Описание: Prudent, verifiable, and timely corporate accounting is a bedrock of our modern capitalist system. In recent years, however, the rules that govern corporate accounting have been subtly changed in ways that compromise these core principles, to the detriment of the economy at large. These changes have been driven by the private agendas of certain corporate special interests, aided selectively--and sometimes unwittingly--by arguments from business academia With Political Standards, Karthik Ramanna develops the notion of "thin political markets" to describe a key problem facing technical rule-making in corporate accounting and beyond. When standard-setting boards attempt to regulate the accounting practices of corporations, they must draw on a small pool of qualified experts--but those experts almost always have strong commercial interests in the outcome. Meanwhile, standard setting rarely enjoys much attention from the general public. This absence of accountability, Ramanna argues, allows corporate managers to game the system. In the profit-maximization framework of modern capitalism, the only practicable solution is to reframe managerial norms when participating in thin political markets. Political Standards will be an essential resource for understanding how the rules of the game are set, whom they inevitably favor, and how the process can be changed for a better capitalism.
The Earth receives 174 Petawatts (PW) of incoming solar radiation at the upper atmosphere. Approximately 30 % of its radiation is reflected back to space while the rest of 71 % (124 PW) is absorbed by clouds, oceans and land masses. The world cumulative solar PV installed capacity reached almost 398 Gigawatts (GW) in 2018. This is only about 0.3 % of solar energy utilization from the sun. There is a wide gap of utilization is noticed due to lack of technology. In 1931 selenium cell efficiency of 1% invented then in 1980 thin films cell efficiency of 6-7% introduced. After 2013, efficiency of 18 to 21% achieved by crystalline silicon technology. In India, the installed capacity of till 2018 is of 350 GW which includes renewable and non-renewable energy sources. In that the cumulative installed solar capacity is only about 25 GW. By 2022, India wants to install 175 Gigawatt (GW) of renewable power capacity which corresponds to around half of its total electricity production.
To achieve this capacity by improving solar cell efficiency from 20 % to 40 %, augmentation of grid infrastructure, massive subsidies and skilled manpower of 3 lakhs persons for the next three years to achieve the planned target. Most of the world's population lives in areas with solar insolation levels of 150 to 300 watts/m or 3.5 to 7.0 kWh/m per day. In India, the per capita electricity consumption from 2017 to 2018 was around 1150 to 2000 kWh. The electricity demand in the country will grow at 7 % between FY 2017 and FY 2022 and 57 % of the total electricity capacity will be generated from renewable sources by 2027 as per Central Electricity Authority (CEA). In 2011, a report by the International Energy Agency (IEA) found that solar energy technologies such as photovoltaic, solar hot water and concentrated solar power could provide a third of the world's energy by 2060.
Описание: Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.What You'll Learn Understand machine learning algorithms using RMaster the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithmsSee industry focused real-world use casesTackle time series modeling in RApply deep learning using Keras and TensorFlow in RWho This Book is ForData scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
Автор: Karthik, Lavanya Название: Neel on wheels ISBN: 9383331879 ISBN-13(EAN): 9789383331871 Издательство: Неизвестно Рейтинг: Цена: 949.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: My big brother Neel has wheels Neel's wheelchair transforms itself to fight dragons and monsters and chase away scary creatures of the night.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
