Goods in transit refers to merchandise and other inventory items that have been shipped by the seller, but have ..I.. been received by the purchaser. To illustrate goods in transit, let's use the following example. Company J ships a truckload of merchandise on December 30 to Customer K, which is located 2,000 miles away. The truckload of merchandise arrives at Customer K on January 2. Between December 30 and January 2, the truckload of merchandise is goods in transit. The goods in transit requires special attention if the companies issue financial statements as of December 31. The reason is that the merchandise is the inventory of one of the two companies. However, the merchandise is not physically present at either company. One of the two companies must add the cost of the goods in transit to the cost of the inventory that it has in its possession.

      The terms of the sale will indicate which company should report the goods in transit as its inventory as of December 31. If the terms are FOB shipping point, the seller (Company J) will record a December sale and receivable, and ..II..  include the goods in transit as its inventory. On December 31, Customer K is the owner of the goods in transit and will need to report a purchase, a payable, and must add the cost of the goods in transit to the cost of the inventory which is in its possession.

      If the terms of the sale are FOB destination, Company J will not have a sale and receivable until January 2. This means Company J must report the cost of the goods in transit in its inventory on December 31. (Customer K will not have a purchase, payable, or inventory of these goods until January 2.)

       (Adapted from http://www.accountingcoach.com/blog/what-are-goods-in-transit) 

Words that went extinct

By Kimberly Joki

    Dictionaries incorporate new words every year. Some are pop culture inventions like jeggings, photobomb, and meme. Other words, like emoji and upvote, spring up from technology and social media. Dictionaries respond by creating definitions for anyone who cares to know what a twitterer is. And thank goodness they do; you can learn what an eggcorn is simply by turning a few pages in your trusty updated dictionary.

    Interestingly, not all newly added words are recent developments. The Oxford English Dictionary June 2015 new words list included autotune, birdhouse, North Korean, and shizzle! North Korea was founded in 1948. The initial release of the autotuner audio processor was in 1997. Before adding a slang term like shizzle, dictionary publishers weigh the current popularity, predicted longevity, and other factors. Just this year alone, the Merriam-Webster Dictionary welcomed about 1,700 new arrivals.

    With more and more words coined every year, dictionaries couldn’t possibly add them all to their existing word banks. Can you imagine a dictionary containing all the words ever used in English? It would be impossible to lift! With each yearly edit, dictionary editors must discard some words to make room for new ones.

    (…)

    The Sami languages, spoken in Finland, Norway, and Sweden, reportedly include more than 150 words related to snow and ice. In the 1590s, the English language had a word for recently melted snow—snowbroth. Now, English speakers simply call it water or melted snow. In fact, words that are markedly specific seem more vulnerable to extinction. A 19th-century dictionary included Englishable, a term to describe how appropriate a word is for the English language. However, English is a dynamic language, always accepting and abandoning words. Apparently, Englishable itself isn’t Englishable; it’s now obsolete.

    Do you favor any infrequently used words? If so, use them now and often. . . A word’s best defense against extinction is regular use.

(Source: http://www.grammarly.com/blog/2015/words-that-went-extinct/)

TEXT II

The backlash against big data

[…]

Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.

The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.

There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.

(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)