This Redpaper introduces the integration between two IBM products that you might like to consider when implementing a modern agile solution on your Z systems. The document briefly introduces Operational Decision Manager on z/OS and Machine learning on z/OS. In the case of Machine Learning we focus on the aspect of real-time scoring models and how these can be used with Business Rules to give better decisions. Note: Important changes since this document was written: This document was written for an older release of Operational Decision Manager for z/OS (ODM for z/OS). ODM for z/OS 8.9.1 required the writing of custom Java code to access a Watson Machine Learning for z/OS Scoring Service (this can be seen in ). Since that time ODM for z/OS version 8.10.1 has been released and much improves the integration experience. Integrating the two products no longer requires custom Java code. Using ODM for z/OS 8.10.1 or later you can use an automated wizard in the ODM tooling to: Browse and select a model from Watson Machine Learning Import the Machine Learning data model into your rule project Automatically generate a template rule that integrates a call to the Watson Machine Learning scoring service Download and read this document for: Individual introductions to ODM for z/OS and Machine learning Discussions on the benefits of using the two technologies together Information on integrating if you have not yet updated to ODM for z/OS 8.10.1 For information about the machine learning integration in ODM for z/OS 8.10.1 see IBM Watson Machine Learning for z/OS integration topic in the ODM for z/OS 8.10.x Knowledge Center
This book investigates in detail the concepts and principles of green chemistry and related methodologies, including green synthesis, green activation methods, green catalysis, green solvents, and green design to achieve process intensification while at the same time ensuring process safety and promoting ecological civilization and environmental protection. Moreover, it incorporates elements of chemical management and chemical education, highlighting chemists’ responsibility to protect humankind and foster green and sustainable development in chemistry. Combining Chinese and Belarus wisdom, this book is intended for those working in the chemical industry who are interested in environmental protection and sustainable development, as well as undergraduate and graduate students who are interested in green chemistry and related technologies.
Backscattering Spectrometry reviews developments in backscattering spectrometry and covers topics ranging from instrumentation and experimental techniques to beam parameters and energy loss measurements. Backscattering spectrometry of thin films is also considered, and examples of backscattering analysis are given. This book is comprised of 10 chapters and begins with an introduction to backscattering spectrometry, what it can and what it cannot accomplish, and some ""rules of thumb"" for interpreting or reading spectra. The relative strengths and weaknesses of backscattering spectrometry in the framework of materials analysis are outlined. The following chapters focus on kinematics, scattering cross sections, energy loss, and energy straggling; backscattering analysis of thin films of various degrees of complications; the influence of beam parameters; and mass and depth resolutions and their relationships to the mass and energy of projectiles. Many examples of backscattering analysis are also presented to illustrate the capability and limitation of backscattering. Backscattering applications when combined with channeling effects are considered as well. The final chapter provides a list of references on the applications of backscattering spectrometry. This monograph will be a useful resource for physicists.
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