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"Global tea science: Current status and future needs would suit the
library of any botanical enthusiast or academic, with its
comprehensive collection of relevant chapters. The humble tea leaf
has might roots indeed." (Plant Science Bulletin - Botanical
Society of America) Tea is the most widely-consumed beverage in the
world. Like other crops, tea cultivation faces a number of
challenges. With the challenge of climate change and the
competition for scarce resources, there is a need to make tea
cultivation more efficient and sustainable. Cultivation needs also
to be more resilient to biotic and abiotic stresses, whether it be
pests or more extreme weather (e.g. drought) associated with global
warming. Fortunately, there is a range of research addressing these
challenges. Drawing on an international range of expertise, this
collection summarises this research by focusing on ways of
improving the cultivation of tea at each step in the value chain,
from breeding through to harvest. Part 1 reviews advances in
breeding. Part 2 discusses improvements in cultivation techniques.
The book then discusses plant protection and chemistry before
concluding with sustainability issues. As the need for more
interdisciplinary and collaborative research increases, this
collection will be a standard reference for the tea research
community by summarising key research trends in each topic and
putting them in the context of tea cultivation as a whole.
Graph representations are pervasive in scientific and social
computing. They serve as vital tools to model the interplay between
different interacting entities. This monograph delves into the
problem of community detection, which is one of the most widely
used graph operations toward scientific discovery. Community
detection refers to the process of identifying tightly-knit
subgroups of vertices in a large graph. These sub-groups (or
communities) represent vertices that are tied together through
common structure or function. Identification of communities could
help in understanding the modular organization of complex networks.
However, owing to large data sizes and high computational costs,
performing community detection at scale has become increasingly
challenging. This monograph presents a detailed review and analysis
of some of the leading computational methods and implementations
developed for executing community detection on modern day multicore
and manycore architectures. The intention is to: a) define the
problem of community detection and highlight its scientific
significance; b) relate to challenges in parallelizing the
operation on modern day architectures; c) provide a detailed report
and logical organization of the approaches that have been designed
for various architectures; and d) provide insights into the
strengths and suitability of different architectures for community
detection, and a preview into the future trends of the area. While
the focus is on community detection, the challenges, and techniques
to overcome the challenges, transcend to several other graph
problems that have applications in science and data analytics.
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