text:
If any of these elements describe you, then you already possess the core characteristic of all successful Life Coaches. Life Coaching is a wonderful career where you can earn a living by sharing what you know, and more importantly, who you are, to positively impact the lives of others. Life Coaching is a growing field where those trained to a level of professional excellence are in high demand.
As a Certified HLC Life Coach you can generate an excellent full-time or part time income with exceptional tax benefits. That is because as an HLC student you will be taught HLC's Coach's Proven Success System, with its clinically recognized training systems, which will prepare you to be a professional certified life coach. That's why upon graduation as a Certified Spiritual Life Coach [CSLC] you will know everything that you need to know to build a successful Life Coaching Practice.
A Life Coach is someone you hire to "coach" you how to do something or learn something that will improve the quality of your life. You do not hire a Life Coach if you think you might be mentally ill or mentally disturbed. If you feel that you might have mental health issues you need to hire a psychologist or psychiatrist, not a Life Coach.
A professional Life Coach is trained to "coach" the competent how to become more competent in whatever area they have been certified to Life Coach.
The important thing to note here is that not all Life Coaches receive formal training. To date there are no federal or state certification requirements to be a Life Coach or requirements for Life Coaching schools to regulate their curriculum. However, this may change in the very near future as more states begin to look at this as an actual occupation similar in nature to being a therapist.
Most people have heard the term "Life Coach"; so most of us have a general idea of what a Life Coach does, but what most people don't know is that there are several different types of life coaching specialties. A few of the more common types of Life Coach specialties are:
It is important to hire a certified Life Coach who specializes in the type of coaching you need help with. Meaning, if you want to be coached by someone you would see as a guest on the Oprah Winfrey show, a spiritual Life Coach, don't hire an executive Life Coach who specializes in working with corporate executives who need to improve their management and organizational skills.
Do you want to hire a certified Spiritual Life Coach?
If your self-help library has books and audio albums by authors such as Doreen Virtue, Deepak Chopra, Gandhi, Pema Chodron, A Course in Miracles, Eckhart Tolle, Stuart Wilde, Eric Butterworth, Conversations With God, Abraham-Hicks Materials, Wayne Dyer, Phil McGraw, John Gray, Marianne Williamson, Anthony Robbins, [etc.] or if you are familiar with organizations such as HayHouse, One, TED, Unity, Science of Mind, [etc] then hiring a Spiritual Life Coach will serve you well.
HLC's certified Spiritual Life Coaches are taught how to facilitate the HuMethod™ The HuMethod™ is a college level professional training system that includes textbooks, training manuals, forms and subjective measuring techniques that allow its Life Coaches to maintain, measure and quantify how to successfully use proprietary clinically field-tested exercises to empower their clients to quickly achieve their self-help objectives.
HuMethod™ professional training system that was developed upon 54 clinically proven, spiritually-based, emotional and psychological exercises, which are sub-categorized into Diagnostic, Multi-sensory, Dis-Creation, Self-mastery, Self-parenting and Meditative exercises that when performed as prescribed will create the permanent behavioral changes that you and your clients seek.


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* Molly E. Holzschlag wrote: >The first article is on properly specifying character sets. > >http://www.webstandards.org/learn/askw3c/dec2002.html > >Discussion can take place here. [...] There are several ways of specifying the character set for a particular document. Which of the following methods (or combination thereof) does the W3C recommend, and why? * Have the server administrator set the proper encoding via the HTTP headers returned by the Web server * Have the author add the encoding with a meta element * XHTML authors can add the character encoding using the XML prolog [...] The term "character set" should be avoided, see <http://www.w3.org/MarkUp/html-spec/charset-harmful.html>. The third item should be rephrased to match the prolog. It should be "XML declaration", not "XML prolog". [...] These three ways of providing the character encoding of a document are not equivalent. When trying to figure out the character encoding of a resource, user agents will try, in this order: * The HTTP Content-Type header sent by the server * The XML declaration (only for XHTML documents) * The HTML/XHTML meta element * Other ways. There are algorithms to guess the character encoding, for example [...] The XML declaration should be ignored for XHTML documents delievered as text/html (the HTML WG says, user agents should not use any heuristics to determine whether a document is HTML or XHTML and thus parse all XHTML documents delivered as text/html as beeing HTML and thus processing instructions (the XML declaration is a processing instruction from an HTML point-of-view) are to be ignored) (and this is what most user agents do), this information should be added to the document, it is otherwise confusing. [...] However, in at least two cases, this is simply not possible: * The document author does not have any way to configure the server to send the proper HTTP Content-Type header * The document is not served via HTTP. It's a standalone document, or served via MIME [...] MIME also has a Content-Type header, hence this is not a valid exception. [...] In these cases, an HTML document should provide the character encoding via a meta element, and an XML document must provide it via the XML declaration. [...] No, XML documents do not need to have an XML declaration in these cases if they are encoded using one of the default encodings (us-ascii for text/xml, utf-8 or utf-16 for most other cases). [...] Example of an HTML 4.01 document written in French with a UTF-8 encoding: <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">   <meta http-equiv="content-type" content="text/html; charset=UTF-8"> <title>Exemple de document HTML 4.01</title>
Portrait Intérieur

Rainer-Maria Rilke

Ce ne sont pas des souvenirs
 qui, en moi, t'entretiennent ;
 tu n'es pas non plus mienne
 par la force d'un beau désir.
   [...] The "
"s have to be "
"s. regards.Received on Tuesday, 10 December 2002 00:28:28 UTC
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subset: commoncrawl


text: Phobias consist of a fear of objects or situations such as heights, animals, flying, snakes, spiders, etc? The phobia invokes a panic attack or anxiety response but the person realizes that the fear is excessive or unreasonable. If the fear involves a fear of social or performance situations then it is referred to as Social Phobia.


text:
Phil Tippett is the Oscar-winning stop-motion animator and designer behind some of the greatest fantasy creatures and sci-fi set pieces in cinema history. From his humble beginnings as an alien patron in the iconic Cantina sequence from 'Star Wars: A New Hope', to pioneering stop-motion techniques used throughout 'Empire Strikes Back' and 'Return of the Jedi', to seamlessly merging practical animation and CGI in Jurassic Park and beyond. In 'My Life in Monsters', VICE chronicles Tippett's legendary life work, illustrating the process behind his greatest creations, the emotional hardships of transitioning into Hollywood's digital revolution, and completing his return-to-form, stop-motion opus with the brutal, dystopian 'Mad God'.
WATCH NEXT: LARPing Saved My Life - http://bit.ly/1Yu04Zz
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text:
Life with Derek is a half-hour comedy series about a modern blended family, with two type-A 15 year-olds, Casey (Ashley Leggat) and Derek (Michael Seater). Each episode is an alpha-teen comic war between two self-obsessed teenagers in a take-no-prisoners fight for control of their house, their school, and their world. On the fateful day when Nora McDonald (Joy Tanner) and George Venturi (John Ralston) decided to get married and merge their two families into one, there was a little detail they overlooked: they each had an individualist, controlling, sub-species of the human race â?? an eldest child. You really should not, on any account, have two of them in the same place. For Casey MacDonald and Derek Venturi, their own, genetic, siblings were never a real threat. But NOW? Two self-actualising, domineering eldest teenage children forced to co-habit the same house? The situation could be compared to having two north poles, two captains of the same ship, two alpha dogs in the same wolf pack.
Air dates are in Eastern Time Zone


text:
Modern communications technology demands smaller, faster and more efficient circuits. This book reviews the fundamentals of electromagnetism in passive and active circuit elements, highlighting various effects and potential problems in designing a new circuit. The author begins with a review of the basics - the origin of resistance, capacitance, and inductance - then progresses to more advanced topics such as passive device design and layout, resonant circuits, impedance matching, high-speed switching circuits, and parasitic coupling and isolation techniques. Using examples and applications in RF and microwave systems, the author describes transmission lines, transformers, and distributed circuits. State-of-the-art developments in Si based broadband analog, RF, microwave, and mm-wave circuits are reviewed. With up-to-date results, techniques, practical examples, illustrations and worked examples, this book will be valuable to advanced undergraduate and graduate students of electrical engineering, and practitioners in the IC design industry. Further resources for this title are available at www.cambridge.org/9780521853507.
• Combines electromagnetics and circuits, building on new circuit knowledge to teach classic material with a new focus, and bridging the gap between RF and microwave circuit theory • Further resources, including exercises, solutions and lecture notes, are available online at www.cambridge.org/9780521853507 • Presents up-to-date results, techniques, applications and practical examples from the field, including measurement techniques, to allow the reader to gain real-world knowledge of electromagnetics
1. Introduction; 2. Capacitance; 3. Resistance; 4. Ampère, Faraday and Maxwell; 5. Inductance; 6. Passive deivce design and layout; 7. Resonance and impedance matching; 8. Small-signal high-speed amplifiers; 9. Transmission lines; 10. Transformers; 11. Distributed circuits; 12. High-speed switching circuits; 13. Magnetic and electrical coupling and isolation; 14. Electromagnetic propagation and radiation; 15. Microwave circuits.


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The Internal Revenue Service ("IRS") has issued two pieces of guidance that provide relief to retirement plan sponsors and administrators from the penalties that may be assessed under the Internal Revenue Code (the "Code") for delinquent Forms 5500 filings. Plans that fail to timely file Form 5500 series annual reports can be subject to penalties under both Title I of the Employee Retirement Income Security Act ("ERISA") and the Code. IRS penalties for delinquent Forms 5500 filings can reach $15,000 for each late return, plus interest.
The first piece of guidance, Revenue Procedure 2014-32 ("Rev. Proc. 2014-32"), sets forth a temporary pilot program that provides relief from Form 5500 late filing penalties for retirement plans that are ineligible to participate in the DOL's Delinquent Filer Voluntary Compliance Program (the "DFVC Program"). The second piece of guidance, Notice 2014-35, modifies the requirements for retirement plans to qualify for IRS late filing penalty relief by requiring ERISA-covered retirement plans to file Forms 8955-SSA with the IRS in addition to satisfying the requirements of the DFVC Program.
Rev. Proc. 2014-32. Rev. Proc. 2014-32 establishes a temporary one-year pilot program providing relief from Form 5500 late filing penalties to plan administrators and sponsors of retirement plans that are subject to the filing requirements of the Code but not subject to Title I of ERISA. These plans include:
- small business plans that provide benefits only for the owner and the owner's spouse, and plans of business partnerships that cover only partners and their spouses (collectively, "one-participant plans"); and
- certain foreign plans.
NOTE: Relief is not available where the IRS has already issued a CP-283 Notice (Penalty Charge on Your Form 5500 Return) to a plan sponsor or administrator in relation to the delinquent Form 5500.
Under Rev. Proc. 2014-32, the IRS will not impose any penalty for delinquent Forms 5500/5500-EZ filings if the applicant submits a complete Form 5500 series return, including all required schedules and attachments, for each plan year for which the applicant seeks relief. All returns submitted must be sent to the IRS in paper format and cannot be submitted electronically via the DOL's EFAST2 filing system.
Delinquent returns submitted under the pilot program must be marked, in red letters at the top margin of the first page, "Delinquent return submitted under Rev. Proc. 2014-32, Eligible for Penalty Relief." In addition, a completed paper copy of the Transmittal Schedule provided in the Appendix of Rev. Proc. 2014-32 must be attached to the front of each delinquent return.
No penalty or payment is required under the temporary pilot program. However, IRS has indicated that if the temporary program is replaced with a permanent program, a fee or other payment will be required. Delinquent filers can submit an application for penalty relief under the temporary pilot program from June 2, 2014 until June 2, 2015.
Notice 2014-35. Since 2002, IRS has not imposed penalties relating to delinquent Forms 5500 filings where a plan administrator or sponsor has satisfied the requirements of the DOL's DFVC Program. The DOL's DFVC program allows plans that fail to timely file their Forms 5500 to submit the late reports and pay a reduced civil penalty. Retirement plans participate in the DFVC Program by filing an application and submitting the late Forms 5500.
Notice 2014-35 updates the terms for retirement plans covered by Title I of ERISA to obtain relief from IRS penalties for failure to timely comply with the Code's Form 5500 filing requirements. To obtain relief, the delinquent Form 5500/5500-SF must be filed electronically via EFAST2 in accordance with the requirements of the DOL's DFVC Program, and the delinquent Form 8955-SSA must be filed in paper format with the IRS. If these requirements are met, IRS will not impose penalties for untimely filed Forms 5500/5500-SF and 8955-SSA.
NOTE: If a Form 8955-SSA is filed pursuant to Notice 2014-35, the filer must check the box on Line C, Part I (Special extension) on Form 8955-SSA, and enter "DFVC" in the space provided on Line C.
Delinquent Forms 8955-SSA must be filed with the IRS no later than 30 days after completing the DFVC filing for the late Form 5500/5500-SF, or December 1, 2014, whichever is later. This requirement applies to all DFVC filings submitted via EFAST2 (i.e., all DFVC filings after December 31, 2009), regardless of whether the filing was submitted before the release of Notice 2014-35.
NOTE: Form 8955-SSA must be used for years prior to 2009 even though Schedule SSA would have been filed for those years if the filing had been timely submitted to DOL.
For example, if a DFVC filing for a delinquent 2008 Form 5500 was submitted in 2012 and Schedule SSA was never filed for the 2008 plan year, a paper Form 8955-SSA must be filed with IRS for the 2008 plan year no later than December 1, 2014.
Action Steps for Plan Sponsors and Administrators. Plan sponsors and administrators for one-participant plans and foreign plans are advised to confirm that all required Form 5500 filings have been completed. Any outstanding filings discovered should be corrected while the opportunity to do so at no cost exists via the temporary pilot program created by Rev. Proc. 2014-32. Plan sponsors and administrators should also monitor the development of the temporary program to see if it becomes permanent.


text:
Creagh, TA, Leahy, AL, Bouchier Hayes, JJ, Tormey, William, Leader, M and Broe, PJ (1993) Oxygen free radicals and acute pancreatitis: fact or fiction. Ir J Med Sci, 162 . pp. 497-498. [Journal article]
Full text not available from this repository.
|Item Type:
|Journal article
|Faculties and Schools:
|Faculty of Life and Health Sciences > School of Biomedical Sciences
Faculty of Life and Health Sciences
|ID Code:
|Deposited By:
|Prof William Tormey
|Deposited On:
|02 Nov 2012 15:26
|Last Modified:
|02 Nov 2012 15:26
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text:
Nokia 9 Reveals Itself In Benchmarks: Dual Cam & More!
Nokia is making news for quite a while now, as they're planning a grand comeback in a country that has shown an enormous amount of love towards them, in the past. Yes, Nokia is coming back to India with the all new Nokia 9.
Many of us became sad when Nokia announced their exit a few years back. We know how we missed Nokia all these years, from the memories of their indestructible device: The Nokia 3310 and their well-praised pure-view technology which enhanced the camera quality a lot.
Nokia has recently launched the iconic Nokia 3310 with a new tinge of colors and features. It's a refreshed version of the old 3310, and oh boy, It has just become cuter.
We were aware of the new lineup, Nokia has for India. i.e. Nokia 6, Nokia 9.etc
Well, Nokia 9 got leaked in benchmarks, and we just can't wait for it to compete with the likes of the Galaxy S8 or the OnePlus 5.
A recent Geekbench leak shows us that, Nokia 9 is truly a flagship device considering the fact that it has got the heart of the latest Snapdragon 835, coupled with 4GB RAM.
Nokia 9 rumored specifications:
- A 5.3 inch Quad HD Screen with a brightness of 530 nits.
- 4GB RAM with 64GB inbuilt memory
- 13mp+13mp dual rear cameras along with OIS, EIS, and 4K Video recording capabilities.
- An 8mp front camera
- Fingerprint Sensor and
- 3500mAh battery
These are impressive specifications, enough to compete with the likes of iPhone 7 and Google Pixel.


text:
The Students of the School of Russian Classical Ballet attached to the Russian Centre of Science and Culture presented an impressive performance of a Ballet Show "The Stone Flower" at the Kamani Auditorium, New Delhi on April 29, 2012.
About 150 students of the School from India, Russia, U.S.A., Israel, Australia and other countries, attired in their colourful customs, performed a superb and spectacular show with which the audience was overwhelmed.
The function was attended by Mr. Fyodor A. Rozovskiy, Director, Russian Centre of Science and Culture.
The hall at the Kamani Auditorium was packed to its capacity, occupied by a large gathering representing a cross-section of the Capital, mostly parents of the students who took part in the annual ceremony of the Russian School of Classical Ballet.
The audience was overwhelmed by the superb show performed by the young children, both Indian and Russian budding artists, attired in their colourful costumes.
The show, The Stone Flower, was conceived by the well-known Russian composer S. Prokofiev, based on the Russian Ural folk tale by P. Bazhov. The students had their year-long training under the distinguished guidance of the Russian teacher and choreographer Ms. Galina Lyakhova.
The entire group of young performers were given a standing ovation at the conclusion of the show. The best performers as well as the teacher were felicitated by Mrs. Natalya Rozovskaya.


text:
- Review
- Open Access
Large-scale spatial population databases in infectious disease research
© Linard and Tatem; licensee BioMed Central Ltd. 2012
- Received: 11 January 2012
- Accepted: 20 March 2012
- Published: 20 March 2012
Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.
- Human population
- Global
- Infectious diseases
- Spatial demography
- Health metrics
Mapping and modelling methods used to study the spatial distribution and spread of vector-borne and directly transmitted infectious diseases are becoming increasingly widespread and sophisticated as the field of spatial epidemiology grows. Spatial epidemiology is defined as "the study of spatial variation in disease risk or incidence" [1], and its aims are both to describe and to understand these variations [2], with the ultimate objective being to assist public health decision making. Interactions between pathogens, vectors and hosts, and between these agents and their environment determine spatial variations in disease risk and make the transmission of vector-borne and other infectious diseases an intrinsically spatial process [1, 3].
Most studies on infectious disease dynamics are not spatially-explicit, i.e. elements are not explicitly localized in space. Models are typically based on the metapopulation concept, which considers isolated subpopulations subject to colonization and extinction dynamics [4–6]. If the species of interest is a parasite, colonization means infection and a local extinction occurs when the host dies or recovers [5]. This approach is spatially-implicit, as it avoids the use of geographical maps to locate elements. In the majority of non-spatial mathematical models of infectious diseases, the total population is assumed to be constant [7], but population data have been included, for instance, in non-spatial models of HIV [8], pertussis [9], malaria [7], or in global burden of disease calculations [10–16]. However, the spatial nature of infectious diseases, and particularly spatial heterogeneities in transmission and spread, make risk maps and spatially-explicit models of disease incidence valuable tools for understanding disease dynamics and planning public health interventions [1, 2, 17]. Defining the extent of infectious diseases as a public health burden and their distribution and dynamics in time and space are critical to scoping the financial requirements, for setting a control agenda and for monitoring.
The emergence of spatially-explicit studies in infectious disease research has been supported by improvements in spatial data and tools such as remote sensing and geographical information systems (GIS) [18–23], as well as advances in spatially-explicit modelling methods [17, 24]. GIS are commonly used to combine spatial data from different sources, for mapping disease and for performing spatial analyses to identify the causal factors of observed spatial patterns such as cluster detection or landscape fragmentation analyses [20, 25]. In addition, the growth in computing, data collection and the centralization of epidemiological data, has lead to an increase in the sophistication and complexity in the mapping and modelling of infectious disease risks.
Among the agents involved in the disease transmission process, human hosts play a crucial role as their density [26], spatial location, demographic characteristics (e.g. age-risk profiles [27–30]) and behaviour [31–33] determine their exposure to infection. Any approach that requires the use of modelled disease rates or dynamics requires reasonable information on the resident population for the time period one is intending to estimate risk. Where risks and spread of diseases are heterogeneous in space, population distributions and counts should ideally be resolved to higher levels of spatial detail than large regional estimates. Accurate and detailed information on population size and distribution are therefore of significant importance for deriving populations at risk and infection movement estimates in spatial epidemiological studies [34]. For many low-income countries of the World, where disease burden is greatest, however, spatially detailed, contemporary census data do not exist. This is especially true for much of Africa, where currently available census data are often over a decade old, and at administrative boundary levels just below national-level [35, 36].
Modelling techniques for the spatial reallocation of populations within census units have been developed in an attempt to overcome the difficulties caused by input census data of varying resolutions. National census population data can be represented as continuous gridded population distribution (or count) datasets through the use of spatial interpolation algorithms. Here, we firstly review and compare the methods used in the construction of existing large-scale population datasets, and secondly review applications of these datasets in past studies of disease risk and dynamics.
Mapping humans
Spatial demographic data
Our knowledge of human distribution in many areas of the World remains surprisingly poor. A growing interest in the global mapping of human populations emerged in the 1990s [37, 38]. Until then, the only information on the spatial distribution of people came from maps showing the location of towns, cities and administrative boundaries on one hand and sparse, inconsistent population data coming from national censuses or demographic surveys on the other [39]. Wright (1936) provided one of the first examples of the combination of demographic and spatial data to build a population density map of the Cape Cod region in the United States [40]. Improvements in demographic and spatial data availability and the development of methods to combine them led to the creation of global population density datasets.
The link between demographic data and a spatial reference system is essential for geographical analyses. Census data collected at the administrative unit level must be related to an accurate boundary dataset [37]. This has in the past often been neglected, mainly due to a lack of GIS technology, knowledge, resources and methods, as well as computing infrastructure [37, 39, 42], but efforts are now being made across the world to link census data with digital administrative boundaries.
Population distribution modelling methods
A variety of methods for converting population count data from irregular administrative units to regular grids have been developed since the 1990s [43, 44] and have led to the emergence of differing global gridded population datasets. The quality and accessibility of population and spatial data have been improving, making collaborations between demographers and geographers stronger [39, 42, 43]. In addition, GIS technologies are becoming increasingly available and accessible and computer power is continuously improving, allowing the processing of larger and more detailed datasets [37, 39, 42].
Areal weighting assumes that the population is uniformly distributed within each administrative unit. The population assigned to a grid cell is simply the total population of the administrative unit divided by the number of cells in the administrative unit. Every grid cells of an administrative unit has therefore the same population value [45]. This method was used to construct the Gridded Population of the World (GPW) database, versions 2 and 3 [42, 46]
Pycnophylactic interpolation starts with the areal weighted method, but smoothes population values using the weighted average of nearest neighbours, while preserving the summation of population data to the original population per areal unit [47]. Pycnophylactic interpolation was used to generate GPW version 1 [48, 49].
Dasymetric modelling involves using ancillary data - often this may include satellite derived land cover data - to redistribute populations within administrative units [45, 50]. Weightings are attributed to the different land cover classes and the population is redistributed accordingly. For example, the Global Rural Urban Mapping Project (GRUMP) uses a similar approach to GPW, but incorporates urban-rural extents and their corresponding populations in the spatial reallocation of census counts [43, 44]. The urban-rural extent information is generated by a variety of input data that include census data, online web sources and National Imagery and Mapping Agency (NIMA) database of populated places [46]. The recently produced AfriPop dataset, which covers the African continent at a fine spatial resolution, also used land cover data to redistribute populations [51, 52]. Other kinds of ancillary data such as the slope or roads can be used for dasymetric mapping.
More sophisticated modelling approaches - called smart interpolation - involve modelling the finescale distribution of populations using a range of satellite and other ancillary data. For example, an accessibility surface developed from road networks and populated places can be used to redistribute people, as was done in the construction of the UNEP database [53–56]. The LandScan dataset is another example of smart interpolation, where various ancillary data such as roads, slope, land cover and nighttime lights are used to determine the probability of population occurrence in cells. Populations are spatially reallocated within each areal unit using modelling approaches based on these probability coefficients [57–59].
Existing gridded global and continental population datasets and their main characteristics.
Level of transparency in data and methodology used
Spatial resolution
Year(s) represented
Distribution policy
Gridded Population of the World
National Center for Geographic Information and Analysis (NCGIA), University of California;
Center for International Earth Science Information Network (CIESIN), Columbia university
GPW1: pycnophylactic; GPW2 and GPW3: areal-weighted
2.5 arcminutes (~5 km)
1990, 1995, 2000, 2005, 20101, 20151
Global Rural Urban Mapping Project
Center for International Earth Science Information Network (CIESIN), Columbia university; International Food Policy Research Institute; The World Bank; Centro Internacional de Agricultura Tropical
30 arcseconds (~1 km)
1990, 1995, 2000
2000, 2004
LandScan Global Population database
Oak Ridge National
30 arcseconds (~1 km)
year of release
1998; yearly from 2000 to 2010
UN Environment Programme global population datasets
United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), Sioux Falls
2.5 arcminutes (~5 km)
1996, 2004
AfriPop population dataset for Africa
AfriPop project: University of Oxford, University of Florida and Université Libre de Bruxelles
3 arcseconds (~100 m)
Uncertainty and error
Given the different input data and the different modelling methods used, the existing gridded population datasets described above clearly differ. Different sources of error and uncertainty are associated with these population datasets, which generally arise from (i) the input data, (ii) temporal projections and (iii) the modelling procedure used.
To adjust population counts to one target year, either inter-censal growth rates or national-level growth rates - often from the United Nations [64] - are used. These national estimates are derived from fertility, mortality and international migration numbers, a method that is inevitably associated with uncertainties [65]. In addition, growth rates can vary substantially within countries, introducing uncertainties when using national-level estimates, and are dependent upon the urban-rural definition used when using urban and rural growth rates.
Modelling approaches using ancillary datasets only increase population distribution model accuracies over the simple gridding (areal weighting) of census data if the ancillary data is more detailed and complete spatially than the input census data, and can be detrimental to modelling accuracies otherwise [35, 62]. The extent to which ancillary data can improve population model accuracies depends on the resolution of census data, and decreases when the resolution of census data becomes finer.
The validation of large-scale population distribution datasets is problematic as no independent source exists that could serve this purpose globally. Map accuracies can be tested in target regions where reference data are available at a finer spatial resolution than the map produced [57, 62, 66, 67]. Recent studies have shown that using certain methods of downscaling increases consistently the mapping accuracy over simple areal weighting of administrative unit census data [35, 63]. Until now, validation efforts at the global level have been limited to comparing results with population totals reported by the UN (in the case of GPW and GRUMP) [39]. Another way to evaluate the accuracy of population datasets is to use geospatial metrics to compare spatial datasets [68]. These metrics quantify differences in the spatial structure of datasets and analyse properties such as spatial correlations within and between datasets [68, 69]. The accuracy of urban extent datasets can be more easily assessed using expert opinion [70] or a set of independent test sites derived from medium or high resolution remote sensing imagery [71, 72]. In any case, a future priority should be to design methods for the incorporation of uncertainty explicitly.
Application of global population distribution data in studies of disease risk and dynamics
Infectious disease-related studies that have utilized large-scale spatial population databases (adapted from [34])
Population map used [Reference]
Populations at risk
GPW [108, 122, 123, 132, 134, 135, 138], Landscan [109, 133], UNEP [107], GRUMP [110, 113–115, 124, 127, 128, 139]
Clinical cases
Intervention coverage
Funding coverage
Risk mapping
Infection movements
Urbanization effects
Populations at risk
Risk mapping
Landscan [89]
Epidemic modelling
Risk mapping
Yellow fever
Populations at risk
Populations at risk
Risk mapping
Populations at risk
Landscan [120]
Risk mapping
Bovine TB
Risk mapping
Prevalence analyses
Landscan [76]
Risk mapping
Incidence analyses
Trends in emerging diseases
Health of schoolchildren
Input data for spatial transmission modelling
Population density and growth are significant drivers for the emergence of different categories of infectious diseases [73]. Jones and colleagues (2008) examined the relationship between the spatial distribution of emerging infectious disease events of different kinds - including vector-borne and non-vector-borne - and population data from GPW3 [73]. For directly-transmitted diseases, the spatial distribution of people is determinant as it controls person-to-person contacts and therefore the spread of the disease. Gridded population data have been used in spatio-temporal models that simulate contacts between infectious and susceptible people and the spatial spread of the disease [74–76]. Even if less obvious, the link between indirectly-transmitted diseases - i.e. infections that require an external agent for transmission to occur, such as a vector, an animal host or the environment - and population density can also be determinant. The effect however varies according to the disease considered, mainly because population size and distribution can modify the habitat of disease vectors or hosts and hence increase or decrease disease incidence. For example, trypanosomiasis is expected to decline in large parts of Africa because of the growth in human population and the expansion of agriculture at the expense of tsetse fly habitat [77]. Urbanization in Africa is also expected to reduce malaria transmission risk [78]. Other transmitting agents can live in urbanized or highly populated places, making the disease transmission risk higher in these areas, such as poultry responsible for avian influenza [79].
Gridded population datasets have been widely used to examine the relationships between infectious disease incidence and population size and distribution. Globally, population density has an opposed impact on dengue and yellow fever: vector preferences mean that dengue risk is higher in highly populated urban areas, whereas yellow fever risk is higher in rural areas [80]. Several authors included gridded population data as a risk factor in disease mapping and modelling [79, 81–85]. When the objective is to study the impact of factors other than population (e.g. climate, ecological or socio-economic variables) on disease transmission, gridded population data can be used as an offset variable in statistical models, i.e. to control for population count differences by analysing rates instead of absolute values [86, 87].
Besides spatial models that simply study the statistical association between disease risk or incidence and population density in order to map disease risk, more sophisticated spatially-explicit models have been developed to study the spatial diffusion of infectious diseases. Several of such spatially-explicit models have successfully used gridded population datasets as input data, for example for creating risk maps [88, 89], for the calculation of a global malaria transmission stability index [90], or to study the potential economic impacts of avian influenza in Nigeria [91]. All of these models were developed at the grid cell level, making gridded population datasets particularly useful. Gridded population data have also been used to develop agent-based simulation models at the regional level [92, 93] and at the global level [94]. Whatever the spatial approach for modelling - patch, distance, group or network - population data are essential, as these models generally require the generation of a virtual society with an appropriate distribution of people. Population distribution datasets have been used to randomly distribute households in study areas according to local population densities [92–94]. In these models, gridded population data provide valuable input datasets mainly because of their wide coverage, consistent spatial detail and availability in the public domain. Moreover, most spatially-explicit models are grid cell-based, meaning that the gridded population datasets are ready to use without any further processing.
Human interactions and movements are crucial for disease spread. However, the complexity of human mobility and its multiscale nature make comprehensive data on movements difficult to obtain [95]. A recurrent issue emerging from large-scale modelling of infectious diseases is therefore the mobility patterns and the spatial details required for transport network data [95]. Gridded population data provide a useful baseline for developing mobility networks. Several authors have combined gridded population data with transport networks to simulate the large-scale spread of infectious diseases [95–100]. In low-income countries, data on population flows are rare and a recent study on the risk of introducing malaria to areas targeted for elimination used a database of bilateral migrant stocks and the GRUMP dataset to evaluate international population movements [101]. Even if population datasets help in the construction of large-scale mobility databases, significant further work is required to fill the data gaps.
Endemic disease health metrics
Population distribution datasets constitute an essential denominator required for many infectious disease studies. It is well known that disease transmission is focal and heterogeneous [102–105], partially due to the clustered nature of human population distribution. As the precision and detail of disease risk mapping improves, spatial population datasets that capture these patterns are therefore required if the sizes of populations at risk (PAR) are to be more accurately quantified.
PAR of some infectious diseases have been estimated based on gridded population datasets, principally malaria [78, 106–115], helminth infections [116–119], human African trypanosomiasis [120] and dengue and yellow fevers [80, 121]. Typically, disease risk maps are spatially overlaid onto population distribution datasets to quantify the number of people residing in specific risk zones or classes, and thus derive PAR numbers. This commonly used method of combining population and prevalence data to assess PAR was also used to study co-infection of diseases that show a clear geographic overlap, such as malaria and helminth infections [122, 123]. This method is simple to implement and provides a useful assessment of PAR. However, PAR assessments generally use the different existing population datasets interchangeably to provide such estimates. Moreover, uncertainties inherent in the population datasets are rarely acknowledged in such calculations. As already described above, existing gridded population datasets clearly differ, especially where census population data are spatially coarse, and both input-based and process-based uncertainties contribute to great variations in mapping precision (Figure 3). As a consequence, large variations in PAR estimates can result from the choice of population dataset, particularly in low-income countries where census data are often spatially and temporally poor [34]. Specific estimates for pregnant women and children have been also derived from population distribution datasets, by combining gridded population data with age, sex and fertility data from the United Nations [124, 125]. However, given that the demographic composition of populations varies substantially within countries, using such national-level demographic estimates introduces additional uncertainties, as already mentioned before.
The combination of endemic disease risk maps with human population counts in the 'at risk' regions presents opportunities for designing the targeting of interventions such as resource allocation, vaccine campaigns or epidemic prevention measures to regions where they will have most cost-effective or burden-reducing impact [80, 108, 111]. For example, PAR estimates of malaria enabled the derivation of intervention coverage estimates [126], the intervention costs for reducing the malaria burden [127], malaria elimination feasibility [128], and the actual funding coverage [129]. The use of gridded population datasets in these studies facilitated more precise estimates of PAR than estimates based on aggregated population data by administrative units. An accurate assessment of 'at risk' populations also presents opportunities for designing disease surveillance and early warning systems for epidemics in the populations [130, 131].
The size of PAR is expected to vary in the future as a response to environmental and demographic changes. Some authors have attempted to assess the future PAR of malaria according to different scenarios [132–135]. While climate factors are predicted to cause limited changes in PAR of malaria for the year 2050 [134] and even a reduction in the size of PAR in Africa in the coming decades [133], demographic changes could significantly increase PAR. Applying forecasted population growth rates to gridded population data enables the derivation of estimates of future global population distribution [132, 135]. The combination of such projected data based on GPW2 with climate scenarios showed that population growth likely will have a larger effect than climate change on future global PAR of malaria estimates [135]. Moroever, a study examining the combined effects of climate, population and urbanisation changes confirmed the likely dominant effect of population growth in the increasing size of malaria PAR estimates in Africa [132]. Dengue fever risk in the future was also estimated based on population and climate projections for 2055 and 2085 [121].
Spatial methods and tools are now widely used in infectious disease research and have led to significant advances in our understanding of disease dynamics, surveillance and control [1, 2, 17, 18]. Population distribution datasets are becoming increasingly important inputs to these models. During the past decade, a number of advances have been made in GIS technologies that have allowed demographers and GIS specialists to begin to map the spatial distribution of human populations globally at an unprecedented level of detail. We have shown in this paper how useful large-scale gridded population datasets are for the calculation of PAR of infectious diseases and for disease risk mapping and modelling. Gridded population datasets allow the user to select geographic boundaries of interest independently from administrative boundaries. Population datasets capture spatial heterogeneities observed in disease transmission risks, making PAR calculations significantly more accurate than can be reached with aggregated population data.
Nevertheless, a number of issues and challenges remain, that, if resolved, would permit a more refined analysis of the spatial distribution of human population around the globe, and reduced uncertainty resulting from their use in epidemiological studies. Population distribution modelling methods have raised several issues and challenges such as the lack of comparability of statistical data from different countries and sources [43], the lack of standard definitions for what constitutes an urban area [136], the need for extensive source and metadata information, and the difficulties in validating the existing population datasets or measuring which existing dataset is the most accurate. The construction of contemporary, well-validated and well-documented spatial demographic datasets should be a priority in order to reduce uncertainties in spatial epidemiological studies [34]. In the absence of a more institutionalized mechanism to generate updated and freely available population datasets, data sharing should be encouraged between projects. Each dataset is for instance built upon similar population data linked to administrative boundaries and a standardized database framework that would encourage sharing of new and improved datasets between projects would greatly facilitate the data production and benefit the users.
Several extensions of population datasets would be particularly useful for infectious disease research (as well as other health related fields such as disaster risk management or conservation), for policy and planning. The most useful one would be to improve information on population attributes of interest. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. It is not feasible for global population databases to generate on-demand maps for each variable of interest, nevertheless the potential to leverage current freely available population databases appears large, as was recently discussed in Tatem et al. [137]. These authors proposed a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications.
Over the next few years, improvements in population distribution modelling methods and infectious disease distribution mapping will allow further refinements in PAR estimation and intervention targeting. Continued efforts to resolve the remaining challenges in accurate spatial population datasets construction will be required to obtain the full benefits from these potentially powerful methodologies.
CL is supported by the Fonds National de la Recherche Scientifique (F.R.S./FNRS). AJT is supported by grants from the Bill and Melinda Gates Foundation (#49446, and #OPP1032350). AJT also acknowledges funding support from the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. This work forms part of the AfriPop and AsiaPop Projects http://www.afripop.org, http://www.asiapop.org, principally funded by the Fondation Philippe Wiener - Maurice Anspach, and the Malaria Atlas Project (MAP, http://www.map.ox.ac.uk), principally funded by the Wellcome Trust, U.K. The F.R.S./FNRS, the Fondation Philippe Wiener - Maurice Anspach, the Bill and Melinda Gates Foundation and the Wellcome Trust have no intellectual or editorial input into the content of this Review.
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Weekends Sept. 22nd through Oct. 28th
: 10am until last entry to flashlight maze at 9pm
: 10am-6pm
~Extended Hours Columbus Day Weekend~
Saturday Oct. 6:
10am - last entry to flashlight maze at 9pm
Sunday Oct. 7
: 10am - last entry to flashlight maze at 9pm
Monday Oct. 8
: 10am-6pm
PUMPKINS: Pick-your-own
or select from our many pre-picked varieties
CORN MAZE Theme:
It's a Jungle Out There!
FLASHLIGHT MAZE: Saturday Nights
Lantern-lit hay ride available
visit with friendly ponies
by appointment - email us for details
Our Farm in unlike any other. Come see our beautiful fields, stone walls, old barns, cows, and ponies. It's a great place
to bring the family, friends and neighbors. Feel free to pack
a lunch and find a spot at a picnic table. Do not forget
to take lots of pictures!
Email : email@example.com
Call : 401-397-7500
"This has to be one of Rhode Island's most picturesque farms"
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What our Visitors are Saying.....
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39 Cucumber Hill Road
Foster, RI 02825
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Profession: Videographer & Editor
Teaching Experience: I have a TEFL qualification however I have not used it in a classroom setting for any prolonged period of time. I have taught Monks in Laos for a week in a monastery and Indian children in the Himalayas again for a week. My main experience comes from my travels and conversing with different people. I understand how to communicate with a range of different levels of English and adjust myself accordingly to their needs.
Interests: Travel
Video Editing
Education: University of York
BA Hons Philosophy
Spoken Languages: English (Native)
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The ATO has issued guidance on claiming mobile phone, internet and home phone expenses.
It says that if you use any of these for work purposes, you may be able to claim a deduction if there are records to support your claims. But the ATO points out that use for both work and private use will require you working out the percentage that "reasonably relates" to your work use. (more…)


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That is a picture of Lake Michigan, taken during my run this morning. My first solo run since the first week of July. And it's hard for me to even count that early July run as an actual run because it was too short and I had to stop every block for traffic and I felt like crap and it was raining, etc… Honestly, I haven't been running for almost six weeks now. The last time I logged anything into Training Peaks was, I think, the first week of June. Sorry, Coach.
I went out on Sunday with a friend who is, like me, just getting back into the swing of things after taking some time off. He ran a 200 mile Ragnar last month and needed some time to get his legs and mind back. He was the perfect person to go out with for an easy trot. He's an obviously experienced runner, born of a family chock-full of running phenoms (his uncle held a world record for the fastest 10k run by, like, an 8-year-old boy or some such craziness) and I could tell that on any other day, if we were both in peak condition, he'd have made me work. Hard. But it wasn't any other day. It was Sunday. After a long rest period. And we were both happy with an easy, conversational pace and a pleasant 40 minutes along the lakefront.
I had no plans to run this morning but the minute I woke up, I felt the urge. And not only the urge, but also the ability. As in: I felt like I had the power to get up, get dressed and go for a run. The flesh was willing and the mind was strong. Anyone who has ever been active for any length of time knows that it's a boon when the body and mind are in sync. It doesn't always happen. Sometimes your mind is OK but you're body is wiped. Sometimes your body is fine but your mind is like, "Fuck you. It's 5AM and it's raining. We are staying in bed." In either of these instances, it's normally possible for one to override the other and you get up and you get out and you do your thing.
But when both mind and body go AWOL, it's really hard to motivate. This has happened to me before. Actually it happens more regularly than I think I've ever been willing to admit. And most of the time, I muscle through. Why? Because…
Because I fear what will happen if I don't. Which is what? Probably nothing. Nobody has money riding on whether or not I'm going to place at the next sprint tri on Key Biscayne. I muscle through because I fear what others will think of me if I don't. Maybe people will think I'm weak. Maybe people will think I'm lazy. Maybe people will think I'm not a good athlete. Maybe. Maybe. Maybe.
Who Gives a Fuck?
Obviously I do. Or I have. In the (not so distant) past. So for the last month, I've been trying to worry less about what my not-training "means" to other people (and I've been through enough therapy to know that, at the end of the day, it means NOTHING to other people) and more about what I can do to feel better. So I went to see this acupuncturist/chiropractor/herbal doctor and settled on a plan to eat clean, take a boat-load of supplements, sleep when my body was telling me to sleep, and focus on being a playwright. So far so good. Ish.
When I woke up this morning and felt the desire to run, I couldn't fight it. I've been listening to my body about other shit, so why not listen to it when it asked me to move? I wanted to run. And that made me happy. I don't think I've ever gotten out of the house so quickly. I was up and out the door in about seven minutes. Just enough time to pee, dress, strap on my Garmin and go. I ran pretty much the same route my friend and I had run on Sunday. Down towards the lake and then along the lakefront and then back. Right around 4 miles. And that felt fine.
For the last, oh, two years, every time I've gone out for a run, I've had some kind of time and/or pace goal in mind. 10 minute warmup then 1 mile build to lowest point of zone 5, then 1.5 miles all out, then cool down to zone 2. Or 30min at 10K pace + 15 seconds. Or 90 minutes in HR Z1-2 NO ZONE 3! This morning, my goal was to go out and enjoy running. Funny how goals change.
But now I'm on the couch, under a blanket, with my laptop on my legs, writing this blog post, feeling my eyelids getting heavy. I had a smoothie immediately after the run that included frozen berries, almond milk, 1 tsp maca powder, 1 tsp Omega oil, one B complex, 2 multi-minerals, 1 probiotic and a scoop of Perfect Food. Then I had a cup of mate and two gluten free waffles with sunflower butter. So…where's the energy?
Why do I want to take a nap at 11:42 in the morning? What am I not doing right? Is this STILL caffeine withdrawal? Should I have ignored my body this morning when it begged me to get out on the road? Did I really so exhaust myself over the course of the last six months that it's going to take another six months to get my energy levels back up to what they were? And really, what were they? Have I ever really had any sustained energy? Or have I just been faking it? The truth is, I have been tired for the last ten years. But how is it possible that I've been training and competing in amateur sports that whole time? Why do I feel alternately great and crappy? Energized and fatigued? Calm and anxious?
These are the questions I've got rolling around in my brain right now. They're sharing space with the questions I've got about Oedipus and narcissists and the drama of family dynamics and how to be an adult relating to other adults. And I have to work very hard to make sure that the questions I'm asking about my health don't completely take over that space; overpower the questions I have to be asking about my work and my writing and my personal life. But this is tricky because those questions all probably, in one way or another, point to the same answer.
I know it's not just about coffee and it's not just about gluten or dairy. It's not just about booze or casein or yeast. It's not just about migraines and balance issues and yo-yo dieting. It's not just about hay fever or eating too much soy. It's not about pestides and GMOs and soil depletion and how none of our food has any nutritional value anymore. Well, maybe it's a little about all of that. Honestly though, I think it's far more likely that this is about me, and so many other young American women, fighting to hit one or two or twenty unattainable goals and making ourselves sick in the process.


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The Grand Prismatic Spring is Yellowstone's largest hot spring.
Kalyan V. Srinivas, Dreamstime
The Grand Canyon of the Yellowstone is a popular hiking area in the park.
Galyna Andrushko, Dreamstime
Moose Falls at Yellowstone National Park
Jason P Ross, Dreamstime
A wolf roams around its home in Yellowstone National Park.
Photograph by Marcel Schauer, Dreamstime
Yellowstone's Old Faithful geyser erupts more frequently than any of the other large geysers at the park.
Lorcel G, Dreamstime
Click the full-screen arrows in the upper right to read the captions!
A Swainson's hawk soars over tree-lined mountains, gushing waterfalls, and hot springs called geysers. The land below is part of Yellowstone National Park. Straddling the states of Wyoming, Montana, and Idaho, Yellowstone was the first national park ever formed.
The area became widely known for its beauty in the 19th century. Native Americans had already been living in the region for thousands of years. But until the late 1800s, few others were aware of the awe-inspiring landscape. In 1871 the U.S. government sent geologist Ferdinand Hayden to explore the terrain. Hayden brought photographer William Jackson along with him. At the time, photography was a new art form and the equipment was very slow—it took Jackson 30 minutes to take just one shot!
After several weeks, Hayden and his team returned from their trek with hundreds of photos and drawings of the region. They presented the pics to lawmakers, who were amazed by the canyons, geysers, bubbling mud pools, and other natural wonders that the images revealed. In 1872 the government enacted a special law to conserve some two million acres of land in the area and protect the wildlife living on it.
Today Yellowstone—named for the Yellowstone River that cuts through the land—welcomes some three million visitors a year who come to camp, hike, and raft, as well as check out the park's amazing natural features and wild animals.
The area is home to bears, bison, wolves, and ravens. Creating Yellowstone inspired the formation of more national parks in the United States. And it inspired other countries around the world to create protected parks as well.
Text by Andrea Silen, NGS Staff


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Credit reports are gaining popularity with employers faced with the task of recruiting honest, reliable employees. Some employers use credit reports to screen applicants for sensitive positions, such as cashiers or couriers. Other employers use credit reports to give them a general indication of an applicant's financial honesty and personal integrity. But many employers may not know that a federal law, the Fair Credit Reporting Act (FCRA), governs their use of credit reports.
This brochure briefly explains the FCRA and then describes how you, as an employer, can use credit reports. It also discusses your legal responsibility under the FCRA to notify applicants if information in their credit reports influenced your decision not to hire them.
The Act
The FCRA, which has been in effect since 1971, is designed to protect the privacy of information in credit reports and to ensure that information supplied by credit bureaus about consumers is as accurate as possible. The law specifically permits credit bureaus to release to employers credit reports for employment purposes. While the FCRA does not supersede fair employment laws, it allows employers to review credit records for the purpose of evaluating anyone they may hire, promote, reassign, or retain, consistent with other laws.
The Disclosure Notice
When a decision to deny employment is based on information in a credit report, the employer must disclose this fact to the applicant -- along with the name and address of the credit bureau making the report. This is important because some credit reports may contain errors. The disclosure notice allows an applicant to obtain a free copy of the credit report and check it for accuracy and completeness.
The disclosure is required even if credit-report information was not the main reason the applicant was turned down. It may have played a small part in the overall decision, but the applicant still must be notified.
The disclosure requirement also pertains to any current employee who applies (and is turned down) for a new position or whose employment is terminated because of information in a credit report.
A written disclosure is not required. However, employers often provide written notices -- and keep copies of them for two years -- to show compliance with the FCRA.
Some Common Situations
The following examples illustrate situations where the notice must be given to job applicants.
* A business receives 100 applications for a cashier position. It obtains credit reports on all of them and dismisses 50 from further consideration based on information contained in the credit report. The remaining 50 have good credit histories. All but ten of them are ultimately rejected for other reasons.
The first 50 applicants are entitled to the notice. Their rejections were based solely on their credit reports.
* A person with an unfavorable credit history is denied employment. Although the credit history was considered a negative factor, the applicant's lack of relevant experience for the position was even more important.
The applicant is entitled to the notice because the credit report played a part -- however minor -- in the employer's decision.
* An employer is looking for an employee to fill a particularly sensitive position. It rejects one applicant with a good credit repayment history because the report shows a debt load that may be too great for the proposed salary. Another applicant is rejected because the credit report shows only one trade line, and the employer prefers to hire someone who has demonstrated financial responsibility.
Both applicants are entitled to notices. If any information in the report influences an adverse decision -- even though the information may not be otherwise thought of as "negative" -- the disclosure must be given.
* An employer obtains credit reports on applicants. It does not send rejection letters to those not hired. These applications are simply filed and later discarded.
Employers are not excused from their notification responsibilities merely because they had not communicated with the applicants. Once an applicant is no longer under consideration, in part because of something in a credit report, the notice must be given.
Noncompliance with FCRA
Employers who do not provide the disclosure when it is required may face several consequences. Under the FCRA, individuals can sue employers in federal court for actual damages suffered from an FCRA violation that occurs because of negligence. A person who successfully sues under the FCRA is entitled to recover court costs and a reasonable attorney's fee. The law also permits suing for punitive damages if it is established that the employer willfully violated the law.
In addition, the FTC (and other Federal agencies) may sue users of credit reports who do not comply with the FCRA. Most employers (except banks and a few other employers) are subject to the FTC's jurisdiction.
Facts for Business from the Federal Trade Commission
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Members of the Dublin Film Critics Circle (DFCC) have voted Pixar's Inside Out the best film of 2015.
There was stiff competition from a number of other heavy hitters, including Mad Max: Fury Road, which was a winner with critics groups elsewhere in the world. In the end, Max was left to wander the desert in second.
Myroslav Slaboshpytskiy's The Tribe, a Ukrainian film set among the deaf, and Todd Haynes's Carol, a story of lesbian romance from the 1950s were next in line with the recently released Star Wars: The Force Awakens and the less hyped Tangerine, a no-budget film shot on iPhones, sharing fifth.
John Crowley's Brooklyn, which is being acclaimed this award's season was named as best Irish film, but shared this honour with Conor Horgan's documentary on Panti Bliss, Queen of Ireland.
Daisy Ridley was named as the best international breakthrough thanks to her performance in Star Wars: The Force Awakens, while the best breakthrough in the domestic category went to Tallaght's Jordanne Jones who appeared in I Used to Live Here.
Best Film
Best Irish
Best Documentary
Best Actress
Best Actor
Best Director
Daisy Ridley (Star Wars The Force Awakens) / Kitana Kiki Rodriguez and Mya Taylor (Tangerine)
Breakthrough Irish
Jordanne Jones (I Used to Live Here)