BBC Scotland have been running various "things" under the banner "Scotland Writes", the latest being "Write Here, Write Now" encouraging people to write a sitcom set in Scotland. Why not, I thought, having failed miserably to enter their compeition looking for new drama writers. I'm a funny guy, I can make people laugh (in more ways than one). The problem is that I'm picky about which sitcoms I watch, consider most of them to be unfunny, and aimed at people older then me - the "Terry and June Syndrome" I call it, or "Keeping Up Appearancitis". The sitcoms I like are things like "The Mighty Boosh", or "The IT Crowd", or going further back a few years, "Black Books". The only recent sitcom I laughed at was "Miranda" and I've got a copy of the first episode of that series right in front of me to give me a steer in how to lay out a script.


"Yucca nana is a beautiful, dwarf yucca that grows in eastern Utah and western Colorado on the sandstone substrates and in pinon-juniper forests of the Colorado Plateau. Mature rosettes are not more than around a foot in diameter (30 cm) and many are smaller than that. The common name is dwarf yucca, or doll-house yucca. Often single but also forming small clumps, Yucca nana is one of the loveliest of the yucca clan and it's surely among the smallest. (The specific epithet means "small" or "miniature".) There is some dispute about whether this plant can be justified to be a separate species, or whether it's a subspecies of the slightly larger Yucca harr... read moreimaniae. Either way, there's no doubt that this compact and cold-tolerant plant would be a wonderful addition to various rock gardens and succulent collections for enthusiasts. Plus the filamentous leaves are extra decorative in pots or confined spaces!"
This plant survived the winter of 2013-2014 here in Montreal, QC unscathed. Very hardy, has been in the ground 2 winters but has not shown any sign of pups yet. It's growing near a south facing wall.
Purchased these from high country gardens a couple years ago . They like to spread and form small sharp porcupine like colonies . I purchased four now I have about a dozen . I am an organic gardener so I pull weeds manually and this is just sheer painful joy to weed around lol . The flowers seem out of scale with the plants also . The flowers look like a full size yucca but the plant itself is only around 10 or twelve inches high . Does not bloom every year but when it does wow !
I just bought a house from an amazing gardener in her 80's. She planted 6 Yucca harrimaniae on the property as small plants. They are now HUGE- about 2 1/2 feet tall spikes and I'm told the flowers will stand taller. I dug one up to give to a friend to find it was 4 seperate plants with an extensive bulbous root system. I now nothing about these plants. I do know they thrive here in northern Ohio and that is amazing. All I read is that it is a midwestern plant. I have been told they are valuable, which is why I dug one up to give to a friend. I plan on putting several rock gardens in the yard, so I will transplant some to those. Question: How do I move these without damaging the bulbs? It seems, as big as these are, I couldnt dig far enough from the plant without cutting a bulb.... read more Thanks for the info, in advance.
This yucca has handled the wet, cold Arkansas winters and steamy summers very well.
this is a common stemmless Yucca of the Rocky Mountain states that was, along with Yucca glauca, one of my least favorite plants in my youth- got stabbed by them all the time while hiking about the New Mexican mesas and canyons. Bayonet is a good description of this plant, and though I now find it somewhat ornamental, its pugnacity keeps me from giving it a 'thumbs up'.


How do you change a blank into a Mc Trudie stock? The bluish colored stock in the photo is a Mc Trudie pattern which is a Steve McGee pattern with a smaller pistol grip and the stock is lighter (I've done a lot of wood rasping and sanding to modify to fit the Mc Trudie pattern ). This stock will be used for F TR and I'm doing a lot of lightning to the stock to make weight so that I can use a heavy barrel with the Stolle Panda action.


Hearing loss can come with old age but there are some preventative measures you can take.
If you have a loved one that is suffering from hearing loss, it can be difficult to know when they may need help. However, if you find telephone conversations difficult because they can't hear you, or you go for a visit and can't hear over the television, they definitely need some help. If you think your loved one is in the beginning stages of hearing loss, here are some tips for preserving what hearing they have left.
We all know that repeated loud noises are a common source for hearing loss, and damage becomes more apparent with age. Music that's too loud, lawn mowers, leaf blowers, even motorcycles or other loud engines can make that damage even worse. If they are around a lot of loud noises, keep ear plugs to reduce the decibels to a comfortable level or even noise-reducing headphones they can wear over their ears can help.
Sometimes, there isn't any damage at all! Earwax can build up and cause clogs in anyone's ears, but especially the elderly. Never put anything in their ear canals, especially cotton swabs, as they aren't effective in cleaning earwax out of ears. Take them to their doctor and they can clean their ears out. The doctor can also let you both know if there is damage to their ear canal or ear drum.
The elderly, just like children, are susceptible to ear infections and they can cause hearing loss. To help prevent ear infections, make sure everyone, like you, or a caretaker, wash their hands regularly. Sometimes ear infections piggyback with other illnesses, like a cold, so stay alert for ear pain or discharge if your loved one has a cold.
Sometimes, medicines can cause hearing loss. If you think it may be a side effect of a medication, talk to their doctor about switching medicines or lowering the dose to prevent permanent hearing loss.
This entry was posted on Friday, January 26th, 2018 at 12:21 pm. Both comments and pings are currently closed.


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Panthers vs. Rangers Odds, Picks, Predictions
Panthers vs. Rangers Odds, Picks, Predictions: Look to the Over at MSG
Scott Cullen
January 23, 2023 1:15 PM EST • 3 min 17 sec read. Twitter link
NEW YORK, NEW YORK - MARCH 25: Chris Kreider #20 of the New York Rangers celebrates his second period goal against the Pittsburgh Penguins and is joined by Mika Zibanejad #93 (L) at Madison Square Garden on March 25, 2022 in New York City. Photo by BRUCE BENNETT / GETTY IMAGES NORTH AMERICA / Getty Images via AFP.
The Florida Panthers hope to gain ground in the Eastern Conference playoff race when they pull into Madison Square Garden to face the New York Rangers on Monday. Read on for our best Panthers vs. Rangers pick based on top odds.
The Panthers will need to improve their road performance if they are going to get to the postseason. Florida ranks 25th with a .444 points percentage away from FLA Live Arena.
Nevertheless, a 4-0-1 record in the past five games has moved Florida to within three points of the second Wild Card spot in the Eastern Conference.
The Rangers are more secure in their positioning, sitting in third place in the Metropolitan Division. Oddly enough, this comes despite the Blueshirts not exactly crushing it at Madison Square Garden. Their 11-9-4 home record ranks 18th in terms of points percentage.
Here is our best Panthers vs. Rangers NHL pick (odds via FanDuel Sportsbook; pick confidence based on a 1-to-5-star scale).
Check out our NHL best bets.
Panthers vs. Rangers Game Info
Date: Monday, Jan. 23, 7 p.m. ET
TV: ESPN+, TVAS, MSG, BSFLX
Location: Madison Square Garden, New York, NY
Panthers vs. Rangers Odds
Check out our complete list of NHL odds!
Panthers vs. Rangers Odds Analysis
The FanDuel moneyline opened with the Rangers as favorites, at -125, compared to Florida at +104. Now that the Panthers have confirmed that third-stringer Alex Lyon will start in net and 67% of the early consensus moneyline wagers are on the Rangers, the home side is now -152 while the Panthers are priced at +125.
The total opened at 6.5 with a decided lean towards the Over, priced at -124, while the Under was +102. With 55% of the action on the Under, the Over is now at -118, with the Under at -104 at FanDuel.
Panthers vs. Rangers Pick
Panthers-Rangers Over 6.5 (-118) ⭐⭐⭐
With Sergei Bobrovsky and Spencer Knight injured, the Panthers will have Lyon starting in net for a second straight game.
Lyon is a 30-year-old journeyman who has started 18 NHL games across the past six seasons, posting a .896 save percentage in 26 total appearances. He is a good AHL goaltender who has a .911 save percentage in 48 games over the past two seasons, and he stopped 29 of 32 shots against Minnesota in Florida's 5-3 win on Saturday.
While the market appears to consider Lyon a downgrade in goal for the Panthers, it's not like goaltending has been a strength for Florida, which ranks 21st in the league with a .894 team save percentage.
Judging by the morning skate, it looks like the Rangers are going with backup Jaroslav Halak in net for this game. Halak has had three strong starts since the holiday break, stopping 73 of 78 shots, but his save percentage remains a pedestrian .901. He has allowed a dozen goals on 77 shots in three home starts.
The Panthers lead the league with 34.4 shots for per 60 minutes during five-on-five play, while Rangers rank 14th. Considering the goaltending matchup, those shots should turn into goals and push this game Over.
Check out our Stanley Cup odds.
Panthers-Rangers Best Odds
FanDuel (-118)
DraftKings FanDuel BetMGM Caesars PointsBet
Over 6.5 Over 6.5 Over 6.5 Over 6.5 Over 6.5
-120 -120 -120 -120 -121
Although the total is 6.5 across the top-rated sportsbooks, it has settled into nearly unanimous agreement at -120 for the Over. PointsBet is a tiny outlier at -121, but the rest of the top-rated sportsbooks have landed on the same price.
The goaltending matchup appears to be the biggest driver of this number, but unless a star is suddenly out of the lineup, there should not be anything else that has a dramatic effect on the total.
Panthers-Rangers pick made 1/23/2023 at 11:45 a.m. ET.
Here are our top-rated sportsbooks:
PointsBet: Earn Five Bet Credits Up to $100 Each | Read our PointsBet Review
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Scott Cullen Twitter link
Scott Cullen is SportsbookReview.com's Sports Betting Analyst and is one of the most well-known sports betting writers on the web. He wrote about fantasy sports for TSN for many years, and he has also written for The Athletic, NHL.com, The Hockey News, Monkey Knife Fight, and McKeen's Hockey. Born in Kitchener, Ontario, Canada, Cullen was an Economics major at Wilfrid Laurier University in Waterloo, Ontario and played for the Laurier Golden Hawks hockey team.

redpajama_set_name: RedPajamaCommonCrawl


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Kangaroos skipper says England will be tough to crack
Cameron Smith of Australia and Sean O'Loughlin of England with the trophy. Bradley Kanaris
by Gilbert Gardiner, Herald Sun
AUSTRALIAN captain Cameron Smith has declared England a "big hurdle" in the Kangaroos bid to be the best in the world.
Smith, 34, who will equal Clive Churchill on 35 consecutive Tests tonight, said England poses a significant threat in the Rugby League World Cup opener at AAMI Park.
The loser will be forced to play a semi-final in New Zealand, most likely against the powerhouse Kiwis.
"It's always something that's spoken about leading into big games - who is favourite, who is underdogs," Smith said.
"We want to be the best we can be. We want to build on what we've achieved the last 18 months."
The Kangaroos are undefeated in seven Tests under coach Mal Meninga, including a big win over England in the Four Nations last year.
"We're not shy in saying we want to be No.1 in the world and win every game we play," Smith said.
"That's what we are about. It's a pretty big hurdle for us against England.
"I don't know if they've been given enough credit for this match.
"We know we have to play extremely well (tonight) to win.
"If we don't win that really puts a lot of pressure on us to then go on and win the remaining matches and travel out of the country to play a semi-final."
The Kangaroos will field three debutants in the crunch game, including Melbourne Storm premiership prop Jordan Mclean, Dane Gagai and Wade Graham.
Significantly, it's also the last time Smith runs out alongside Billy Slater and Cooper Cronk in Melbourne.
Cronk is expected to sign with Sydney Roosters for 2018.
"I think we've got a bit of a handle on it (emotion) now," Smith said.
"It was quite an emotional week leading into the grand final."
The "Big Three" were brought to tears behind closed doors during an emotional jersey presentation before the NRL decider.
"I think the three of us are hoping that we can play another six games together but I don't know maybe that emotion may come back again in the (World Cup) final because we still don't know what's happening next year."
The Kangaroos have focused on combinations and execution this week, but unbeaten coach Meninga conceded the team could be at its most vulnerable.
"They're (England) one of the major forces in the game of rugby league, they probably think we're at our most vulnerable tomorrow night so again we're prepared for that," Meninga said.
"They are a definite threat, they got a really strong team across 20-24 players, I would anticipate them competing to be in the final this year.
"We want to kick off to a good start to aid our campaign.
"We want to be winners and we want to be successful in this World Cup and the best way to do that is play really well tomorrow night."
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text:
Windstream $19.99 15 - 100 Mbps DSL 99%
Windstream $75.00 15 - 100 Mbps Fiber-optic 12%
Vyve Broadband $39.99 105 - 105 Mbps Cable 91%
Plant Telephone Company $34.95 25 - 10 Mbps DSL 2.434%
Provider: Vyve Broadband
At 100%, broadband coverage in the 31535 ZIP code is comparable to the rest of Georgia - 95%, and comparable to broadband coverage across the U.S. - 95%.
The vast majority of those living in the 31535 ZIP will have access to DSL, which covers 99% of households. Cable internet is also an option for many, offering service to 91% of the area. Fiber-optic is less available but still a popular internet option for roughly 12% of those living in the 31535 ZIP code.
The largest internet providers serving the 31535 ZIP code are Windstream and Vyve Broadband bringing DSL, fiber-optic, and cable options to the area.
DSL internet from Windstream is available to nearly all in the 31535 ZIP code, covering 99% of households. Some will also have the option of fiber-optic from Windstream, which covers 12% of the area. Douglas's third most-available internet provider, Vyve Broadband, offers cable to around 91% of residents in the 31535 ZIP code.
Windstream DSL internet
DSL from Windstream offers broadband to all households in the 31535 ZIP code, covering 99% of the area.
DSL internet from Windstream uses telephone lines to deliver service, which often leads to lower prices and higher availability, though download speeds are likely to be slower than cable or fiber-optic internet.
Internet prices for Windstream start at $19.99/mo.*, which is significantly lower than the average starting price for internet in Douglas, $40.98.
Windstream customers who choose the lowest-priced plan can expect speeds up to 15 Mbps, which comes to an estimated $1.33 per Mbps, about the same as the average cost per Mbps for the lowest priced plans in Douglas, $1.05.
For those who value internet speed as much as cost, Windstream offers download speeds up to 100 Mbps and upload speeds up to 8 Mbps in 31535 ZIP code. These download speeds are available to 99% of the area, and are about the same as the average max speeds from all Douglas's providers, 70 Mbps.
Windstream fiber-optic internet
Fiber-optic from Windstream offers broadband to a limited number of households in the 31535 ZIP code, covering 12% of the area.
Fiber-optic service from Windstream is popular for its fast download and upload speeds, even during peak usage times, but may be less available throughout 31535 ZIP code than other internet types.
Internet prices for Windstream start at $75/mo.*, which is significantly higher than the average starting price for internet in Douglas, $40.98.
Windstream customers who choose the lowest-priced plan can expect speeds up to 15 Mbps, which comes to an estimated $5.00 per Mbps, significantly higher than the average cost per Mbps for the lowest priced plans in Douglas, $1.05.
Vyve Broadband cable internet
Cable from Vyve Broadband offers broadband to nearly all households in the 31535 ZIP code, covering 91% of the area.
Vyve Broadband's cable network is likely to offer faster speeds than DSL or satellite service, but 31535 residents in densely populated areas may experience slowed speeds during peak usage times.
Internet prices for Vyve Broadband start at $39.99/mo.*, which is about the same as the average starting price for internet in Douglas, $40.98.
Vyve Broadband customers who choose the lowest-priced plan can expect speeds up to 105 Mbps, which comes to an estimated $0.38 per Mbps, about the same as the average cost per Mbps for the lowest priced plans in Douglas, $1.05.
For those who value internet speed as much as cost, Vyve Broadband offers download speeds up to 105 Mbps and upload speeds up to 10 Mbps in 31535 ZIP code. These download speeds are available to 91% of the area, and are about the same as the average max speeds from all Douglas's providers, 70 Mbps.
Satellite internet from HughesNet and Viasat offers 100% availability in 31535 and the greater Douglas area. HughesNet starting prices are significantly higher than average in Douglas at $59.99/mo. for speeds up to 25 Mbps. Viasat starting prices are higher than average in Douglas at $50/mo. for speeds up to 12 Mbps. Satellite internet is a less appealing option for many in the 31535 ZIP, however, as service typically comes with high latency and low data allowances.
Vyve Broadband is the fastest internet provider in the 31535 ZIP code with download speeds up to 105 Mbps. These speeds are available to 91% of households in 31535. The next fastest internet provider in the 31535 ZIP code is Windstream, offering download speeds up to 100 Mbps to 99% of the area.
Windstream offers the cheapest internet in the 31535 ZIP with service starting at $19.99/mo. This is significantly less than the average starting price for internet in Douglas, $40.98.
Windstream is the largest fiber-optic internet service provider serving the 31535 ZIP code, covering 12% of the city.
How do I get cheaper internet?
Using your own router is a good way to cut internet costs without having to downgrade your internet plan. You can also try negotiating for a lower price with your current provider, or switch to another internet provider to take advantage of introductory pricing and special promotions.
Home page Georgia Douglas 31535


We used data on 100% of traditional Medicare beneficiaries from 51 nationally representative geographical regions for the years 2005–2010.
We constructed networks of physicians based on their shared patients. In the fixed-threshold networks and adaptive-threshold networks, we included data on all patient-physician encounters to form the physician-physician ties, and then subsequently thresholded some proportion of the strongest ties. In contrast, in the episode-based approach, only those patient-physician encounters that occurred within shared clinical episodes treating specific conditions contributed towards physician-physician ties.
We extracted clinical episodes in the Medicare data and investigated structural properties of the patient-sharing networks of physicians, temporal dynamics of their ties, and temporal stability of network communities across the two approaches.
The episode-based networks accentuated ties between primary care physicians (PCPs) and medical specialists, had ties that were more likely to reappear in the future, and appeared to have more fluid community structure.
Constructing physician networks around shared episodes of care is a clinically sound alternative to previous approaches to network construction that does not require arbitrary decisions about thresholding. The resulting networks capture somewhat different aspects of patient-physician encounters.
The ever-increasing availability of electronic administrative and social interaction data has made it possible to study human behavior at unprecedented scale and depth (Lazer et al. 2009; Onnela et al. 2007). These types of data also provide the opportunity to study the behavior of groups of individuals in different social or organizational contexts, such as a healthcare system, where the outcomes would be expected to be related to the structure of these interactions.
Social networks are a natural way to capture the structure of interactions among a group of people, where a network node corresponds to an individual and a network tie corresponds to a relationship between the two individuals. Administrative data have recently been used to study physician-physician information sharing networks, which consist of individual physicians as nodes with ties defined by patient-sharing relationships among physicians (Landon et al. 2012; Landon et al. 2013; Mandl et al. 2014; Ong et al. 2016; Pollack et al. 2012; Pollack et al. 2013), i.e., networks. At a more fundamental level, however, physician-patient encounter data form a bipartite network consisting of two types of nodes, physicians and patients, and they detail physician-patient interactions from which physician-physician interactions are intuited. The term bipartite refers to any network consisting of two types of nodes such that each edge connects a node from one category with a node from the other category; the bipartite networks studied here form either patient-physician ties or episode-physician ties. These bipartite networks can be projected to unipartite networks consisting of physicians only, where ties reflect patient-sharing between physicians. This standard practice is useful because it enables the study of connections among physicians, but unfortunately the optimal method for identifying meaningful information sharing relationships using administrative data is not clear. For instance, in some cases, such identified relationships between two physicians might be spurious (i.e., not reflective of a true relationships), resulting from a common tie to another physician with whom both physicians share information. For example, a patient may see a primary care physician as well as a gastroenterologist for an ulcer and an ophthalmologist for cataracts. While the primary care physician likely has interactions with both physicians, the gastroenterologist and ophthalmologist are unlikely to interact with each other regarding this patient's care.
Prior approaches to identifying physician social networks based on shared patients have typically projected all interactions in the underlying bipartite networks to a unipartite network, and have then thresholded ties in the unipartite network based on their strength (i.e., the number of shared patients) to retain the most important connections (Landon et al. 2012). The rationale of connecting physicians via shared patients is the notion that a shared patient is a conduit for the diffusion of clinical practices and information among the physicians who provide care to that patient. While this logic is reasonable if all of the patient-physician encounters are tied to the same episode of care for a particular condition, necessitating at least some information sharing among the physicians about the episode, the patient visits to a host of physicians could be associated with several different clinical episodes, and physicians providing care to a patient in the context of one clinical episode may not need to be informed about the details of another clinical episode. Consequently, if there were little or no transfer of information across different clinical episodes for the given patient, the standard approach would lead one to conclude the network to be denser, or more connected, than is the case. An episode-based network construction, therefore, might offer an improvement over existing methods because it results in less dense networks comprised of physician-physician ties that are expected to have greater likelihood to correspond to real connections among the physicians. The episode-based approach also removes the somewhat arbitrary and unprincipled step of network thresholding. Although statistical arguments can be applied for selecting an optimal threshold, the episode-based network approach avoids the thresholding problem altogether by relying on a different network construction method that focuses on clinically-relevant connections. The end product of the episode-based approach is a network that is a superposition of the connections induced by each individual episode. This approach generalizes one used in some prior research that focused on episodes of care for a particular clinical condition such as prostate cancer or diabetes, that necessarily are limited to physicians treating that condition (Pollack et al. 2014; Pollack et al. 2012; Pollack et al. 2013). Other research has used networks constructed around episodes of care similar to the approach that we take to examine care and outcomes for Medicare beneficiaries undergoing a coronary artery bypass grafting (CABG) (Hollingsworth et al. 2015) (Hollingsworth et al. 2016) (Jordan et al. 2018) (Funk et al. 2017). These papers, however, examined networks constructed around just a single procedure or clinical condition. In contrast, our paper presents a first systematic exploration of the differences between networks constructed from all patient-physician encounters, two different thresholded variants of such networks, and networks constructed around episodes of care. Rather than examining care around a specific episode type, we use data across all episodes and offer these resulting networks as an alternative to constructing networks of all patient-physician encounters with or without thresholding. In addition to examining nodal or dyadic properties, we also examine longitudinal changes in the community structure of these networks, which can be used to identify and study the evolution of teams of physicians that provide care to their patients.
In this paper, we use administrative data from the Medicare program to construct physician networks for 51 hospital referral regions (HRRs) in the US using these two different approaches, the standard patient-based network construction method (Landon et al. 2012) and a new episode-based network construction method introduced here. We characterize the differences in the networks in terms of some commonly used network summary statistics. Instead of examining only a single network snapshot (or cross-section), we construct a sequence of networks, based on annual data from 2005 to 2010. In addition to descriptive analyses at the level of network ties, we identify longitudinal network communities and characterize the stability of community structure over time in these annual "multi-slice" networks obtained using the two different methods of network construction. Network communities can be defined as sets of nodes that are densely connected to other nodes in the same community but only sparsely to nodes in other communities. In our setting, network communities are potentially relevant units of analysis because they go beyond individual physician pairs and identify "clusters" of connected node pairs within the networks, i.e., groups of physicians that jointly provide care to patients.
We used data on 100% of traditional Medicare beneficiaries from 51 hospital referral regions (HRRs) for the years 2005–2010. A total of 50 HRRs were randomly sampled with probability proportional to their size; the 51st HRR was Boston, which was included because of our familiarity with it. This was the maximum amount of data that we were permitted to purchase. We defined encounters with physicians based on paid claims in the carrier file. We excluded claims for non-direct patient care specialties or specialties where individual physicians are not typically selected by patients (e.g., anesthesia, radiology). We identified all evaluation and management services, and included procedures with a relative value unit (RVU) value of at least 2.0 in order to capture surgical procedures that often are reimbursed via bundled fees that include pre- and post-procedure assessments. We excluded claims for laboratory and other services not requiring a physician visit; we also excluded claims generated from physicians who saw fewer than 30 Medicare patients during any year or who practice outside of the included HRRs.
We identified discreet episodes of care using Symmetry Episode Treatment Groups (ETG), version 8.3 (Optum, Eden Prairie, Minnesota), which is in widespread use nationally. Each episode of care groups clinically related services delivered to a patient with a specific condition over a defined period of time into one of about 600 different episode types, which reflect treatment for both chronic diseases (e.g., diabetes) and acute conditions (e.g., pneumonia, ankle fracture). A total of 92.2% of patient visits are assigned to episodes, and 46.5% of episodes have more than 1 visit associated with them. Each episode has a start date, end date, and a unique episode number. The start date is defined as the first visit date of the episode and usually is preceded by a "clean" period of varying length during which no related encounters occurred. The end date is defined as the last visit date of the episode. Acute episodes are defined as episodes that are expected to resolve and the end date is defined after no additional related visits have occurred for a defined period of time, which varies by type of episode. Note that the start and end dates of acute episodes are distinct from the time windows used to construct the networks. This is in contrast to chronic episodes, which are defined to last an entire calendar year for each year when present.
All networks are constructed using 1-year time windows. To construct episode-based bipartite networks, for each HRR, we consider a sequence of physician-episode pairs that occurred during the time window, where each episode consists of one patient and one or more physicians who provided care to the patient during the given medical episode. To construct patient-based bipartite networks, for each HRR, we consider a sequence of physician-patient encounters that took place during the entire calendar year. Importantly, there is no requirement that the physicians provide care to a patient for a particular clinical condition (episode), and therefore the latter approach results in a set of physician-physician ties that is a superset of the ties induced by the former approach. For both methods, after constructing bipartite graphs, for each HRR and each calendar year, we construct corresponding unipartite networks of physicians by projecting the underlying bipartite networks consisting either of physician-episode ties or physician-patient ties. Intuitively, patient i (or episode i) in a bipartite network having degree ki induces a clique of ki nodes among the physicians he or she is connected to, where a k-clique is a fully connected subgraph of k nodes and k(k − 1)/2 edges; the projection of the entire bipartite graph is then the summation of the cliques induced by all patients (or all episodes), where each patient (each episode) contributes precisely one clique to the projected unipartite graph. See Fig. 1 for a schematic on network construction.
The resulting two unipartite graphs for any given HRR and year typically have different numbers of edges, making it difficult to interpret differences in network properties between them. In particular, the patient-based network graphs have many more edges than the episode-based network graphs, and it is likely that those with the lowest numbers of shared patients do not represent true information sharing ties(Barnett et al. 2011). We therefore threshold the patient-based unipartite networks in one of two ways. First, we maintained ties in the top 80% of ties (as defined at the individual node) based on their strength (quantified as the number of shared patients), an approach that we have used previously (Landon et al. 2012; Landon et al. 2013). Second, we created an adaptive threshold for each HRR by removing ties from weakest to strongest until the number of ties remaining matched to within 1% of the number of ties in the corresponding episode-based network. We call the former fixed-threshold networks and the latter adaptive-threshold networks. Note that both networks are unipartite since they consist of physicians only.
Using physician specialty data from the American Medical Association physician Masterfile, we characterized each physician, and therefore each node, as a primary care provider (P), medical specialist (M), or surgical specialist (S). Since each tie, by definition, consists of a pair of physician nodes, we can stratify all ties into six categories: P-P, S-S, M-M, P-S, P-M, and S-M. For example, a tie between a medical specialist and a primary care physician would be placed in the P-M category (directionality does not matter, this category simply represents a pairing of physicians of certain designations). We use LXY to denote the number of ties between a provider of type X and a provider of type Y in a given HRR and a given time window; for example, LPM stands for the number of ties in a given network between primary care physicians and medical specialists. We use L to denote the total number of ties in the given network, and define the proportions of tie types as lXY = LXY/L; for example, here lPM stands for the proportion of network ties that connect primary care physicians and medical specialists in a network for a given HRR and given time window.
Tie persistence refers to the lifetime of a tie in a sequence of networks constructed over consecutive time windows (annual in this case) for a given HRR. For simplicity, consider two graphs G(t1) and G(t2) constructed for two consecutive years, say for years 2008 and 2009, respectively. We compute tie persistence by first considering the number of ties that exist in both graphs (a tie exists in both if it connects the same two physicians in each graph) in each of the six categories of specialty combinations, and we then divide these numbers by the number of ties in each category in the first graph in the sequence, here G(t1). In a longer sequence of graphs, for a tie to persist it must be present in all graphs in the sequence. A concept related to tie persistence is that of tie reappearance. Given a longer sequence of annual graphs, some ties may persist for a few years, then disappear, only to reappear in a later year. Because a tie can only reappear after it has disappeared, the number of tie reappearances is either equal to, or one less, than the number of tie disappearances. Because the observations (ties) may reappear later, censoring of observations is not an issue in this analysis.
To learn about structural properties of the episode-based and patient-based networks at the mesoscopic scale, the scale between microscopic (e.g., node degree) and macroscopic (e.g., graph diameter) structural properties, we use community detection (Fortunato 2010; Newman 2012; Porter, Onnela, and Mucha 2009) to identify groups of densely connected nodes in the networks. In our setting, network communities can be seen to correspond to groups of physicians who jointly provide care to a group of patients. We use a variant of a popular method of modularity maximization (Newman 2006), the so-called multi-slice method (Mucha et al. 2010), which makes it possible to detect network communities in a sequence of temporally ordered networks. This means that rather than detecting network communities separately and independently in each network corresponding to a given HRR and time window, the network communities can now extend over various network slices (time points—years in our case) for each HRR. For example, it is possible for two separate communities to merge together at some point and, similarly, an existing community may split into two or more communities. Community detection is a difficult mathematical and computational problem that requires special software. We refer the reader to the supplement for more details. To quantify the stability of communities over time in each HRR, we used a metric that is related to the entropy of a node's community assignment in each year (see Additional file 1). For example, if a node is assigned to the same community in each of the six annual slices (in the sequence of six networks corresponding to the six years of data under consideration), it is said to have minimal normalized entropy regarding its community assignment, whereas if a node is assigned to a different community each year, it has maximal normalized entropy in this regard. In simplified terms, entropy can be considered to measure the variability of a node's community assignment from year to year. Communities that are stable in time may correspond to groups of physicians who provide coordinated care to patients over longer periods of time without significant churn in their membership, i.e., both the group of physicians and the structure of relationships among them remain stable over time.
Table 1 shows descriptive statistics for the episode-based and the three versions of the patient-based networks from 2005 to 2010 constructed from annual time windows. The episode-based networks ("Episode", 1st block in the table) include all relevant ties from shared episodes, with no threshold. The full patient-based network ("Patient", 4th block in the table) includes all patient-sharing ties and has also not been thresholded. The two other types of patient-based networks incorporate thresholds: the adaptive-threshold patient networks ("Patient (A)", 2nd block in the table), where ties have been removed from weakest to strongest until the number of remaining ties matched to within 1% of the number of ties in the corresponding (same year, same HRR) episode-based network, and the fixed-threshold patient networks whose weights have been thresholded at the 80th percentile ("Patient (80%)", 3rd block in the table) for each node, the approach we have followed previously. For each network, the number of nodes and ties increases over time, the latter more than the former, leading to increasing average degree over time. Average degrees are essentially identical across episode-based and adaptive-threshold networks by construction (small discrepancies are due to non-uniqueness of edge weights in the thresholding process), but these average degrees are about 30% higher than for networks thresholded at the 80th percentile. Clustering coefficients however are very similar across the first three network types; as expected, non-thresholded networks have substantially higher degree and much higher average clustering coefficient. The proportion of different tie types is essentially identical across adaptive-threshold networks and fixed-threshold networks (fixed 80th percentile threshold), so our subsequent analyses focus on differences between episode-based networks and adaptive- threshold patient networks (the first two blocks in the table). As is clear from the table, episode-based networks contain a greater proportion of ties among medical specialist (roughly 38% vs. 30%) and fewer PCP – medical specialist ties (roughly 12% vs. 19%). In general, episode networks appear to accentuate ties among PCPs and ties among medical specialists, giving less weight to other tie types.
Figure 2 shows the distribution of tie survival times in adaptive-threshold networks and episode-based networks. The distributions are similar overall, although the proportion of ties that persist for only 1-year is greater in episode-based networks (72.4% vs. 67.4%). These numbers are comparable to a recent study using Medicare data and the Dartmouth Atlas, which found that that 70.7% of ties between PCPs and other physicians that were present in 2012 persisted in 2013, and additional shared patients in 2012 increased the odds of being connected in 2013 (DuGoff et al. 2017). Thus, tie persistence time in episode-based networks, 1.56 years, is somewhat smaller than mean tie persistence time in adaptive-threshold networks (1.72 years). In contrast, taken over all HRRs and all years, there were a total of 2,744,224 tie reappearances events in the episode-based networks and 2,437,351 in the adaptive networks. Since our data cover 6 years and 51 HRRs, to make these numbers more interpretable, we compute the average number of tie reappearances per HRR per year which is 8968 in the former category and 7965 in the latter. This demonstrates that there were 12.6% more tie reappearance events in the episode-based networks, suggesting that in those networks ties are more likely to reappear around specific episodes of patient care, thus leading to greater overall persistence over time.
Figure 3 shows the three pairwise plots of normalized entropies computed for fixed-threshold, adaptive-threshold, and episode-based networks. Each dot represents an HRR, and in general the mean entropies are reasonably strongly correlated. The plots also indicate that while normalized entropies for fixed-threshold and adaptive-threshold networks are comparable, episode-based networks have somewhat higher entropy values. We summarize the distribution of normalized entropy for the three ways of constructing networks in Table 2. Median entropy for fixed-threshold and adaptive-threshold networks is comparable at 0.114 and 0.118, respectively, whereas the corresponding value for episode-based networks is 0.189. Since adaptive-threshold networks and episode-based networks have the same number of edges for each HRR, their values are not confounded with the network density, and we still find that episode-based networks have 60% higher normalized entropy than adaptive-threshold networks.
One potential confounder in this latter analysis is that the number of communities detected varies for each network type. The median number of communities for fixed-threshold networks is 10 (range 4–20), for adaptive-threshold networks 9 (range 3–20), and for episode-based networks 14 (range 7–21). The larger number of communities in episode-based networks could potentially inflate the observed entropy values because there are more communities in which to be assigned. To investigate this, we regressed normalized entropy against the number of communities for each of the three network types using linear models, resulting in three separate models. We estimated the value of entropy by setting the predictor, the number of communities, to the mean number of communities observed in the adaptive-threshold networks (9.7), which resulted in entropy values of 0.12 (95% CI: 0.10–0.14) for fixed-threshold networks, 0.12 (CI: 0.10–0.14) for adaptive-threshold networks, and 0.17 (CI: 0.14–0.20) for episode-based networks. We also estimated these numbers at the mean number of communities observed in the episode-based networks (13.7), which resulted in entropy values of 0.15 (95% CI: 0.13–0.18) for fixed-threshold networks, 0.15 (CI: 0.11–0.18) for adaptive-threshold networks, and 0.19 (CI: 0.17–0.21) for episode-based networks. Therefore, the episode-based approach results in greater values for entropy whether we compute the expected entropy at the mean number of communities in the adaptive-threshold networks or at the mean number of communities in the episode-based networks. This suggests that increased entropy is a characteristic of networks constructed using only patient-physician encounters within shared episodes of care.
We compared three different methods of constructing patient-sharing networks of physicians, which we termed fixed-threshold networks, adaptive-threshold networks, and episode-based networks. Comparing adaptive-threshold networks and episode-based networks, which had the same number of ties, we found that episode-based networks retained more PCP-PCP ties and specialist-specialist ties. Ties in both types of networks appeared and disappeared in time, but in episode-based networks ties had somewhat lower persistence times and ties that had existed in the past were more likely to reappear in the future.
Weak ties present a special challenge to all approaches to network construction, a problem that is aggravated by the presence of what is effectively measurement noise. It is a common finding that large-scale weighted networks have strongly right-skewed tie strength distributions, meaning that most network ties are weak. Applying a single relatively large threshold to patient-based ties will eliminate most if not all of them, and many of these will be true information sharing relationships. In contrast, the episode-based approach applied without thresholding, as we have done here, is guaranteed to preserve all ties, including weak ties, that pertain to the episodes of interest. The tradeoff between (thresholded) patient-based and (non-thresholded) episode-based networks, as regards weak ties, is that the former will have many false negatives but few false positives, whereas the latter may have some false positives but few false negatives. Our observations regarding lower persistence but higher reappearance of ties suggest that episode-based networks may be more sensitive to detecting ties that are weak but meaningful. Because episode-based ties are not thresholded, they may be based on few or even just a single shared patient and would thus be expected to decay faster over time. However, to the extent that these weak ties reflect genuinely important interactions among physicians, we would expect those ties to reappear should to opportunity arise.
We also studied longitudinal network communities, examining their stability using an entropy-based metric. We focused on network communities because they arguably correspond to functional groupings of physicians and therefore could directly affect delivery of care. Although the changes in network structure at the level of individual ties were not very large, their net effect was that communities in episode-based networks were more dynamic. Measured by their relative entropy, episode-based networks had 60% greater normalized entropy than adaptive-threshold networks. This finding appears to be at least in part due to episode-based networks having more communities, but even after a simple adjustment for the number of communities, episode-based networks had greater entropy. Part of the explanation almost certainly has to do with the persistence and reappearance of ties, but it is possible that there are additional factors, such as the extent to which tie appearance and disappearance events are correlated in different regions of the network, that give rise to this outcome.
The fundamental reason for some type of network thresholding arises from the fact that non-thresholded networks tend to be very densely connected and also include ties that are likely to be spurious. This a problem from the point of view of interpretation, as it is unlikely that weak or sporadic ties are significant conduits of information, but it additionally presents challenges for computing some of the commonly used network metrics and measures. Our past work has made use of the fixed-threshold approach to arrive at sparser networks, but his approach requires imposing a relatively arbitrary threshold, which might not maximize the number of true information sharing ties maintained. An important benefit to the episode-based approach is that it provides a solution to the thresholding problem that is based on clinical interactions and theory rather than a relatively arbitrary cut point. Across all HRRs, the episode-based networks (and therefore the adaptive-threshold networks) eliminated on average 70.6% of all edges, effectively retaining the top 30% of the strongest and most clinically relevant physician-physician ties.
Although it is possible to consider the thresholding problem from statistical grounds, where it could be cast as a variable selection problem with the ties corresponding to variables, episode-based networks offer a domain-specific approach to the thresholding problem. Moreover, episode-based networks are clinically intuitive and the retained ties are more likely to correspond to information-sharing ties. Notwithstanding their face validity, episode-based networks appear to have more dynamic community structure, which presents some challenges for interpretation. One possible reason for this is that thresholded networks retain connections among high-volume providers, leading to fewer communities and falsely stabilizing network structure over time.
Our study has strengths and limitations. A key strength of our paper is the use of Medicare patient-physician encounter data and data on episodes of care to compare different ways of constructing physician-physician networks. Another strength is the examination of the resulting networks not only at the level of individual physicians, but also at the level of groups of physicians, here conceptualized as network communities, as well as the extension of this analysis to multiple years of data. Because we detected the network communities using a specific method, it is possible that other approaches could yield different results, although lack of longitudinal community detection methods prevents a more thorough investigation of this possibility. Our analyses were also confined to the 51 HRRs for which data were available and included only fee-for-service Medicare patients, but given that 50 of the included HRRs were randomly sampled from all HRRs (the 51st HRR, Boston, was included by design because of our familiarity with it), we expect our results to generalize to all HRRs. For the included HRRs, we had a 100% sample of traditional Medicare beneficiaries. The software that we used to generate the episodes of care is proprietary, which potentially makes replication of these results more difficult, but this problem can be alleviated by using a public domain episode grouper (Centers for Medicare & Medicaid Services 2011).
Medicare patient-physician encounter data continue to present vast opportunities for investigating provision of medical care. The size and complexity of these data present many challenges for data analysis and substantive interpretation. The episode-based approach we have introduced in this paper is informed by clinical intuition and uses a theory-driven approach wherein we require the two physicians to be linked through shared care of an episode for a particular patient. This allows us to eliminate ties that likely are spurious without requiring an arbitrary threshold to be assigned. These factors make the episode-based method preferable to other approaches in most research settings. In addition, the episode-based approach allows for a much more generalized approach to network construction; for example, as some prior studies have done, one could base these networks on specific types of episodes, which might be helpful, for instance, for studying specific procedures or conditions, such as cancer (Pollack et al. 2014; Pollack et al. 2012; Pollack et al. 2013). These types of analyses are not a potential extension of the standard thresholding approach but rather a restriction that could be applied on conjunction with an episode-based approach. Fundamentally, we see the episode-based approach, compared to the patient-based approach, as giving rise to measurements that are less contaminated with noise, and therefore these more precise measurements of physician-physician interactions are more likely to reveal the true underlying structure of these networks. In conclusion, filtering networks by clinical episodes of care is a step towards making research that uses these data more powerful, understandable and actionable for patients and physicians.
The authors wish to thank Laurie Meneades for her expert assistance with data extraction and processing.
This work was supported by 1R01CA174468 from the National Cancer Institute.
Data were obtained from the US Centers for Medicare and Medicaid Services (CMS). Due to data use agreement restrictions, we cannot share our project data with other investigators, but Medicare data can be obtained from CMS.
All authors developed the study design and wrote the manuscript. JPO performed the analysis. All authors read and approved the final manuscript.


This pair of rustic decorative corner brackets would make a lovely addition to any home or garden space. They can be used as intended to support a shelf, or as stand-alone decor. Each bracket is 1" thick and measures 21" x 21".


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text:
Q: Error parsing data org.json.JSONException: type java.lang.String cannot be converted to JSONObject Currently my PHP file that is used to interface between the MySQL server and my Android app looks like:
<?php
// array for JSON response
$response = array();

if (isset($_GET['porfileID'])) {
     $ID = $_GET['porfileID'];

    // include db connect class
    require_once __DIR__ . '/db_connect.php';

    // connecting to db
    $db = new DB_CONNECT();

    // get all products from products table
    $result = mysql_query("SELECT * FROM profiles_profilelists WHERE ProfileID = '$ID'") or die(mysql_error());

    // check for empty result
    if (mysql_num_rows($result) > 0) {
        // looping through all results
        // products node
        $response["lists"] = array();

        while ($row = mysql_fetch_array($result)) {
        // temp user array
        $lists = array();
        $lists["ProfileListID"] = $row["ProfileListID"];
        $lists["ProfileListName"] = $row["ProfileListName"];

        // push single product into final response array
        array_push($response["lists"], $lists);
        }
        // success
        $response["success"] = 1;

        // echoing JSON response
        echo json_encode($response);
    } else {
        // no products found
        $response["success"] = 0;
        $response["message"] = "No products found";

        // echo no users JSON
        echo json_encode($response);
    }
} else {
    // required field is missing
    $response["success"] = 0;
    $response["message"] = "Required field(s) is missing";

    // echoing JSON response
    echo json_encode($response);
}
?>

This works fine however when I add:
$result2 = mysql_query("SELECT * FROM profiles_profilelistitems WHERE ProfileListID = ".$row["ProfileListName"]) or die(mysql_error());

$lists["ListCount"] = mysql_num_rows($result2);

To my Php so it looks like:
<?php

// array for JSON response
$response = array();

if (isset($_GET['porfileID'])) {
     $ID = $_GET['porfileID'];

    // include db connect class
    require_once __DIR__ . '/db_connect.php';

    // connecting to db
    $db = new DB_CONNECT();

    // get all products from products table
    $result = mysql_query("SELECT * FROM profiles_profilelists WHERE ProfileID = '$ID'") or die(mysql_error());

    // check for empty result
    if (mysql_num_rows($result) > 0) {
        // looping through all results
        // products node
        $response["lists"] = array();

        while ($row = mysql_fetch_array($result)) {
        // temp user array
        $lists = array();
        $lists["ProfileListID"] = $row["ProfileListID"];
        $lists["ProfileListName"] = $row["ProfileListName"];

        $result2 = mysql_query("SELECT * FROM profiles_profilelistitems WHERE ProfileListID = ".$row["ProfileListName"]) or die(mysql_error());

        $lists["ListCount"] = mysql_num_rows($result2);

        // push single product into final response array
        array_push($response["lists"], $lists);
        }
        // success
        $response["success"] = 1;

        // echoing JSON response
        echo json_encode($response);
    } else {
        // no products found
        $response["success"] = 0;
        $response["message"] = "No products found";

        // echo no users JSON
        echo json_encode($response);
    }
} else {
    // required field is missing
    $response["success"] = 0;
    $response["message"] = "Required field(s) is missing";

    // echoing JSON response
    echo json_encode($response);
}
?>

I end up with the above error.
The complete trace is:
09-03 17:05:57.836: E/JSON Parser(675): Error parsing data org.json.JSONException: Value You of type java.lang.String cannot be converted to JSONObject
09-03 17:05:59.617: E/AndroidRuntime(675): FATAL EXCEPTION: AsyncTask #1
09-03 17:05:59.617: E/AndroidRuntime(675): java.lang.RuntimeException: An error occured while executing doInBackground()
09-03 17:05:59.617: E/AndroidRuntime(675):  at android.os.AsyncTask$3.done(AsyncTask.java:299)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.FutureTask$Sync.innerSetException(FutureTask.java:273)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.FutureTask.setException(FutureTask.java:124)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:307)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.FutureTask.run(FutureTask.java:137)
09-03 17:05:59.617: E/AndroidRuntime(675):  at android.os.AsyncTask$SerialExecutor$1.run(AsyncTask.java:230)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1076)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:569)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.lang.Thread.run(Thread.java:856)
09-03 17:05:59.617: E/AndroidRuntime(675): Caused by: java.lang.NullPointerException
09-03 17:05:59.617: E/AndroidRuntime(675):  at com.test.app.MyLists$LoadAllLists.doInBackground(MyLists.java:267)
09-03 17:05:59.617: E/AndroidRuntime(675):  at com.test.app.MyLists$LoadAllLists.doInBackground(MyLists.java:1)
09-03 17:05:59.617: E/AndroidRuntime(675):  at android.os.AsyncTask$2.call(AsyncTask.java:287)
09-03 17:05:59.617: E/AndroidRuntime(675):  at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:305)
09-03 17:05:59.617: E/AndroidRuntime(675):  ... 5 more

The code that the trace links to from within the app:
protected String doInBackground(String... args) {
            // Building Parameters
            List<NameValuePair> params = new ArrayList<NameValuePair>();
            params.add(new BasicNameValuePair("porfileID", Integer.toString(userID)));
            // getting JSON string from URL
            JSONObject json = jParser.makeHttpRequest(url_all_products, "GET", params);

            // Check your log cat for JSON reponse
            Log.d("All Products: ", json.toString());

            try {
                // Checking for SUCCESS TAG
                int success = json.getInt(TAG_SUCCESS);

                if (success == 1) {
                    // products found
                    // Getting Array of Products
                    products = json.getJSONArray("lists");
                    productsList.clear();
                    // looping through All Products
                    for (int i = 0; i < products.length(); i++) {
                        JSONObject c = products.getJSONObject(i);

                        // Storing each json item in variable
                        String id = c.getString("ProfileListID");
                        String name = c.getString("ProfileListName");
                        //String count = c.getString("ListCount");
                        // creating new HashMap
                        HashMap<String, String> map = new HashMap<String, String>();

                        // adding each child node to HashMap key => value
                        map.put("ProfileListID", id);
                        map.put("ProfileListName", name);

                        // adding HashList to ArrayList
                        productsList.add(map);
                    }
                } else {

                }
            } catch (JSONException e) {
                e.printStackTrace();
            }

            return null;
        }

And finally the json parser I am using
public class JSONParser {

    static InputStream is = null;
    static JSONObject jObj = null;
    static String json = "";

    // constructor
    public JSONParser() {

    }

    // function get json from url
    // by making HTTP POST or GET method
    public JSONObject makeHttpRequest(String url, String method,
            List<NameValuePair> params) {

        // Making HTTP request
        try {

            // check for request method
            if(method == "POST"){
                // request method is POST
                // defaultHttpClient
                DefaultHttpClient httpClient = new DefaultHttpClient();
                HttpPost httpPost = new HttpPost(url);
                httpPost.setEntity(new UrlEncodedFormEntity(params));

                HttpResponse httpResponse = httpClient.execute(httpPost);
                HttpEntity httpEntity = httpResponse.getEntity();
                is = httpEntity.getContent();

            }else if(method == "GET"){
                // request method is GET
                DefaultHttpClient httpClient = new DefaultHttpClient();
                String paramString = URLEncodedUtils.format(params, "utf-8");
                url += "?" + paramString;
                HttpGet httpGet = new HttpGet(url);

                HttpResponse httpResponse = httpClient.execute(httpGet);
                HttpEntity httpEntity = httpResponse.getEntity();
                is = httpEntity.getContent();
            }

        } catch (UnsupportedEncodingException e) {
            e.printStackTrace();
        } catch (ClientProtocolException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        }

        try {
            BufferedReader reader = new BufferedReader(new InputStreamReader(
                    is, "iso-8859-1"), 8);
            StringBuilder sb = new StringBuilder();
            String line = null;
            while ((line = reader.readLine()) != null) {
                sb.append(line + "\n");
            }
            is.close();
            json = sb.toString();
        } catch (Exception e) {
            Log.e("Buffer Error", "Error converting result " + e.toString());
        }

        // try parse the string to a JSON object
        try {
            jObj = new JSONObject(json);
        } catch (JSONException e) {
            Log.e("JSON Parser", "Error parsing data " + e.toString());
        }

        // return JSON String
        return jObj;

    }
}

A: If you are getting error only after introducing the PHP line then you may have error in the query itself. Try dumping the query to get more insights.
SELECT * FROM profiles_profilelistitems WHERE ProfileListID = ".$row["ProfileListName"]

should be
SELECT * FROM profiles_profilelistitems WHERE ProfileListID = '".$row["ProfileListName"]."'"

A: Problem is in php only , rewrite query or check php . I have also faced same problem , and problem is not in android or anything , it is in php only , and usually in the query . If query seems correct then also rewrite query once .

redpajama_set_name: RedPajamaStackExchange


text:
» Astronomy News
» This Week's Amazing Sky
Best Total Eclipses and Sky Sights of the Century—Guaranteed!
Evan Zucker
Select ratingGive Best Total Eclipses and Sky Sights of the Century—Guaranteed! 1/5Give Best Total Eclipses and Sky Sights of the Century—Guaranteed! 2/5Give Best Total Eclipses and Sky Sights of the Century—Guaranteed! 3/5Give Best Total Eclipses and Sky Sights of the Century—Guaranteed! 4/5Give Best Total Eclipses and Sky Sights of the Century—Guaranteed! 5/5Cancel rating
Take your vitamins and stick around for the next century's super-spectacles, including the longest total solar eclipse in U.S. history (2045) or the spectacular return of Halley's Comet in 2061.
The Magnificent Seven (Four for Canada) Total Eclipses
Totality causes humans and animals alike to moan and babble, as normally invisible deep-pink prominences leap from the Sun's edge like nuclear geysers. Alas, this ineffable experience of totality happens just once every 360 years, on average, from any given site on Earth.
August 21, 2017, will bring the first American totality: the 120-mile-wide shadow will slash the country from coast to coast like a calligraphy brushstroke. See the Almanac's 2017 Total Solar Eclipse Guide and Map!
Another American totality occurs on April 8, 2024, followed by the longest eclipse in U.S. history (a 6-½ minute totality) on August 12, 2045, that again will cross the country from the Pacific to the Atlantic — an inspiration, perhaps, for today's observers to stay healthy.
After a shorter totality in 2052 over Georgia, the country then receives a rare present: two total solar eclipses within a single year, on May 11, 2078, and May 1, 2079. Finally, the century closes with a totality for the northern and Atlantic states in September of 2099. And that's it — the only occasions in the next 100 years in which stay-at-home Americans can stand fully in the Moon's shadow.
For Canadian eclipse addicts, the April 8, 2024, event will also be seen from the Maritime provinces. The next one, on August 23, 2044, actually begins at sunrise on the border with Montana, then hightails it northward through the western Prairies toward the North Pole. After that, it's a long wait until the eclipse of May 1, 2079, visible from the Maritimes, and the totality of September 14, 2099, seen in southwestern Canada.
Great Comets
In terms of sheer spectacle, the closest runner-up to solar totality is probably Earth's encounter with a Great Comet. While 1996's Hyakutake and 1997's Hale-Bopp did indeed break a 20-year Great Comet drought, neither was as spectacular — that is, bright, with a long tail — as some of the finest historical visitors. The most demanding comet lovers desire a comet with both qualities, like Halley's memorable 1910 visit, or the "Great January comet" of that same extraordinary year. While most spectacular comets have initially uncharted orbits of thousands of years and therefore visit us with no advance notice, the one trusty short-period comet that can be predicted is also the most famous of all — Halley's comet.
Unfortunately, during Halley's most recent visit, in 1985-86, Earth was in nearly the worst possible position, the equivalent of the outfield bleacher seats. But the Earth/Halley geometry will be wonderful for its return in 2061. Then, it should span half the sky. Moreover, it will float in front of the stars of the Big Dipper, making it prominent for observers in the United States and Canada.
The finest reliable showers will continue to be summer's Perseids of August 11, which will slowly creep to August 12 as the century advances, and December's rich Geminid display on December 13, which will also migrate ahead one night toward century's end. Anyone can predict which years these will appear at their best by looking up the phases of the Moon for those dates. Meteors are greatly diminished by a Moon that falls between the first and last quarter phases. See more information and viewing tips for the Perseid Meteor Shower and the Geminid Meteor Shower.
Of course, for true spectacle, observers will be looking for a meteor "storm," the 50-to-100-shooting-stars-per-second display that happened in 1799, 1833, and 1966. Right now, it appears that the on-again, off-again 33-⅓-year periodicity of the Leonids should continue, giving us good opportunities in 2033, 2066, and 2099.
Truly awesome conjunctions require a meeting of at least two of the three planets that can attain dazzling brilliance (Venus, Jupiter, and, rarely, Mars), or the Moon with one or more of these. We'll throw in bright but not briliant Saturn and Mercury only when a conjunction involving them is ultra-close. To qualify, the celestial targets must pass extremely near each other in the night sky — perhaps even merge into a single, ultrabright, alien-looking sky-object. (While events involving Venus usually occur in twilight, the conjunctions below remain visible long enough to stand out against a satisfyingly dark backdrop.)
The following list presents a comprehensive list of the best planetary events of the 21st century that can be seen during the nightfall-to-10 P.M. period when most people are willing to venture out.
Dates of Dazzling Conjunctions
J=Jupiter V=Venus M=Mars S=Saturn Merc=Mercury
In all these cases, face west toward the fading evening twilight.
April 5, 2000 M, J
May 10, 2002 M, V
June 30, 2007 V, S
December 1, 2008 V, J, Moon
February 20, 2015 V, M, Moon
June 30, July 1, and July 15 Yr? V, J
December 20, 2020 J, S
March 1, 2023 V, J
December 1-2, 2033 J, M
April 13, 2038 J, Uranus
February 23, 2047 V, M
May 13, 2066 V, M
July 1, 2066 V, S
March 14, 2071 V, J
June 21, 2074 V, J
June 27. 2074 V, J, Moon
June 28, 2076 M, J
October 31, 2076 M, S, Moon
November 7, 2080 M, S, J
November 15, 2080 M, S, J
November 17, 2080 M, S, J, Ura, Moon
December 24, 2080 V, J
April 28, 2065 J, M, Moon
June 13, 2085 J, V, M
Paste this article to a refrigerator you plan to keep for ten decades. But there's no substitute for keeping your eyes wide open after nightfall—for many of the best celestial spectacles, such as awesome long-period comets, Northern Lights, and bolides (exploding meteors), arrive with little or no warning, brilliant bombshells in the heavens.
For your annual "calendar of the heavens," be sure to pick up your copy of The Old Farmer's Almanac!
Welcome to "This Week's Amazing Sky," the Almanac's hub for everything stargazing and astronomy. Bob Berman, longtime and famous astronomer for The Old Farmer's Almanac, will help bring alive the wonders of our universe. From the beautiful stars and planets to magical auroras and eclipses, he covers everything under the Sun (and Moon)! Bob, the world's mostly widely read astronomer, also has a new weekly podcast, Astounding Universe!
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The results showed that the prototype digital modules met the requirements of the pedagogical usability criteria mentioned. It also facilitated the integration of GSP in mathematics teaching.... The 2008 Annual report of the Chief Medical Officer, Safer Medical Practice (Donaldson, 2009) spelled out the importance of simulator training to improve patient safety and clinicians' performance and to enable experience to be gained without practice on patients.
the advantages of using computerised accounting software By ITS Education Asia Let me start this article by saying I am a qualified accountant who has taught …... Here are a few serious advantages that elearning tools bring to workforce training. Benefits Of Using eLearning Tools For Staff Training According to the US Bureau of Labor Statistics, there were close to 80 million people born between the years 1976 and 2001 – the generation that is often referred as millennials or Generation Y.
'One of the most important (principles of good teaching) is the need for planning. Far from compromising spontaneity, planning provides a structure and context for both teacher and students, as well as a framework for reflection and evaluation' (Spencer, 2003, p. 25).
Here are a few serious advantages that elearning tools bring to workforce training. Benefits Of Using eLearning Tools For Staff Training According to the US Bureau of Labor Statistics, there were close to 80 million people born between the years 1976 and 2001 – the generation that is often referred as millennials or Generation Y.


text:
BUSINESS NEWSLETTER 6 January 2021
THIRD NATIONAL LOCKDOWN
The UK is in national lockdown and like the first lockdown in March, we cannot leave our homes except for certain reasons such as medical, food, exercise and for those who cannot work from home.
The rules for can be seen here:
Northern Ireland: Coronavirus (COVID-19) regulations guidance: what the restrictions mean for you | nidirect
All we can do is hope the vaccine is rolled out efficiently to those most vulnerable and that this new lockdown slows the spread of the virus down. In the meantime we will continue to support you throughout this troubling period, and we will be processing your payroll, accounts, tax returns and grants as usual.
The lockdown announcement can be seen here:
Below is our latest summary of Government information. If you need help please talk to us, we are here to listen and advise our clients – we are with you all the way.
New lockdown grants announced
Businesses in the retail, hospitality and leisure sectors are to receive a one-off grant worth up to £9,000, the Chancellor announced on 5th January.
The Chancellor announced one-off top up grants for retail, hospitality and leisure businesses worth up to £9,000 per property to help businesses through to the Spring. In addition there is a £594 million discretionary fund also made available to support other impacted businesses.
The grants will be provided on a per-property basis to support businesses through the latest restrictions, and is expected to benefit over 600,000 business properties, worth £4 billion in total across all nations of the UK.
A further £594 million is also being made available for Local Authorities and the Devolved Administrations to support other businesses not eligible for the grants, that might be affected by the restrictions. Businesses should apply to their Local Authorities.
The one-off top-ups will be granted to closed businesses as follows:
£4,000 for businesses with a rateable value of £15,000 or under
£6,000 for businesses with a rateable value between £15,000 and £51,000
£9,000 for businesses with a rateable value of over £51,000
any business which is legally required to close, and which cannot operate effectively remotely, is eligible for a grant
Business support is a devolved policy and therefore the responsibility of the devolved administrations, which will receive additional funding as a result of these announcements in the usual manner:
the Scottish Government will receive £375 million
the Welsh Government will receive £227 million
the Northern Ireland Executive will receive £127 million
We will keep you up to date with further details as and when the Governments release information.
See: £4.6 billion in new lockdown grants to support businesses and protect job
For England, the eligibility guidance can be seen at:
Travel advice: Coronavirus (COVID-19)
The Government has announced guidance for British people travelling overseas during the pandemic if they are legally permitted to travel internationally.
The guidance varies for England, Scotland, Wales and Northern Ireland and links can be found on the website below.
On the Government website there is a travel planning checklist, links to individual countries rules and Consular help.
See: Travel advice: coronavirus (COVID-19)
List of private providers of coronavirus testing
The Department of Health and Social Care is aware of the private sector (non-NHS) providers published on this page who have declared that they meet the government's minimum standards for:
Test to Release for international travel
Domestic COVID-19 testing
If you have to travel abroad then this page provides a list of private providers of tests. Typically they offer a 24-hour fast test or standard service.
See: List of private providers of coronavirus testing
Critical workers and vulnerable children who can access schools or educational settings – England
The list of critical workers (key workers) and vulnerable children and young people who can access full-time education provision in the case of future restrictions on education during the coronavirus (COVID-19) outbreak has been updated at 5th January.
Critical workers are defined as Parents whose work is critical to the coronavirus (COVID-19) and EU transition response including those who work in health and social care and in other key sectors outlined in the following sections. Children with at least one parent or carer who is a critical worker can go to school or college if required:
Health and social care;
Education and children;
Key public services;
Local and national government;
Food and other necessary goods;
Public safety and national security;
Transport and border; and
Utilities, communication and financial services.
See: Children of critical workers and vulnerable children who can access schools or educational settings
The Coronavirus Job Retention Scheme rules have been updated
In view of the national lockdown the Government has released Information about employees unable to work because they have caring responsibilities.
See: Check which employees you can put on furlough to use the Coronavirus Job Retention Scheme
Haulier advice site locations
At the Department for Transport's haulier advice sites HGV drivers can:
Find out about the rules and documents needed to move goods between the UK and EU.
Complete a free border readiness check to ensure they have the correct documentation to cross the EU border.
Get help using the Check an HGV is ready to cross the border service.
HGV drivers can also use the free live webchat function to complete a border readiness check between 8am and 8pm, 7 days a week.
Testing for HGV drivers using the Port of Dover or Eurotunnel
How HGV drivers get their COVID-19 test before leaving the UK for France.
Worker, Temporary Worker and Student sponsors
There is updated advice for Worker, Temporary Worker and Student sponsors in the UK who are sponsoring those affected by coronavirus (COVID-19).
If your student or employee is absent
If you're sponsoring a student who is distance learning
If you have issued a Certificate of Sponsorship (CoS) or a confirmation of acceptance for studies (CAS) and the sponsored employee or student has not yet applied for a visa
If you're sponsoring a student who's waiting for their permission as a Student or Child Student application to be decided
If you're sponsoring employees who are working from home
If you're sponsoring an employee who's waiting for their work visa application to be decided
If you cannot pay the salaries of sponsored employees because you've temporarily reduced or ceased trading
Scanned documents
If you need more information about the measures in place for students and their sponsors
Coronavirus Immigration Help Centre


text:
Technology|'Prospective Threat' of Chinese Spying Justifies Huawei Ban, U.S. Says
'Prospective Threat' of Chinese Spying Justifies Huawei Ban, U.S. Says
The Chinese telecommunications giant Huawei is fighting a United States law that prohibits federal agencies from using the company's equipment.CreditCreditFred Dufour/Agence France-Presse — Getty Images
By Raymond Zhong
BEIJING — The Trump administration says the mere potential for Beijing to influence the Chinese technology giant Huawei is enough to justify a law passed last year restricting federal agencies' business with the company.
Huawei has challenged the law in federal court, arguing that Congress produced no proof that the company was a security threat before acting against it, and that it was being punished without due process.
The United States government's latest argument in the case, outlined in a court filing this week, suggests that the Trump administration believes it may not have to produce conclusive evidence of past wrongdoing by Huawei to uphold at least some of its actions against the company.
Huawei executives have long expressed frustration over American officials' clamping down on the company without presenting evidence that Beijing could use Huawei products for espionage, as Washington has claimed for years.
European and Asian officials have also complained privately that recent American intelligence briefings have not demonstrated clearly how the Chinese government has used Huawei to steal information.
"We remain confident in our case," a Huawei spokesman said in a statement. The government's motion does not address the "fundamental" issue, the statement says, which is "that the law selectively imposed substantial restrictions on sales of Huawei equipment for the purpose of driving Huawei out of the United States."
The company, China's leading maker of telecommunications equipment and smartphones, has found itself at the center of the vast trade and technology conflict between China and the United States. In May, the Trump administration curbed Huawei's ability to buy American parts and technology, potentially impairing its business worldwide. But after meeting with China's leader, Xi Jinping, last weekend, President Trump said he would ease some of those restrictions.
Congress prohibited federal agencies and contractors from using Huawei equipment as part of a spending bill passed last year. That led the company to sue the United States government in federal court in March, arguing that the law was a "bill of attainder," or an unconstitutional legislative punishment. A few months later, Huawei filed a motion asking the court to accelerate the suit.
In a court motion this week, United States government lawyers responded by saying that Congress's purpose was not to punish Huawei, but to protect American networks from Chinese cyberattacks. The law's "prophylactic" intent means that it is not a bill of attainder, the government says in its motion.
The motion also argues that it does not matter that Congress did not conclusively show that Huawei had done something wrong in the past.
Congress enacted the law, the motion says, because it determined that widely held suspicions about the company's ties to the Chinese state made "Huawei's products particularly susceptible to the prospective threat of wrongdoing by the Chinese government."
The legislative branch has "substantial leeway," the motion says, to make "predictive" judgments of this sort.
Huawei has repeatedly said it is a privately owned company and does not take orders from the Chinese government.
Follow Raymond Zhong on Twitter: @zhonggg.
A version of this article appears in print on , Section B, Page 3 of the New York edition with the headline: Spying Threat Alone Justifies Huawei Ban, U.S. Says. Order Reprints | Today's Paper | Subscribe
Huawei Revs Up Its U.S. Lawsuit, With the Media in Mind
Huawei Sues U.S. Government Over What It Calls an Unfair Ban
Delay in Huawei Ban Is Sought by White House Budget Office


My business mentor the incomparable MP reminds us often to "keep the main thing the main thing." If you're a loyal reader, you know what's at the center of my vision board that hangs above the center of my desk. If you're new here, I won't keep you in the metaphorical dark. At the center of my vision board it says: Domestic rabbits are safe and protected.
That's my main thing. That's what my heart beats for. That's my life's purpose.
Yesterday, I helped coordinate the rescue of Linus. I share his story with you today in hopes that one day that sentence in the center of my vision board comes true.
Some days in Rescue are more difficult than others. We don't share these stories and pictures to make you angry or sad; we share these stories to increase awareness, in hopes that no more rabbits have to suffer like Linus is right now.
Just before the weekend, we received an email from a man who found a lop-eared rabbit who had been abandoned in eastern Suffolk County. After several exchanges, we were able to ascertain that the rabbit had an injury to his mouth, so this case became a priority among the dozens we are alerted to weekly.
With no available foster space, we turned to the Town of Brookhaven Animal Shelter. Like most shelters on Long Island, they aren't typically set up to accept rabbits but they have been remarkably gracious and generous with helping many rabbits lately. The finder brought this little injured lop to the shelter yesterday just before closing… and the rabbit's condition was worse than anticipated. He got immediate medical attention and we are grateful to the doctors and staff at the shelter.
He has a hole through his face and jaw. His underside is inflamed and possibly infected. He cannot eat on his own. Our volunteers who met him said that all he wanted was snuggles and kisses despite the pain he must be feeling.
Due to his extremely fragile condition, Linus, named by the volunteers, is spending a few days in a medically-equipped foster home. If Linus survives these critical days, he will be in search of a foster home to help him recuperate.
Someone did this to him. A person, possibly out of ignorance, let him "go." Now Linus is fighting for his life.
Hundreds of rabbits are abandoned on Long Island every year. Not all are as lucky as Linus to have a second chance.
To donate toward his care, visit http://longislandrabbitrescue.org/index.htm and click Donate on the right.
Share his story. Thank you for your support.