Three-dimensional (3D) printing technology, also called additive manufacturing (AM), has recently come into the spotlight because of its potential high-impact implementation in applications ranging from personal tools to aerospace equipment. Even though 3D printing technology has only recently emerged as a hot topic, its history can be traced back to 1983 when the first 3D printer was created by Charles W. Hull, co-founder of 3D Systems.

3D Printing Techniques Using Polymers
• Fused Deposition Modeling (FDM)
• Selective Laser Sintering (SLS)
• Stereolithography (SLA)
• Near-Field Electrospinning (NFES)

Popular Applications

Fashion: At the 2016 New York Fashion Week, two unique 3D printed dresses were unveiled. These masterpieces were produced through a collaboration between fashion designers and the 3D printing company, Stratasys.4 The complex designs (e.g., mixing a variety of interlocking weaves, biomimicking natural animal textures) and cutting-edge material (e.g., nano-enhanced elastomeric 3D printing material) gave the dresses durability and flexibility.

Regenerative medicine: The area of regenerative medicine has also achieved impressive applications within the 3D printing field. Dr. Anthony Atala’s team from the Wake Forest Institute for Regenerative Medicine has successfully used 3D printing technology to fabricate living organs and tissue (including muscle structures, and bone and ear tissue).5,6 These bioprinted body parts are capable of generating functional replacement tissue.

Aerospace: NASA has also been implementing 3D printing techniques and 3D printers to develop materials that allow astronauts to repair or replace essential parts and build structures in space. NASA recently collaborated with researchers at Washington State University to fabricate a replica of a moon rock using raw lunar regolith simulant and 3D laser printing technology.

Construction: The assembly of modular construction materials using giant 3D printers for use in the housing industry has gained significant interest, especially for poorer countries, during natural disasters, or sudden emergencies. Some 3D companies have succeeded in building houses or bridges with cement, sand, or concrete materials.10–12

4D Printing

The rapidly decreasing cost, improved software design, and increasing range of printable materials have helped to bring about a new technology called four-dimensional (4D) printing. 4D printing provides printed objects with the ability to change form or function with time according to various stimuli such as heat, water, current, or light (Figure 1A).13 The essential difference between 4D printing and 3D printing is the addition of smart design, or responsive materials that cause time-dependent deformations of objects.
This review covers both 3D and 4D printing processes and shows the materials related to different printing types.

The process of 3D & 4D Printing Technology

3D printing is the process of fabricating objects by building up materials layer by layer. he 3D printing process from modeling to final printing. Based on the use of computer-aided design (CAD) describing the geometry and the size of the objects to be printed, a complicated 3D model is created in a printable standard tessellation language (STL) file format. Then, it is sliced into a series of digital cross-sectional layers in accordance with the layer thickness setting.
Upon completion of the model, the object is fabricated by a 3D printer through the layer-by-layer fabrication process based on a series of 2D layers to create a static 3D object. 3D printing can involve different types of materials such as thermoplastic polymer, powder, metal, UV curable resin, etc.

Four-dimensional printing incorporates a time component to the 3D printed objects, making the design process more important. 4D-printed structures must be preprogrammed in detail based on the transforming mechanism of controllable smart materials that incorporate time dependent material deformations. 3D structures that self-fold based on the thermal activation of spatially variable patterns printed with a variety of shape memory polymers. Each polymer has a different thermal-dependent behavior that can make the box self-fold in a time-sequential manner based on smart design and thermomechanical mechanisms.14 The choice of materials for 4D printing is significant, however, because most 3D printing materials are designed only to produce rigid, static objects. Recently, some smart shape alloy/polymer memory materials have been developed to utilize their self-assembled behaviors driven by heat, UV, or water absorption-driven. For example, the temperature-responsive artificial hand was printed with a temperature-responsive TPU (thermal polyurethane) filament. It has the ability to contract or expand in response to specific temperatures. In addition, multi-materials having different environmental behaviors are also useful in 4D printing. A research group at the Massachusetts Institute of Technology used two different materials with different porosities and water-absorption abilities to print transformable structures.16,17 It was composed of a porous water-absorbing material on one side and a rigid waterproof material on the opposite side. When exposed to water, the water-absorbing side increased in volume while the other side remained unchanged, resulting in shape deformation.

Future Prospects

Three-dimensional printing technology is highly versatile and efficient with respect to design, fabrication, and applications. 4D printing may be of great importance in the future due to its potential to redefine manufacturing-related industries. However, the technology must be further refined before it can replace conventional manufacturing methods. Therefore, future research and investment in 3D and 4D printing technologies are imperative to bring about improvements in essential areas including materials, printer systems, and product markets.

References:
https://www.sigmaaldrich.com/IN/en/technical-documents/technical-article/materials-science-and-engineering/3d-printing/3d-and-4d-printing-technologies
https://dl.uctm.edu/journal/node/j2020-6/17_20-115_p_2094-2104.pdf
https://www.frontiersin.org/articles/10.3389/fbioe.2019.00164/full
https://www.sftichallenge.govt.nz/our-research/projects/spearhead/additive-manufacturing-and-3d-and-or-4d-printing-of-bio-composites/

The post 3D & 4D Printing and Additive Manufacturing appeared first on iValuePlus.

So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.
Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.
For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.
This work paved the way for the automation and formal reasoning that we see in computers today.

Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.
On a broad level, we can differentiate both AI and ML as:

Artificial Intelligence
Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence”, which means “a human-made thinking power.” Hence we can define it as,
The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.
Based on capabilities, AI can be classified into three types:
Weak AI
General AI
Strong AI

Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.
As we know it today, AI is symbolized with Human-AI interaction gadgets by Google Home, Siri, and Alexa, by the machine-learning-powered video prediction systems that power Netflix, Amazon, and YouTube. These technological advancements are progressively becoming essential in our daily lives. They are intelligent assistants who enhance our abilities as humans and professionals — making us more productive.
In contrast to machine learning, AI is a moving target, and its definition changes as its related technological advancements turn out to be further developed. Possibly, within a few decades, today’s innovative AI advancements ought to be considered as dull as flip-phones are to us right now.

Machine Learning
Machine learning is about extracting knowledge from the data. It can be defined as,
Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data.
Machine learning works on algorithm which learn by it?s own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of dogs, it will only give result for dog images, but if we provide a new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender system, for Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.
It can be divided into three types:
Supervised learning
Reinforcement learning
Unsupervised learning

For instance, if you provide a machine learning model with many songs that you enjoy, along with their corresponding audio statistics (dance-ability, instrumentality, tempo, or genre). It oughts to be able to automate (depending on the supervised machine learning model used) and generate a recommender system as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do.

In a simple example, if you load a machine learning program with a considerable large dataset of x-ray pictures along with their description (symptoms, items to consider, and others), it oughts to have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on. The machine learning model looks at each picture in the diverse dataset and finds common patterns found in pictures with labels with comparable indications. Furthermore, (assuming that we use an acceptable ML algorithm for images) when you load the model with new pictures, it compares its parameters with the examples it has gathered before to disclose how likely the pictures contain any of the indications it has analyzed previously.

The type of machine learning from our previous example, called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous datasets fed.

Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model trains with unlabeled data).

Reinforcement learning, the third popular type of machine learning, aims at using observations gathered from the interaction with its environment to take actions that would maximize the reward or minimize the risk. In this case, the reinforcement learning algorithm (called the agent) continuously learns from its environment using iteration. A great example of reinforcement learning are computers reaching a super-human state and beating humans on computer games.

Machine learning can be dazzling, particularly its advanced sub-branches, i.e., deep learning and the various types of neural networks. In any case, it is “magic” (Computational Learning Theory), regardless of whether the public, at times, has issues observing its internal workings. While some tend to compare deep learning and neural networks to the way the human brain works, there are essential differences between the two.

The post Artificial Intelligence & Machine Language appeared first on iValuePlus.

What is Data Science?

Data science is the study of “data”. It basically involves different methods which can be developed around recording, storing, &analyzing data across different domains so as to effectively extract meaningful information. The goal of data science is to create perception and knowledge from data — which can be both structured and unstructured.

Data science is related to computer science, but is altogether a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, using scientific methods, processes, algorithms and systems. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. It is more closely related to the “mathematics field of Statistics”, which includes the collection, organization, analysis, and presentation of data. It is around the concept to “unify statistics, data analysis, machine learning and their related methods”.

Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. Data science should not be confused with data analytics. Both fields are ways of understanding big data, and both often involve analyzing massive databases using R and Python.

Various companies have petabytes of user data that may use data science to develop effective ways to store, manage, and analyze the data. Different scientific methods can be used to run tests and extract results that can provide meaningful insights about their users.

Below is a practical example of Data Science

“GOOGLE: MACHINE-LEARNING FOR METASTASIS

Location: Mountain View, California

How it’s using data science: Google hasn’t abandoned applying data science to health care. In fact, the company has developed a new tool, LYNA, for identifying breast cancer tumors that metastasize to nearby lymph nodes. That can be difficult for the human eye to see, especially when the new cancer growth is small. In one trial, LYNA — short for Lymph Node Assistant —accurately identified metastatic cancer 99 percent of the time using its machine-learning algorithm. More testing is required, however, before doctors can use it in hospitals”.

“How Data Science can help in COVID-19?”

The main cause of the wide spread of the coronavirus is the lack of information about the early-stage symptoms. This has led to a situation where people are not aware whether they are affected or not. They travel from one place to another with no clue that they are carrying the virus with them.

Now, the governments have started collecting the information of citizens such as their travel history and medical records. This has resulted in the collection of “huge data of citizens”. Countries have already started processing this data with the help of “Big Data tools”.

The processing of the data of billions of citizens involves removing “redundancy, scaling the data, and structuring” for further use. This is only possible with the help of various essential tools of “Big Data”.Many of these sources pull from data provided by trusted bodies such as the U.S Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). They also include direct links to those places so that people have quick, easy access to reliable information.

– After the collection and processing of such huge data, the government authorities analyze and visualize it. The collection can happen following the below basic steps:

• Help the people around you how to interpret data/information

• Translate information into more languages

• Prepare data related to the response

• Analyze data that is not directly related to the response

• Research using existing disaster response datasets

– By analyzing the data and visualizing the trends in it, Data Science helps the governments make estimates about the scope of further spread of the disease, the available medical infrastructure to admit affected patients, and the budget required for all of this.

– With the help of these estimations, Data Science is helping the governments make arrangements for medical facilities and capital to spend on their citizens.

The “US Centers of Disease Control (CDC)” is working with researchers at the “machine-learning department of Carnegie Mellon University” to forecast the spread of coronavirus.

– The team built a machine-learning model that processes data collected from several sources such as flu-related Google searches, Twitter activity, and web traffic to predict the spread of the virus.

Significant efforts have been made by the scientific community as a whole to offer a unique opportunity to the data science community.

-One such example is the effort to create “The COVID-19 Open Research Dataset (CORD-19)”, an extensive machine-readable collection of coronavirus literature available. CORD-19 is a resource of over 52,000 scholarly articles, including over 41,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.

• There are thousands of JSON files, each containing the research paper text details including their references.

• Due to the text being unstructured there are data quality issues including (but not limited to) correctly identifying the primary author’s country. This definitely has to be cleaned up.

• Upon cleaning up the data, we can apply various NLP algorithms to it to gain some insight and intuition into this data.

– The science community can answer high-priority scientific questions related to COVID-19 with the help of such datasets, data mining and other extraction techniques.

Data Science Can Give Accurate Pictures of Coronavirus Outcomes [Situation Awareness]

Medical professionals and others must get correct and up-to-date information about how the coronavirus situation changes day by day. Several organizations, including Johns Hopkins University, IBM and Tableau, have released interactive databases that offer real-time views of what’s happening with the virus.

Using these databases can inform, the number of confirmed cases, fatalities and recoveries.

Data Science Can Help Track the [“Spread”]

Data science specialists have also concluded that graph databases are instrumental in showing them how COVID-19 spreads. For example, BlueDot was able to predict the early spread of the illness from Wuhan to other Asian cities based on airline ticketing data.

• A graph database shows links between people, places or things.

• Scientists refer to each of those entities as a node, and the connections between them are the “edges.” The results give a visual representation of the relationship between things, if any.

• In the early days of the coronavirus outbreak, Chinese data scientists built a graph database tool called Epidemic Spread.

• It allowed people to type in identifying information associated with the journeys they took, such as a flight number or even a car’s license plate.

• Mobile phone data can play a key role in tracking the movement of people to help identify where the disease is likely to spread.

• The database would then tell those users whether anyone with a confirmed coronavirus case took those same trips and may have spread it to fellow passengers.

• Big data analytics can cross reference disease data against high-risk senior residents down to postcode level and the incidence of factors such as diabetes or obesity.

Data Scientists Can Handle [“Contact Tracing”]

Contact tracing is an effective way to slow COVID-19. It involves getting in touch with a person’s close contacts after that individual tests positive for the virus and telling them to self-isolate. Contact tracing is time-consuming, although it’s getting easier as more people take social distancing seriously.

They “created a mobile phone-based solution” to eliminate the need for people to call the contacts manually. Instead, those parties get text messages confirming the need for self-isolation. The researchers clarify that their approach would be most effective if it gets support from national leaders and is not an effort primarily spearheaded by independent app developers.

No nations are using this method yet. Given the market penetration of mobile phones and the familiarity people have with receiving texts, however, it’s easy to see why this approach makes sense.

Data Science [“Managing the pandemic”]

Data science can play a central role in analyzing the large-scale testing of people

• AI is also being used to accelerate drug development to treat COVID-19

• Google’s Deep Mind AI system is being used to identify characteristics of the virus that may help to understand how it functions.

• UK-based BenevolentAI, which is using AI to identify promising existing treatments for other illnesses that could be effective in treating COVID-19.

• The more data there is, the more accurate these predictions can be, and the better the pandemic can be managed.

Data Scientists to Find [“Possible Cures”]

Besides the race to restrict the COVID-19 spread, scientists are working as quickly as possible to uncover effective treatments.

Two graduates of the data science program at Columbia University have turned to machine learning to help.

• The typical process of antibody discovery in a lab takes years.

• This approach, however, takes only a week to screen for therapeutic antibodies with a high likelihood of success.

The team taking this approach says this method is less costly than traditional ones, too. Humans are still part of the process because they have to test the gene sequences identified as most promising by the machine learning algorithm. However, using this expedited method could be crucial in efficiently finding interventions that work for coronavirus patients.

Data Scientists to[“Predict Future Outbreaks”]

The data that is collected from this pandemic will be invaluable in understanding how best to deal with future outbreaks.

Global disease surveillance will be important part in the battle against future pandemics.

The more data we collect, the better data science and AI will be able to help us.

References:

www.builtin.com; https://www.kdnuggets.com/; https://databricks.com/; https://www.orange-business.com/

The post “How Data Science can help in COVID-19?” appeared first on iValuePlus.