316
Faculty Sections / Re: আকস্মিকভাবে পরপারে পাড়ি জমিয়েছিলেন যেসব বিজ্ঞানী
« on: May 16, 2018, 09:18:39 PM »
অনেক কিছু জানা গেলো। ধন্যবাদ শেয়ার করার জন্যে।
News:
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317
Software Engineering / Re: Elearn Course Create and Enrolment key setup video
« on: May 16, 2018, 09:13:12 PM »
Its really important for all of us.
318
Protect your Health/ your Doctor / Re: দ্রুত ওজন কমানোর মসলা
« on: May 16, 2018, 01:35:43 PM »
উপকারি পোষ্ট
319
Health Tips / Re: রোজায় শ্বাসকষ্ট
« on: May 16, 2018, 01:33:02 PM »
শ্বাসকষ্ট রোগীদের জন্যে দরকারী পোষ্ট
320
Teaching & Research Forum / Self-driving cars and humans face inevitable collisions
« on: May 16, 2018, 10:28:57 AM »
In 1938, when there were just about one-tenth the number of cars on U.S. roadways as there are today, a brilliant psychologist and a pragmatic engineer joined forces to write one of the most influential works ever published on driving. A self-driving car’s killing of a pedestrian in Arizona highlights how their work is still relevant today – especially regarding the safety of automated and autonomous vehicles.
James Gibson, the psychologist in question, and the engineer Laurence Crooks, his partner, evaluated a driver’s control of a vehicle in two ways. The first was to measure what they called the “minimum stopping zone,” the distance it would take to stop after the driver slammed on the brakes. The second was to look at the driver’s psychological perception of the possible hazards around the vehicle, which they called the “field of safe travel.” If someone drove so that all the potential hazards were outside the range needed to stop the car, that person was driving safely. Unsafe driving, on the other hand, involved going so quickly or steering so erratically that the car couldn’t stop before potentially hitting those identified hazards.
The driver of the car on the right judges a safe path through obstacles based on their movements and perceived stopping distances. Adapted from Gibson and Crooks by Steven Lehar
However, this field of safe travel isn’t the same for driverless cars. They perceive the world around them using lasers, radar, GPS and other sensors, in addition to their on-board cameras. So their perceptions can be very different from those presented to human eyes. At the same time, their active response times can be far faster – or sometimes even excessively slow, in cases where they require human intervention.
I have written extensively on the nature of human interaction with technology, especially concerning the coming wave of automated automobiles. It’s clear to me that, if people and machines drive only according to their respective – and significantly different – perceptual and response abilities, then conflicts and collisions will be almost inevitable. To share the road safely, each side will need to understand the other much more intimately than they do now.
Interplay of movement and view
For human drivers, vision is king. But what drivers see depends on how they move the car: Braking, accelerating and steering change the car’s position and thereby the driver’s view. Gibson understood this mutual interdependence of perception and action meant that when faced with a particular situation on the road, people expect others to behave in specific ways. For instance, a person watching a car arrive at a stop sign would expect the driver to stop the car; look around for oncoming traffic, pedestrians, bicyclists and other obstacles; and proceed only when the coast is clear.
A stop sign clearly exists for human drivers. It gives them a chance to look around carefully without being distracted by other aspects of driving, like steering. But an autonomous vehicle can scan its entire surroundings in a fraction of a second. It need not necessarily stop – or even slow down – to navigate the intersection safely. But an autonomous car that rolls through a stop sign without even pausing will be seen as alarming, and even dangerous, to nearby humans, because they’re assuming human rules still apply.
What machines can understand
Here’s another example: Think about cars merging from a side street onto a busy thoroughfare. People know that making eye contact with another driver can be an effective method of communicating with each other. In a split section, one driver can ask permission to cut in and the other driver can acknowledge that yes, she will yield to make room. How exactly should people have this interaction with a self-driving car? It’s something that has yet to be established.
Pedestrians, bicyclists, motorcycle riders, car drivers and truck drivers are all able to understand what other human drivers are likely to do – and to express their own intentions to another person appropriately.
An automated vehicle is another matter altogether. It will know little or nothing of the “can I?” “yes, OK” types of informal interaction people engage in every day, and will be stuck only with the specific rules it has been provided. Since few algorithms can understand these implicit human assumptions, they’ll behave differently from how people expect. Some of these differences might seem subtle – but some transgressions, such as running the stop sign, might cause injury or even death.
What’s more, driverless cars can be effectively blinded if their various sensory systems become blocked, malfunction or provide contradictory information. In the 2016 fatal crash of a Tesla in “Autopilot” mode, for example, part of the problem might have been a conflict between some sensors that could have detected a tractor-trailer across the road and others that likely didn’t because it was backlit or too high off the ground. These failures may be rather different from the shortcomings people have come to expect from fellow humans.
A Tesla ‘Autopilot’ system steers directly for a barricade.
As with all new technologies, there will be accidents and problems – and on the roads, that will almost inevitably result in injury and death. But this type of problem isn’t unique to self-driving cars. Rather, it’s perhaps inherent in any situation when humans and automated systems share space.
Reference:
https://theconversation.com/self-driving-cars-and-humans-face-inevitable-collisions-93774
James Gibson, the psychologist in question, and the engineer Laurence Crooks, his partner, evaluated a driver’s control of a vehicle in two ways. The first was to measure what they called the “minimum stopping zone,” the distance it would take to stop after the driver slammed on the brakes. The second was to look at the driver’s psychological perception of the possible hazards around the vehicle, which they called the “field of safe travel.” If someone drove so that all the potential hazards were outside the range needed to stop the car, that person was driving safely. Unsafe driving, on the other hand, involved going so quickly or steering so erratically that the car couldn’t stop before potentially hitting those identified hazards.
The driver of the car on the right judges a safe path through obstacles based on their movements and perceived stopping distances. Adapted from Gibson and Crooks by Steven Lehar
However, this field of safe travel isn’t the same for driverless cars. They perceive the world around them using lasers, radar, GPS and other sensors, in addition to their on-board cameras. So their perceptions can be very different from those presented to human eyes. At the same time, their active response times can be far faster – or sometimes even excessively slow, in cases where they require human intervention.
I have written extensively on the nature of human interaction with technology, especially concerning the coming wave of automated automobiles. It’s clear to me that, if people and machines drive only according to their respective – and significantly different – perceptual and response abilities, then conflicts and collisions will be almost inevitable. To share the road safely, each side will need to understand the other much more intimately than they do now.
Interplay of movement and view
For human drivers, vision is king. But what drivers see depends on how they move the car: Braking, accelerating and steering change the car’s position and thereby the driver’s view. Gibson understood this mutual interdependence of perception and action meant that when faced with a particular situation on the road, people expect others to behave in specific ways. For instance, a person watching a car arrive at a stop sign would expect the driver to stop the car; look around for oncoming traffic, pedestrians, bicyclists and other obstacles; and proceed only when the coast is clear.
A stop sign clearly exists for human drivers. It gives them a chance to look around carefully without being distracted by other aspects of driving, like steering. But an autonomous vehicle can scan its entire surroundings in a fraction of a second. It need not necessarily stop – or even slow down – to navigate the intersection safely. But an autonomous car that rolls through a stop sign without even pausing will be seen as alarming, and even dangerous, to nearby humans, because they’re assuming human rules still apply.
What machines can understand
Here’s another example: Think about cars merging from a side street onto a busy thoroughfare. People know that making eye contact with another driver can be an effective method of communicating with each other. In a split section, one driver can ask permission to cut in and the other driver can acknowledge that yes, she will yield to make room. How exactly should people have this interaction with a self-driving car? It’s something that has yet to be established.
Pedestrians, bicyclists, motorcycle riders, car drivers and truck drivers are all able to understand what other human drivers are likely to do – and to express their own intentions to another person appropriately.
An automated vehicle is another matter altogether. It will know little or nothing of the “can I?” “yes, OK” types of informal interaction people engage in every day, and will be stuck only with the specific rules it has been provided. Since few algorithms can understand these implicit human assumptions, they’ll behave differently from how people expect. Some of these differences might seem subtle – but some transgressions, such as running the stop sign, might cause injury or even death.
What’s more, driverless cars can be effectively blinded if their various sensory systems become blocked, malfunction or provide contradictory information. In the 2016 fatal crash of a Tesla in “Autopilot” mode, for example, part of the problem might have been a conflict between some sensors that could have detected a tractor-trailer across the road and others that likely didn’t because it was backlit or too high off the ground. These failures may be rather different from the shortcomings people have come to expect from fellow humans.
A Tesla ‘Autopilot’ system steers directly for a barricade.
As with all new technologies, there will be accidents and problems – and on the roads, that will almost inevitably result in injury and death. But this type of problem isn’t unique to self-driving cars. Rather, it’s perhaps inherent in any situation when humans and automated systems share space.
Reference:
https://theconversation.com/self-driving-cars-and-humans-face-inevitable-collisions-93774
321
Teaching & Research Forum / Plug-and-play diagnostic devices
« on: May 16, 2018, 10:27:16 AM »
Plug-and-play diagnostic devices
Modular blocks could enable labs around the world to cheaply and easily build their own diagnostics.
Anne Trafton | MIT News Office
May 16, 2018
Press Inquiries
SHARE
COMMENT
Researchers at MIT’s Little Devices Lab have developed a set of modular blocks that can be put together in different ways to produce diagnostic devices. These “plug-and-play” devices, which require little expertise to assemble, can test blood glucose levels in diabetic patients or detect viral infection, among other functions.
“Our long-term motivation is to enable small, low-resources laboratories to generate their own libraries of plug-and-play diagnostics to treat their local patient populations independently,” says Anna Young, co-director of MIT’s Little Devices Lab, lecturer at the Institute for Medical Engineering and Science, and one of the lead authors of the paper.
Using this system, called Ampli blocks, the MIT team is working on devices to detect cancer, as well as Zika virus and other infectious diseases. The blocks are inexpensive, costing about 6 cents for four blocks, and they do not require refrigeration or special handling, making them appealing for use in the developing world.
“We see these construction kits as a way of lowering the barriers to making medical technology,” says Jose Gomez-Marquez, co-director of the Little Devices Lab and the senior author of the paper.
Elizabeth Phillips ’13, a graduate student at Purdue University, is also a lead author of the paper, which appears in the journal Advanced Healthcare Materials on May 16. Other authors include Kimberly Hamad-Schifferli, an associate professor of engineering at the University of Massachusetts at Boston and a visiting scientist in MIT’s Department of Mechanical Engineering; Nikolas Albarran, a senior engineer in the Little Devices Lab; Jonah Butler, an MIT junior; and Kaira Lujan, a former visiting student in the Little Devices Lab.
Customized diagnostics
Over the past decade, many researchers have been working on small, portable diagnostic devices based on chemical reactions that occur on paper strips. Many of these tests make use of lateral flow technology, which is the same approach used in home pregnancy tests.
Despite these efforts, such tests have not been widely deployed. One obstacle, says Gomez-Marquez, is that many of these devices are not designed with large-scale manufacturability in mind. Another is that companies may not be interested in mass-producing a diagnostic for a disease that doesn’t affect a large number of people.
The Little Devices Lab researchers realized that they could get these diagnostics into the hands of many more people if they created a kit of modular components that can be put together to generate exactly what the user needs. To that end, they have created about 40 different building blocks that lab workers around the world could easily assemble on their own, just as people began assembling their own radios and other electronic devices from commercially available electronic “breadboards” in the 1970s.
“When the electronic breadboard came out, that meant people didn’t have to worry about building their own resistors or capacitors. They could worry about what they actually wanted to use electronics for, which is to make the entire circuit,” Gomez-Marquez says.
In this case, the components consist of a sheet of paper or glass fiber sandwiched between a plastic or metal block and a glass cover. The blocks, which are about half an inch on each edge, can snap together along any edge. Some of the blocks contain channels for samples to flow straight through, some have turns, and some can receive a sample from a pipette or mix multiple reagents together.
The blocks can also perform different biochemical functions. Many contain antibodies that can detect a specific molecule in a blood or urine sample. Those antibodies are attached to nanoparticles that change color when the target molecule is present, indicating a positive result.
These blocks can be aligned in different ways, allowing the user to create diagnostics based on one reaction or a series of reactions. In one example, the researchers combined blocks that detect three different molecules to create a test for isonicotinic acid, which can reveal whether tuberculosis patients are taking their medication.
The blocks are color-coded by function, making it easier to assemble predesigned devices using instructions that the researchers plan to put online. They also hope that users will develop and contribute their own specifications to the online guide.
Better performance
The researchers also showed that in some ways, these blocks can outperform previous versions of paper diagnostic devices. For example, they found that they could run a sample back and forth over a test strip multiple times, enhancing the signal. This could make it easier to get reliable results from urine and saliva samples, which are usually more dilute than blood samples, but are easier to obtain from patients.
“These are things that cannot be done with standard lateral flow tests, because those are not modular — you only get to run those once,” says Hamad-Schifferli.
The team is now working on tests for human papilloma virus, malaria, and Lyme disease, among others. They are also working on blocks that can synthesize useful compounds, including drugs, as well as blocks that incorporate electrical components such as LEDs.
The ultimate goal is to get the technology into the hands of small labs in both industrialized and developing countries, so they can create their own diagnostics. The MIT team has already sent them to labs in Chile and Nicaragua, where they have been used to develop devices to monitor patient adherence to TB treatment and to test for a genetic variant that makes malaria more difficult to treat.
Catherine Klapperich, associate dean for research and an associate professor of biomedical engineering at Boston University, says the MIT team’s work will help to make the diagnostic design process more inclusive.
“By reducing the barriers to designing new point-of-care paperfluidics, the work invites nonexperts in and will certainly result in new ideas and collaborations in settings all around the world,” says Klapperich, who was not involved in the research. “The practical demonstrations of the system presented here are poised to be immediately useful, while the possibilities for others to build on the tool are large.”
The researchers are now investigating large-scale manufacturing techniques, and they hope to launch a company to manufacture and distribute the kits around the world.
“We are excited to open the platform to other researchers so they can use the blocks and generate their own reactions,” Young says.
The research was funded by a gift from Autodesk and the U.S. Public Health Service.
Reference:
http://news.mit.edu/2018/plug-and-play-diagnostic-devices-0516
Modular blocks could enable labs around the world to cheaply and easily build their own diagnostics.
Anne Trafton | MIT News Office
May 16, 2018
Press Inquiries
SHARE
COMMENT
Researchers at MIT’s Little Devices Lab have developed a set of modular blocks that can be put together in different ways to produce diagnostic devices. These “plug-and-play” devices, which require little expertise to assemble, can test blood glucose levels in diabetic patients or detect viral infection, among other functions.
“Our long-term motivation is to enable small, low-resources laboratories to generate their own libraries of plug-and-play diagnostics to treat their local patient populations independently,” says Anna Young, co-director of MIT’s Little Devices Lab, lecturer at the Institute for Medical Engineering and Science, and one of the lead authors of the paper.
Using this system, called Ampli blocks, the MIT team is working on devices to detect cancer, as well as Zika virus and other infectious diseases. The blocks are inexpensive, costing about 6 cents for four blocks, and they do not require refrigeration or special handling, making them appealing for use in the developing world.
“We see these construction kits as a way of lowering the barriers to making medical technology,” says Jose Gomez-Marquez, co-director of the Little Devices Lab and the senior author of the paper.
Elizabeth Phillips ’13, a graduate student at Purdue University, is also a lead author of the paper, which appears in the journal Advanced Healthcare Materials on May 16. Other authors include Kimberly Hamad-Schifferli, an associate professor of engineering at the University of Massachusetts at Boston and a visiting scientist in MIT’s Department of Mechanical Engineering; Nikolas Albarran, a senior engineer in the Little Devices Lab; Jonah Butler, an MIT junior; and Kaira Lujan, a former visiting student in the Little Devices Lab.
Customized diagnostics
Over the past decade, many researchers have been working on small, portable diagnostic devices based on chemical reactions that occur on paper strips. Many of these tests make use of lateral flow technology, which is the same approach used in home pregnancy tests.
Despite these efforts, such tests have not been widely deployed. One obstacle, says Gomez-Marquez, is that many of these devices are not designed with large-scale manufacturability in mind. Another is that companies may not be interested in mass-producing a diagnostic for a disease that doesn’t affect a large number of people.
The Little Devices Lab researchers realized that they could get these diagnostics into the hands of many more people if they created a kit of modular components that can be put together to generate exactly what the user needs. To that end, they have created about 40 different building blocks that lab workers around the world could easily assemble on their own, just as people began assembling their own radios and other electronic devices from commercially available electronic “breadboards” in the 1970s.
“When the electronic breadboard came out, that meant people didn’t have to worry about building their own resistors or capacitors. They could worry about what they actually wanted to use electronics for, which is to make the entire circuit,” Gomez-Marquez says.
In this case, the components consist of a sheet of paper or glass fiber sandwiched between a plastic or metal block and a glass cover. The blocks, which are about half an inch on each edge, can snap together along any edge. Some of the blocks contain channels for samples to flow straight through, some have turns, and some can receive a sample from a pipette or mix multiple reagents together.
The blocks can also perform different biochemical functions. Many contain antibodies that can detect a specific molecule in a blood or urine sample. Those antibodies are attached to nanoparticles that change color when the target molecule is present, indicating a positive result.
These blocks can be aligned in different ways, allowing the user to create diagnostics based on one reaction or a series of reactions. In one example, the researchers combined blocks that detect three different molecules to create a test for isonicotinic acid, which can reveal whether tuberculosis patients are taking their medication.
The blocks are color-coded by function, making it easier to assemble predesigned devices using instructions that the researchers plan to put online. They also hope that users will develop and contribute their own specifications to the online guide.
Better performance
The researchers also showed that in some ways, these blocks can outperform previous versions of paper diagnostic devices. For example, they found that they could run a sample back and forth over a test strip multiple times, enhancing the signal. This could make it easier to get reliable results from urine and saliva samples, which are usually more dilute than blood samples, but are easier to obtain from patients.
“These are things that cannot be done with standard lateral flow tests, because those are not modular — you only get to run those once,” says Hamad-Schifferli.
The team is now working on tests for human papilloma virus, malaria, and Lyme disease, among others. They are also working on blocks that can synthesize useful compounds, including drugs, as well as blocks that incorporate electrical components such as LEDs.
The ultimate goal is to get the technology into the hands of small labs in both industrialized and developing countries, so they can create their own diagnostics. The MIT team has already sent them to labs in Chile and Nicaragua, where they have been used to develop devices to monitor patient adherence to TB treatment and to test for a genetic variant that makes malaria more difficult to treat.
Catherine Klapperich, associate dean for research and an associate professor of biomedical engineering at Boston University, says the MIT team’s work will help to make the diagnostic design process more inclusive.
“By reducing the barriers to designing new point-of-care paperfluidics, the work invites nonexperts in and will certainly result in new ideas and collaborations in settings all around the world,” says Klapperich, who was not involved in the research. “The practical demonstrations of the system presented here are poised to be immediately useful, while the possibilities for others to build on the tool are large.”
The researchers are now investigating large-scale manufacturing techniques, and they hope to launch a company to manufacture and distribute the kits around the world.
“We are excited to open the platform to other researchers so they can use the blocks and generate their own reactions,” Young says.
The research was funded by a gift from Autodesk and the U.S. Public Health Service.
Reference:
http://news.mit.edu/2018/plug-and-play-diagnostic-devices-0516
322
Teaching & Research Forum / AI technology as small and medium-sized enterprises should evaluate today
« on: May 16, 2018, 12:00:48 AM »
AI technology as small and medium-sized enterprises should evaluate today
by Micael Corneliusson, 2018-04-25
With the advancement of technology and the furious rapid development, it is not easy to keep up with. We are therefore launching an article series today, where we present different technologies that will change the conditions for small and medium-sized companies, both in terms of competition and the struggle for customers. First out: artificial intelligence (AI).
When we hear the term artificial intelligence, we usually think of movies like an I robot where robots live and work with people. However, it's a long way till AI can write books and replace people in their work. It writes Hubspot in a post that is the basis of this article .
But the fact is that today there is AI in the middle of our society:
Alexa and Siri (assistants in the phone)
chat Bots
Facebook photos tagged on Facebook
"Discover weekly" on Spotify
At the time of writing, AI can read text, understand languages, visually translate the world and process large amounts of data. In the future, AI will be a part of all industries.
The features we have mentioned above are called ANI (artificial narrow intelligence), which means that the artificial intelligence is designed to solve only one problem, not several. However, there is also AGI (artificial general intelligence). And it's AGI that we usually associate with AI - the human-like robot that can clean, cook and bring conversations. According to experts, however, it will last for at least 40 years before we can expect the AGI to work fully.
Therefore, artificial intelligence is important for small and medium-sized companies
Generally speaking, AI is important to all companies because it contributes a thing - convenience and relieve.
For example, Teslas cars are self-learning. They can read about their surroundings and predict and also prevent accidents. At the same time, Netflix understands what movies you usually watch, what makes you feel then contributes with suggestions.
AI will be able to simplify and improve many of our analogous processes that we have today. Because AI will be a major part of our future, it is important that all companies evaluate what AI can do for them.
Thus, small and medium-sized companies can use AI
To get started, we recommend that you implement one or more of the various software and features available on the market. More than that, it's hardly profitable to hire an own AI developer.
A first step could be a chat babe. This fine can help your business to take care of incoming customer and prospect questions, and push them forward in their purchase. And this without having to lift one finger!
In a survey like Facebook is behind, 67 percent of respondents say they will send more messages to companies that have a chat feature. 53 percent also say they will be more likely to buy from a company that has a chat function.
With the figures from the survey in mind, it's a good start for companies to start thinking about implementing an AI chat bot in their business.
Summary
Artificial intelligence will play a major role in the market in the future. In order not to slip, it is important that small and medium-sized companies already take part in the supply. A chat could be a good start .
Want more tips on how technology can help your business? Download the CEO's guide to how IT can help in the growth phase!
http://content.koneo.se/blogg/ai-tekniken-som-sma-och-medelstora-foretag-bor-utvardera-redan-idag?utm_campaign=VD%20-%20Markus&utm_content=70082150&utm_medium=social&utm_source=linkedin
by Micael Corneliusson, 2018-04-25
With the advancement of technology and the furious rapid development, it is not easy to keep up with. We are therefore launching an article series today, where we present different technologies that will change the conditions for small and medium-sized companies, both in terms of competition and the struggle for customers. First out: artificial intelligence (AI).
When we hear the term artificial intelligence, we usually think of movies like an I robot where robots live and work with people. However, it's a long way till AI can write books and replace people in their work. It writes Hubspot in a post that is the basis of this article .
But the fact is that today there is AI in the middle of our society:
Alexa and Siri (assistants in the phone)
chat Bots
Facebook photos tagged on Facebook
"Discover weekly" on Spotify
At the time of writing, AI can read text, understand languages, visually translate the world and process large amounts of data. In the future, AI will be a part of all industries.
The features we have mentioned above are called ANI (artificial narrow intelligence), which means that the artificial intelligence is designed to solve only one problem, not several. However, there is also AGI (artificial general intelligence). And it's AGI that we usually associate with AI - the human-like robot that can clean, cook and bring conversations. According to experts, however, it will last for at least 40 years before we can expect the AGI to work fully.
Therefore, artificial intelligence is important for small and medium-sized companies
Generally speaking, AI is important to all companies because it contributes a thing - convenience and relieve.
For example, Teslas cars are self-learning. They can read about their surroundings and predict and also prevent accidents. At the same time, Netflix understands what movies you usually watch, what makes you feel then contributes with suggestions.
AI will be able to simplify and improve many of our analogous processes that we have today. Because AI will be a major part of our future, it is important that all companies evaluate what AI can do for them.
Thus, small and medium-sized companies can use AI
To get started, we recommend that you implement one or more of the various software and features available on the market. More than that, it's hardly profitable to hire an own AI developer.
A first step could be a chat babe. This fine can help your business to take care of incoming customer and prospect questions, and push them forward in their purchase. And this without having to lift one finger!
In a survey like Facebook is behind, 67 percent of respondents say they will send more messages to companies that have a chat feature. 53 percent also say they will be more likely to buy from a company that has a chat function.
With the figures from the survey in mind, it's a good start for companies to start thinking about implementing an AI chat bot in their business.
Summary
Artificial intelligence will play a major role in the market in the future. In order not to slip, it is important that small and medium-sized companies already take part in the supply. A chat could be a good start .
Want more tips on how technology can help your business? Download the CEO's guide to how IT can help in the growth phase!
http://content.koneo.se/blogg/ai-tekniken-som-sma-och-medelstora-foretag-bor-utvardera-redan-idag?utm_campaign=VD%20-%20Markus&utm_content=70082150&utm_medium=social&utm_source=linkedin
323
Use of Technology in Education / Re: Moodle is on
« on: May 15, 2018, 11:05:22 PM »
Please share with us your innovation in your style.
324
Higher Education / Re: 10 trends changing global higher education
« on: May 15, 2018, 10:53:26 PM »
True fact indeed.
326
Business Administration / Re: 'Uber-Pathao' ride-share's impact on Dhaka
« on: May 15, 2018, 12:24:04 AM »
good and useful information.
327
Faculty Sections / Re: স্যাটেলাইট কিভাবে কাজ করে?
« on: May 14, 2018, 03:43:01 PM »ভালো জিনিস শেয়ার করছেন।
328
Cancer / Re: Broccoli is a cancer-preventing superfood
« on: May 14, 2018, 12:32:24 PM »
Thanks for sharing
329
Nutrition and Food Engineering / Re: লিভারের কর্মক্ষমতা বাড়ায় লবঙ্গ
« on: May 14, 2018, 12:32:09 PM »
Thanks for sharing
330
Permanent Campus of DIU / Re: ব্যস্ততায় ভরা জীবনে যখন একটুও দম ফেলার সময় নেইঃ অন্তর মহলে শান্তির আগমন...
« on: May 14, 2018, 12:31:55 PM »
Thanks for sharing