A lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications? In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans, and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions.
A lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications? In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans, and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions.
Over the past half century, reverse osmosis (RO) has grown from a nascent niche technology into the most versatile and effective desalination and advanced water treatment technology available. However, there remain certain challenges for improving the cost-effectiveness and sustainability of RO desalination plants in various applications. In low-pressure RO applications, both capital (CAPEX) and operating (OPEX) costs are largely influenced by product water recovery, which is typically limited by mineral scale formation. In seawater applications, recovery tends to be limited by the salinity limits on brine discharge and cost is dominated by energy demand. The combination of water scarcity and sustainability imperatives, in many locations, is driving system designs towards minimal and zero liquid discharge (M/ZLD) for inland brackish water, municipal and industrial wastewaters, and even seawater desalination. Herein, we review the basic principles of RO processes, the state-of-the-art for RO membranes, modules and system designs as well as methods for concentrating and treating brines to achieve MLD/ZLD, resource recovery and renewable energy powered desalination systems. Throughout, we provide examples of installations employing conventional and some novel approaches towards high recovery RO in a range of applications from brackish groundwater desalination to oil and gas produced water treatment and seawater desalination.
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