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COVID-19: Buhari extends Lagos, Ogun, FCT Lockdown by 14 days
President Muhammadu Buhari has extended the current restriction of movement in Lagos, Ogun and the Federal Capital Territory (FCT) by another 14 days to curb the spread of Coronavirus disease in the country.
The President, who confirmed this in a nationwide broadcast in Abuja on Monday, however said "the previously issued guidelines on exempted services shall remain".
He said the extension was informed by the advice given to him by the Presidential Task Force on COVID-19.
"Having carefully considered the briefings and Report from the Presidential Task Force and the various options offered, it has become necessary to extend the current restriction of movement in Lagos and Ogun States as well as the FCT for another 14 days effective from 11:59 pm on Monday, 13th of April, 2020.
"I am therefore once again asking you all to work with Government in this fight.
"This is not a joke. It is a matter of life and death. Mosques in Makkah and Madina have been closed.
"The Pope celebrated Mass on an empty St. Peter's Square. The famous Notre Dame cathedral in Paris held Easter Mass with less than 10 people.
"India, Italy and France are in complete lockdown. Other countries are in the process of following suit. We cannot be lax.
"The previously issued guidelines on exempted services shall remain.
"This is a difficult decision to take, but I am convinced that this is the right decision. The evidence is clear," he said
According to him, the repercussions of any premature end to the lockdown action are unimaginable.
"We must not lose the gains achieved thus far. We must not allow a rapid increase in community transmission. We must endure a little longer," he added.
The News Agency of Nigeria (NAN) reports that the President had on March 29 addressed the nation over the novel Coronavirus pandemic where he directed the cessation of all movements in Lagos, Ogun and the FCT for an initial period of 14 days with effect from March 30, to check the spread of the disease.
The NCDC says as at 9.30 p.m on April 12, there are 323 confirmed cases of COVID19 pandemic in the country, while 85 have been discharged with 10 deaths.
There is also a reduction of new positive cases of the pandemic and majority in stable condition, 85 were treated and discharged.
The number of new cases as at April 12 were fewer as compared to April 11 with 13 new cases of virus.
Lagos remains the epicentre with 176 cases, 56 cases in FCT, 20 in Osun, 12 in Edo, 11 in Oyo, 11 in Bauchi, 6 in Akwa Ibom, 5 in Katsina 5, Kwara 4, Kaduna 6, 7 in Ogun 7, 2 each in Enugu, Ekiti, Delta, Ondo and Rivers, and 1 each in Benue, Niger, Anambra and Kano States.
The agency has also tested over 5,000 Nigerians for COVID-19 across the country.
COVID-19: Nigeria not expecting upsurge of fatality – FG
Military dismisses alleged tribal interest in Taraba, Benue conflict


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Long-term care threatens to sap seniors' savings
Resource type: News
The Dallas Morning News | June 17, 2009
AARP is an Atlantic grantee.
by Bob Moos
Kay Paggi has been the bearer of bad news more times than she cares to remember.
The senior-care coordinator has helped hundreds of Dallas families find long-term care for frail parents. Almost always, they think Medicare will pick up most of the tab, the same as it does for hospital stays and doctor visits."They're shocked when I tell them that Medicare doesn't pay for long nursing home stays," she said. "All of a sudden, they have this look of horror as they realize their parents will have to foot the bill. Those retirement nest eggs go poof."
Over 20 years, Paggi has seen many seniors spend almost their last dollar on personal care at home or an assisted-living community before turning to a nursing home and ultimately relying on Medicaid, the health care program for the poor.
"Our system of paying for long-term care in this country is broken," she said. "Unless you have a big family or thick wallet, you'll run into trouble in old age."
As lawmakers debate how to pay for acute care for 46 million uninsured Americans, advocates for older or disabled adults are urging the Obama administration and Congress to discuss long-term care's financing problems, too.
Possible reforms range from providing more federal money for home care to creating a national insurance pool that would protect people against long-term care expenses.
"It doesn't make sense to figure out how to pay for the hospital care of a heart attack victim but overlook the day-to-day problems of someone living with congestive heart failure," said Howard Gleckman, a senior research associate at the Urban Institute and author of Caring for Our Parents.
Long-term care includes everything from help with bathing, dressing and household tasks to assistance with taking medications. It may be provided by family members or paid caregivers in the home or at a facility like an adult day care center or nursing home.
Ten million Americans require long-term care. But by 2030, 37 million boomers will be living with more than one chronic condition such as arthritis or diabetes, said Larry Minnix, president of the American Association of Homes and Services for the Aging.
Health care and long-term care go hand in hand, experts say.
"If the chronically ill forget to take their medications or they can't get to their doctors' offices, their health will deteriorate, and they'll wind up back in the hospital," said James Swan, professor of applied gerontology at the University of North Texas.
Lawmakers won't wring savings out of the health care system without managing chronic illnesses effectively, and that will mean making long-term care more accessible and affordable, he said.
Boomers coming
The nation's long-term care system is at risk of collapsing when aging boomers need help in a decade or two, experts warn.
Medicare covers short nursing home stays and some home health care, but those services are intended mainly for patients recovering from illnesses or injuries. The federal health care program isn't set up to meet the day-to-day needs of the chronically ill.
Nor is private insurance expected to become the primary means for financing long-term care, though its role will grow. High premiums will remain a big hurdle for many, said Diane Rowland, executive vice president of the Kaiser Family Foundation.
For a 60-year-old individual, annual premiums for a typical policy average $2,329, according to Kaiser and the Avalere Health consulting firm.
For a couple the same age, premiums average $3,096 a year.
Most long-term care is provided by unpaid family members who often make personal sacrifices. The typical caregiver is a middle-age daughter who must quit her job or scale back her hours to look after a parent at home.
Paid care can quickly exhaust life savings. The median cost of a private room at a Dallas nursing home is $54,750 a year, while the median charge for an assisted-living community is $33,456, according to Genworth Financial.
As older or disabled adults have spent their way into poverty, Medicaid has become the anchor of long-term care and now accounts for almost half of the $200 billion that the government, private insurers and individuals pay for such help each year.
"Medicaid was supposed to act only as a safety net for the poorest, but it has become the single largest source of financing for long-term care," said Doni Van Ryswyk, manager of the North Central Texas Area Agency on Aging.
Medicaid could stretch its dollars by caring for more people at home instead of in nursing homes, said Elaine Ryan, vice president of government relations for AARP.
"It would be a win-win for Medicaid and its beneficiaries," she said.
Medicaid could care for three people with home and community-based services for what it spends on one nursing home resident, according to the AARP Public Institute.
"And nine of 10 older adults prefer to remain in their homes anyway," Ryan said.
In Texas . . .
Texas has done better than other states in shifting to home and community-based programs, but many people still must wait one to two years for the care because of state budgetary pressures, said Lue Taff, director of the elder support program at the Senior Source in Dallas.
Though Texas and other states are required by law to provide nursing home beds to eligible Medicaid recipients, they have the discretion to limit their home and community-based services.
As part of health care reform, advocates are pressing Congress to give states a bigger federal match so that they can afford to expand in-home programs and lessen the need for more expensive nursing home care.
The American Association of Homes and Services for the Aging, a trade group, is also urging lawmakers to create a national insurance pool that would pay for itself. Workers would contribute to the fund through payroll deductions and receive a cash benefit if they became disabled.
"For essentially the price of a cup of coffee each day, people could have an average long-term care benefit of $75 a day," Minnix said. "If such an insurance program were in place and everyone participated, Medicaid's costs would be cut in half."
The national pool's success would depend on how many workers participated, experts say.
"You've got to make any such insurance pool mandatory for it to be affordable," said Joshua Wiener, director of aging research for the RTI International think tank. "Otherwise, only the people who know they'll need the coverage will buy it, and the premiums will be too high."
Supporters say workers would be automatically enrolled in the fund, though anyone could opt out. That would spread the risk across a broad population and keep premiums low.
Minnix said the disabled could use the cash benefit to pay for any services they needed. Workers who wanted additional coverage could buy private insurance.
Past debates over long-term care have been stymied by disagreements over whether it should be financed with public or private money, said Judith Feder, professor of public policy at Georgetown University.
"That rhetoric obscures the facts," she said. "We already have a mixed public-private system, and we always will. The real choice is whether to keep today's dysfunctional system or create something that won't buckle under millions of aging boomers."
View more news ›

redpajama_set_name: RedPajamaCommonCrawl


Take a look at some of the amazing smart city mobility applications enabled by the internet of things.
Signify, Cisco and Panduit discuss how investing in an IoT connected lighting infrastructure enables you to capture real-time data about your building operations and usage.
What does quality of light mean today when the internet of things takes it beyond photometrics? Read our latest article to discover more.
Smart cities could make people feel better. Literally.
Is it possible to create smart, human cities that enable people to live well-balanced lives suitable to their own needs?
From 5G to M2M we take a look at the landscape of enabling technologies that make up an IoT ecosystem.
Read an article from Terri Wills, CEO of the World Green Building Council, about the powerful impact green buildings have on our environment.
This white paper investigates the important effects that lighting can have on the user (employee) within the office trinity: user, organization, and building.


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When They're Best
High in vitamin C, red bell peppers are not only tasty but may also reduce the risk of certain chronic or aging-related health conditions. Red bell peppers (also called sweet peppers) deliver a large volume of food with few calories, carbohydrates, or fat, and they have the most nutrients of all the sweet peppers.
They are available year-round in grocery stores and are a low-calorie vegetable, delicious eaten cooked or raw in salads, soups, casseroles, and more.
Red Bell Pepper Nutrition Facts
One cup of chopped, raw red bell pepper (149g) provides 39 calories, 1.5g of protein, 9g of carbohydrates, and 0.5g of fat. Red bell peppers are an excellent source of vitamin C and vitamin A. The following nutrition information is provided by the USDA.
Sodium: 6mg
Vitamin C: 190mg
Potassium: 314.4mg
Vitamin A: 233.9mcg
Vitamin E: 2.4mg
Vitamin K: 7.3mcg
The non-fiber carbohydrates in red bell peppers are mostly glucose and fructose, both of which are naturally occurring sugars. Raw green bell peppers have slightly fewer carbs and less fiber than their red counterparts, with 6.9g of carbohydrate and 2.5g fiber per cup.
The glycemic index of bell peppers has not been studied. They are presumed to have no effect on your blood sugar due to their low carb content.
Bell peppers are very low in fat, and most of that fat is healthy polyunsaturated fat.
As is typical for vegetables, bell peppers have only a small amount of protein. For a balanced diet, be sure to include protein sources such as legumes, nuts, dairy, meat, or fish in your diet.
Red bell peppers are packed with nutrients, including vitamin C, vitamin A, and vitamin B6. Vitamin C, perhaps one of the best-known antioxidants, offers a variety of health benefits: supporting immune function, facilitating cell repair, aiding in the absorption of folate, and assisting in collagen production for healthy bones, skin, and hair.
Vitamin C also facilitates iron absorption, so it may help prevent iron deficiency anemia (by building the body's stores of iron) or aid in the recovery from mild anemia. Red peppers also contain some iron.
Red bell peppers deliver other key antioxidants that help protect cells from oxidative damage. These include carotenoids such as lycopene, beta carotene, lutein, and zeaxanthin.
Bell peppers are also a source of vitamin E, vitamin K, folate, potassium, and manganese. A large red bell pepper provides 1.3 milligrams of manganese, contributing 55% to 75% of your daily needs for that mineral.
Understanding Carotenoids in Food
One cup of chopped, raw red bell pepper (149g) provides 39 calories, 77% of which come from carbs, 13% from protein, and 10% from fat.
Bell peppers are a low-calorie, low-fat source of carbohydrates, including fiber, as well as many nutrients such as vitamin C, vitamin A, potassium, magnesium, zinc, and vitamin E.
Thanks to the vitamins, minerals, and antioxidants they supply, red bell peppers may play a protective role by preventing or slowing certain health conditions.
May Lower Cancer Risk
Researchers are studying the association between cancer risk and the intake of carotenoid-rich foods, like sweet bell peppers.
May Improve Heart Health
Red peppers appear to reduce cardiovascular risk by exerting powerful antioxidant effects—the antioxidants in the sweet pepper temper the activity of free radicals that cause cellular damage. Free radicals are directly implicated in developing many human ailments, including cancer, cardiovascular disease, neural disorders, diabetes, and arthritis.
Research shows that the free radical-fighting antioxidants in red bell peppers are activated at different temperatures:
At 35oC (95oF), the phenols and flavonoids in red peppers could disarm free radicals linked to cardiovascular disease.
At 50oC (122oF), the same antioxidants could disarm a free radical linked to certain types of cancer.
At 65oC (149oF), phenol and flavonoid concentrations were at their highest.
These findings suggest that raw bell peppers are heart-protective, but cooked bell peppers may exert stronger antioxidant effects. However, this study was done in a lab, not in humans. More research is needed to understand how these nutrients behave in humans.
How Free Radicals Damage the Body
May Protect Eyesight
Macular degeneration is the leading cause of vision loss in the United States, affecting more than 10 million Americans. It occurs when the central portion of the retina, known as the macula, begins to deteriorate. The macula is mainly composed of a carotenoid, known as zeaxanthin, which is found in oranges, tangerines, and vegetables like red peppers.
Some research shows that an increased intake of dietary zeaxanthin can help maintain the integrity of the macula. Zeaxanthin is a major component of orange bell peppers but is also found in high concentrations in red bell peppers.
The American Academy of Ophthalmology recommends supplementing with 2mg zeaxanthin per day for people with advanced macular degeneration.
May Promote Bone Health
The high manganese content in sweet peppers may also help prevent bone loss because trace elements like manganese (present in red bell peppers), copper, and zinc are characteristically low in people with the disease.
Bell pepper food allergies are rare. However, 50% to 60% of people who are allergic to latex may have latex-fruit syndrome and cross-react to proteins in bell peppers similar to those in latex. You might feel a tingling or itch in your mouth after eating bell peppers. Similarly, people with pollen allergies may have cross-reactions to certain foods.
In rare cases, this can be more serious and trigger throat swelling or anaphylaxis. People with this syndrome may also be sensitive to other fruits, including avocado, banana, chestnut, fig, and kiwi. Talk to your doctor about how to manage your diet if you have a latex allergy.
Some people find that eating bell peppers causes digestive symptoms, such as gas. These effects, however, are more commonly associated with green peppers instead of red ones.
Bell peppers (Capsicum annuum) come in various colors, including green, red, yellow, purple, orange, white, and even brown, and in sizes from mini to softball-sized. Red bell peppers tend to be the sweetest of the bunch.
Bell peppers of all colors are, scientifically speaking, the same plant. Their color is determined by how long they stay on the vine, with green first, followed by red or another color. Red peppers contain the most nutrients, making them the healthiest choice among bell peppers.
Peppers can also be purchased frozen, roasted in cans or jars, or dried and ground (as in paprika). Note that jarred roasted red peppers can often have a large amount of sodium and fat if they are stored in oil.
Jalapeño Pepper Nutrition Facts and Health Benefits
While most bell pepper varieties, especially green ones, are typically available all year, red bell peppers are freshest during the summer and fall. Choose fresh peppers that are brightly colored and plump with smooth, unblemished skin—the more intense the color, the better. The vividness of color is an indicator of ripeness, flavor, and a high concentration of nutrients.
Avoid peppers that have soft spots, nicks, wrinkles, or pits. You'll also want to look for a bright green stem, which indicates freshness.
Store peppers whole in the refrigerator. They should last for about a week this way. Once washed and cut, they will deteriorate more rapidly and should be used within a few days. You can freeze them whole or cut them up and put them in an airtight container.
Red bell peppers can serve as a great snack or an addition to meals. Eat them as is or with your favorite dip, or slice them and add them to eggs, salads, or sandwiches.
Peppers can be stuffed with meat, beans, and whole grains; baked, grilled, sauteed, pureed for soups and dips; or used in chilis, stews, sauces, and condiments. You can eat bell pepper seeds, but most people cut away the seeds and the core before consuming because the texture and taste aren't usually appealing.
Traditional Turkey Stuffed Peppers
Peppers, sweet, red, raw. FoodData Central. U.S. Department of Agriculture.
Pepper, sweet, green, raw. FoodData Central. U.S. Department of Agriculture.
National Institutes of Health Office of Dietary Supplements. Vitamin C: Fact sheet for health professionals.
National Institutes of Health Office of Dietary Supplements. Manganese: Fact sheet for health professionals.
Fiedor J, Burda K. Potential role of carotenoids as antioxidants in human health and disease. Nutrients. 2014;6(2):466-88. doi:10.3390/nu6020466
Shotorbani N, Jamei R, Heidari R. Antioxidant activities of two sweet pepper Capsicum annuum L. varieties phenolic extracts and the effects of thermal treatment. Avicenna J Phytomed. 2013;3(1):25-34.
Centers for Disease Control and Prevention. Learn about age-related macular degeneration.
Eisenhauer B, Natoli S, Liew G, Flood VM. Lutein and zeaxanthin--food sources, bioavailability and dietary variety in age-related macular degeneration protection. Nutrients. 2017;9(2). doi:10.3390/nu9020120
Boyd K. Vitamins for AMD. American Academy of Ophthalmology.
Maioli C, Tagliabue L, Cioni F. Osteoporosis and mineral nutrition. A literature review. Progr Nutr. 2018;20(3):305-12. doi:10.23751/pn.v20i3.6861
Nucera E, Rizzi A, Buonomo A, et al. The clinical meaning of positive latex sIgE in patients with food/pollen adverse reactions. Int J Immunopathol Pharmacol. 2012;25(2):445-53. doi:10.1177/039463201202500214
Aghajanpour M, Nazer MR, Obeidavi Z, Akbari M, Ezati P, Kor NM. Functional foods and their role in cancer prevention and health promotion: a comprehensive review. Am J Cancer Res. 2017;7(4):740-769.
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The Printing Works opened in September 2010 as a live exhibit in the attractive village setting of the Cobblestones Museum and we celebrated our 8th anniversary on 25 September 2018.
 Our object is to display and demonstrate the technique of letterpress printing, using the type and tools of the trade that have been used for centuries.
The Works is equipped with seven working presses from small table-top presses to two large treadle-powered platen presses. The fonts of type range from text size, through to large wood characters typically used for posters.
Visitors can enjoy a print on one of these presses !
and all students have the opportunity to print. A school resource kit is also provided which covers the evolution of the alphabet, word search and 'Scrabble' packs, and "mind reading" challenges.
The layout of the typecase showing the most frequently used lower case letters - largest compartments - grouped together for quick selection with one hand while the other holds the composing stick.
The Printing Works enjoys numerous group visits to the Museum particularly from schools when students are shown the way of life in the Wairarapa almost a century ago, compared to today. Visits by tour groups, many in post-conference programmes, senior citizen, art and service groups are regular. In the Printing Works they learn of the roles of those working in the printing industry decades ago and the techniques involved.
The process of setting type and preparing it for the press was captured on film in October 2018 as a sequence for a forthcoming documentary marking the anniversary of the Women's Suffrage movement.
School group visits are popular. Students from Solway Primary School, Masterton, Ponatahi Christian School, Carterton Primery, Hadlow Preparatory School Masterton, Kahutara Primary, and Berhampore Primary school from Wellington visited in 2018.
In September 2018 sequences of traditional letterpress printing were filmed for TV documentaries by two companies.
 Full-day workshops are popular with participants in recent months from Wellington, Hawkes Bay and Featherston. Workshops can be held in other locations. A graphic art tutor from Featherston used her workshop time in November to print items for the local primary school fair.
The Works was a hive of activity for the Museums' 2018 Activities Day in October....and we had a dose of Steam Punk!


PCI is located in the area Buffalo, USA (America), and the colocation data center is carrier neutral. The data center was last updated on 22-10-2008.
owned and operated by Personal Computers, Inc..
Where is the data center located? Located in a secure area.
What size is it? Approximately 4500 Sq. Ft.
If you are interested in receiving a quote for Personal Computers, Inc., please try our free quote service.


Huawei unveiled smartphone Honor Magic 2.
The main specs are: 6.4″ FHD+ OLED Curved 2.3D Glass / Kirin 980 / 6GB or 8GB RAM / 128GB or 256GB ROM, and it comes in three colors, including Blue, Red and Black.
This camera is equipped with 3 front (main camera + 3D Depth sensor) / 3 rear (standard + super wide angle + black and white) camera. The front camera uses slide method because it is a bezel-less full screen.
In addition, there is no separate fingerprint sensor for Magic 2, because it uses the display fingerprint recognition method. Pre-order already has started and shipping and sales start on the 6th of November.
Recently, several smartphones have applied pop-ups or slides, and it seems like these types of products may continue to emerge in the future.
Although Huawei Mate 20 Pro supports IP68 waterproofing, but it is a bit difficult to use it for actual diving.
Above image is a dive case for underwater, and it allows you to withstand up to 5 meters of water pressure for an hour.
The sales price for dive case is CNY 599 ($86).
Galaxy Note 9 White Color has been leaked via Evan Blass' Twitter.
There is a difference between the device image and its authenticity, but the White version of Galaxy Note 9 seems likely to be released soon.
The OnePlus 6T adds a new color Thunder Purple color to the existing Mirror Black / Midnight Black.
It is available only in China, but is expected to be available in other countries.


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U.K. must stop penalizing victims for crimes they were forced to commit, says TIP report
Human TraffickingLaw & Policy
The U.K. is failing to provide adequate support for trafficking victims, according to the 2022 Trafficking in Persons report. The report is particularly critical of how the government penalizes victims who are forced to commit crimes by their traffickers.
Punished for being trafficked
The U.S. State Department's annual report highlights that during the reporting period, "the government penalized some victims for unlawful acts traffickers compelled them to commit."
Part 5 of the recently passed Nationality and Borders Act requires the Home Office to take a survivor's criminal record into account when deciding whether to identify them as a modern slavery victim.
This practice is concerning, especially given that almost half of all potential trafficking victims referred in the last year had been forced to commit crimes by their exploiters.
The most common offences included growing marijuana, transporting drugs, and irregular migration, according to Maya Esselmont, director of U.K.-based charity, After Exploitation.
The report calls for the U.K. to "ensure victims are not penalized for unlawful acts, including immigration violations, traffickers compelled them to commit."
Inadequate support for victims
Observers working on the report also felt that "longterm care and reintegration support for victims were inadequate." Potential victims often have to wait for a long time before they can access support through the National Referral Mechanism (NRM), according to the report.
Securing identification and support was particularly difficult for victims without a secure immigration status. The report states:
[The government] created a dual system for identifying victims among individuals who lacked secure immigration status and introduced legislation that experts widely believed would hinder identification and protection efforts.
The next administration must step up
Esselmont told openDemocracy that the report should be "a serious wake-up call" for the next U.K. administration. We agree.
That's why the Freedom United community is campaigning for the U.K. government to pass the U.K. Victim Support Bill, which provides victims with guaranteed support during the National Referral Mechanism period, and for 12 months afterwards, including special leave to remain for foreign victims who need it.
Join us in calling for this change so that all trafficking victims are given the best possible chance at recovery, regardless of their nationality or immigration status. Sign the petition today.
Chip in and help end modern slavery once and for all.
Freedom United is interested in hearing from our community and welcomes relevant, informed comments, advice, and insights that advance the conversation around our campaigns and advocacy. We value inclusivity and respect within our community. To be approved, your comments should be civil.
A few things we do not tolerate: comments that promote discrimination, prejudice, racism, or xenophobia, as well as personal attacks or profanity. We screen submissions in order to create a space where the entire Freedom United community feels safe to express and exchange thoughtful opinions.
Spain debates sex work law that will increase risk of exploitation
A dangerous new law to criminalize sex work is currently being debated in the Spanish parliament. The proposed legislation seeks to criminalize...
Thursday January 26, 2023Jan 26, 2023
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Ukraine war potentially increasing number of known trafficking victims
Monday January 23, 2023Jan 23, 2023
No Uyghur justice in the U.K.
Wednesday January 18, 2023Jan 18, 2023
Migrant workers in U.S. to receive protection when reporting labor abuses
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Walnut Creek, CA – The Contra Costa Transportation Authority (CCTA) has been named the 2017 International Partnering Institute Partnering Champion and awarded the International Partnering Institute's (IPI) highest honor. The IPI Partnering Champion award recognizes organizations that foster collaborations and work to develop high-trust relationships and organizational cultures.
The award aims to highlight the work that CCTA has done in championing partnership with agencies such as the Metropolitan Transportation Commission/Bay Area Toll Authority and cities across the county from San Pablo to Oakley.


Even though L.A. and Santa Monica almost merge together, the spectacular beach city of Santa Monica is undoubtedly a separate entity. Here you will spot celebrities, surfers, business professionals and hordes of students, all drawn to the endless summer and sunshine that the city offers with more than 300 days of sunshine per year. The laid-back lifestyle is much more distinct here than it is in its pulsating neighbor Los Angeles. This balance between the relaxing beach and the glamour of Hollywood makes Santa Monica an excellent destination for your TEFL/TESOL course.
Even for Americans, California is known as one of the most attractive states to live in and it also draws in countless visitors from across the world every year. The most famous attraction in Santa Monica is its pier, built over 100 years ago in 1906. The city also marks the end of the famous route 66, which goes from Chicago, Illinois, all the way down to the pier in Santa Monica over a total of 2,451 miles (3,945 km). Left and right off the pier lies a beachfront area of 3.5 miles of pure golden sand where surfing culture has developed since the 30's. Today, it is also a popular spot for picnics, cycling and working out. Many famous TV shows and movies have also been set here, including the Terminator, Gilmore Girls, 90210, Castle, Star Trek, Grey's Anatomy, and many more.
If you decide to take a TEFL/TESOL course in Santa Monica, you get a 2-in-1 deal: Santa Monica and Los Angeles! Due to the proximity of the two cities and the good transportation network, you can commute to and from both with ease. Why not take a walk around Hollywood or Beverly Hills on the weekend? Go and check out the famous Madame Tussaud's wax museum or take a celebrity house tour guiding you to the homes of the rich and famous. Take in a movie premier at the Chinese Theater and see your favorite actors in person or simply relax on the famous sands of Venice Beach and Malibu.
Choosing to stay in Santa Monica during your TEFL/TESOL course will certainly not lead to disappointment. Who could resist the golden beaches, crystal clear waters and the glitz and glamour? With its laid-back lifestyle and a vibrant nightlife, earning your TEFL/tesol certificate in Santa Monica is sure to be an experience of a lifetime and something you will never forget!
Do you love sunshine, blue skies and the beach? Then, our TEFL/TESOL training center in Santa Monica might be the ideal location for you. Located right next to glamorous Los Angeles, Santa Monica will fascinate you with its beautiful Californian coastline setting. Even though the school is located in the downtown area, it's only minutes away from the golden beaches of the West Coast. Do you also fancy amusement parks such as Disneyland or Universal Studios? Add the glamour of Hollywood into the mix and you may have found your dream campus!
In Santa Monica you will be studying at an ESL school with 10 classrooms, a photocopying area and an extensive resource library. Wi-Fi is also available throughout the school building. A snack shop, as well as complimentary tea and coffee are available on-site. The area around the campus also offers everything you need from coffee shops, restaurants and bars to public transportation.
The low average number of 10 students per course at our Santa Monica campus enables you to get the most out of your experience. Throughout your stay you will receive the best hands-on training from our highly experienced trainers. As the course takes place within an operating language school, you will also be able to test your new skills in the classroom on real English language students. Course times are from 5:30 p.m. to 10:30 p.m. Wednesday to Friday and from 9:00 a.m. to 6:00 p.m. on Saturday and Sunday, every third week of the month.
If you are traveling from outside the city and require accommodation for your stay in Los Angeles, you will find a seemingly endless array of options. As one of the world's major tourist destinations, there should be something available to suit most budgets, from inexpensive hostels right through to 5-star luxury resorts. Please note that due to the overwhelming size of the L.A. area, and the infamously busy roads, we highly recommend that you choose accommodation that is within easy reach of our training center.
If you do not want to choose your own accommodation in the city, we are happy to recommend one option that is affordable, located close to our school, and has been used many times by previous trainees. This option offers a comfortable, apartment-style room that includes a fully-equipped kitchen, daily breakfast, and optional access to Wi-Fi internet and laundry facilities. During your stay you can save on your overall budget by preparing your own meals in your private kitchen that includes a refrigerator, stovetop, microwave and coffee maker. Once we have processed your application for our course in Los Angeles you will receive full details on booking your accommodation.


text:
Q: Potential abuse of observer pattern I have a MeetingRoom object that contains a list of Employees, there is a function on each employee called SetMeetingDate, this method has to take into account the state of all other employees in the MeetingRoom in particicular the IsReadyForMeeting property.
An individual employee for example can only successfully call SetMeetingDate(Date date) when every employee in the ChatRoom has IsReadyForMeeting set to true.
I have thought about a couple of ways to implement this functionality, I can do something where the employee checks the state of the other employees by going via the MeetingRoom object like this:
    public void SetMeetingDate(DateTime date)
    {
        if (!MeetingRoom.AllEmployeesAreReady)
        {
            throw new ApplicationException("all employees are not ready");
        }

        MeetingDate = date;
    }

which seems to me to have the responsibility in the wrong place.
A second approach that I can use is to set up an observer so that the Employee class will inform the MeetingRoom object that it has set a meeting date and the MeetingRoom class will raise the exception if MeetingRoom.AllEmployeesAreReady is false thus moving the responsibility to the MeetingRoom object and the code for the employee would look like this:
    public void SetMeetingDate(DateTime date)
    {
        MeetingDate = date;

        Observer.MeetingDateAdded();
    }

And the Meeting Room would then have a method like this:
    public void MeetingDateAdded()
    {
        if (!AllEmployeesAreReady)
        {
            throw new ApplicationException("all employees are not ready");
        }
    }

This use of the observer has been met with opposition from other developers in my team and  I can see why but I can't think of a better way to implement the requirement, is there an easy/different approach that can be used in this situation?

A: This doesn't look like a reasonable place to apply the Observer Pattern.
What I do notice is that all of your examples reference local state, so I assume they are part of some sort of Meeting object.  Consequently, the code
public void SetMeetingDate(DateTime date)
{
    if (!MeetingRoom.AllEmployeesAreReady)
    {
        throw new ApplicationException("all employees are not ready");
    }

    MeetingDate = date;
}

is perfectly valid, since MeetingRoom and MeetingDate are clearly local properties of your "Meeting" class.
If you prefer something a bit more self-contained, then do something like this:
public static MeetingDate SetMeetingDate(MeetingRoom room, DateTime date)
{
    if (!room.AllEmployeesAreReady)
        throw new InvalidOperationException("All employees are not ready");

    return new MeetingDate(date, room.Employees);
}

or somesuch.

A: You should go with your first solution.
Your first solution is better because it's simpler (but maybe show the user a message instead of throwing an exception if it seems more appropriate).
Head First Design Patterns says it best:

First of all, when you design, solve things in the simplest way possible. Your goal should be simplicity, not "how can I apply a pattern to this problem." Don't feel like you aren't a sophisticated developer if you don't use a pattern to solve a problem. Other developers will appreciate and admire the simplicity of your design. That said, sometimes the best way to keep your design simple and flexible is to use a pattern.

It's usually best to use design patterns only when your alternatives all seem far too complicated. Let KISS and YAGNI be your guides when it comes to deciding whether to use a design pattern.

A: I'd take a different approach, hopefully a clearer one.
// This is a filter function, needlessly wordy in Java.
List<Employee> getUnreadyParticipants(DateTime meetingMoment) {
  ArrayList<Employee> unreadyParticipants = new ArrayList<>();
  for (Employee e : this.getMeetingParticipants()) {
    if (!e.readyForMeeting(meetingTime)) unreadyParticipants.add(e);
  }
  return unreadyParticipants;
}

public void setMeetingDate(DateTime meetingMoment) {
  ArrayList<Employee> unreadyParticipants = getUnreadyParticipants(meetingMoment);
  if (unreadyParticipants.size() > 0) {
    throw new InvalidOperationException(
      "Participants not ready: " + printListNicely(unreadyParticipants)
    );
  }
  // whatever else is the logic
}

Reasons:

*

*Checking time compatibility is MeetingRoom's concern, not Employee's.

*The list of unready participants is available for diagnostics.

*There are no flags and other pieces of state that denote readiness (like .AreAllEmployeesReady) which may go out of sync with the list of participants.

redpajama_set_name: RedPajamaStackExchange


text:
from the inside scoop of every club and player, to shedding some light on in-depth onfield and off-field matters, an experience that feels like you're a part of it!
Player power rules the day
Football is the world's greatest sport. I think almost all of its lovers are in agreement that football has made them feel things that...
Ratul Ghosh -
Super Cup but for whom?
While most of the European leagues are getting back to normalcy at the start...
No rest but football
2021 has been a silly old year for English football. Capacity crowds were allowed...
Just not good enough
Portugal lost to Serbia in their recent World Cup qualifier as they failed to...
Narco football
Before 1990, Colombia's best performances at the international football stage was a second-place finish...
Culture reboot needed
As the clock winded down and Pep Guardiola's Manchester City pretended to be playing football seriously at Old Trafford, there was an atmosphere of absolute dejection around the stadium. The crescendo peaked two weeks...
City's trail for Magpies?
The football world was in a frenzy when it was finally announced that Saudi Arabia's Public Investment Fund (PIF), Britain's PCP Capital Partners and Britain's RB Sports and Media would take over the ownership...
PIF, MBS AND NEWCASTLE
Steven Gerrard in the Newcastle dugout? Kylian Mbappe donning the famous black and white jersey? St. James' Park bowing down to his excellency Mohammed Bin Salman? The possibilities are endless following the takeover of...
THE FUTURE OF SPANISH FOOTBALL
Luis Enrique managed to beat Italy's long-standing unbeaten run of 37 games under Mancini and he did that with a young and promising set of players. Spain also played a young side in the...
SKY BLUES FLYING FINALLY
Coventry City have had one of the most tumultuous decades a club can have in any league. The Championship regulars joined the Football League back in 1919, spending years across all four divisions. They...
A shambolic start to a Premier League campaign, Arsenal have been disastrous in their opening three fixtures of the Premier League. Mikel Arteta's men started their campaign with a defeat against the newly promoted...
Final beckons!
Arsenal vs Liverpool After the wild and heart-stopping end to the first-leg, round two between these two institutions of football should serve up...
Football is the world's greatest sport. I think almost all of its lovers are in agreement that football has made them feel...
Foxes and Spurs meet midweek
An intriguing midweek clash between two teams possessing the capability to make it into Europe if a good stretch of form arrives. Leicester...
Wazza working wonders!
Wayne Rooney is nothing if not a fighter. The Manchester United and England stalwart with Evertonian roots represented all three teams with...


The Library Book Club was founded in 2008 on the premise that reading literature enriches life and improves our capacity to see the world through the eyes of another.
Discussions take place at 12pm in the Library Conference Room (in Library Administration) unless otherwise noted. Tea and refreshments are served, and participants are welcome to bring a lunch. For reminders and updates about Book Club events, follow the Library on Facebook.
The Library Book Club uses Collaborate Ultra videoconferencing software to bring distance students and faculty into our conversations. Request a link to any discussion by contacting Harold Henkel at harohen@regent.edu.
For more information about the Library Book Club contact Harold Henkel at 757-352-4198 or harohen@regent.edu.
For the second straight year, we begin our reading season with...a film. Makala tells the story of Kasongo, a Congolese man who has dreams of purchasing a plot of land for his family. He sees his opportunity to earn money by selling charcoal from a hardwood tree that he has felled and baked in an earthen oven. Loading up the bags of charcoal onto the back of his bicycle, Kasongo sets off across treacherous roads to sell his product at market. Before returning to his village, Kasongo attends a revival service and prays for God's protection and strength. Professors of cinema Pete Fraser and Andrew Quicke will lead a discussion immediately following this thought-provoking and inspiring film.
An 1855 tale of English ecclesiastical life, this work from the author's Barsetshire series relates the humor and pathos that ensue when a kindly clergyman becomes the subject of a scandalous tabloid treatment charging him with financial impropriety. Features a cast of amusingly realistic and memorable characters, naturalistic dialogue, and consummate plotting. Professor of Literature Dr. Pete Fraser will lead our discussion. Distance students and faculty are invited to join the discussion via Collaborate Ultra.
One of Eudora Welty's most loved short stories, A Worn Path (1941) is the tale of an elderly black woman's long walk to town at Christmastime to purchase medicine for her sick grandson. According Welty, the creative stimulus for the story came from the "indelible" image of an old black woman she once saw crossing a wintry field. The theme of the story is "the deep-grained habit of love." First published in the Atlantic Monthly, A Worn Path is available free on the magazine's website. Professor of literature Dr. Michael Crews will lead our discussion. Distance students and faculty are invited to join the discussion via Collaborate Ultra.
The Secret Sharer is a tale of a test of leadership: Am I ready to take responsibility? In this short work, a young captain faces a moral dilemma, the resolution of which will show whether he has the maturity to lead and be entrusted with the ship and the lives of his crew. In addition to the story, we will also read a critical essay by Harvard Business School professor of ethics Joseph Badaracco.


Log into your amazon account using your existing e-mail and password, or create a new account.
On the lower right side of the page you will see a box indicating "choose your own charitable organization." Please type in the search box "Parish of St. Bernadette and School." Only one selection will come up. Please make sure to select St. Bernadette Parish School, Burien.
You are all done! You will now see on the upper left side of the home page that you are supporting St. Bernadette Parish School!


2 Bedroom Apartment To Rent in London for £3,683 per Calendar Month (Calculated).
Modern two double bedroom apartment located on the 1st floor of this well presented purpose built development in the heart of Marylebone. There is an excellent on-site management team for any maintenance queries throughout the tenancy and a 24 hour porter for added security.
Some parking is available by separate negotiation on a first come, first served basis.
Extremely well connected with easy access to Regents Park and surrounding amenities.
Being centrally located in Marylebone, it is just a 0.2 mile walk to Great Portland Street tube station (Hammersmith & City, Circle and Metropolitan lines) and a 0.3 mile walk to Regents Park station (Bakerloo line).
Oxford Circus station (Central, Victoria and Bakerloo lines) is also only a 0.5 mile walk away and beneficial for all of the local amenities and shops within easy reach, as well as a 0.6 mile walk to Goodge Street station (Northern line).


The following post was adapted from a post that originally appeared on RealFoodLiz.com. This list is not exhaustive. This is a primer. Using the ideas set forth in the Purely Primal Skincare Guide means you will not be exposed to any unnecessary chemicals and toxic substances that can be found in conventional skin care products!
There are lots of wonky ingredients in conventional – and heck, even "organic" and "natural" skin care products, but these top my list of ingredients to avoid. Why? Not just because they're associated with skin irritation and allergic reactions (to say the least!) but because they're flat-out unnecessary! The use of these ingredients isn't to benefit your beauty – it's to benefit THEIR bottom line. Avoid whenever possible!
Avoiding these potentially irritating ingredients is important! When trying to help or heal your skin, using sensitizing chemicals doesn't help!
Gluten: That's right, Gluten. This pesky protein is present in many personal care products under names like Hydrolized Wheat Protein or Wheat Germ. If you're avoiding gluten in your food, or if you have a wheat allergy, you may want to avoid this in your skin care routine. Even the best products may still contain wheat protein, so be aware!
Triclosan: This is the antibacterial agent in most antibacterial soaps. It kills good AND bad bacteria, and encourages surviving bacteria to mutate. It may be an endocrine (hormone) disruptor.
Antibacterial soap is different from Hand Sanitizers, which IDEALLY should be alcohol-based (see next item), and have utility in acute situations (occasionally in the home, always in airport bathrooms).
Benzalkonium Chloride: You'll find this in a wide range of cleansers and as the substitute antibacterial ingredient in "Alcohol-Free" hand sanitizer. Its long-term use is associated with impaired immune function and extreme skin sensitivity.
DEA: Also known as Diethanolamine and Cocamide DEA. It's been labeled as potentially carcinogenic and may react with other ingredients in cosmetics to create other, delightful carcinogenic offspring.
Propylene Glycol: This is used to give many shampoos their smooth, gliding texture and will keep the product from hardening. It's a skin irritant and kissing cousin to (though, contrary to popular belief, NOT identical to) the main ingredient in antifreeze.
Phthalates: These endocrine (hormone) disruptors that may be present despite NOT being listed in the Ingredients section.
Aluminum: A key ingredient in deodorant/anti-perspirants, aluminum doesn't sit on your skin and nicely ask those sweat glands to please take a break for the day. Aluminum enters and blocks the sweat ducts, accumulates in body tissue, and may be linked to breast cancer and Alzheimer's disease.
Parabens: Any ingredient that ends in "paraben," like methylparaben, propylparaben, isobutylparaben, is one and the same. They are preservatives that extend the shelf life of all those cute "herbal" products at the drugstore. They penetrate the skin and may be endocrine (hormone) disruptors.
Sodium Lauryl Sulfate (SLS) and Sodium Laureth Sulfate (SLES): SLS was labeled mutagenic from the early 1980s, yet it's still used in everything from shampoos to engine cleaners. It may cause immune damage. It makes your shampoos and soaps foamy, lathery and bubbly while messing with your skin's ability to retain moisture. Therefore, you "need" an expensive and junk-filled conditioner or moisturizer! SLES is a kind of "mini-me/little brother" version of SLS, and no more necessary than its older sibling.
Do you have anything to add to the list? Join the conversation on Facebook!
Did you enjoy Skincare Saturday? Never miss a post! Sign up to get it delivered directly to your inbox!


I took particular interest in a recent Politifact article highlighting House Speaker John Boehner's assessment that "There's no plan from Senate Democrats or the White House to replace the sequester." This statement should not seem right to anybody who follows politics, the news, or simply has not been living under a rock for the past few months (years?). Have the democrats really dropped the ball, or is this more political jockeying by the Do-Nothing GOP? Unsurprisingly, it is the latter. The President and Senate democrats have proposed plans, just not the plans their opponents agree on. Politifact gave Boehner's comment its worst possible rating "Pants on Fire".
Democrats, both in the White House and Capitol Hill, have proposed alternatives to the sequester that involve cutting bloated programs and closing tax loopholes to raise revenue. The "Sequester", as most know by now, cuts programs indiscriminately of their importance to overall economic and social security and without taking into consideration whether the program runs efficiently or not. This undesirable result was meant to be undesirable in the hopes of forcing congress into passing a more acceptable deal. Unfortunately, Congress was unable to envision its own incompetence, and the sequester became fiscal policy starting last Friday.
So the Do-Nothing-GOP has decided the democrats have not offered an alternative plan because they have made it their party's goal to strike down any plan the Democrats offer. This sounds more like self-fulfilling economic suicide than two sides working towards an agreement that will work for the American people.
So what does the GOP require in a plan? It requires that tax loopholes that are closed must be met with equal reductions in government spending. In an effort to be "fiscally responsible", the GOP has taken any proposal that will raise government revenue off the table.
1) The U.S. was running a budget surplus until Bush gave that surplus away in the form of tax breaks (notice the blue line sharply going down around 2000) and spending on the "war on terror" (notice how the red line goes up when around the same time period).
2) Federal government receipts are at their lowest point since the 1960s. This is partially due to Bush Era tax cuts (which have expired for the wealthiest Americans thanks to "Fiscal Cliff" negotiations, which is probably what the current small uptick represents) and partially due to exploitation of tax loopholes (and other forms of tax evasion, such as moving profits abroad).
3) Government spending peaked during the American Recovery and Reinvestment Act of 2009, and has since been on the decline since.
Government spending is supposed to rise during a recession, the only reason this is a problem is because the surplus secured under President Clinton was squandered during a period of economic prosperity by President Bush. Instead of pursuing counter-cyclical fiscal policy, (save up during the good times to spend during the bad times) Bush did the opposite. Therefore, we have had to rely on deficit spending instead of spending out of a "rainy day fund". While not exact science, if government receipts had stayed at Clinton-era levels, it appears our deficit would be about half of what it currently is (around 40% of GDP instead of 80%).
In an ideal world, we would be able to pass another stimulus program to jump-start the economy and reduce unemployment. As interest rates remain low, the government could worry about paying back this deficit once the economy is producing at its full potential. The deficit is a manufactured problem, a legacy of "starve-the-beast" fiscal policy championed by the GOP. The problem is that starve-the-beast does not work, you can reduce the amount of resources the government has, but you cannot reduce the programs people rely on to survive (especially not during times of high unemployment). What you get instead is a large government deficit.
A balanced approach to deficit reduction would be reasonable during healthy economic times. In the current economic climate, however, the red line will continue to come down on its own as the economy recovers and less people rely on entitlement programs. The blue line is the one that requires government action.
"He had written a piece suggesting that if only Republicans knew how much Obama has been willing to offer, they might be willing to make a deal. Jonathan Chait set him straight, informing him that no matter what Obama put on the table, Republicans would find a way to say that it's not enough. And sure enough, a Twitter exchange lets Klein watch that process in real time, as a top Republican consultant, confronted with evidence that Obama has already conceded what he said was all that was needed, keeps adding more demands.
The GOP has no interest in getting a deal done if that deal involves raising revenues. This is an absurd position, as government revenue is at its lowest point in decades. A balanced approach to avoiding the "Sequester" is not what the doctor ordered; fiscal stimulus and greater government revenue is the optimal fiscal policy for the American public. But the idea that we have a spending problem, and not a revenue problem, is wrong. A balanced approach is still better than the alternative, but the GOP is refusing to consider even a balanced plan to end the Sequester.
By refusing to consider any deal increasing revenue, the GOP has doubled down on its "Do-Nothing" approach to governance, to the detriment of the American people and American economy. The GOP manufactured this deficit with "starve-the-beast" fiscal policy, now it is manufacturing a need to reform entitlement programs NOW (these are long term issues, while the Sequester and stubbornly high unemployment are immediate problems that are not being addressed).
The GOP is the party of the 1%. and the 1% are not being hurt as badly as the rest of us by the Sequester, so why should the GOP budge if it's constituents are happy? Hopefully in 2014 the GOP receives 1% of the seats in Congress; representation based on those it truly serves. It has become clear that the Democrats need a complete majority in the Federal government if there is any hope of reversing the high unemployment and inequality and low levels of social mobility that have come to define contemporary America.
Categories: Blog Posts, Economic Outlook | Tags: Barack Obama, Democratic Party (United States), do-nothing congress, Fiscal policy, GOP, John Boehner, Republican, Senate, sequester, starve the beast, United States, White House | Permalink.


text:
Комвос () е праисторическо и антично селище край мъгленското село Драгоманци (Апсалос), Гърция, обитавано от неолита до късната римска епоха.

Местността е разположена веднага до крайните югоизточни къщи на Драгоманци. Открити са частично запазени землянки, датиращи от ранния неолит (6500 – 5900/5800 г. пр. Хр.). Важно откритие в Комвос е керамичната пещ от късната бронзова епоха (1600 – 1100 г. пр. Хр.), както и няколко отпадъчни ями. Те очевидно принадлежат към работната зона на разположено на юг селище, което все още не е разкрито. Пещта е кръгла с диаметър от 1,15 m и ямата на нагряващата камера е в земята. Решетката над ямата е запазена непокътната. Това е пещ с усъвършенствана технология, разпространена в средиземноморския регион, но рядкост в Македония. Открити са и участък от път и гробове от късния римски период.

В 2012 година селището е обявено за защитен археологически паметник.

Бележки

Археологически обекти в Гърция
Новокаменна епоха в Гърция
Паметници на културата в дем Мъглен

redpajama_set_name: RedPajamaWikipedia


text:
Pharus glaber  es una especie  gramínea, perenne, siempre verde, arbustiva, que  alcanza hasta 2 metros de altura. Es nativa de América (EE. UU., México, el Caribe, Cuba, América Central y Sudamérica hasta Argentina). Soporta hasta - 5 °C y, en algunos países, se la utiliza como planta medicinal.

Sinonimia
 Pharus angustifolius  (Nees) Döll
 Pharus brasiliensis Raddi
 Pharus glochidiatus J.Presl
 Pharus lancifolius Desv. ex Ham.
 Pharus lappulaceus Aubl.
 Pharus lappulaceus Lam.
 Pharus lappulaceus subsp. glaber (Kunth) Judz. ex Berendsohn & Araniva
 Pharus micranthus Schrad. ex Nees

Referencias

Bibliografía
 USDA, ARS, National Genetic Resources Program. GRIN. National Germplasm Resources Laboratory, Beltsville, Maryland.  (6 ene 2008)

Enlaces externos
 http://zipcodezoo.com/Plants/P/Pharus_lappulaceus.asp

glaber
Plantas ornamentales


Before you pull the RV up and start looking for a place to pitch the kids tent, I should let you know the camping area is long gone – over 40 years gone. If you're reading this, chances are you've been on the Waupaca Chain o'Lakes and heard someone mention the Camp or maybe you've driven by the entrance sign.
Camp Cleghorn is an association of 41 cottage owners located on the south-east corner of Columbia lake. The camp was founded in 1897 as a summer camp with the mission of educating Campers on the virtues of temperance and persuading them to forswear "demon" rum and all other alcoholic beverages.
Of course if you want people to listen to you, it helps to offer something nice in return – or so I'm told by the time-share sales guys. And what's nicer or more picturesque than staying on the Chain o'Lakes, which back in the 1800's was known as the "Killarneys of America." If you're curious about the journey from temperance camp to current association, click About Us for the short version. For the true history buff - click Cleghorn History.
A major part of the camp's heritage is the chapel. The temperance message was both preached and orated at one of the first buildings erected on camp grounds. Summer worship services are offered 9:15 Sunday mornings throughout the summer. Come attend our non-denominational services held in the historic 100-year-old Cleghorn Chapel. Click for here for information and dates. If you are vacationing in the area and don't have a church home, we invite you to join us.
If you have some bits of history to add to the camps collection or you are looking for someone you met at Camp years ago feel free to Contact Us.


text:
from __future__ import print_function
import sys

import pandas as pd
import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, Activation, Flatten, merge
from keras.layers import Embedding
from keras.layers import Convolution1D, MaxPooling1D
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from keras.layers import SimpleRNN, GRU, LSTM
from keras.regularizers import l2

from data_util import load_mr
import cPickle as pickle

import ml_metrics as metrics

"""
   following https://gist.github.com/xccds/8f0e5b0fe4eb6193261d
   to do 1d-CNN on different text classification tasks

"""

def cnn1d_w2vembd(X_train, Y_train, X_test, Y_test, nb_classes,
                  maxlen,
                  nb_filter, filter_length, batch_size, nb_epoch, optm):
    """
    - CNN-1d on 3d sensor which uses word2vec embedding
    - MOT

    :param <X, Y> train and test sets
    :param nb_classes # of classes
    :param maxlen max of n char in a sentence
    :param nb_filter
    :param filter_length
    :param batch_size
    :param nb_epoch
    :param optm
    :return:
    """
    pool_length = maxlen - filter_length + 1

    model = Sequential()

    model.add(Convolution1D(nb_filter=nb_filter,
                            filter_length=filter_length,
                            border_mode="valid",
                            activation="relu", input_shape=(maxlen, 300)))
    model.add(MaxPooling1D(pool_length=pool_length))
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=optm)

    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)

    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              validation_split=0.1, show_accuracy=True, callbacks=[earlystop])

    classes = earlystop.model.predict_classes(X_test, batch_size=batch_size)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))  # accuracy only supports classes
    print('Test accuracy:', acc)
    # return(acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return (kappa)

def qw_kappa_loss(y_true, y_pred):
    return metrics.quadratic_weighted_kappa(y_true, y_pred)

def cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
                   maxlen, vocab_size, embd_dim,
                   nb_filter, filter_length, batch_size, nb_epoch, optm):
    """
    - CNN-1d on text input (represented in int)
    - MOT
    - dropout + L2 softmax

    :param <X, Y> train and test sets
    :param nb_classes # of classes
    :param maxlen max of n char in a sentence
    :param vocab_size
    :param embd_dim
    :param nb_filter
    :param filter_length
    :param batch_size
    :param nb_epoch
    :param optm optimizer options, e.g., adam, rmsprop, etc.
    :return:
    """
    pool_length = maxlen - filter_length + 1

    model = Sequential()
    model.add(Embedding(vocab_size, embd_dim, input_length=maxlen))
    model.add(Dropout(0.25))

    model.add(Convolution1D(nb_filter=nb_filter,
                            filter_length=filter_length,
                            border_mode="valid",
                            activation="relu"))
    model.add(MaxPooling1D(pool_length=pool_length))

    model.add(Flatten())
    model.add(Dropout(0.5))

    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=optm)

    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)

    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              validation_split=0.1, show_accuracy=True, callbacks=[earlystop])

    classes = earlystop.model.predict_classes(X_test, batch_size=batch_size)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test accuracy:', acc)
    # return(acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return (kappa)

def cnn_var_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
                     maxlen, vocab_size, embd_size,
                     nb_filter, batch_size, nb_epoches, optm):
    ngram_filters = [2, 5, 8]

    input = Input(shape=(maxlen,), name='input', dtype='int32')
    embedded = Embedding(input_dim=vocab_size, output_dim=embd_size, input_length=maxlen)(input)

    convs = [None, None, None]
    # three CNNs
    for i, n_gram in enumerate(ngram_filters):
        pool_length = maxlen - n_gram + 1
        convs[i] = Convolution1D(nb_filter=nb_filter,
                                 filter_length=n_gram,
                                 border_mode="valid",
                                 activation="relu")(embedded)
        convs[i] = MaxPooling1D(pool_length=pool_length)(convs[i])
        convs[i] = Flatten()(convs[i])

    merged = merge([convs[0], convs[1], convs[2]], mode='concat', concat_axis=1)
    merged = Dropout(0.5)(merged)
    output = Dense(nb_classes, activation='softmax', name='output')(merged)

    model = Model(input, output)
    model.compile(optm, loss={'output': 'categorical_crossentropy'})
    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
    model.fit(X_train, Y_train,
              nb_epoch=nb_epoches, batch_size=batch_size,
              validation_split=0.1, callbacks=[earlystop])

    probs = earlystop.model.predict(X_test, batch_size=batch_size)
    classes = np_utils.categorical_probas_to_classes(probs)

    acc = np_utils.accuracy(classes,
                            np_utils.categorical_probas_to_classes(Y_test))
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes,
                                             np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return acc

def cnn_var_selfembd_other(X_train, Y_train, X_test, Y_test, nb_classes,
                           Other_train, Other_test, k,
                           maxlen, vocab_size, embd_size,
                           nb_filter, batch_size, nb_epoches, optm):
    """
    cnn1d using varying filter lengths
    note need using Graph
    :param X_train:
    :param Y_Train:
    :param X_test:
    :param Y_test:
    :param nb_classes:
    :param maxlen:
    :param vocab_size:
    :param embd_size:
    :param batch_size:
    :param nb_epoches:
    :param optm:
    :return:
    """
    ngram_filters = [2, 5, 8]
    nd_convs = ['conv_' + str(n) for n in ngram_filters]
    nd_pools = ['pool_' + str(n) for n in ngram_filters]
    nd_flats = ['flat_' + str(n) for n in ngram_filters]

    model = Graph()
    model.add_input(name='input', input_shape=(maxlen,), dtype=int)

    model.add_node(Embedding(vocab_size, embd_size, input_length=maxlen),
                   name='embedding', input='input')
    # three CNNs
    for i, n_gram in enumerate(ngram_filters):
        pool_length = maxlen - n_gram + 1
        model.add_node(Convolution1D(nb_filter=nb_filter,
                                     filter_length=n_gram,
                                     border_mode="valid",
                                     activation="relu"),
                       name=nd_convs[i], input='embedding')
        model.add_node(MaxPooling1D(pool_length=pool_length),
                       name=nd_pools[i], input=nd_convs[i])
        model.add_node(Flatten(), name=nd_flats[i], input=nd_pools[i])
    model.add_node(Dropout(0.5), name='dropout', inputs=nd_flats, merge_mode='concat')

    # CNN for other
    pos_f_len = 10
    pos_pool_len = maxlen - pos_f_len + 1
    model.add_input(name='other_input', input_shape=(maxlen, k), dtype='float')
    model.add_node(Convolution1D(nb_filter=nb_filter,
                                 filter_length=pos_f_len,
                                 border_mode='valid',
                                 activation='relu',
                                 input_shape=(maxlen, k)),
                   name='poscnn', input='other_input')
    model.add_node(MaxPooling1D(pool_length=pos_pool_len),
                   name='pospool', input='poscnn')
    model.add_node(Flatten(), name='posflat', input='pospool')
    model.add_node(Dropout(0.5), name='posdropout', input='posflat')

    model.add_node(Dense(nb_classes, activation='softmax'), name='softmax',
                   inputs=['dropout', 'posdropout'],
                   merge_mode='concat')

    model.add_output(name='output', input='softmax')
    model.compile(optm, loss={'output': 'categorical_crossentropy'})  # note Graph()'s diff syntax

    # early stopping
    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
    model.fit({'input': X_train, 'other_input': Other_train, 'output': Y_train},
              nb_epoch=nb_epoches, batch_size=batch_size,
              validation_split=0.1, callbacks=[earlystop])
    # Graph doesn't have several arg/func existing in Sequential()
    # - fit no show-accuracy
    # - no predict_classes
    classes = model.predict({'input': X_test, 'other_input': Other_test},
                            batch_size=batch_size)['output'].argmax(axis=1)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))  # accuracy only supports classes
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    # return kappa
    return acc

def cnn_other(Y_train, Y_test, nb_classes,
              Other_train, Other_test, k,
              maxlen,
              nb_filter, filter_size, batch_size, nb_epoches, optm):
    """
    cnn1d using varying filter lengths
    note need using Graph
    :param Y_Train:
    :param Y_test:
    :param nb_classes:
    :param maxlen:
    :param vocab_size:
    :param embd_size:
    :param batch_size:
    :param nb_epoches:
    :param optm:
    :return:
    """
    model = Graph()

    # CNN for other
    pos_pool_len = maxlen / 2 - filter_size + 1
    model.add_input(name='other_input', input_shape=(maxlen, k), dtype='float')

    model.add_node(Convolution1D(nb_filter=nb_filter,
                                 filter_length=filter_size,
                                 border_mode='valid',
                                 activation='relu',
                                 input_shape=(maxlen, k)),
                   name='poscnn', input='other_input')
    model.add_node(MaxPooling1D(pool_length=5),
                   name='pospool', input='poscnn')

    # 2nd CNN
    model.add_node(Convolution1D(nb_filter=nb_filter * 2,
                                 filter_length=filter_size,
                                 border_mode='valid',
                                 activation='relu'),
                   name='cnn2', input='pospool')
    model.add_node(MaxPooling1D(pool_length=10),
                   name='cnn2_pool', input='cnn2')

    model.add_node(Flatten(), name='posflat', input='cnn2_pool')
    model.add_node(Dropout(0.5), name='posdropout', input='posflat')

    model.add_node(Dense(nb_classes, activation='softmax'), name='softmax',
                   input='posdropout')
    model.add_output(name='output', input='softmax')
    model.compile(optm, loss={'output': 'categorical_crossentropy'})  # note Graph()'s diff syntax

    # early stopping
    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
    model.fit({'other_input': Other_train, 'output': Y_train},
              nb_epoch=nb_epoches, batch_size=batch_size,
              validation_split=0.1, callbacks=[earlystop])
    # Graph doesn't have several arg/func existing in Sequential()
    # - fit no show-accuracy
    # - no predict_classes
    classes = model.predict({'other_input': Other_test},
                            batch_size=batch_size)['output'].argmax(axis=1)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))  # accuracy only supports classes
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return acc

def cnn_var_w2vembd(X_train, Y_train, X_test, Y_test, nb_classes,
                    maxlen,
                    nb_filter, batch_size, nb_epoches, optm):
    ngram_filters = [2, 5, 8]

    input = Input(shape=(maxlen, 300,), name='input', dtype='float32')

    convs = [None, None, None]
    # three CNNs
    for i, n_gram in enumerate(ngram_filters):
        pool_length = maxlen - n_gram + 1
        convs[i] = Convolution1D(nb_filter=nb_filter,
                                 filter_length=n_gram,
                                 border_mode="valid",
                                 activation="relu")(input)
        convs[i] = MaxPooling1D(pool_length=pool_length)(convs[i])
        convs[i] = Flatten()(convs[i])

    merged = merge([convs[0], convs[1], convs[2]], mode='concat', concat_axis=1)
    merged = Dropout(0.5)(merged)
    output = Dense(nb_classes, activation='softmax', name='output')(merged)

    model = Model(input, output)
    model.compile(optm, loss={'output': 'categorical_crossentropy'})
    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
    model.fit(X_train, Y_train,
              nb_epoch=nb_epoches, batch_size=batch_size,
              validation_split=0.1, callbacks=[earlystop])

    probs = earlystop.model.predict(X_test, batch_size=batch_size)
    classes = np_utils.categorical_probas_to_classes(probs)

    acc = np_utils.accuracy(classes,
                            np_utils.categorical_probas_to_classes(Y_test))
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes,
                                             np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return acc

def cnn_multi_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
                       maxlen, vocab_size, embd_size,
                       pos_train, pos_test, pos_embd_dim,
                       dp_train, dp_test, dp_embd_dim,
                       nb_filter, batch_size, nb_epoches, optm):
    """
    cnn1d using multi-inputs, i.e., word, POS, DP
    word using varying filter lengths
    note need using Graph
    :param X_train:
    :param Y_Train:
    :param X_test:
    :param Y_test:
    :param nb_classes:
    :param maxlen:
    :param vocab_size:
    :param embd_size:
    :param batch_size:
    :param nb_epoches:
    :param optm:
    :return:
    """
    ngram_filters = [2, 5, 8]
    nd_convs = ['conv_' + str(n) for n in ngram_filters]
    nd_pools = ['pool_' + str(n) for n in ngram_filters]
    nd_flats = ['flat_' + str(n) for n in ngram_filters]

    model = Graph()
    model.add_input(name='input', input_shape=(maxlen,), dtype=int)

    model.add_node(Embedding(vocab_size, embd_size, input_length=maxlen),
                   name='embedding', input='input')
    # three CNNs
    for i, n_gram in enumerate(ngram_filters):
        pool_length = maxlen - n_gram + 1
        model.add_node(Convolution1D(nb_filter=nb_filter,
                                     filter_length=n_gram,
                                     border_mode="valid",
                                     activation="relu"),
                       name=nd_convs[i], input='embedding')
        model.add_node(MaxPooling1D(pool_length=pool_length),
                       name=nd_pools[i], input=nd_convs[i])
        model.add_node(Flatten(), name=nd_flats[i], input=nd_pools[i])
    model.add_node(Dropout(0.5), name='dropout', inputs=nd_flats, merge_mode='concat')

    # POS CNN
    nb_pos = 15
    pos_f_len = 3
    pos_pool_len = maxlen - pos_f_len + 1
    model.add_input(name='posinput', input_shape=(maxlen,), dtype=int)
    model.add_node(Embedding(nb_pos, pos_embd_dim, input_length=maxlen),
                   name='posembd', input='posinput')
    model.add_node(Convolution1D(nb_filter=nb_filter,
                                 filter_length=pos_f_len,
                                 border_mode='valid',
                                 activation='relu'),
                   name='poscnn', input='posembd')
    model.add_node(MaxPooling1D(pool_length=pos_pool_len),
                   name='pospool', input='poscnn')
    model.add_node(Flatten(), name='posflat', input='pospool')
    model.add_node(Dropout(0.5), name='posdropout', input='posflat')

    # DP CNN
    nb_dp = vocab_size
    dp_f_len = 3
    dp_pool_len = maxlen - dp_f_len + 1
    model.add_input(name='dpinput', input_shape=(maxlen,), dtype=int)
    model.add_node(Embedding(nb_dp, dp_embd_dim, input_length=maxlen),
                   name='dpembd', input='dpinput')
    model.add_node(Convolution1D(nb_filter=nb_filter,
                                 filter_length=dp_f_len,
                                 border_mode='valid',
                                 activation='relu'),
                   name='dpcnn', input='dpembd')
    model.add_node(MaxPooling1D(pool_length=dp_pool_len),
                   name='dppool', input='dpcnn')
    model.add_node(Flatten(), name='dpflat', input='dppool')
    model.add_node(Dropout(0.5), name='dpdropout', input='dpflat')

    model.add_node(Dense(nb_classes, activation='softmax'), name='softmax',
                   inputs=['dropout', 'posdropout', 'dpdropout'],
                   merge_mode='concat')

    model.add_output(name='output', input='softmax')
    model.compile(optm, loss={'output': 'categorical_crossentropy'})  # note Graph()'s diff syntax

    # early stopping
    earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1)
    model.fit({'input': X_train, 'posinput': pos_train, 'dpinput': dp_train,
               'output': Y_train},
              nb_epoch=nb_epoches, batch_size=batch_size,
              validation_split=0.1, callbacks=[earlystop])
    # Graph doesn't have several arg/func existing in Sequential()
    # - fit no show-accuracy
    # - no predict_classes
    classes = model.predict({'input': X_test, 'posinput': pos_test, 'dpinput': dp_test}
                            , batch_size=batch_size)['output'].argmax(axis=1)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))  # accuracy only supports classes
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return kappa
    # return acc

def lstm_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
                  maxlen, vocab_size, embd_dim,
                  batch_size, nb_epoch, optm):
    """
    - LSTM  on text input (represented in int)
    - fully-connected model

    :param <X, Y> train and test sets
    :param nb_classes # of classes
    :param maxlen max of n char in a sentence
    :param vocab_size
    :param embd_dim
    :param batch_size
    :param nb_epoch
    :param optm optimizer options, e.g., adam, rmsprop, etc.
    :return:
    """

    model = Sequential()
    model.add(Embedding(vocab_size, embd_dim, input_length=maxlen))
    model.add(Dropout(0.25))

    # model.add(LSTM(100, return_sequences=True))
    model.add(LSTM(50))

    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=optm)

    earlystop = EarlyStopping(monitor='val_loss', patience=2, verbose=1)

    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
              validation_split=0.1, show_accuracy=True, callbacks=[earlystop])

    classes = earlystop.model.predict_classes(X_test, batch_size=batch_size)
    acc = np_utils.accuracy(classes, np_utils.categorical_probas_to_classes(Y_test))  # accuracy only supports classes
    print('Test accuracy:', acc)
    kappa = metrics.quadratic_weighted_kappa(classes, np_utils.categorical_probas_to_classes(Y_test))
    print('Test Kappa:', kappa)
    return (kappa)

def test_mr_embd():
    nb_words = 18000
    maxlen = 64
    embd_dim = 100
    X_train, Y_train, X_test, Y_test, nb_classes = load_mr(nb_words, maxlen, 'self')
    cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
                   maxlen, nb_words, embd_dim,
                   100, 5, 32, 20, 'rmsprop')

def test_mr_w2v():
    maxlen = 64
    X_train, Y_train, X_test, Y_test, nb_classes = load_mr(0, maxlen, 'w2v')
    cnn1d_w2vembd(X_train, Y_train, X_test, Y_test, nb_classes,
                  maxlen,
                  100, 5, 32, 20, 'rmsprop')

if __name__ == "__main__":
    np.random.seed(1337)  # for reproducibility

    print('=' * 50)
    print("mr selfembd CNN")
    test_mr_embd()

redpajama_set_name: RedPajamaGithub


text:
require 'test_helper'

class GarbageBagTest < ActiveSupport::TestCase

  def setup
    @user = User.create(name: 'John', email: 'johndoe@example.com',
                        password: 'password', password_confirmation: 'password')
    @garbage_bag = GarbageBag.new(name: 'Green', status: 'ok',
                                  user_id: @user.id)
  end

  test 'valid garbage bag' do
    assert @garbage_bag.valid?
  end

  test 'name should be present' do
    @garbage_bag.name = ''
    assert_not @garbage_bag.valid?
  end

  test 'status should be present' do
    @garbage_bag.status = ''
    assert_not @garbage_bag.valid?
  end

  test 'user id should be of an existing user' do
    @garbage_bag.user_id = -1
    assert_not @garbage_bag.valid?
  end

  test 'status should be either ok or full' do
    @garbage_bag.status = 'I think it is full'
    assert_not @garbage_bag.valid?
  end

  test 'name should not be too long' do
    @garbage_bag.name = 'x' * 51
    assert_not @garbage_bag.valid?
  end

  test 'name should be unique' do
    duplicate_garbage_bag = @garbage_bag.dup
    @garbage_bag.save
    assert_not duplicate_garbage_bag.valid?
  end
end


text:
Q: Salesforce multiselect picklist value as Journey builder entry event is not working as expected I have a journey builder event which fires when there is a update in the lead object. The entry criteria is based on the values of multiselect picklist. The multiselect picklist values are A, B and C. Based on the value changes a new email must be triggered for each value. For example if some one adds A as new value then email related to A should be fired.
I have used the entry condition like field contains A or contains B or Contains C.
However the rule fires only when i completely remove the values from the field and add them again. Updating along with existing values not triggering email.

A: These triggers are only invoked if changed from a non-matching value to a matching value. So if your field has the possible values A, B, C and D and your entry criteria is field = A OR field = B OR field = C the journey is only triggered if the original value is D and is changed to either A, B or C. If you change from A to B for example, your journey won't be triggered.

When a record is updated, the rule runs only if the record meets rule criteria after the update. The rule does not run when a record that previously met rule criteria is updated.(Source: Journey Builder Documentation: Salesforce Data Event)


We recently published an article about Solo 401(k) contribution rules and deadlines for S-Corporation Owners. That article can be found here. But what about a situation where the business that adopts the solo 401(k) is not taxed as an s-corporation? What if the business is taxed as a sole proprietorship (e.g., a single-member LLC) or a partnership, as is the case with most LLC's? Does that change anything? According to the IRS, the answer is yes.
DO SOLO 401(k) CONTRIBUTION LIMITS DIFFER IF THE BUSINESS IS TAXED AS A SOLE PROPRIETORSHIP OR PARTNERSHIP?
In our prior article written by KKOS Partner Mat Sorensen there was an illustration of a business owner named Sally who owns an s-corporation and that article outlined what solo 401 contributions would look like for her based on net income of $120,000. For purposes of this article, we'll call her "S-Corp Sally". In order to highlight the differences in solo 401(k) contributions between an s-corporation versus a partnership or sole proprietorship, here's an example of what solo 401(k) contributions would look like for a partnership or sole proprietorship using the same net income figures as Mat's example with the s-corporation.
Susan, age 46, is a technology consultant who owns an LLC that has adopted a solo 401(k) in December 2015. She is the only owner/member of the LLC and since she has not elected to have her LLC taxed as an s-corporation, by default her business is taxed as a sole proprietorship. We'll call her "Sole Prop Susan". She has $120,000 in net income for the year, just like S-Corp Sally. All of Sole Prop Susan's net income will be reported on Schedule C of her personal tax form 1040, i.e., no wage income / no W-2. If she decided to take the maximum allowable contribution based on her net income, it would look like this.
Employee Contributions (Elective Deferral) – The 2015 maximum employee contribution for Sole Prop Susan is $18,000. Since she has net income in excess of $18,000, she can contribute the maximum employee contribution of $18,000. This employee contribution amount will be combined with her employer contribution and reported on line 28 of her tax form 1040.
Employer Contributions – Calculating the maximum employer contribution for Sole Prop Susan is not as simple as with S-Corp Sally. S-Corp Sally's employer contribution was computed by taking 25% of her wage/salary to arrive at the amount of her employer contribution. With Sole Prop Susan, there is a more involved computation to arrive at her employer contribution. A step-by-step calculation can be found in IRS Publication 560. In short, the IRS reduces net income by the deductible portion of her self-employment tax and also reduces the maximum percentage from 25% to 20%. There are about 20 additional steps or calculations that must be performed according to the worksheet provided in Publication 560 but basically, if Sole Prop Susan elected to make an employee contribution of $18,000 then her employer contribution would be limited to $22,200 because her overall contribution from both employer and employee contributions would be $40,200.
In the end, Sole Prop Susan would have contributed and saved $40,200 for retirement and reported it on line 28 of her tax form 1040. This is an above the line tax deduction. Not bad. If she were in a 20% tax bracket and a 5% bracket for state taxes that saves her approximately $10,000 in federal and state taxes.
Note: If Susan were in an entity taxed as a partnership rather than a sole proprietorship, and the net income allocable to her from the partnership on Schedule K-1 was $120,000 then the contribution amounts above would remain the same and she would still end up with a maximum contribution limit of $40,200.
WHY THE LARGER SOLO 401(K) CONTRIBUTION LIMIT FOR SOLE PROP SUSAN?
You may notice that Sole Prop Susan was able to make a larger solo 401(k) contribution than S-Corp Sally even though their net income was exactly the same. S-Corp Sally's maximum contribution amount was $30,500. The difference was that S-Corp Sally took a wage/salary that was less than her net income, e.g. $70,000. Sole Prop Susan had to pay self-employment taxes on all $120,000 whereas S-Corp Sally did not. As an s-corporation owner, there are competing interests to pay the least amount self-employment tax as possible with making the maximum 401(k) contribution – by taking the smallest salary/wage possible (so long as it is reasonable), this reduces your self-employment tax liability but it also limits your solo 401(k) contribution, as was the case with S-Corp Sally. The IRS gets you coming or going.
IS ANYTHING ELSE DIFFERENT BETWEEN AN S-CORP SOLO 401(k) AND A PARTNERSHIP/SOLE PROP 401(K)?
Everything else about the solo 401(k) that is adopted by an s-corporation is the same as a solo 401(k) that is adopted by a sole proprietorship/partnership. First, the deadline to adopt the solo 401(k) and have contributions count towards 2015 is still December 31, 2015 regardless of how your business is taxed. Second, the total overall annual contribution limit is $53,000 in 2015 or $59,000 if you are age 50 or older. Finally, both employee and employer contributions can be made up to the company's tax return deadline INCLUDING extensions. The only difference is that with a "sole proprietorship/partnership Solo 401(k)", you don't have the W-2 deadline.
In sum, when it comes to solo 401(k) contributions, all else being equal, how your company is taxed may affect the maximum 401(k) contribution you can make. Either way, the solo 401(k) contribution limits are very robust, especially compared to an IRA. Contact our office to find out more about setting up a solo 401(k) plan for your business.
By Kevin Kennedy, Associate Attorney at the Phoenix, Arizona office of Kyler Kohler Ostermiller, and Sorensen, LLP ("KKOS Lawyers").


text:
<?php
namespace Admin\Controller;

use Zend\Db\TableGateway\TableGateway;
use Zend\Mvc\Controller\AbstractActionController;
use Zend\View\Model\ViewModel;
use Admin\Model\Admin;
use Admin\Form\AdminForm;

class AdminController extends AbstractActionController
{

    private $adminTable;
    //CRUD
    //R- Retrieve
    public function indexAction()
    {
        return new ViewModel(array(
            'admins' => $this->getAdminTable()->fetchAll(),
        ));
    }

    //C - Create
    public function addAction()
    {
        $form = new AdminForm();
        $form->get('submit')->setValue('Add');

        $request = $this->getRequest();
        if ($request->isPost()) {
            $admin = new Admin();
            $form->setInputFilter($admin->getInputFilter());
            $form->setData($request->getPost());

            if ($form->isValid()) {
                $admin->exchangeArray($form->getData());
                $this->getAdminTable()->saveAdmin($admin);

                // Redirect to list of albums
                return $this->redirect()->toRoute('admin');
            }
        }
        return array('form' => $form);
    }

    //U - Update
    public function editAction()
    {
        $id = (int) $this->params()->fromRoute('id', 0);
        if (!$id) {
            return $this->redirect()->toRoute('admin', array('controller'=>'admin',
                'action' => 'edit'
            ));
        }

        // Get the Album with the specified id.  An exception is thrown
        // if it cannot be found, in which case go to the index page.
        try {
            $admin = $this->getAdminTable()->getAdmin($id);
        }
        catch (\Exception $ex) {
            return $this->redirect()->toRoute('admin', array(
                'action' => 'index'
            ));
        }

        $form  = new AdminForm();

        $request = $this->getRequest();
        if ($request->isPost()) {
            $form->setInputFilter($admin->getInputFilter());
            $form->setData($request->getPost());

            if ($form->isValid()) {
                $data = $form->getData();
                $this->getUsersTable()->update($data, array('id' =>$this->id));

                // Redirect to list of albums
                return $this->redirect()->toRoute('admin');
            }
        }

        return array(
            'id' => $id,
            'form' => $form,
        );
    }

    //D - Delete
    public function deleteAction()
    {
        $id = (int) $this->params()->fromRoute('id', 0);
        if (!$id) {
            return $this->redirect()->toRoute('admin');
        }

        $request = $this->getRequest();
        if ($request->isPost()) {
            $del = $request->getPost('del', 'No');

            if ($del == 'Yes') {
                $id = (int) $request->getPost('id');
                $this->getAdminTable()->deleteAdmin($id);
            }

            // Redirect to list of albums
            return $this->redirect()->toRoute('admin');
        }

        return array(
            'id'    => $id,
            'admin' => $this->getAdminTable()->getAdmin($id)
        );
    }

    public function getAdminTable()
    {
        if (!$this->adminTable) {
            $sm = $this->getServiceLocator();
            $this->adminTable = $sm->get('Admin\Model\AdminTable');
        }
        return $this->adminTable;
    }

}


text:
Q: How to fix SQL for AWS Athena I'm moving SQL from Redshift to AWS Athena, but I'm having trouble because the SQL is giving me errors.
SELECT abc_name AS bca_name,
CASE
  WHEN name = 'aaa' THEN 'AAC'
  WHEN name = 'bbb' THEN 'BBA'
  WHEN name = 'ccc' THEN 'CCB'
END AS abc
FROM master_table
WHERE abc IS NOT NULL;

This SQL gave me an error: it says that there is no abc column because abc in the where clause is read earlier than in the select clause.
How can I get this to work?

A: This is a common SQL problem.  You can't refer to an alias defined in the SELECT clause in the WHERE clause of the same query.  But you don't even need to use the alias to get your desired logic, just use a WHERE IN (...):
SELECT abc_name AS bca_name,
       CASE name WHEN 'aaa' THEN 'AAC'
                 WHEN 'bbb' THEN 'BBA'
                 WHEN 'ccc' THEN 'CCB' END AS abc
FROM master_table
WHERE name IN ('aaa', 'bbb', 'ccc');

If you really did need to refer to the alias, you would have to either use a subquery or just repeat the CASE expression.  For the former option:
SELECT bca_name, abc
FROM
(
    SELECT abc_name AS bca_name,
           CASE name WHEN 'aaa' THEN 'AAC'
                     WHEN 'bbb' THEN 'BBA'
                     WHEN 'ccc' THEN 'CCB' END AS abc
    FROM master_table
) t
WHERE abc IS NOT NULL;


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