Lee Godden

Hanri Mostert

Hao Zhang

Giuseppe Bellantuono

Hannah Wiseman

The low-carbon transition is ongoing everywhere. This Handbook, written by a group of senior and junior scholars from six continents and nineteen countries, explores the legal pathways of decarbonisation in the energy sector. What emerges is a composite picture. There are many roadblocks, but also a lot of legal innovation. The volume distils the legal knowledge which should help move forward the transition. Questions addressed include the differences between the decarbonization strategies of developed and developing countries, the pace of the transition, the management of multi-level governance systems, the pros and cons of different policy instruments, the planning of low-carbon infrastructures, the roles and meanings of energy justice. The Handbook can be drawn upon by legal scholars to compare decarbonisation pathways in several jurisdictions. Non-legal scholars can find information to be included in transition theories and decarbonization scenarios. Policymakers can discover contextual factors that should be taken into account when deciding how to support the transition.

Handbook of Energy Law in the Low-Carbon Transition

IBM Redbooks

Samantha Buhler

Guanjun Cai

John Goodyear

Edrian Irizarry

Nora Kissari

Zhuo Ling

Nicholas Marion

Aleksandr Petrov

Junfei Shen

Wanting Wang

He Sheng Yang

Dai Yi

Xavier Yuen

The exponential growth in data over the last decade coupled with a drastic drop in cost of storage has enabled organizations to amass a large amount of data. This vast data becomes the new natural resource that these organizations must tap in to innovate and stay ahead of the competition, and they must do so in a secure environment that protects the data throughout its lifecyle and data access in real time at any time. When it comes to security, nothing can rival IBM® Z, the multi-workload transactional platform that powers the core business processes of the majority of the Fortune 500 enterprises with unmatched security, availability, reliability, and scalability. With core transactions and data originating on IBM Z, it simply makes sense for analytics to exist and run on the same platform. For years, some businesses chose to move their sensitive data off IBM Z to platforms that include data lakes, Hadoop, and warehouses for analytics processing. However, the massive growth of digital data, the punishing cost of security exposures as well as the unprecedented demand for instant actionable intelligence from data in real time have convinced them to rethink that decision and, instead, embrace the strategy of data gravity for analytics. At the core of data gravity is the conviction that analytics must exist and run where the data resides. An IBM client eloquently compares this change in analytics strategy to a shift from "moving the ocean to the boat to moving the boat to the ocean," where the boat is the analytics and the ocean is the data. IBM respects and invests heavily on data gravity because it recognizes the tremendous benefits that data gravity can deliver to you, including reduced cost and minimized security risks. IBM Machine Learning for z/OS® is one of the offerings that decidedly move analytics to Z where your mission-critical data resides. In the inherently secure Z environment, your machine learning scoring services can co-exist with your transactional applications and data, supporting high throughput and minimizing response time while delivering consistent service level agreements (SLAs). This book introduces Machine Learning for z/OS version 1.1.0 and describes its unique value proposition. It provides step-by-step guidance for you to get started with the program, including best practices for capacity planning, installation and configuration, administration and operation. Through a retail example, the book shows how you can use the versatile and intuitive web user interface to quickly train, build, evaluate, and deploy a model. Most importantly, it examines use cases across industries to illustrate how you can easily turn your massive data into valuable insights with Machine Learning for z/OS.

Turning Data into Insight with IBM Machine Learning for z/OS

Nonparametric Variable Selection and Model Building Via Likelihood Basis Pursuit

Tetsuya Sumi

Shoji Fukuoka

Hajime Nakagawa

Sediments, which constitute the surface of the Earth, start their journey to rivers with the energy obtained from rainfalls, fl oods and other natural processes. Due to transport of sediments, rivers develop with various appearances and functions, and play a crucial role in the activities of human beings and the life cycles of other species. River

Advances in River Sediment Research

PH Dependence of Morphine Absorption in Rabbit Oral Epithelia in Vitro

Hao Zhang

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