposed in this paper applies an Artificial Neural Network to complete the missing data of a hydrological streamflow time series. Through comparison between the results obtained by correlational techniques and the proposed methodology, it is concluded that the later can complete the series in a more realistic way and with great precision.