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charts are compiled. But is it from me. We will be back at two o'clock | :00:00. | :00:00. | |
but now here on BBC News it is time for Click. | :00:00. | :00:08. | |
This week, watch out, pollution. We will clean up the city with a bird? | :00:09. | :00:16. | |
No, a plane? No. It is a flying fish drone. This week is the BBC's so I | :00:17. | :00:47. | |
can breathe season, looking at ways to tackle air pollution around the | :00:48. | :00:51. | |
world. We are out on the streets of London to test a new camera from a | :00:52. | :00:56. | |
thermal imaging company. It has a sensitivity to a range of gases | :00:57. | :01:01. | |
which are invisible to the human night. The camera is supposed to be | :01:02. | :01:05. | |
used by experts who know what they are looking for in numbers and | :01:06. | :01:08. | |
colours that they see a bid is really supposed to be used in | :01:09. | :01:12. | |
industrial locations as well where you are looking for gas leaks. But, | :01:13. | :01:18. | |
I must say, even here I can see sprays coming from some of the | :01:19. | :01:22. | |
exhaust pipes through this camera that I cannot see with my eyes. Now, | :01:23. | :01:33. | |
if you want to tackle air pollution problems across a city, you have to | :01:34. | :01:36. | |
know where the pollution is coming from and at what time of day. That | :01:37. | :01:40. | |
is something that Mark has been investigating. Poorer quality, as a | :01:41. | :01:49. | |
result of pollution, poses a serious risk to public health. It is a huge | :01:50. | :01:53. | |
problem. The global burden of disease data now suggests that a | :01:54. | :01:58. | |
lack of clean air is the third leading cause of death in the world | :01:59. | :02:01. | |
after high blood pressure and smoking. But whether it triggers | :02:02. | :02:06. | |
allergy or asthma, understanding the exact challenges that pollution | :02:07. | :02:10. | |
causes, especially in a city, can be tricky. The levels of pollution in | :02:11. | :02:19. | |
cities can vary a lot between individual streets. The more precise | :02:20. | :02:22. | |
the information is, the better we can come up with strategies to | :02:23. | :02:26. | |
improve things. We can identify areas with particular problems. | :02:27. | :02:33. | |
Action to gather that even more precise data about pollution is | :02:34. | :02:36. | |
being taken on the other side of the Atlantic, in Chicago. Because of the | :02:37. | :02:42. | |
location of Chicago in the midwest and the fact that it is a large | :02:43. | :02:46. | |
city, it is something of a transport hub for road, rail and air | :02:47. | :02:51. | |
travellers. All those different vehicles don't do any favours for | :02:52. | :02:57. | |
the air quality in the city. Here, a system is being installed which has | :02:58. | :03:02. | |
been dubbed a fitness track for a city. It is called the array of | :03:03. | :03:06. | |
things when it is completed it will be a citywide network of sensors | :03:07. | :03:12. | |
fitted to lampposts and polls. The rate will monitor a variety of | :03:13. | :03:16. | |
things from local climate to traffic levels and the air quality of the | :03:17. | :03:20. | |
city. Eventually, all of the data the array gathers will be made | :03:21. | :03:31. | |
available online for anybody to use. We have come just outside of Chicago | :03:32. | :03:37. | |
to the Argonne National laboratory. It is part of the US Department of | :03:38. | :03:42. | |
energy and is the birthplace of the array of things. The donor is really | :03:43. | :03:55. | |
into air quality, so they are really excited. Here, the team behind the | :03:56. | :04:01. | |
array continue to refine the centre boxes and the technology they can | :04:02. | :04:05. | |
tame, blazing with city officials and arranging the continual roll-out | :04:06. | :04:08. | |
of the network across the city. This is the guts, if you like, of the | :04:09. | :04:14. | |
array of things. Which party you if the air quality sensor? This one is | :04:15. | :04:20. | |
the air quality sensor. It is an elegiac cell at tuned to a Pacific | :04:21. | :04:26. | |
type of chemical. This is a carbon monoxide one, this is the hydrogen. | :04:27. | :04:30. | |
And it will record the total level of gas. Installation of the array | :04:31. | :04:37. | |
began towards the end of 2016. By the end of 2018, 500 nodes are | :04:38. | :04:41. | |
planned for the network, spread across different parts of the city. | :04:42. | :04:47. | |
Charlie Kaplan is the project lead. He took me on a whistlestop tour of | :04:48. | :04:50. | |
some of the city's earlier census sites. So, Charlie, this is the site | :04:51. | :04:57. | |
of one of your first sensors, isn't it? This is one of the first six. | :04:58. | :05:03. | |
This one here does the air quality, not just the general air quality but | :05:04. | :05:08. | |
this one will tell us seven different gases and so that means we | :05:09. | :05:12. | |
can say, well, this one is reading this gas particularly high and we | :05:13. | :05:16. | |
know that that that is associated with a diesel truck. The new ones | :05:17. | :05:21. | |
that we are putting in we have added an sensor for particles. What we can | :05:22. | :05:25. | |
do with a particle sensor is we can look at the very fine particles that | :05:26. | :05:30. | |
are measured by EPA and other organisations. The smaller particles | :05:31. | :05:37. | |
are the ones you cannot see but they are quite dangerous. They will go | :05:38. | :05:40. | |
straight into your bloodstream. The large ones are what triggers | :05:41. | :05:43. | |
allergy. So if you are somebody with allergies related to asthma, you | :05:44. | :05:47. | |
will be able to use the data from these nodes to look at Poland across | :05:48. | :05:53. | |
the city and you may decide to change your cycle route to school or | :05:54. | :05:58. | |
work, a sum may be where the pollen concentration is around the city. | :05:59. | :06:02. | |
Chicago was not alone when it comes to pollution monitoring. We have a | :06:03. | :06:08. | |
system also in London which combines historical pollution data with | :06:09. | :06:11. | |
current pollution measurements to provide an hourly update of | :06:12. | :06:18. | |
pollution levels across the city. The rollout in Chicago continues. | :06:19. | :06:21. | |
The array of things nodes have been installed in other US cities with | :06:22. | :06:25. | |
one in Seattle and another in Denver and there is interest in the city -- | :06:26. | :06:30. | |
system internationally as well. The data generated by the array of | :06:31. | :06:35. | |
things will be used by researchers, scientists and healthcare | :06:36. | :06:37. | |
professionals to get a better picture of the effects of poor air | :06:38. | :06:42. | |
quality and pollution. When it comes to turning this information into | :06:43. | :06:46. | |
action, that is job of local government. These two employees | :06:47. | :06:56. | |
works of the city of Chicago and working out how the array of things | :06:57. | :07:00. | |
can help city look at a range of issues. We have pockets of increased | :07:01. | :07:06. | |
rates of asthma among our children that doctors have known about for | :07:07. | :07:09. | |
quite sometime but they do not have a lot of information on why they | :07:10. | :07:14. | |
happen in certain areas of the city. The role of the array is to help us | :07:15. | :07:20. | |
understand the issues with air quality in Chicago in a detailed | :07:21. | :07:24. | |
level because you cannot fix a problem if you cannot define it and | :07:25. | :07:27. | |
understand it. We think about how heavy pollutant vehicles, say, if we | :07:28. | :07:33. | |
installed hundreds of miles of biplanes, there is clear research | :07:34. | :07:39. | |
showing that inhaling diesel fumes, especially by cyclists as they ride | :07:40. | :07:43. | |
alongside traffic, can harm them. It helps us to picture and take a good | :07:44. | :07:48. | |
look at where the bike avenues are and how that corresponds with the | :07:49. | :07:53. | |
system. If you have a school or a vulnerable location close to an area | :07:54. | :07:57. | |
that has increased air quality challengers, the data from the array | :07:58. | :08:01. | |
of things will give us the ability to define a policy that will address | :08:02. | :08:05. | |
that. A good example here in Chicago will be a quickly growing | :08:06. | :08:10. | |
neighbourhood on the west side. It has evolved into one of our | :08:11. | :08:14. | |
trendiest residential and entertainment district. But it has | :08:15. | :08:18. | |
also been crisscrossed by any number of street level railroads. By | :08:19. | :08:23. | |
looking at data and using data we will make decisions more confidently | :08:24. | :08:27. | |
and we will know we'd better than many other cities have the ability | :08:28. | :08:31. | |
to know that because of the data that we collect. Here, the | :08:32. | :08:35. | |
technology has a role to play in the fight against poor air quality. But | :08:36. | :08:42. | |
the bigger pollution busting powers relate to local and national | :08:43. | :08:49. | |
government. That was market in Chicago. In London, I'm checking out | :08:50. | :08:56. | |
a pollution monitoring device with a difference. I will give you a | :08:57. | :09:02. | |
clue... This is the launch pad. With this water tank, they can launch | :09:03. | :09:07. | |
their prototype. They even have their own in tunnel. Imperial | :09:08. | :09:18. | |
College in London have a drone that can fly through the air, dive into | :09:19. | :09:22. | |
the water and then leapt out again. Splash! All the while, gathering | :09:23. | :09:28. | |
data to give us a greater understanding of pollution levels | :09:29. | :09:31. | |
above and below the surface. The plan is to release a swarm of them | :09:32. | :09:39. | |
into an area of concern. This is our response to extreme environments or | :09:40. | :09:44. | |
post disaster applications such as after floods toxic spills, or I'll | :09:45. | :09:49. | |
spills, nuclear accidents or so numb is. They are different classes of | :09:50. | :09:53. | |
applications and capital abilities that they had to do something. This | :09:54. | :10:00. | |
low-cost tool brings an enormous value compared to many other methods | :10:01. | :10:03. | |
such as the human going there with a full protective suit. I was going to | :10:04. | :10:08. | |
say, we have seen a lot of quite robots and we have seen a lot of | :10:09. | :10:12. | |
flying robots. It never occurred to me that is quite difficult to get an | :10:13. | :10:17. | |
underwater robot over great distances quickly and, so, you have | :10:18. | :10:23. | |
combined the two. That is hard-core. So, yes, we will just die of it in | :10:24. | :10:28. | |
the water and then died out and fly it that way. In some applications it | :10:29. | :10:34. | |
is not even accessible through the water, in floods or ice you may not | :10:35. | :10:39. | |
get there. On the other side, and aerial beacon may not be able to get | :10:40. | :10:43. | |
the information that local people need so combining the two makes | :10:44. | :10:49. | |
sense. During a dive, the drone fills with water and then by | :10:50. | :10:53. | |
releasing carbon dioxide from its on-board gas chamber it forces the | :10:54. | :10:58. | |
water back out as a high-powered jet which thrusts the drone back | :10:59. | :11:01. | |
upwards, propelling it into the air. And in the wings unfold and it comes | :11:02. | :11:07. | |
out of the water and it beautifully becomes this flying birdlike thing. | :11:08. | :11:12. | |
It is quite graceful. That was a very romantic description. Now you | :11:13. | :11:18. | |
know what sort of guy I am and what I get excited about. There is a | :11:19. | :11:22. | |
beautiful part of it which makes it elegant. And elegance in nature that | :11:23. | :11:27. | |
makes it effective as well. Having the folding wings might be beautiful | :11:28. | :11:31. | |
but for us it allows us to reduce the drag that it would experience of | :11:32. | :11:36. | |
the dives in the water and allows it to dive more deeply, as well is | :11:37. | :11:42. | |
protecting the wings an impact. Hello and welcome to the week in | :11:43. | :11:47. | |
Tech. A week which saw Airbus revealed plans for a hybrid car that | :11:48. | :11:52. | |
flies. When Jaguar Land Rover revealed a search and rescue vehicle | :11:53. | :11:57. | |
that is home to a heatseeking drain. And when high polluter showed off a | :11:58. | :12:01. | |
500 metre long test tunnel from which it hopes to fire passengers at | :12:02. | :12:06. | |
around 600 miles an hour. That would be a two second journey. Time to | :12:07. | :12:10. | |
scream. Testing begins soon. It was also the week in which the | :12:11. | :12:16. | |
revelation was televised. According to WikiLeaks, does the CIA can | :12:17. | :12:21. | |
listen in on targets using Samsung TVs. Even when users think they have | :12:22. | :12:25. | |
switched off. A range of other surveillance methods were exposed | :12:26. | :12:31. | |
including a spy department dedicated to hacking the products of apple. | :12:32. | :12:35. | |
WikiLeaks say that the CIA is out of control. Apple and Google say they | :12:36. | :12:40. | |
have plug the holes and Samsung said it takes privacy seriously and will | :12:41. | :12:43. | |
be listening closely to the concerns of its customer. Facebook was left | :12:44. | :12:48. | |
red-faced when the BBC pointed out its platform was being used by | :12:49. | :12:52. | |
convicted paedophiles to share sexualised images of children. And | :12:53. | :12:55. | |
because the BBC shared the images with Facebook to help clean up its | :12:56. | :13:00. | |
platform, Facebook reported the BBC to the police, accusing the | :13:01. | :13:03. | |
corporation of distributing images of child exploitation. Want to buy a | :13:04. | :13:09. | |
cheap house? This one took only 24 hour was to print and cost $10,000. | :13:10. | :13:23. | |
artificial intelligence ahead of us, it is no surprise that tech giants | :13:24. | :13:26. | |
are investing big time in data sensors. Super brains to make | :13:27. | :13:31. | |
intelligent decisions in the cloud. But is this the best tactic? Here's | :13:32. | :13:35. | |
Dave Lee. And video is taking a different | :13:36. | :13:44. | |
approach. -- NVIDIA. It wants to do all that computation on this. NVIDIA | :13:45. | :13:47. | |
is best known for creating chips to handle high-end graphics, at | :13:48. | :13:50. | |
increasingly the company is looking to apply that computer power to data | :13:51. | :13:55. | |
and AI. This week it introduced Jetson T X two, the latest in their | :13:56. | :13:59. | |
line of what are essentially supercomputers on a chip. So, the | :14:00. | :14:06. | |
Jetson TX2 is really for artificial intelligence at the age, devices | :14:07. | :14:10. | |
like robots, drones, portable medical devices, which need a lot of | :14:11. | :14:13. | |
intelligence, but they are really small and they have small power. So | :14:14. | :14:17. | |
Jetson is going to give them the level of performance they need to do | :14:18. | :14:20. | |
artificial intelligence in that small size. So a drone that has | :14:21. | :14:25. | |
artificial intelligence on board is going to help find people that are | :14:26. | :14:29. | |
missing in the wilderness, say, and find them and deliver them first aid | :14:30. | :14:35. | |
and supplies. Experimenting with the new gear, people said it has many | :14:36. | :14:38. | |
practical applications. There are many reasons why you might want to | :14:39. | :14:42. | |
keep your computer power on a local device like this. For starters, it | :14:43. | :14:45. | |
is much more secure, because your data is not being sent to and from | :14:46. | :14:49. | |
the cloud constantly. That means some decisions are made quicker, | :14:50. | :14:53. | |
which, if you are riding in a soft driving car, you will probably | :14:54. | :14:58. | |
appreciate. -- self driving. There are many microcomputers on the | :14:59. | :15:02. | |
market and most of them strive to be as cheap as possible. Not NVIDIA's. | :15:03. | :15:06. | |
The Jetson TX2 will cost at least $400. | :15:07. | :15:15. | |
It is that time of year again. I've arrived at London's wearable | :15:16. | :15:23. | |
technology show. Only some of the highlights don't seem to actually be | :15:24. | :15:28. | |
wearable. Well, I've always thought that one of the most natural uses | :15:29. | :15:32. | |
for augmented reality would be to provide such now have in a car. -- | :15:33. | :15:38. | |
satnav. That is one of the functions this device provides. It has this | :15:39. | :15:42. | |
section on the dashboard, when images reflected onto this small | :15:43. | :15:45. | |
piece of glass, and then we also have this dial on the steering wheel | :15:46. | :15:49. | |
which allows you to run through various functions. Things like being | :15:50. | :15:52. | |
able to change or music, or answering phone calls without overt | :15:53. | :15:55. | |
in your eyes away from that route straight ahead. The only thing is | :15:56. | :15:59. | |
that you are actually changing the length of focus, so even though I'm | :16:00. | :16:03. | |
looking in the same direction, looking at the screen does take my | :16:04. | :16:06. | |
attention away from the road a little. Probably for less time than | :16:07. | :16:10. | |
a separate satnav screen over there, though. Smart rings, vibrating | :16:11. | :16:16. | |
coats, sportswear tracking your every move. It has all been thought | :16:17. | :16:23. | |
of. The market for wearables reached an all-time high in 2016, with 102.4 | :16:24. | :16:31. | |
million devices shipped. But the focus has shifted away from smart | :16:32. | :16:34. | |
devices connecting to multiple apps to simpler ones connecting to just | :16:35. | :16:40. | |
one, and that seems to be a trend reflected here. If you are | :16:41. | :16:44. | |
travelling somewhere on foot and you need to find your way, then some | :16:45. | :16:48. | |
satnav in your shoes would of course be ideal. This device has been | :16:49. | :16:53. | |
around a little while, which can attach to the laces of a pair of | :16:54. | :16:58. | |
trainers. But now it also slips inside and insole, so if it is time | :16:59. | :17:02. | |
to turn left, well, your left foot will vibrate. Time to turn right, | :17:03. | :17:06. | |
and your right foot well. Last year we featured a different type of | :17:07. | :17:10. | |
vibrating in Seoul. This is a prototype which is aimed at the | :17:11. | :17:13. | |
elderly or firm to help them maintain balance. This year, the | :17:14. | :17:18. | |
same company has a different product, a device for people | :17:19. | :17:21. | |
suffering from Parkinson's. It will shine this laser light in front of | :17:22. | :17:25. | |
each foot to help them put each but steadily in front of the other. | :17:26. | :17:30. | |
Within Parkinson's there is a symptom called freezing of gate, | :17:31. | :17:37. | |
which is fairly common. -- gait. It makes an individual feel as if they | :17:38. | :17:41. | |
are glued to the floor at any moment during walking. As you can imagine, | :17:42. | :17:44. | |
if your feet are suddenly not following you, you become quite | :17:45. | :17:48. | |
prone to falling. Researchers found you can use visual triggers and | :17:49. | :17:53. | |
sensory cues to enable a person to continue walking and taken another | :17:54. | :17:58. | |
step. And another insole on display. This seems to be a theme this year. | :17:59. | :18:02. | |
This time it is a personal safety alarm. If you want to activate it | :18:03. | :18:06. | |
you type your feet together twice and you'll selected emergency | :18:07. | :18:09. | |
contacts will be told there is an issue. -- your selected. To switch | :18:10. | :18:13. | |
it off, you type your feet together three times. Some products on show | :18:14. | :18:18. | |
were more finished than others, but overall it was a good glimpse at how | :18:19. | :18:22. | |
some of the latest wearable tech is looking right now. | :18:23. | :18:29. | |
That was Lara. Now, if you are a parent, like me, it has probably | :18:30. | :18:33. | |
crossed your mind that your kids might be using technology a bit too | :18:34. | :18:37. | |
much. How long are they spending on their phones? How much are they | :18:38. | :18:42. | |
texting? But the popularity of texting amongst young people isn't | :18:43. | :18:47. | |
all bad. Sumeet Dowson has been exploring how one organisation is | :18:48. | :18:52. | |
using it to deal with serious issues for young people. | :18:53. | :18:58. | |
Every Monday morning, this woman spends four hours texting with | :18:59. | :19:04. | |
people in need. She is a volunteer counsellor for crisis text line, a | :19:05. | :19:08. | |
free support service in the United States. Councillors and textures | :19:09. | :19:15. | |
remain anonymous for privacy reasons. -- texters. We have a lot | :19:16. | :19:19. | |
of middle schoolers who are concerned about what is going on and | :19:20. | :19:23. | |
they reach out to us during the day. They might be concerned about | :19:24. | :19:26. | |
sitting alone at lunch, for example. We have texters texting in because | :19:27. | :19:29. | |
they are in a domestic violence situation. Most texters are young, | :19:30. | :19:36. | |
under the age of 25. People tell us everything. They spill their guts. | :19:37. | :19:40. | |
Typically by the third message. Nobody overhears you, you don't have | :19:41. | :19:44. | |
to wait, even to be in a quiet place a quiet moment. The millions of | :19:45. | :19:48. | |
messages exchanged on crisis text lines make up a data sets teeming | :19:49. | :19:53. | |
with mental health insights. It reveals when texters struggle with | :19:54. | :19:56. | |
eating disorders and where they have suicidal thoughts. The data was also | :19:57. | :20:02. | |
used to build an algorithm. The model essentially performs triage by | :20:03. | :20:06. | |
analysing each word in the message. So a person who is thinking about | :20:07. | :20:10. | |
arming themselves would have a higher priority in the queue than | :20:11. | :20:13. | |
somebody who is out after a breakup. -- harming. We quickly learned there | :20:14. | :20:18. | |
were other things that were even more high-risk, that we didn't think | :20:19. | :20:27. | |
of or didn't know. Things like #kms, which means "Kill myself". | :20:28. | :20:30. | |
Conversations which reference things like either broken, Tylenol, and | :20:31. | :20:36. | |
Bill, draino, all the household drugs that are within reach. The | :20:37. | :20:42. | |
data is another mice and texters can opt out of data sharing. To promote | :20:43. | :20:48. | |
mental health research some data is shared with researchers. Scientists | :20:49. | :20:51. | |
at Stanford use natural language processing to study about 3 million | :20:52. | :20:55. | |
text messages. They uncovered five phases in the conversations. The | :20:56. | :20:59. | |
introduction, problem setting, exploration, problem-solving and the | :21:00. | :21:04. | |
wrapup. The best councillors were really quick to get through this | :21:05. | :21:08. | |
problem exploration phase. They were really good at getting to the heart | :21:09. | :21:11. | |
of the issue to understand that, and they were quicker to move on in the | :21:12. | :21:15. | |
conversation, which means that they then had more time to spend in this | :21:16. | :21:19. | |
problem-solving phase. At the end of the chat, texters can rate their | :21:20. | :21:24. | |
experience and the council. The researchers found that effective | :21:25. | :21:27. | |
councillors avoided canned responses and able to shift the texter's | :21:28. | :21:33. | |
outlook. We built an algorithm set that could measure different kinds | :21:34. | :21:36. | |
of perspective change from talking, using lots of negative words, to | :21:37. | :21:40. | |
talking about more positive words, to talk about how much you focus on | :21:41. | :21:44. | |
the past versus the present and future, and how much you focus on | :21:45. | :21:48. | |
versus other people. The next step is to create training tools for | :21:49. | :21:52. | |
councillors, like real-time feedback on the conversation, and exploring | :21:53. | :21:56. | |
the potential of a conversational agent. A robot. While data science | :21:57. | :22:02. | |
and tech gets these self-professed data nerds that the crisis text line | :22:03. | :22:06. | |
very excited, it will not use chat box. -- bots. Every messages read | :22:07. | :22:17. | |
and reply to buy a human. We couldn't let you go without | :22:18. | :22:20. | |
mentioning this mind controlled robot that responds really well to a | :22:21. | :22:28. | |
certain thought. In collaboration with Boston University, MIT's | :22:29. | :22:31. | |
computer science and artificial intelligence laboratory has | :22:32. | :22:34. | |
published a system which allows human uses to correct a robot's | :22:35. | :22:39. | |
mistakes by thought alone. It uses the signal we produce when we detect | :22:40. | :22:42. | |
a mistake. It is called the error potential. The user wears an EEG cap | :22:43. | :22:50. | |
and watch as the robot sought paint and wire into two bins. If they see | :22:51. | :22:53. | |
the robot making a wrong choice, they simply think, that's wrong! The | :22:54. | :22:58. | |
cap picks up that thought and the robot will correct its mistakes. We | :22:59. | :23:04. | |
are interested in exploring the possibility of combining the | :23:05. | :23:07. | |
potential -- error potential with other types of signals, which might | :23:08. | :23:14. | |
be easily reliable. Even though these are baby steps there are | :23:15. | :23:18. | |
tremendous applications that could happen in the home, on the factory | :23:19. | :23:25. | |
line, or in the floors, so this technology can help support people | :23:26. | :23:28. | |
in their daily activities, whether they are at work, at play, or in | :23:29. | :23:34. | |
transportation. Pretty interesting stuff, although admittedly, I think | :23:35. | :23:38. | |
that is still in the far future. So how about I tell you about something | :23:39. | :23:43. | |
in the more immediate future? Next week, click is going to India. We | :23:44. | :23:50. | |
will be travelling across the country to meet the people working | :23:51. | :23:54. | |
hard to change lives, save lives, and maybe one day try out a new | :23:55. | :24:02. | |
life. I can't wait. It is going to be brilliant. Join us on Twitter | :24:03. | :24:06. | |
throughout the week for more techies and behind-the-scenes photos and we | :24:07. | :24:09. | |
will see you next week in India. -- Tech news. | :24:10. | :24:17. |