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Xilinx Bolsters Automotive Solution Offerings with First FPGA IP for Controller Area Networks
New addition to Xilinx LogiCORE library supports critical CAN IP protocol - delivers more flexibility and functionality to XA automotive PLDs
September 19, 2005, SAN JOSE, Calif., –Xilinx, Inc. (NASDAQ: XLNX) today announced the immediate availability of a fully verified intellectual property (IP) core for implementing controller area networks (CAN) in automotive designs. The IP offering, developed by the Xilinx Hyderabad team, complements the expansion of its market-leading Xilinx® Automotive (XA) family of programmable logic devices (PLDs). The CAN LogiCORE™ solution provides a full set of features and capabilities to quickly implement the increasingly popular CAN protocol with the Xilinx Spartan™ and Virtex™ Platform FPGAs, delivering a complete and flexible hardware/software solution to address the dynamic requirements of the automotive market. For more information visit www.xilinx.com/ipcenter.
The CAN soft IP core, which conforms to the ISO 11898-1 CAN 2.0A/B standards, can be used standalone or with the embedded MicroBlaze™ and PowerPC™ processors from Xilinx. The CAN IP solution can be used with the entire line of XA FPGA offerings.
"Flexibility is essential to the automotive market. The combination of our XA silicon solutions with this critical piece of functionality provides a natural fit for developing next-generation automotive applications," said Kevin Tanaka, Automotive marketing and product planning manager at Xilinx. "By using our pre-built hardware and software solutions, designers can drastically reduce development time with ultimate flexibility and freedom of choice for a wide range of functions."
CANs Worldwide Adoption
Controller area networks have been adopted as a standard protocol for European and U.S. automakers, and also growing in popularity in Japan and Asia-Pacific. The CAN protocol is most commonly utilized in gateways, body and door electronics, automated HVAC (Heating, Ventilation, Air Conditioning), dashboard and other driver information and safety-related applications. Due to its rapid adoption, most automotive designs now require at least a single CAN, and sometimes multiple CANs, in addition to other automotive networking protocols.
Specifically, the Xilinx CAN LogiCORE solution is appealing for its ability to allow designers the flexibility to program as many CAN channels as needed for each automotive platform, thus reducing the high costs of over-design for the application. In addition, the core can be combined with multiple protocols to optimize gateway applications as an alternative to generic solutions. The CAN LogiCORE IP itself also offers configurable FIFO depth (from 2 to 64 messages), and receive filtering support. Designers also have access to a full suite of third-party IP support available through the Xilinx AllianceCORE™ program.
License Pricing and Availability
The Xilinx CAN LogiCORE IP (DO-DI-CAN) is immediately available and priced at retail USD $18,000 (a one time, site license charge).
About Xilinx XA
The Xilinx Automotive (XA) product family is the industry's leading programmable logic family developed specifically for automotive applications. Xilinx automotive PLDs are already on the road in applications such as GPS, infotainment, communication, driver assistance and driver comfort. For more information, visit www.xilinx.com/automotive.
About Xilinx
Xilinx is the worldwide leader in complete programmable logic solutions. For more information, visit www.xilinx.com.
Contact Xilinx, Inc.
Fill out this form for contacting a Xilinx, Inc. representative.
Xilinx, Inc. Hot IP
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Spartan-3 LogiCORE Endpoint PIPE for PCI Express (PCIe)
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See Xilinx, Inc. IP >>
Xilinx Launches Next Generation of Video over IP Connectivity Solutions to Address Emerging All IP-Based Networks
Tamba Releases New Ultra-Low Latency Ethernet Cores for Xilinx UltraScale 20nm FPGA - Lowest Latency Achieved on an FPGA Device
Xilinx and Open-Silicon Announce Hybrid Memory Cube Controller IP for All Programmable FPGAs
Xilinx Expands All Programmable Artix-7 FPGA Family with New Automotive Qualified Devices
Xilinx FPGA Development Platforms Jump Start Innovation for Smarter Automotive System Designs at SAE Convergence 2010
See Xilinx, Inc. Latest News >>


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Home > Magazine > Magazine Detail
Wittmann one step closer to the DTM title
Event Reports, Featured
Alexander S
Marco Wittmann is one step closer to winning the DTM title after he has won the race 1 at Nurburgring and with three races remaining, he has a solid advantage in the overall standings.
Mercedes driver Lucas Auer had a fastest lap time in qualifying but had to serve a five-place grid penalty, so Marco Wittmann started from pole position. He was followed by another BMW driver Tom Blomqvist and two Audis of Jamie Green and Edoardo Mortara, while Daniel Juncadella at the 5th was the best placed Mercedes driver.
Marco Wittmann, the winner of race 1 at Nurburgring
The championship leader Wittmann had a comfortable 2.5s lead while Mortara moved up to the 3rd and desperately tried to overtake Blomqvist. The two had some big battles and contacts but both were able to continue the race. Behind them, there were also some incidents and after one of them, so Bruno Spengler, who was running 6th, got a drive-through penalty, just like Timo Scheider who was on P11. However, Juncadella was the biggest loser of the moment as he dropped to the back of the grid after one of the incidents.
Tom Blomqvist did well to take place 2nd place
Wittmann calmly brought the race to the end, claiming his third win of the season and eighth in DTM career. Blomqvist defended the 2nd place while Green late in the race managed to overtake Mortara for the 3rd. Timo Glock finished 5th while the remaining points went to Paul Di Resta, Lucas Auer, Maxime Martin, Robert Wickens, and Maxi Gotz.
Click here for full results
The DTM race 2 of the season's penultimate round will go on Sunday, 15:10 CET.
Photo: dtm.com
DTM at Norisring: Third win for Rast, Spengler returns to top podium spot
Audi triumphed at Nürburgring 24 Hours for the fifth time
DTM at Misano: Wins for Wittmann, Müller; championship lead for Rast

redpajama_set_name: RedPajamaCommonCrawl


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(Return to All Events)
When: Tuesday, October 12, 2021
9:00 AM - Wednesday, October 13, 2021 3:30 PM
Where: CVPA: College of Visual and Performing Arts	285 Old Westport Road, Dartmouth, MA
See: https://www.umassd.edu/cvpa/news/ceramics-symposium.html
Description: Artists Highlight Legacy of Colonialism in 2-Day Symposium
The University of Massachusetts Dartmouth Ceramics Club is proud to present: Can you see us now? A Diversity, Equity, & Inclusivity Ceramics Symposium, October 12th and 13th. Three contemporary artists, Natalia Arbelaez, Leila Babirye, and Courtney M. Leonard will create and discuss their investigations of ancestral origin, personal identity, and the legacy of colonization.
All three artists will demonstrate their techniques, present slide lectures of their work, and participate in a panel discussion. Demonstrations will take place in the tents behind the CVPA building at the main campus. The artists will present their slide lectures and participate in a panel discussion, moderated by Dr. Pamela Karimi, in the Grand Reading Room, 122, in the Claire T. Carney library. We will have an open dialogue about making personal ceramic work, issues facing today's and future generations of BIPOC-LGBTQ artists and encouraging critical discourse and sharing the issues and challenges of being a BIPOC-LGBTQ artist and interfacing with systems such as art institutions, including academia.
This is a free event! Don't miss out on this unique opportunity! Everyone is welcome and encouraged to attend!
9:00-12:30 Artist Demonstrations in tents behind the CVPA
12:30-1:30 Lunch break on site
1:30-3:30 Artist Demonstrations in tents behind the CVPA
3:30-5:00 Artist Slide Presentations in the Library's Grand Reading Room, 122
1:30-3:30 Artists Panel Discussion in the Library's Grand Reading Room, 122
Symposium concludes
Natalia Arbelaez is a Columbian American artist, born and raised in Miami, Florida, to immigrant parents. Her terracotta sculptures often involve figures interacting with one another or conveying emotion with their facial expressions. She draws from her Colombian family's immigration story to tell a larger cultural history of hybridization that is a result of migration.
Leilah Babirye was forced to flee Uganda when she was outed as a lesbian by a local newspaper. She sought asylum in the United States, when she was threatened by her country's virulent homophobic laws. Babirye's work often amplifies the disparity between the patriarchal systems of British colonialization and the matriarchal clan systems of historical Uganda. In addition to clay, she uses wood and found objects, including debris found on the streets.
Courtney M. Leonard is an artist and filmmaker, of the Shinnecock nation, which occupied the territory between Long Island and Connecticut. Leonard's work is an exploration and documentation of historical ties to place and sustainability. In collaboration with national and international museums, cultural institutions, and indigenous communities, Leonard's practice investigates narratives of cultural viability as a reflection of environmental record.
For more information, please contact Elizabeth K. Alvarez at ekaplan@umassd.edu
This event is sponsored by the Frederick Douglass Unity House, The Office of Diversity, Equity and Inclusivity, and the College of Visual and Performing Arts.
Contact: > See Description for contact information


My good friend Simon Sabin used the term 'invertability' on a Connect item he logged today.
Essentially, Simon's noticed that there are lots of people that use year(someDate), but that the system doesn't understand that this function doesn't affect the order of the items in the index. month(someDate) does, but if you're already using year(someDate), then the combination of the two doesn't change.
This is one of the keys to SARGability, which I've written about before, like at http://bit.ly/sargability. I've also raised a Connect item myself about it.
However, the term 'invertability' is interesting, and ties into the Inverse Predicates concept that I've also used before, like at http://bit.ly/inversepredicates. The idea is that you might have applied a function to a column, creating a predicate that isn't sargable, but if you (or the system) can tell how to invert it, then you can make a predicate that can be easily handled by the Query Optimizer. Currently, the system doesn't understand the invertability of all the functions (even easy ones like the YEAR function), but it's something which would make SQL a lot faster if it did.
I'm going to let you read those various posts yourself, and encourage you to vote for Simon's connect item. But as well as that, I'm going to encourage you to consider the SARGability of the predicates in your query.
Edit: Simon's written a post on this now.
No, this post has nothing to do with airport security, and nothing to do with marijuana.
Being honest, this post is only half about Hash Matches and Probe Residuals. It's more about the types of predicates you can see mentioned in a SQL Server Execution Plan (or Query Plan, but I prefer to call them Execution Plans) – but you may well see some described as a Probe Residual when you look at the properties of a Hash Match operator.
The main point of this post is: Some of these predicates can be really bad, even if they're part of things which seem really good (like Seeks or Merge Joins).
Let's consider a join. Two streams of data, from which matching rows must be found. These matching rows will be the ones that satisfy the join conditions, expressed through predicates listed in the ON clause and/or the WHERE clause. In fact, these predicates might only involve one side of the join, such as OrderDate <= '2011-03-22T00:00:00.000'. There are plenty of times when a join condition will incorporate a one-sided predicate like this – imagine a scenario in which matching rows can be easily located in an index, but only those that match an additional condition are allowed to be included.
In a join there are these two streams of data, joined by one of a few different operators. This operator could be a Hash Match; it could be a Merge Join. It could even be a Nested Loop, although then the predicates are (generally) handled in the second (lower) data source. In fact, let's start with that scenario.
When a Seek is performed, there are two main kinds of predicates that can be included. One is the Seek Predicate, and one is simply listed as Predicate. I prefer to call this second one the Residual Predicate. It's the leftover one, after the Seek has been performed. This often happens when SARGability isn't possible. SARGability is about being able to use an index effectively (with a seek), so if you have a predicate which doesn't allow SARGability (for example, being able to consider the last letter of a character string.
You'll see here that for each ProductSubcategory, we look up the Products for it. We have [s].[ProductSubcategoryID] before the Seek is done (the Nested Loop calls the Seek operator using each one), but although it can quickly seek to the right row(s) involved, it performs an additional check, making sure that the Product Name ends in a 'y'.
The index lets the system immediately search for the rows needed. It can seek to the rows in rf_ix_Product_Subcat_inc that match the SubcategoryID, but this is only half the story. Having applied this Seek Predicate, there's still the matter of the last letter of the Product Name, which isn't something that can be checked easily with the index. The values are there, but each row that the Seek finds must be checked individually, with this leftover, or Residual, Predicate.
Hopefully this helps show why I want to call the Predicate here a Residual Predicate.
But this Residual Predicate must be tested on every row that is fetched by the Seek. That could well be a lot of rows, if the Seek isn't particularly selective.
The Seek might feel really nice, and might be returning very few rows. But the effort could be a lot larger than you expect if most of the work is being done in the Residual Predicate.
The Nested Loop operator pulls rows from the first stream of data, and passes the required values down to the next row of operators, pulling a stream of data which is then simply joined. All the rows that come in from the second stream of data are known to be matches with the row that provided the values, so that there is relatively little work in doing the actual join.
In a Merge Join or Hash Match, things are slightly different. The predicate checking happens in the actual join operator. However, there are still two types of predicates – the main ones and the residuals.
In a Merge Join, the two data streams are ordered by columns appropriate for the join, but there could still be a leftover predicate.
Consider this query (but I've dropped the extra index at this point). It's a very similar query to before, but I'm forcing a Merge Join with a Join Hint, and I've thrown in a predicate which is non-SARGable from the perspective of either table. This is part of the clue to the problem – it's a non-SARGable predicate. Mind you, it could seem perfectly SARGable. It might simply be that the stream of data being used for the Merge Join isn't ordered by all the columns involved in the join.
When hovering over the Merge Join operator in this query's plan, we see this tooltip. You'll notice that it has a section called "Where (join columns)" which shows that the join is being done on the ProductSubcategoryID.
However, the other predicate isn't mentioned. It's nowhere in the tooltip. Hitting F4 shows the Properties window, and this is where we find it, in a property called Residual.
Interestingly, it re-checks that the ProductSubcategoryID columns match, but the important thing is that it's here (and only here) that Residual Predicate is tested.
This Residual Predicate must be tested on every combination of rows that match the 'Where (join columns)' predicate. That could well be a lot of rows, if those rows aren't particularly selective.
With a Hash Match, the join is done by first applying a Hash function to columns involved in the join, using the resultant Hash Key to populate the data (including all the required columns) into a Hash Table. Once that has been done for one stream of data, the second stream is pulled in, and the Hash function applied to the columns from the second stream. The result of each row from the second stream is used in a Probe of the Hash Table. However, predicates which don't work nicely with the Hash Key concept (such as the 'blah' predicate I used earlier) are considered residual.
So when candidate rows are identified via the Probe, the Probe Residual still needs to be tested. Just like the Predicate after the Seek Predicate was done, and the Residual after the Where (join columns) are handled.
This Probe Residual must be tested on every combination of rows that satisfies the Hash Key Probe. That could well be a lot of rows, if that probe isn't particularly selective.
When you're trying to tune your query, you need to consider how many rows are being matched by each section of the join.
The predicate featuring the ListPrice column is always going to be treated as residual. It's something that can only be tested once both values are known, and is considered a non-SARGable predicate. Regardless of what type of join is done, the ListPrice predicate is handled as a Residual.
For this query, the answer is hopefully obvious. Rewriting the predicate as p1.ListPrice = p2.ListPrice will resolve it nicely, but an example you have might not be so straightforward.
Residual predicates can be expensive, and the bottleneck of your query might not be obvious from looking at the plan. The mere fact that a Residual in a Merge Join is not shown in the tooltip could mean you miss it significantly. Don't worry – you'll be in good company. Plenty of proper experts miss this.
Luckily, the answer is simple. Look at your Seek Predicates, your Where (join columns) and your Hash Keys Probes, and compare this to the Residuals. If the Residuals are needing to be checked for a lot more rows that you'd like, then you have a tuning opportunity you can leverage. Ideally, Residuals only need to be applied on a tiny number of rows.
Remember, a Residual Predicate will feel like a Scan, because it's not using the Index nicely. Scanning a tiny table might be fine, but scanning a large one could be horrible.
How many people will be with you during 24HOP?
In less than a week, SQLPASS hosts another 24 Hours of PASS event, this time with an array of 24 female speakers (in honour of this month being Women's History Month).
From a licensing and marketing perspective, there is value in knowing how many people are watching the event, but there are no restrictions about how the thing is viewed.
Book a meeting room in your office with a projector, and watch 24HOP in there.
If you're planning to have it streaming in the background while you work, obviously this makes life a bit trickier. But if you're planning to treat it as a training event (a 2-day conference if you like) and block out a bit of time for it (as well you should – there's going to be some great stuff in there), then why not do it in a way that makes it so that other people can see that you're watching it, and potentially join you.
When an event like this runs, we can see how many different 'people' are attending each LiveMeeting session. What we can't tell is how many actual people there are represented. Jessica Moss spoke to the Adelaide SQL Server User Group a few weeks ago via LiveMeeting, and LiveMeeting told us there were less than a dozen people attending. Really there were at least three times that number, because all the people in the room with me weren't included.
I'd love to imagine that every LiveMeeting attendee represented a crowd in a room, watching a shared screen.
If you're not registered for the event yet, get yourself over to the SQLPASS site and make it happen.
Posted on March 9, 2011 October 20, 2017 Author Rob Farley2 Comments on How many people will be with you during 24HOP?
I wrote a post recently about how query tuning isn't just about how quickly the query runs – that if you have something (such as SSIS) that is consuming your data (and probably introducing a bottleneck), then it might be more important to have a query which focuses on getting the first bit of data out. You can read that post here.
In particular, we looked at two operators that could be used to ensure that a query returns only Distinct rows.
The Sort operator pulls in all the data, sorts it (discarding duplicates), and then pushes out the remaining rows. The Hash Match operator performs a Hashing function on each row as it comes in, and then looks to see if it's created a Hash it's seen before. If not, it pushes the row out. The Sort method is quicker, but has to wait until it's gathered all the data before it can do the sort, and therefore blocks the data flow.
But that was my last post. This one's a bit different.
This post is going to look at how Aggregate functions work, which ties nicely into this month's T-SQL Tuesday.
I've frequently explained about the fact that DISTINCT and GROUP BY are essentially the same function, although DISTINCT is the poorer cousin because you have less control over it, and you can't apply aggregate functions.
Just like the operators used for Distinct, there are different flavours of Aggregate operators – coming in blocking and non-blocking varieties. The example I like to use to explain this is a pile of playing cards.
If I'm handed a pile of cards and asked to count how many cards there are in each suit, it's going to help if the cards are already ordered. Suppose I'm playing a game of Bridge, I can easily glance at my hand and count how many there are in each suit, because I keep the pile of cards in order. Moving from left to right, I could tell you I have four Hearts in my hand, even before I've got to the end. By telling you that I have four Hearts as soon as I know, I demonstrate the principle of a non-blocking operation.
This is known as a Stream Aggregate operation. It requires input which is sorted by whichever columns the grouping is on, and it will release a row as soon as the group changes – when I encounter a Spade, I know I don't have any more Hearts in my hand.
Alternatively, if the pile of cards are not sorted, I won't know how many Hearts I have until I've looked through all the cards. In fact, to count them, I basically need to put them into little piles, and when I've finished making all those piles, I can count how many there are in each. Because I don't know any of the final numbers until I've seen all the cards, this is blocking. This performs the aggregate function using a Hash Match. Observant readers will remember this from my Distinct example.
You might remember that my earlier Hash Match operation – used for Distinct Flow – wasn't blocking. But this one is. They're essentially doing a similar operation, applying a Hash function to some data and seeing if the set of values have been seen before, but before, it needs more information than the mere existence of a new set of values, it needs to consider how many of them there are.
A lot is dependent here on whether the data coming out of the source is sorted or not, and this is largely determined by the indexes that are being used. If you look in the Properties of an Index Scan, you'll be able to see whether the order of the data is required by the plan. A property called Ordered will demonstrate this.
In this particular example, the second plan is significantly faster, but is dependent on having ordered data.
In fact, if I force a Stream Aggregate on unordered data (which I'm doing by telling it to use a different index), a Sort operation is needed, which makes my plan a lot slower.
This is all very straight-forward stuff, and information that most people are fully aware of. I'm sure you've all read my good friend Paul White (@sql_kiwi)'s post on how the Query Optimizer chooses which type of aggregate function to apply.
But let's take a look at SQL Server Integration Services.
SSIS gives us a Aggregate transformation for use in Data Flow Tasks, but it's described as Blocking. The definitive article on Performance Tuning SSIS uses Sort and Aggregate as examples of Blocking Transformations.
I've just shown you that Aggregate operations used by the Query Optimizer are not always blocking, but that the SSIS Aggregate component is an example of a blocking transformation. But is it always the case? After all, there are plenty of SSIS Performance Tuning talks out there that describe the value of sorted data in Data Flow Tasks, describing the IsSorted property that can be set through the Advanced Editor of your Source component.
And so I set about testing the Aggregate transformation in SSIS, to prove for sure whether providing Sorted data would let the Aggregate transform behave like a Stream Aggregate. (Of course, I knew the answer already, but it helps to be able to demonstrate these things).
A query that will produce a million rows in order was in order. Let me rephrase. I used a query which produced the numbers from 1 to 1000000, in a single field, ordered. The IsSorted flag was set on the source output, with the only column as SortKey 1. Performing an Aggregate function over this (counting the number of rows per distinct number) should produce an additional column with 1 in it.
If this were being done in T-SQL, the ordered data would allow a Stream Aggregate to be used. In fact, if the Query Optimizer saw that the field had a Unique Index on it, it would be able to skip the Aggregate function completely, and just insert the value 1. This is a shortcut I wouldn't be expecting from SSIS, but certainly the Stream behaviour would be nice.
Unfortunately, it's not the case.
As you can see from the screenshots above, the data is pouring into the Aggregate function, and not being released until all million rows have been seen. It's not doing a Stream Aggregate at all.
An SSIS transformation is a piece of code that runs. It's a physical operation. When you write T-SQL and ask for an aggregation to be done, it's a logical operation. The physical operation is either a Stream Aggregate or a Hash Match. In SSIS, you're telling the system that you want a generic Aggregation, that will have to work with whatever data is passed in.
I'm not saying that it wouldn't be possible to make a sometimes-blocking aggregation component in SSIS. A Custom Component could be created which could detect whether the SortKeys columns of the input matched the Grouping columns of the Aggregation, and either call the blocking code or the non-blocking code as appropriate. One day I'll make one of those, and publish it on my blog. I've done it before with a Script Component, but as Script components are single-use, I was able to handle the data knowing everything about my data flow already.
As per my previous post – there are a lot of aspects in which tuning SSIS and tuning execution plans use similar concepts. In both situations, it really helps to have a feel for what's going on behind the scenes. Considering whether an operation is blocking or not is extremely relevant to performance, and that it's not always obvious from the surface.
In a future post, I'll show the impact of blocking v non-blocking and synchronous v asynchronous components in SSIS, using some of LobsterPot's Script Components and Custom Components as examples. When I get that sorted, I'll make a Stream Aggregate component available for download.


Ramadan is almost upon us, and with it comes the cleansing of the spirit, body, and the thorough home-cleaning before Hari Raya.
Understanding the pressures of rising costs of living especially when preparing for the festive season, Lam Soon's home and personal care brands have come together to delight consumers with the Raya Fiesta Berganda contest that offers 416 prizes over 13 weeks, including 390 weekly prizes of RM100 each, 21 special prizes of RM1,000 each and five grand prizes of RM10,000 for a total of RM110,000 in duit Raya! The contest commenced on April 2 until July 2, 2018.
The participating Lam Soon brands for Raya Fiesta Berganda include Antabax, Malaysia's number 1 Halal antibacterial personal care range1, bio-home a range of eco-friendly home cleaning solutions made from biodegradable and plant-based active ingredients, Orchid feminine wash, May and Fruitale personal care ranges as well as ZIP and Labour household aids.
The Raya Fiesta Berganda contest is so easy to join as most households will be buying these products regularly: Purchase RM20 from any participating Lam Soon brands in a single receipt. Then name any three Lam Soon brands that are participating this contest. Simply Whatsapp your entry to +6012.526.3391 including your full name, IC number and answer. Attach a photograph of your receipt with your entry for verification purposes. If the RM20 purchase receipt includes two or more participating brands there is the chance to win one of the 21 special prize of RM1,000!
audience with our work, and with our positive message!
see my work here until mid-October.
community a bigger platform to showcase our skill, technique and capabilities.
There is a contest and I love shopping online, so this is SGshop Malaysia, wanna know what 3 itmes I have my eyes on. Lets check out them.There's a contest going on at Ayue Idris blog do check out now. There will be 10 winners to win RM500 shopping credits from Ayue to shop online at SGshop.
Apakah Servis yang ditawarkan oleh SGshop? Online shopping service, the an online e commerce of China, like Taobao, Tmall.com and many more. I visited the website and it's in English language good for me because my Chinese language is no good I can easy shopping in Malaysia using their service.
Senarai 3 items yang korang ingin dapatkan dari SGshop using RM500?
The items 1: The waterproof Mini DV camera because I been looking high and low for such camera.
2. light shoes woman, which I wanna to own a pair for myself.
3. bracelet for myself, love the silver jewelry that I saw online.


I know I say the same thing every time I write about a Welsh band, but the amount of involvement I have with the local scene is shocking on my part to say the least. A case in point being this as my very first encounter with Pretty Vicious, a band well on their way to great things hailing from Merthyr. Premiered by Annie Mac as her Hottest Record last night, the band's latest single National Plastics is an explosion of killer riffs and voracious percussion underpinned by their youthful angst and vigour. Guided by gritty vocals and some insatiable guitar layering the South Wales quartet unleash an earth shattering force that's sure to turn heads. Take a listen below for yourselves.
National Plastics will be released on Virgin EMI September 18th.


text:
Changes in freshwater availability across Canada
Bonsal, B R; Peters, D L; Seglenieks, F; Rivera, A; Berg, A
Canada's changing climate report; by Bush, E (ed.); Lemmen, D S (ed.); 2019 p. 261-341
Online - En ligne
on-line; digital
This publication is contained in Bush, E; Lemmen, D S; (2019). Canada's changing climate report
This publication is a translation of Bonsal, B R; Bonsal, B R; Peters, D L; Peters, D L; Seglenieks, F; Seglenieks, F; Rivera, A; Rivera, A; Berg, A; Berg, A; (2019). Évolution de la disponibilité de l'eau douce à l'échelle du Canada, Rapport sur le climat changeant du Canada
pdf (Adobe® Reader®)
Offshore region; Western offshore region; Northern offshore region; Eastern offshore region; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut
Nature and Environment; hydrogeology; environmental geology; geophysics; climate; climate effects; climatology; surface waters; rivers; lakes; groundwater; hydrologic environment; runoff; stream flow; flow regimes; floods; wetlands; deltas; soil moisture; temperature; precipitation; groundwater levels; remote sensing; satellite imagery; groundwater regimes; Gravity Recovery and Climate Experiment (GRACE); climate change; fresh water; drought
block diagrams; schematic representations; location maps; tables; geoscientific sketch maps; time series
National Aquifer Evaluation & Accounting, Groundwater Geoscience
(Summary)
Freshwater availability in Canada is influenced by a multitude of factors: some natural, some as a result of human activity. Changes in precipitation and temperature have a strong influence, both directly and indirectly, through changes to snow, ice, and permafrost. Disturbances of the water cycle by humans (dams, diversions, and withdrawals) make it difficult to discern climate-related changes. Direct measurements of freshwater availability indicators are inconsistent across the country and, in some cases, too sparse to evaluate past changes. In addition, future changes are determined from a multitude of hydrological models, using output from numerous climate models with different emission scenarios. These factors make it challenging to conduct a pan-Canadian assessment of freshwater availability, and even more difficult to determine whether past changes can be attributed to anthropogenic climate change. In this chapter, national and regional studies are considered, along with information on changes in temperature and precipitation from Chapter 4 and changes to the cryosphere from Chapter 5, to assess changes to freshwater availability in Canada.
Past changes in the seasonality of streamflow have been characterized by earlier spring freshets (the increased flow resulting from snow and ice melt in the spring) due to earlier peaks in spring snowmelt, higher winter and early spring flows, and, for many regions, reduced summer flows. These changes are consistent with observed warming and related changes to snow and ice. During the last 30 to 100 years, annual streamflow magnitudes, surface water levels, soil moisture content and droughts, and shallow groundwater aquifers have, for the most part, been variable, with no clear increasing or decreasing trends. This variability corresponds to observed year-to-year and multi-year variations in precipitation, which are partly influenced by naturally occurring large-scale climate variability (see Chapter 2, Box 2.5). However, for many indicators, there is a lack of evidence (particularly in northern regions of the country) to assess Canada-wide past changes in freshwater availability.
Continued warming and associated reductions in snow cover, shrinking mountain glaciers, and accelerated permafrost thaw are expected to continue to drive changes in the seasonality of streamflow. This includes increased winter flows, even earlier spring freshets, and reduced summer flows, as well as corresponding shifts from more snowmelt-dominated regimes toward rainfall-dominated regimes. Annual streamflow is projected to increase in some areas (mainly northern regions), but decline in others (southern interior regions). Thawing permafrost could cause future changes in many northern Canadian lakes, including rapid drainage. The frequency and intensity of future streamflow-driven flooding are uncertain, because of the complexity of factors involved. Projected increases in extreme precipitation are expected to increase the potential for future urban flooding. However, it is uncertain how projected higher temperatures and reductions in snow cover will combine to affect the frequency and magnitude of future snowmelt-related flooding. Lower surface water levels of lakes and wetlands are expected, especially toward the end of this century, under higher emission scenarios (see Chapter 3, Section 3.2), due to higher temperatures and increased evaporation. However, the magnitude of these decreases will depend on how much future precipitation increases offset increased evaporation.
Future increases in drought and decreases in surface soil moisture are anticipated during summer in the southern Canadian prairies and interior British Columbia, where moisture deficits from increased evapotranspiration are projected to be greater than precipitation increases. These changes are expected to be more prominent toward the end of this century under higher emission scenarios; however, there is considerable uncertainty in their magnitude. Groundwater systems are complex, and, although it is expected that changes to temperature and precipitation will influence future levels, the magnitude and even direction of change is not clear. However, in the future, spring recharge of groundwater aquifers over most of the country is anticipated to occur earlier, as a result of earlier snowmelt.
These anticipated changes from anthropogenic climate warming will directly affect the timing and amount of future freshwater supplies, and they may be exacerbated by human management alterations to freshwater systems. The impacts are expected to be more prominent toward the end of this century under higher emission scenarios, given the larger associated climate changes. Of particular concern are impacts in regions that currently rely on snow and ice melt as freshwater sources, as well as continental interior areas, where increased evapotranspiration from warmer temperatures could reduce future water supplies. However, freshwater supplies in all regions of Canada are expected to be affected in one way or another. It is also anticipated that water-related extremes, such as droughts and floods, will intensify these impacts.


text:
Ryan Murphy and Husband David Miller Share Three Kids Together
By Kelly Corbett
Jan. 17 2023, Published 11:58 a.m. ET
Director, producer, and TV writer Ryan Murphy is one of the top names in the entertainment industry. He is the mastermind behind a number of popular TV series like Nip/Tuck, Glee, American Horror Story, and Monster: The Jeffrey Dahmer Story. His ability to create one hit TV show after another is unparalleled — and he's got six Emmys and a Tony to prove it.
But who is Ryan besides, of course, an incredibly brilliant and creative man? Well, he's also a loving husband and father. Keep reading as we discuss his family.
Who is Ryan Murphy's husband?
Ryan Murphy is married to L.A.-based photographer David Miller, who takes on a lot of high-profile projects per his professional Instagram. Prior to working as a photographer, David worked as a commercial location manager for over a decade, during which he teamed up with clients like Nike, Ford, IBM, and Apple, per his website.
According to The New Yorker, Ryan met David in 1996 at a gay hangout in L.A. called Muse. They befriended one another but it wasn't until over 15 years later (in 2010) that they became involved romantically.
Ryan told the outlet, "It was kind of like — I don't want to say, 'Game on,' but we knew what was up, because we'd flirted over the years. We were at an age where we were both serious about wanting a relationship, wanting children."
On July 4, 2012, the pair tied the knot on the beach in Provincetown, Mass., with just an officiant present.
Ryan Murphy and David Miller with son Logan Phineas Miller Murphy
Ryan Murphy and David Miller have three kids together.
Ryan and David welcomed their first son, Logan Phineas Miller Murphy, on Dec. 24, 2012. As their oldest son, he is now at the age where he sometimes walks the red carpet with the couple at events. Logan, as well as Ryan and David's other two children, were all born via surrogate.
Shortly after Logan's birth, Ryan actually introduced the sitcom The New Normal, which was based on his and David's journey to start a family through surrogacy. Sadly, the series only lasted one season.
On Oct. 6, 2014, Ryan and David welcomed their second son, Ford Theodore Miller Murphy. Although the couple usually kept their personal lives private, they did share some details with the public after Ford was diagnosed with neuroblastoma at age 2 and had to undergo aggressive treatment.
According to People, Ryan wrote in a since-deleted Instagram post in 2018 that Ford had an abdominal tumor the size of a tennis ball. He added, "Ford has undergone a huge surgery and several difficult procedures" in an effort to rid his tiny body of cancer.
Fortunately, Ford did make a full recovery. To thank the staff for helping their son, Ryan and David donated $10 million to Children's Hospital Los Angeles. As a result, a wing in the hospital was named after Ford.
Last but not least, Griffin Sullivan Miller Murphy was born on Aug. 18, 2020. He is Ryan and David's youngest son.
In terms of parenting their three boys, Ryan revealed to Entertainment Tonight that he's actually a total softy. In an interview, he said, "I thought I was going to be the great disciplinarian, completely rigid, and I am shocked to tell you that I am the opposite. I am the biggest pushover."
Why Are Ryan Murphy Characters So Intoxicating? A Deep Dive
The Broaddus Family Sold the Watcher House in 2019 — Who Owns the Hellish Property Now?
Is Evan Peters Part of the 'American Horror Story' Season 11 Cast? Here's What We Know


Do you know of any other reason why haproxy might not start? I applied the fixes you describe above, but the service still refuses to start. I don't see anything in the logs to point me in the right direction either.
Thanks for the correction Keith. I have corrected the post.
By doing the above steps I got it working. Maybe double your config. The IP addresses are configured correctly? Proxy ARP address don't work with HA Proxy on pfSense, they either need to be the primary interface address or CARP addresses. The port/s you have HA Proxy listening on aren't being used by another service such as the pfSense webGUI? Other than that I don't know what to suggest.


text:
Q: graph-api: Adaptive card with mention doesn't render date properly It seams that msteams object doesn't allow to render date, when card pushed through graph api in private msteams channel.
Example is like this:
POST https://graph.microsoft.com/v1.0/teams/{team_id}/channels/{channel_id}/messages
{
  "body": {
        "contentType": "html",
        "content": "<attachment id=\"1\"/>"
  },
    "attachments":[
        {
            "contentType":"application/vnd.microsoft.card.adaptive",
            "id":"1",
            "content":"{\"type\":\"AdaptiveCard\",\"body\":[{\"text\":\"<at>Lev</at> hello {{ DATE(2021-04-28T00:00:00Z, SHORT) }}\",\"wrap\":true,\"type\":\"TextBlock\"}],\"version\":\"1.3\",\"msteams\":{\"entities\":[{\"type\":\"mention\",\"text\":\"<at>Lev</at>\",\"mentioned\":{\"id\":\"29:131...Rg\",\"name\":\"Lev\"}}]}}"
        }
    ]
}

Example in teams
Is there some workaround?

A: Please check the below request body
{
    "body": {
        "contentType": "html",
        "content": "<attachment id=\"1\"/>"
    },
    "attachments": [
        {
            "contentType": "application/vnd.microsoft.card.adaptive",
            "id": "1",
            "content": "{    \"$schema\": \"http://adaptivecards.io/schemas/adaptive-card.json\",    \"type\": \"AdaptiveCard\",    \"version\": \"1.0\",    \"body\": [        {            \"type\": \"TextBlock\",            \"text\": \"Your package will arrive on {{DATE(2017-02-14T06:00:00Z, SHORT)}} at {{TIME(2017-02-14T06:00:00Z)}}\",            \"wrap\": true        }    ]}"
        }
    ]
}

A: msteams object is not allowed through Graph API. Only Bots have access to the msteams object. This is by design.
The at mention object when you send from bot, the user will get notify in teams activity and the card will display as shown in below image

and the at mention in adaptive card using graph api post request, the user will not get notify in teams activity and the card will display as shown in below image

redpajama_set_name: RedPajamaStackExchange