Research and forecasts in IT

«Nothing makes you feel stupid quite like reading a scientific journal article» — writes the TV presenter and molecular biologist Adam Ruben. In a way, he's right — many of us get lost in the often confusing language of peer-reviewed papers. But the situation does not have to be hopeless. A bit of effort on the readers' part can go a long way. We looked at the techniques actual scientists use to navigate academic content. And compiled them into this two-part guide (Part 1: Getting Better at Understanding Academic Papers).

«Nothing makes you feel stupid quite like reading a scientific journal article» — writes the TV presenter and molecular biologist Adam Ruben. In a way, he's right — many of us get lost in the often confusing language of peer-reviewed papers. But the situation does not have to be hopeless. A bit of effort on the readers' part can go a long way. We looked at the techniques actual scientists use to navigate academic content.

And compiled them into this two-part guide (part 2).

Even the most honest of scientists are regularly misled by their cognitive biases. They often go to great lengths to find proof for whatever seems logical, while dismissing evidence to the contrary.

Yet this issue is rarely discussed — because it remains an embarrassing subject.

Annotation. This article gives an analogy between the forces of nature and various types of money. A justification for the "money conservation laws" is made. Explanation of the IT-money phenomenon by analogy to physics laws is given, as well as gold and currency money. The transition from the gold and currency to the gold-currency-computing economy is considered. A reasonable assumption is made that the fourth type of money after gold, securities and IT money will be so-called "citation indices" or "ratings", which are similar in their properties to stock indices.

This article is an attempt to understand what money is from the physics and econophysics points of view. Econophysics (economics and physics) is an interdisciplinary research field, applying theories and methods originally developed by physicists to solve problems in economics, usually those including uncertainty or stochastic processes, nonlinear dynamics and evolutionary games.

The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.

Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).

The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.

^{Source: The online counterfeit economy: consumer electronics, a report made by CSC in 2017}

Over the past 10 years, the number of fake goods in the world has doubled. This data has been published in the latest Year-End Intellectual Property Rights Review by the US Department of Homeland Security in 2016 (the most current year tracked). A lot of the counterfeiting comes from China (56%), Hong Kong (36%) and Singapore (2%). The manufacturers of original goods suffer serious losses, some of which occur on the electronics market.

Many modern products contain electronic components: clothes, shoes, watches, jewellery, cars.
Last year, direct losses from the illegal copying of consumer electronics and electronic components in the composition of other goods were about $0.5 trillion.

How to solve this problem?