on 28-Nov-2020 (Sat)

The main objective of this Authority is to complement and strengthen the national effort in conservation of the rich biodiversity of the country, particularly the fauna as per the National Zoo Policy, 1998. Other objectives of this Authority include- enforcing minimum standards and norms for upkeep and healthcare of animals in Indian zoos and to control mushrooming of unplanned and ill-conceived zoos.

National Green Tribunal has been established on 18.10.2010 under the National Green Tribunal Act 2010 for effective and expeditious disposal of cases relating to environmental protection and conservation of forests and other natural resources including enforcement of any legal right relating to environment and giving relief and compensation for damages to persons and property and for matters connected therewith or incidental thereto. It is a specialized body equipped with the necessary expertise to handle environmental disputes involving multi-disciplinary issues.

Basel Convention v Hong Kong Convention
In a related development, Basel Parties disagreed that the International Maritime Organization’s Hong Kong Convention on ship recycling, provided a level of control equal to that of the Basel Convention. Legal experts and developing countries in attendance voiced the concern that because the Hong Kong Convention has no intention of minimising movement of toxic ships to developing countries, it was not sufficient, not legally equivalent, and therefore the Basel Convention must continue to be active to prevent the dumping of old toxic ships on the beaches of developing countries.

The Ban Amendment, adopted in 1994, effectively bans, as of 1 January, 1998, all forms of hazardous waste exports from the 29 wealthiest countries of the Organization of Economic Cooperation and Development (OECD) to non-OECD countries.

The Ban Amendment prohibits all export of hazardous wastes, including electronic wastes and obsolete ships from developed to developing countries.

The deal was brokered by the Indonesia and Switzerland Country Led Initiative (CLI)

Now, the concept of chunks used by Simon in his study of chess players actually came from a famous 1956 paper by George Miller, “The Magical Number Seven, Plus or Minus Two”*. Miller argued that the capacity of working memory is roughly seven chunks. In fact, it turns out that there is variation in that number from person to person, and a substantial correlation between the capacity of an individual's working memory and their general intellectual ability (IQ)*. Typically, the better your working memory, the higher your IQ, and vice versa

Put another way, in Miller's account the chunk was effectively the basic unit of working memory. And so Simon and his collaborators were studying the basic units used in the working memory of chess players. If those chunks were more complex, then that meant a player's working memory had a higher effective capacity. In particular, someone with a lower IQ but able to call on more complex chunks would be able to reason about more complex situations than someone with a higher IQ but less complex internalized chunks.

Okay, that's a speculative informal model. Regardless of whether it's correct, it does seem that internalizing high-level chunks is a crucial part of acquiring expertise. However, that doesn't then necessarily imply that the use of systems such as Anki will speed up acquisition of such chunks. It's merely an argument that long-term memory plays a crucial role in the acquisition of some types of expertise. Still, it seems plausible that regular use of systems such as Anki may speed up the acquisition of the high-level chunks used by experts*. And that those chunks are then at the heart of effective cognition, including our ability to understand, to problem solve, and to create.

Ebbinghaus found that the probability of correctly recalling an item declined (roughly) exponentially with time. Today, this is called the Ebbinghaus forgetting curve:

What determines the steepness of the curve, i.e., how quickly memories decay? In fact, the steepness depends on many things. For instance, it may be steeper for more complex or less familiar concepts. You may find it easier to remember a name that sounds similar to names you've heard before: say, Richard Hamilton, rather than Suzuki Harunobu. So they'd have a shallower curve. Similarly, you may find it easier to remember something visual than verbal. Or something verbal rather than a motor skill. And if you use more elaborate ways of remembering – mnemonics, for instance, or just taking care to connect an idea to other things you already know – you may be able to flatten the curve out*.

But then you need to introduce them to someone else, and so need to bring it to mind. Immediately after that, your probability of recall will again be very high. Ebbinghaus's research suggested that the probability will decay exponentially after the re-test, but the rate of decay will be slower than it was initially. In fact, subsequent re-tests will slow the decay still more, a gradually flattening out of the decay curve as the memory is consolidated through multiple recall events:

These phenomena are part of a broader set of ideas which have been extensively studied by scientists. There are several related terms used for this set of phenomena, but we'll use the phrase “distributed practice”, meaning practice which is distributed in time, ideally in a way designed to maximally promote retention. This is in contrast to cramming, often known as massed practice, where people try to fit all their study into just one session, relying on repetition

While scientists have done a tremendous number of studies of distributed practice, many fundamental questions about distributed practice remain poorly understood. We don't understand in detail why exponential decay of memory occurs, or when that model breaks down. We don't have good models of what determines the rate of decay, and why it varies for different types of memories. We don't understand why the decay takes longer after subsequent recalls. And we have little understanding of the best way of expanding the inter-study intervals. Of course, there are many partial theories to answer these and other fundamental questions. But there's no single, quantitatively predictive, broadly accepted general theory. And so in that sense, we know little about distributed practice, and are probably decades (if not more) away from a reasonably full understanding.

Mathematics only tells you answers to questions in the real world after you have taken the one vital step of choosing which kind of mathemat ics to apply. Even if there were a rival number theory which used the symbols '2', '3', and '+ ', and in which a theorem said "2 + 2 = 3", there would be little reason for bankers to choose to use that theory! For that theory does not fit the way money works. You fit your mathematics to the world, and not the other way around. For instance, we don't apply number theory to cloud systems, because the very concept of whole numbers hardly fits. There can be one cloud and another cloud, and they willcome together and instead of there being two clouds, there will still only be one. This doesn't prove that 1 plus 1 equals 1; it just proves that our number- theoretical concept of "one" is not applicable in its full power to cloud- counting.