Similarities and Differences Between CE Series. I want answer in brief discussion to complete understand.
CE series clearomizers are all top coil clearomizers and can compatible with ego batteries. CE4 is non-rebuildable but others are all rebuildable and removable. CE4 and CE5 have only 1.6 volume capacity. CE6 is usually called vivi nova which has 3 ml volume capacity and have outer thread. CE7 is mini vivi nova and also have outer thread which can compatible with 510 thread battery. For more information, please check: http://youtu.be/zI6Ihq-ipT8
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I have been learning the book "Computer Algebra and Materials Physics" during this period of time.
In the chapter 9 titled by "Final Remarks", the author wrote the following:
In my article, I have omitted several important application of the
group theory and the algebra. One of this is the treatment of magnetic
or color groups. Also I omitted the treatment of Lie algebra,
important in the spin system or the study of angular momentum. As for
the former, a package of color groups is implemented in GAP (The Gap
Group 2017).
Since there is no example, I am still confused about the magnetic space group creating in GAP. It's well known that there are 1651 types of
magnetic space groups in total, as shown below:
I'm not sure if all these groups can be created and manipulated in GAP currently. Any tips or examples
will be appreciated.
Regards,
HZ
We are looking for NewSQL database.
We were using ScyllaDB (Cassandra) which in ~ 10 days test just had fatal crash (all nodes are unresponsive to API, CSQL is working, one node after reset has lost data for > 5 days (100GB), after reset failed to rebuild).
This is the most tragic crash that any DB that we've tested had, so we are looking for something else.
In terms of data we're talking about 200-500TB per month, > 10 nodes.
~ 50k writes a second, ~ 200k reads a second.
We need something that:
supports SQL (Cassandra was a huge headache during design stage, impossible to replace certain aspects of our MySQL database (which is fine, but we are testing new product that has much higher requirements)
can scale horizontally well from 5-10-50 nodes + regional support (we have farms on in 8 main locations, they have to replicate fairly well, but clients from i.e. USA West won't access data in USA East)
can support read/write scaling
we do not use transactions as per se, but want to be able to where X = 'E' and Date > '2020'
I'm using LabVIEW to operate and record data from a wastewater reactor. I currently have a program set up to monitor pH continuously, and then use pH data to turn on either an acid or base pump.
My problem is that I want to monitor and record pH data 24/7, but I only want my acid/base pumps to be activated for one hour, every three hours. Ideally, I'd like to tie these operating times to the computer's clock.
For example, from 10:05 am to 11:05 am, I want my acid and base pumps to use data from the pH sensor to either turn on or remain off depending on the pH measured. My goal pH is 7.0 +/- 0.3. For example, if the pH measured was 6.5, the base pump would turn on until a pH of 6.7 is reached. If the pH measured was 7.5, the acid pump would turn on until a pH of 7.3 was reached. If the pH was 7, both pumps would remain off. So far, my code does this, but pumps are turned on and off constantly.
At 11:05, both pumps would be "deactivated" and turn off, though pH measurement should continue. Then, 3 hours after the initial pump start time (3 hours after 10:05 am = 1:05 pm, or 2 hrs after the 11:05 am stop time) this cycle would start again, running again for one hour. I want this cycle to continue over and over (i.e. pumps on in response to pH measurements for 1 hr, every 3 hours).
Is it possible to do this in LabView? (I'm extremely new to LabVIEW also). Thanks!
Yes, it's certainly possible to do this.
The simplest way to achieve what you describe would be to add extra logic to the pump control code inside your loop. Each loop iteration, get the current time (e.g. with Get Date/Time in Seconds) and calculate whether the pumps should be enabled or not (you might find Quotient and Remainder useful). Then you could use an And function to enable each pump if both the pH calculation and the enabled-time calculation produce a True output.
I'd suggest using functions from the Programming palette for your greater than, less than, And, etc operations as they take less diagram space and are easier to understand than the Express functions in my opinion.
A more sophisticated and scalable approach might be to separate the pH measurement and the pump control into two different loops and use some mechanism to transfer the latest pH value into the pump control loop (a notifier, local variable, functional global or channel wire would all be options here). A state machine would then be a good pattern for the pump control logic.
Im trying to analyse data from cycle accidents in the UK to find statistical black spots. Here is the example of the data from another website. http://www.cycleinjury.co.uk/map
I am currently using SQLite to ~100k store lat / lon locations. I want to group nearby locations together. This task is called cluster analysis.
I would like simplify the dataset by ignoring isolated incidents and instead only showing the origin of clusters where more than one accident have taken place in a small area.
There are 3 problems I need to overcome.
Performance - How do I ensure finding nearby points is quick. Should I use SQLite's implementation of an R-Tree for example?
Chains - How do I avoid picking up chains of nearby points?
Density - How to take cycle population density into account? There is a far greater population density of cyclist in london then say Bristol, therefore there appears to be a greater number of backstops in London.
I would like to avoid 'chain' scenarios like this:
Instead I would like to find clusters:
London screenshot (I hand drew some clusters)...
Bristol screenshot - Much lower density - the same program ran over this area might not find any blackspots if relative density was not taken into account.
Any pointers would be great!
Well, your problem description reads exactly like the DBSCAN clustering algorithm (Wikipedia). It avoids chain effects in the sense that it requires them to be at least minPts objects.
As for the differences in densities across, that is what OPTICS (Wikipedia) is supposed do solve. You may need to use a different way of extracting clusters though.
Well, ok, maybe not 100% - you maybe want to have single hotspots, not areas that are "density connected". When thinking of an OPTICS plot, I figure you are only interested in small but deep valleys, not in large valleys. You could probably use the OPTICS plot an scan for local minima of "at least 10 accidents".
Update: Thanks for the pointer to the data set. It's really interesting. So I did not filter it down to cyclists, but right now I'm using all 1.2 million records with coordinates. I've fed them into ELKI for analysis, because it's really fast, and it actually can use the geodetic distance (i.e. on latitude and longitude) instead of Euclidean distance, to avoid bias. I've enabled the R*-tree index with STR bulk loading, because that is supposed to help to get the runtime down a lot. I'm running OPTICS with Xi=.1, epsilon=1 (km) and minPts=100 (looking for large clusters only). Runtime was around 11 Minutes, not too bad. The OPTICS plot of course would be 1.2 million pixels wide, so it's not really good for full visualization anymore. Given the huge threshold, it identified 18 clusters with 100-200 instances each. I'll try to visualize these clusters next. But definitely try a lower minPts for your experiments.
So here are the major clusters found:
51.690713 -0.045545 a crossing on A10 north of London just past M25
51.477804 -0.404462 "Waggoners Roundabout"
51.690713 -0.045545 "Halton Cross Roundabout" or the crossing south of it
51.436707 -0.499702 Fork of A30 and A308 Staines By-Pass
53.556186 -2.489059 M61 exit to A58, North-West of Manchester
55.170139 -1.532917 A189, North Seaton Roundabout
55.067229 -1.577334 A189 and A19, just south of this, a four lane roundabout.
51.570594 -0.096159 Manour House, Picadilly Line
53.477601 -1.152863 M18 and A1(M)
53.091369 -0.789684 A1, A17 and A46, a complex construct with roundabouts on both sides of A1.
52.949281 -0.97896 A52 and A46
50.659544 -1.15251 Isle of Wight, Sandown.
...
Note, these are just random points taken from the clusters. It may be sensible to compute e.g. cluster center and radius instead, but I didn't do that. I just wanted to get a glimpse of that data set, and it looks interesting.
Here are some screenshots, with minPts=50, epsilon=0.1, xi=0.02:
Notice that with OPTICS, clusters can be hierarchical. Here is a detail:
First, your example is quite misleading. You have two different sets of data, and you don't control the data. If it appears in a chain, then you will get a chain out.
This problem is not exactly suitable for a database. You'll have to write code or find a package that implements this algorithm on your platform.
There are many different clustering algorithms. One, k-means, is an iterative algorithm where you look for a fixed number of clusters. k-means requires a few complete scans of the data, and voila, you have your clusters. Indexes are not particularly helpful.
Another, which is usually appropriate on slightly smaller data sets, is hierarchical clustering -- you put the two closest things together, and then build the clusters. An index might be helpful here.
I recommend though that you peruse a site such as kdnuggets in order to see what software -- free and otherwise -- is available.
We've been trying to figure out why we only achieve writing speed of ~53MBps on UHS104 cards that claim 90MBps.
Due to hardware constraints, clock frequency supplied to the card is only 148.5 MHz (instead of 208MHz).
Does that mean that we should achieve speed of (148.5 * 4)/8 = 74.25MBps?
Or is our caclulation wrong since it assumes that if card guarantees speed of 90MBps on frequency of 208MHz, then it should guarantee speed of 74.25MBps on frequency of 148.5?
The simplified physical layer spec states that for maximum performance you need to write full AU blocks - usually 2 or 4 MByte, otherwise the card will have to copy data around internally when writing across block boundaries. Unfortunately, most of the Speed Class Specification is missing in the 4.13 chapter.
The first AUs may have a different wear level strategy, as they are normally used for the FATs. This could make them slower to write to.