Understanding the basics of SFAS 109

Holtzman, Yair; Nagel, Paul C.

doi:10.1108/02621711111098406

Cited by 2 publications

(2 citation statements)

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“…In order to properly discuss FASB ASC § 740 as it currently stands, and where it will go in the future, the past treatment is key. Prior to the Codification's implementation in 2009, the subject of accounting for income taxes, relative to formal guidance, was known as Statement of Financial Accounting Standards (SFAS) 109 (Holtzman & Nagel, 2011). The earlier treatments of income taxes focused more heavily on the timing differences relative to accounting and taxable income, leading to deferred tax assets/liabilities.…”

Section: Asc § 740mentioning

confidence: 99%

FASB ASC § 740 simplification of income taxes: An evaluation of the effects on U.S. corporate financial statements from 2015‐present

Green

Mason

Boyer‐Davis

2022

J Corp Accounting Finance

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The FASB Codification is a key document in financial accounting as it serves the role of authoritative guidance for a majority of the field. The main problem associated with the Codification comes from its unwieldly complexity, which can give rise to corruption in financial reporting, compliance issues, and general difficulty in application. To combat such issues, FASB has undertaken several projects, referred to as the simplification initiative, to reduce complexity in order to provide better cost-benefit usage of the guidance. Simplification of the Codification also provides several supplemental benefits, such as closer guidance to IFRS standards and reduced corruption activities. An examination of the impacts of the simplification initiative on corporate financial accounting relating to accounting standards update (ASC) § 740, accounting for income taxes demonstrates the benefits of simplification, as well as any detriments that may exist. By looking at the history of simplification and taxes, certain estimations of the effects of future changes may be determined.

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Section: Asc § 740mentioning

confidence: 99%

FASB ASC § 740 simplification of income taxes: An evaluation of the effects on U.S. corporate financial statements from 2015‐present

Green

Mason

Boyer‐Davis

2022

J Corp Accounting Finance

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“…The National Mastitis Council defines Bovine Mastitis (Mast = breast, itis = inflammation) as the inflammation of the mammary gland (udder tissue) in cattle; this inflammation is a response to bacterial, chemical, thermal or physical injury. In dairy cattle, Mastitis is always caused by pathogens (bacteria) that attack the milkproducing tissues in the udder and produce toxins that immediately cause damage in the udder, as illustrated by Jones and Bailey (2009).…”

Section: Cattle Mastitismentioning

confidence: 99%

Detection of dairy cattle Mastitis: modelling of milking features using deep neural networks

Hajaya¹,

Samarasinghe

Kulasiri³

et al. 2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Dairy cattle Mastitis is one of the most notable and costly diseases in dairy industry worldwide. The total Mastitis cost to dairy industry in New Zealand is up near $280 million a year; this cost includes drop in milk production, cattle treatment and other costs. This research includes the examination and analysis of data collected from a commercial robotic dairy farm, in order to design and build a computational model that can help efficient and accurate detection of Mastitis in dairy cattle herds. Accurate Mastitis detection helps cut treatment costs, control the disease, retain milk production levels and maintain milk quality grade. In addition to cutting financial costs, efficient detection helps cows by protecting them and relieving pain caused by the disease. Computational models can help achieve these by helping farmers to adopt timely and suitable cattle treatment regime, and by preventing healthy cows from being infected. For this study, robotic data have been collected for 12 months from a barn-based dairy farm at Makikihi in South Canterbury-New Zealand. At data collection time, that farm was the largest dairy farm in the world in terms of the number of milking-robots under one roof (24 milking robots in one barn). The collected dataset contains sensor data of more than 1,900 cows being milked more than 1.1 million times during the time of data collection. Having about 29,000 milking instances fully labelled (healthy/sick), a deep neural network (DNN) was used to build, train and validate a classification model using variable combinations, including variables that have not been studied before. The model has shown the ability to perform detection tasks with a high level of accuracy; with Specificity (Sp) of 99%, and Sensitivity (Se) of 97%. With this high and stable Sp, and the relatively high Se, the proposed model avoids the problem of false positive alerts. By using deep neural networks to build a Mastitis detection model, this study exploits the main characteristic that gives deep learning predominance compared with other techniques-representation learning, which means that the trained models can extract patterns that used to be ignored by other techniques, to present a robust definition of Mastitis, using real-world sensor data, generated by milking robots in a commercial dairy farm, including data for previously unexploited features. The results of this study allow viewing dairy cattle Mastitis detection from a different angle, which brings about a broader understanding of some of the signs and symptoms of Mastitis, leading to better control and management of the disease.

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Understanding the basics of SFAS 109

Cited by 2 publications

References 0 publications

FASB ASC § 740 simplification of income taxes: An evaluation of the effects on U.S. corporate financial statements from 2015‐present

FASB ASC § 740 simplification of income taxes: An evaluation of the effects on U.S. corporate financial statements from 2015‐present

Detection of dairy cattle Mastitis: modelling of milking features using deep neural networks

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