science Archives | Adamas University

BIOMEDICAL ENGINEERs – Warriors of all time

In COVID-19 crisis, the real warriors were the Biomedical Engineers. They played an important role in medical technology in patient care. They involved themselves in making ventilators and PPE (personal protection equipment) kits to help the COVID patients. Biomedical engineers focus on inventing new devices and develop modern technologies which help in improving human health care system. With the help of the doctors and researchers, Biomedical Engineers are developing equipments to solve clinical problems.

Electing biomedical engineering as métier is extremely rewarding. Biomedical Engineers has the ability to save lives through innovation and modern technology. That’s why experts from Human Resource Department’s planning team suggest that every doctor along with medical studies should be accustomed with biomedical engineering studies.

Biomedical engineering, also known as medical engineering, is a form of engineering associated with the study in the fields of biology and health care system.

The following qualities are required for Biomedical Engineers:

Analytical skills
Communication skills 
Listening skills
Math skills

Apart from these skills, they should have idea of all disciplines ranging from material science to electronics, life science to biomechanics and mathematics to computation. Because of these, Biomedical Engineering is completely interdisciplinary in nature and the Biomedical Engineers possess vast knowledge across of all domains.

AI and ML for a better future

Have you ever considered about what motivates artificial intelligence programmes like Tony Stark’s JARVIS or the common man’s Alexa, Google Assistant, or Siri? These programmes can answer your calls and help you make decisions, but have you ever wondered what motivates them? In what ways does their brain operate? The application of Artificial Intelligence (AI) and Machine Learning is the straightforward response to each and every one of your questions (ML). The mechanical brains are controlled by artificial intelligence, which attempts to simulate human intellect so that they can perform like a human brain. With more and more research being done on AI and ML, there is the potential for AI and ML to assist in training computers to make decisions on their own, which will eventually make our lives easier by reducing the amount of work we have to perform.

This article, which was meticulously researched and penned with the intention of depicting the future reach of AI and ML in India, was created with the goal of assisting students in understanding how this subject will be advantageous to them if they decide to pursue it.

The potential applications of artificial intelligence in India are still in the process of being adopted, but the technology is gradually being put to use to find intelligent solutions to modern problems in almost all of the country’s most important industries, including agriculture, healthcare, education and infrastructure, transportation, cyber security, banking, manufacturing, business, hospitality, and entertainment. Readers who are interested in pursuing a course in artificial intelligence can find helpful information in this article. Candidates will gain some insight into the potential of artificial intelligence in India if they read this article and consider its contents.

Scope of AI in India

Both artificial intelligence and machine learning have a promising future in India and an immense potential to alter every area of the economy for the benefit of society. Artificial intelligence and machine learning have a bright future in India. AI is an umbrella term that incorporates a variety of helpful technologies, such as self-improving algorithms, machine learning, pattern recognition, and large amounts of data. Soon, there will not be a single business or market segment in India that is immune to the effects of this powerful instrument. This is one of the reasons why there is a growing demand for online courses in artificial intelligence in India.

Scope of AI and ML in Education Sector

By utilizing a variety of AI applications such as text translation systems, real-time message to speech, automating mundane and also repeated tasks such as taking presence, automating grading, and also customizing the learning journey based on ability, comprehension, and also experience, artificial intelligence can help our instructors be more effective. Within the purview of Artificial Intelligence education and learning is the consideration of the prospect of utilizing AI-powered rating machines that are able to evaluate solutions in an objective manner. This is being carried out in college and university settings in a step-by-step fashion. Real-time text-to-speech synthesis and text translation are two further AI-based applications in the educational sector.

The Role of AI and ML in the Development of Chatbots

The combination of chatbots in the digital framework or availability via the IVRS system education domain can be transformative in a country as diverse as India. They can be educated on the subject matter, and a large percentage of the students’ doubts can be answered quickly. This reduces the current workload of educators, allowing them to focus on more creative tasks.

The Integration of AI and ML into the Automated Grading System

On a more global scale, methods of machine learning such as Natural Language Processing could be used for automated grading of assessments on systems such as E-PATHSHALA, SWAYAM (Study Webs of Active Learning for Young Aspiring Minds), and DIKSHA. This would apply not only to inquiries that are subjective but also to those that are objective. This is because of the National Education Policy 2019, which places an emphasis on computer and internet literacy.

The Role of AI and ML in the Healthcare Industry

The healthcare industry in India is one of the most rapidly developing and competitive markets in the world. There is a dearth of doctors and services, including competent nurses and technicians, as well as infrastructure. This is one of the primary issues, but there are many others as well, including affordability and accessibility. As a result of the majority of high-quality medical facilities in India being situated in close proximity to tier 1 and tier 2 cities, access to healthcare in India is not uniformly distributed across the country physically. Aside from that, as Artificial Intelligence develops, there will be an increase in efficiency, which will lead to a reduction in the overall cost of medical treatment.

Because AI is able to process vast volumes of data in a short amount of time, it can be of assistance in the creation of medical equipment, as well as in design and innovation. Having a system that is enabled with AI helps to eliminate medical errors and increases overall productivity. Artificial intelligence has the potential to both circumvent access barriers and provide a solution to the accessibility problem by applying early detection followed by suitable diagnostic conclusions.

AI and ML in the Agriculture Sector

In India, agriculture is a major source of income for many people. Traditional farming methods pose a slight challenge for Indian farmers. Thermal imaging cameras can be used to continuously monitor agricultural land to ensure that plants receive adequate water. When it comes to selecting the right crop and the optimum method of sowing, this tool can help you get the most out of your land and save money.

As a result, improved insect control preparation can benefit from the application of artificial intelligence to predict behaviour and investigate parasites. Artificial intelligence-assisted anticipating modelling can be effective for delivering more detailed demand-supply details and for predicting the needs of farmers in terms of agricultural produce.

Automated Vehicles Using AI and ML

In the transportation industry, artificial intelligence offers a lot of potential. Artificial intelligence (AI) has the potential to be useful in a few specific contexts. Since its invention in 1922, autopilot has been used to keep ships, planes, and spacecraft on course. Self-driving cars are another field of research. Self-driving automobiles are being researched by companies around the world, including India. The use of artificial intelligence and machine learning has been prevalent in the design of these automobiles from the beginning. Self-driving cars, according to experts, will have various advantages, such as reducing pollution and eliminating human error from driving.

Artificial Intelligence and Machine Learning for a Smart Home

We are surrounded by artificial intelligence. Most of the time, we don’t even realize we’re interacting with devices powered by artificial intelligence. As an example, we routinely use OK GOOGLE, ALEXA, or CORTANA to execute a variety of chores by simply speaking to them. Artificial Intelligence and Machine Learning are used by these intelligent assistants for voice recognition. Learning from the user’s commands improves their productivity. You may ask a question, play a song, and buy anything online all with the help of this clever assistant.

Applied Artificial Intelligence and Machine Learning in Cybersecurity

Cybersecurity is another area where AI is being applied. Many companies have to deal with a lot of data. A security system is required, for example, in the banking industry or government entities that maintain vast databases containing the personal information of citizens. An good example of this topic is Cognitive Artificial Intelligence (CAI). Additionally, it helps analysts make better judgments by detecting, analysing, and reporting on hazards.

Machine Learning formulae and Deep Learning networks are used to improve and strengthen the AI over time. As a framework and central point of control for safety and security responses, IBM Resilient is an open and agnostic platform.

In the Manufacturing Industry, AI and ML

The industrial sector is a popular target market for AI-based firms from India. In order to assist the manufacturing industry flourish, these companies are developing AI-based solutions. Various types of robots are controlled by artificial intelligence in the workplace. The ability to examine data and forecast the future is a unique AI technology.

Using this AI capabilities, companies may estimate future supply and demand based on data from prior years’ sales or market surveys, allowing them to make faster decisions and better use of existing products. Artificial intelligence (AI) will be widely used in manufacturing in the future years.

Are we having safe drinking water?

Water is one of the most precious, integral and important aspects of almost every living organism. About 3% of the water is fresh water. This fresh water does not contain significant levels of dissolved minerals or salt and two third of that is frozen in ice caps and glaciers and is unavailable for human consumption. In total only about 0.5% of the total water of the planet is accessible for consumption^[1]. So, we have a very limited amount of usable fresh water. Now one of the basic human needs is clean drinking water. Unfortunately, more than one in six people still lack reliable access to this precious resource in this developing world^[2].

India covers for 2.45% of land area and 4% of water resources of the world but represents 17.7% of the world population. If the population increases with the present population growth-rate of 1.9 % per year then the population is expected to cross the mark of 1.5 billion by 2050. According to the Planning Commission, Government of India water demand will increase from 710 BCM (Billion Cubic Meters) which was in 2010 to about 1180 BCM (Billion Cubic Meters) in 2050. Domestic and industrial water usage is expected to increase by 2.5 times. Increase in urbanization in India is putting a drastic stress on civic authorities to provide basic requirement such as safe drinking water, infrastructure and sanitation^[2]. Overpopulation is putting a lot of pressure on available water resources, it is increasing the demand for the portable water, which in turn therefore requires exploration of raw water sources, developing treatment and distribution systems^[3]. In this era of rapid growth of population, management, maintenance and monitoring of water and its quality becomes a problem. Therefore, it is important to ensure that the water quality should be maintained at its desired level and should be monitored on regular basis so as to provide safe and quality drinking water.

As our concern is to know the quality of water, so we have gathered some reports across India to understand the recent scenario. We found that many Institutions/Organizations such as Maharashtra Public Health Laboratory, PES university, BIS (Bureau of Indian Standards), Union Government, Jal Sansthan, Ministry of Jal Shakti etc. had worked on checking the quality of water. They checked the quality of water under 28 parameters^[4].

Number of parameters in which samples are failing [4]

Water Treatment Process

Different communities have different procedures of treating the water. Treatment is on the basis of quality of the source water which enters the treatment plant. Now, the water which enters the treatment plant can be from two areas either surface water or ground water. Treatment and filtration are required more in surface water than ground water. This is because streams, rivers and lakes, contain more sediments like clay, sand, silt, soil particles,chemicals,toxins and germs than ground water^[7].

The basic purposes of water treatment plant or water treatment process:

Production of biologically and chemically safe water for human consumption
Production of water should be appealing to the consumers meaning water should be clear, colourless, pleasant to the taste, odourless, cool, none staining, neither corrosive nor scale forming, and reasonably soft.
Production of water should be accomplished using facilities with reasonable capital and operating costs^[2].

Unit Operations and Unit Process of Water Treatment Units

Sl. No.	Unit Operations and Unit Process	Principle Applications
1	Collection	Before the treatment, water needs to be collected from various sources such as lakes, ponds, rivers, streams, etc^[8].
2	Screening	Screening is the first step in the treatment process that is needed to remove larger items of suspended materials, such as plants, rubbish, large solid chunks of different materials, plastic packets, trees, animals and other debris^[8].
3	Micro strainer	It is used to remove plankton &algae from water^[2].
4	Aeration	In this process water is supplied with air so as to take up oxygen form air. Aeration helps in making the water less corrosive. it helps in expelling soluble gases such as CO₂, H2S and also expels volatile organic compounds. By aeration the taste and odour get improved^[9].
5	Mixing	This process provides rapid& uniform distribution of gases and chemicals into the water^[2].
6	Pre-oxidation	Ozone,potassium permanganate& chlorine, potassium permanganate acts as oxidizing agents in raw water. They retard the microbiological growth and oxidizes the taste, colour and odour causing compounds^[2].
7	Coagulation	Coagulation is the removal of fine particles that are suspended in the water. Rapid mixing of coagulant (positive charge) is done in this method resulting in neutralization with the negative charged particles. Lastly, fine particles come together to form flocs. Aluminium sulphate, ferric chloride, etc are the coagulants used in this process^[9].
8	Flocculation	Flocculation isthe process where water is gently mixed in a flocculation tankto form larger and heavier flocs ^[7].
9	Sedimentation	For sedimentation process a settling tank is used in which the large flocs that are formed in the previous process will fall because of gravity and settle on the floor of this tank^[9].
10	Filtration	Filtration removes dust, parasites, germs, dissolved particles, etc from the water. In this process the clear water that is above the settling tank is passed through filters with different pore sizes and are made up of different materials such as sand, gravel, charcoal, etc., ^[7]
11	Disinfection	This process is needed to destroy thedisease-causing organisms in water. Disinfection is done by oxidative chemicals such as bromine, chlorine, iodine, ozone &potassium permanganate, and also the ultraviolet radiation helps in disinfection^[2].

How is Biotechnology impacting millions of lives?

Are you an avid lover of biology? Are you naturally inclined to apply the principles of biology to create an impact on people’s lives? Well, then the Biotechnology sector is where you may find your dream job.

With the world still reeking of over 520 million deaths due to the COVID-19 pandemic, it is undeniable that the figure could have been in billions or even more had it not been for the Biotechnology sector. Almost everything starting from the diagnosis and immediate treatment to the development of vaccines brought to light the promise that Biotechnology holds as a leading technology in the modern world. To know more about the potential of Biotechnology and the facets of human lives that it is able to impact upon, read further down.

What is Biotechnology?

Biotechnology is an industrial sector that deals with the manipulation of living organisms to create commercial products. For instance, the wealth of knowledge accumulated by cell biologists, botanists, zoologists, molecular biologists, and geneticists have been implemented by genetic engineers to manipulate information carried by the DNA in order to create transgenic animals and plants of commercial importance. Quite undoubtedly, the deepest penetrations of the biotechnology sector has been in the healthcare and agricultural markets. However, there are several other important areas where Biotechnology is making inroads with sustainable solutions. This blog highlights some fields wherein Biotechnological interventions are working wonders.

Vaccine development

Within a few months of the detection of coronavirus, scientists mapped the entire genome of the virus and it helped to understand how the virus operates. Genome mapping being an important technique in Biotechnology, the Biotechnology sector can boast of its towering presence in global markets across the map. Also, the highly effective mRNA-based vaccine for COVID-19 was first tested in cells inside the laboratory which entails practising some basic techniques of Biotechnology. Weighing the outcomes, government organizations and pharmaceutical giants have entered into strong public-private partnerships to pool resources and fund research in the domain of vaccine development.

Next-generation computing-aided drug discovery

Advanced computing technology such as artificial intelligence and machine learning have enabled Biotech companies to automate their processes and scale up operations. This handholding of technologies have enabled to reduce the cost and time required to take new drugs from bench to bedside. The ability to analyze large data sets helps medicine manufacturers to identify treatments based on the root cause of a disease. This holds immense potential to reduce the usual 90 percent failure rate for developing new drugs. Data mining from current clinical trials can also help to predict the effectiveness of treatments down to a molecular level and even predict new or different uses for an existing drug thereby reducing cost and effort of establishing new drugs.

Genome editing

Techniques for manipulating the information present in the DNA, known as gene editing in technical jargon, has come a long way since they were first used to make edits such as addition, deletion, silencing, or replacement of a specific gene. Precise gene editing has been made possible by the advancement of technologies such as the revolutionary CRISPR-Cas9 systems. Engineered nucleases called CRISPRs acting as molecular scissors have unfurled a plethora of applications in gene therapy for the treatment of many conditions including rare genetic disorders and even fatal cancers. Furthermore, gene editing has also allowed the development of improved transgenic plants and animals capable of synthesizing a variety of medically important recombinant human proteins such as Insulin.

Precision medicine

Sequencing the entire human genome, an initiative known as the Human Genome Project, began in 1990 and was completed by 2003. This was another hallmarking achievement of Biotechnology that now allows extensive screening of patients and targeting of interventions. Improvisation of sequencing technologies have reduced the cost of genetic sequencing drastically ever since thereby making personalized gene sequencing affordable. This, in turn, has enabled the development of personalized treatment plans and targeted therapies, which are more effective than less-specific therapies because they focus on a patient’s genetic constitution. Furthermore, the falling cost of sequencing technologies has fostered the development of rapid and inexpensive methods to detect pathogens from clinical samples as well as soil samples.

Boosting agricultural yields

The global population is set to increase by 25% from 7.9 billion in 2022 to 9.7 billion in 2050. The basic need for a growing population is food, and hence its demand for feeding both people and livestock is about to increase proportionately. This automatically necessitates the use of increasing hectares of land for farming while practically cultivable land will keep on reducing as the expanding population of humans keep encroaching onto such lands. Biotechnology offers a solution to this alarming problem through the approach of gene editing. For example, crops such as wheat or corn may be engineered through the transgenic technology to grow in harsher conditions or produce more grain in a smaller area than other crop varieties while providing the same nutritional value. From another perspective, the development of biopesticides can enable protection of crops without the use of harmful chemicals thereby averting environmental damage.

Bioprinting and tissue engineering

Another promising futuristic application of Biotechnology in the medical field is 3D bioprinting, wherein bioprinters are used to develop cell-based scaffolds using a ‘bio-ink’ comprising cells and biomaterials. This empowers one to develop skin, bone, and vascular grafts from the patient’s own cells for personalized medicine. The bioprinting technology has added a major thrust to the field of tissue engineering and regenerative medicine by enabling the creation of autologous tissue grafts for wound healing and organ transplantation.

Conclusion

These trends clearly show that the demand for biotechnology is on the rise. The fact that this particular sector is being able to solve real-life problems related to human health and nutrition has catapulted it to fame. It is also quite evident that biotechnologists need more than just a background in biology, chemistry, or pharmaceutical science to build their careers upon. With innovative solutions rooted to the genetic level, biotechnology is here to stay and offer myriad career opportunities to the brightest minds!

Why should we learn Nanoscience? Impact to our Society

In 1959, Professor Richard Feynman in a public lecturer at California Institute of Technology shared his thought about the strange behaviour of small particles. His lecturer was entitles as: “There’s plenty of room at the bottom”. Professor Feynman actually gave us the idea to enter into a new field of Physics, today it is known as Nanoscience. Professor Feynman in his lecture also talked about “How do we write small”, “Information on a small scale” and the importance of developing better electron microscope. All his novel ideas have created breakthroughs in the field of nanoscience.

Nanoscience enables us to study the properties of system at nanoscale and Nanotecgnology enables us to organize and manipulate the properties and behaviour of a system in atomic or molecular level. Nanoscience has wide prospect and finds application in various different fields. Here I describes application of nanoscience and the scope and prospect in this field.

What is nanoparticle?

A particle has dimension of nanometer size. The question is how small one nonometer is. The 1 nm (1 nm = 10-9 meter) is one billionth of a meter or equivalent to 10 Å (1 Å = 10-8 cm). Nanosized particles of a substance exhibit different properties and behaviours than larger particles of the same substance. Carbon is very common also very abundant material in nature. We are aware of its two different forms; graphite and diamond. During 1985 to 2004, scientists have discovered three new allotropes of carbon. They are known as fullerene (known as Buckminsterfullerene: C60), carbon nanotube and graphene.

Fullerene: In 1985 a group of scientists lead by Prof. Harry Kroto had discovered a small structure in which 60 Carbon atoms are joined together in one unit. The structure is quite similar like a football. In this fullerene structure we could see hexagon + pentagon pattern. Prof. Kroto and his collaborators were awarded the 1996 Nobel Prize in Chemistry. With the advancement of technology various different structures like fullerenes with larger number of carbon atoms (C70, C76, C80, etc.) were synthesized.

Graphene: Graphene is 2-dimensional nano-structure. It is a 2D sheet of single layered carbon atoms arranged in hexagonal lattice. Graphite is actually made of millions of layers of graphene. In 2004 at the University of Manchester, Andre Geim and K. Novoselov produce graphene from graphite using a scotch tape in laboratory. Professor Geim and his co-workers were awarded Nobel Prize for Physics in 2010. Graphene is the most useful and thinnest 2D nanomaterial due to its extremely high electrical conductivity, transparency and tensile strength.

Carbon nanotube (CNT): CNTs are cylindrical nanostructure consists of one or more layer of graphene sheet. Diameter of single-wall CNT (SWCNT) and multi-wall CNTs (MWCNT) may vary from 0.8 to 2 nm and 5 to 20 nm respectively. A single-wall CNTs can be realize as cut-outs from a 2D hexagonal graphene sheet rolled up along one of the Bravais lattice vectors and thereby form a hollow cylinder. CNTs exhibit remarkable electrical conductivity. Single-wall CNTs are metallic but multi-wall CNTs are having small band-gap. CNTs exhibit exceptionally high tensile strength and thermal conductivity. These properties of CNT make them valuable and are used in electronics, optics, biological and biomedical research.

Exciting Properties of Nanoparticles

Super surface activity: Nanoparticles exhibit strong reactivity due to much higher surface to volume ratio. With decrease of particle size the number of particles at the surface increases. This leads to a significant energy contribution to the system from the unsatisfied bonds of the surface atoms. Hence, the surface becomes extremely ‘active’ due to the high available surface energy. This effect finds applications in: adsorption of toxic gases, catalysis, etc.

Superparamagnetism: A ferromagnetic particle behaves like a paramagnet when particle size is made very small. Ferromagnetic solid consists of small magnetic domains and spins are aligned inside the domain. If particle size is reduced to very small size (typically < 20 nm) the entire particle becomes a single domain. With further reduction in particle size (< 5 nm) ferromagnetic property is lost. Therefore in the absence of external field the particle behaves like a paramagnet and in the presence of a field spins are getting aligned leading to a large magnetization, also known as super-paramagnetic behavior.

Super-hydrophobicity: If surfaces are highly hydrophobic (super-hydrophobic) then they are difficult to wet. The contact angle of water droplet may exceed 150^oon a super-hydrophobic surface. Surface roughness is increased at nano-scale therefore actual contact area of the surface decreases and hence the surface becomes non-wetting. The super-hydrophobic coating is used in vehicle windshields and maritime industry.

Why nano-scale become so Important?

Nanoparticles exhibits some unique mechanical, optical, magnetic, and electrical properties that are distinctly different from that of bulk materials. It was found that nanoparticles exhibits enhanced activity when subjected to similar applications. A few are discussed below.

Nano-crystals have lower melting point and has reduced lattice constant (difference can be as large as 1000^oC).
Due to high surface to volume ratio nano-crystals are used for catalysis, drug delivery and energy storage.
Semiconductor nanocrystals have larger band gap than that of bulk semiconductors.
Ferroelectric and ferromagnetic materials lost their ferroelectricity and ferromagnetic property at the nano scale.
A system composed of nano-particles can conducts electricity better.

Applications:

Use of nanotechnology includes sports equipment, vehicle parts, storage of power in batteries, cosmetics, drug delivery and many more. Scientists are working with nonomaterials with a hoped that nanoscience will control our health-care system in future. We all use sunscreens; it contains ZnO or TiO₂nano powder to avert sunburns. Nano-science is combined with bio-science naturally because in general the bio-molecules that we are dealing with (e.g; DNA, RNA, proteins, enzymes) are all within the nanoscale range from 1-100 nm. In November 2012, Scientists at NIST (American National Institute of Standard and Technology) demonstrate that SW-CNTs can protect DNA molecules from oxidation. Here I illustrate some more applications of CNTs in bio-medical research.

CNTs are bio-compatible and having low-level of toxicity.
CNTs are elastic cylindrical tubes with both ends open and therefore can be used in intracellular delivery.
Due to high tensile strength, CNTs filled with calcium and grouped in the structure of bone can act as a bone substitute.
For biomedical application, functionalization is required and it is possible for CNTs. Functionalization may improve biocompatibility and also reduce the toxicity level.

CNTs can enter into cells by binding their tips to the cell membrane receptors. This actually helps in drug delivery.

Is Operations Research useful in Data Science?

“Operations research (OR) is defined as the scientific process of transforming data into insights to making better decisions.”

–The Institute for Operations Research and the Management Sciences (INFORMS)

Introduction:

In the twenty-first century, especially in the last decade, the most trending domain of study is may be Data Science and Data Analytics. In this domain of study, people work with data from different fields and they use different tools and techniques from the domain of Mathematics, Statistics, and Computer Science to study and analyze the data. Then make some conclusion from the data and use them to predict the future of the phenomenon under study. Before the rise of data science as a domain of study, Operations Research analyst and Statisticians are used to do the similar kind of job. Due to these facts, the overlap between the domain of Data Science and the domain of OR is misunderstood. Also, there is a common perception that OR is not useful in for Data Science or Data Analytics. Actually, the marketing of OR products and services which are applied to solve the real world problems leads to this kind of misconception, as most of the time the end-users do not have an understanding or background of OR and data science. Another possible reason may be that the availability of machine learning models which are available as packages of several platforms like Python and do not really contain specific any OR models. In practical, OR tools and techniques are applicable to data science. In fact, a lot of ideas which are used in Artificial Intelligence (AI) and data science problem solving, have cross-pollinated from OR due to the large overlap in the techniques and methods used. In this blog, I try to explore these relations of OR with Data Science and Data Analytics.

Operations Research and Data Science:

Before going to the discussion on the role and relation between Data Science and OR, let us try to understand another very important term called Analytics. According to INFORMS, Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems. There are three distinct types of analytics:

i) Descriptive Analytics gives insight into past events, using historical data;

ii) Predictive Analytics provides insight on what will happen in the future; and

iii) Prescriptive Analytics helps with decision making by providing actionable advice [https://www.informs.org/Explore/Operations-Research-Analytics]. In an INFORMS podcast, depending on organizational backgrounds, Glenn Wegryn divides Analytics into two distinct camps: Data Centric Analytics where data is used to find interesting insights and information to predict or anticipate what might happen; and Decision Centric or Problem Centric Analytics which is used to understand the problem, then determine the specific methodologies and information needed to solve the specific problem. This data centric analytics are done by using Data Science whereas problem centric analytics are done by Operations Research. The above mention figure clearly give an idea about this. From the figure, it is very clear that there is a common point of interest from both the domain. Hence OR plays a very important role in Data Science domain.

Operations Research and Machine Learning:

Machine learning is the area of data science where most of the OR tools and techniques are used. Linear programming and Optimization techniques are fundamental part of the overall machine learning lifecycle. Some of the examples of OR are:

Enabling smart human resource management by forecasting human resource requirements and optimizing daily schedule for resource persons (linear programming model)
Increasing TV program viewership by optimal scheduling of programs’ promotion (linear programming model)
Enabling supply chain transformation by providing AI/machine learning-based recommendations for optimized product utilization
AI-enabled forecasting for retail and eCommerce applications to optimize funnel and customer traffic
Data-driven optimization models for automated inventory management where we need to do warehouse management, inspection and quality control

Operations Research and Artificial Intelligence:

Another important area of data science is Artificial Intelligence where we can observe the use of OR algorithm. AI is used to build an automated system. Now, any real-life system have many decision variables and parameters, so if we want to build an automated system then we have to deal with a lot of decision variables. That’s why operations research algorithm must be a core engine in the system.

An Artificial Intelligence development lifecycle consists of the following steps: (Link)

Descriptive and Predictive steps:

In the first step, we need to define the problem to be solved
In the next step, we need to understand the current state of the problem and accordingly we have to define the work scope
Next we need to develop a Machine Learning model, where the machine learning solution is developed and tested.

Prescriptive steps:

Machine learning outputs or the predictions obtained using machine learning are given as OR inputs. Here, the OR techniques are used to make recommendations based on the outputs from the ML model. This is a critical step for the entire life cycle.
Finally, the solution output is delivered to the client.

Covid-19 impact:

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. (Zhang, Qingpeng, et al. “Data science approaches to confronting the COVID-19 pandemic: a narrative review.” Philosophical Transactions of the Royal Society A 380, No. 2214 (2022)). Covid-19 has a big impact on supply chain strategies also. People from data science community are analyzing “lessons learned” from the pandemic to better prepare and more efficiently and effectively respond to the next disaster, interested people can visit the following for a discussion on it (Link1).

Conclusion:

From the above discussion, it is very much clear that Data Science and Operations Research have some overlapping objectives with clear line of difference between these two domains of study. Also, we observe that there are several OR techniques and algorithms which have important role to play in different topics of data science. In my opinion, operations research together with data science and analytics is going to play a very important role to build the future of us.

Biomass based biofuel generation future in India

Out of some of the hottest trends that have been on the top lists for quite a while are choosing an entrepreneur as the primary occupation and doing an eco-friendly business.

The need of renewable energy is increasing in the world due to rapidly growing human population, urbanization and huge consumption of fossil fuels. Fossil fuel reserve is very limited, and the reserve is getting depleted day by day. The primary sources of energy that can be used as the alternative of fossil fuels are wind, water, solar and biomass-based energy.

Currently biomass as a feedstock for biofuel production is gaining importance. Biomass energy is supplying about 10-15% of total energy demand of the present world. Biomass feedstocks include organic material such as wood, wood-based energy crops, grass, lignucellulosic materials like wheat straw, rice straw, sugarcane baggase, corn, microalgae, agricultural residues, municipal wastes, forest product wastes, paper, cardboard and food waste. Biomass can be converted into biofuels by thermochemical and biochemical conversion. Based on the types of feedstocks or biomass the biofuels derived are divided into different groups i.e. 1^st generation, 2^nd generation, 3^rd generation. 1^st generation biofuels mainly extracted from the food crop-based feedstocks like wheat, barley, sugar and used for biodiesel and by fermentation to produce bioethanol. But first-generation biofuels face the “fuel vs food” debate and also the net energy gain is negative. 1^st generation biofuels production systems also have some economic and environmental limitations. To overcome the drawbacks of 1^st generation biofuels 2^nd generation biofuels have been generated from the non-food crops-based feedstocks like organic wastes, lignocellulosic biomass etc. For biofuel production from these sources rigorous pretreatments are required to make the feedstocks suitable for biodiesel production. This is the major drawback of 2^nd generation biofuel production. Then the attention of the world has been shifted towards 3^rd generation biofuel production entails “algae-to biofuels”. Microalgae is easy to cultivate, has higher photosynthetic rate and growth rate than other plants and there is no food vs. feed dilemma present of using microalgae as feedstock for biofuel production. Presently the attention is also given towards fourth generation biofuel. The former concept of third generation of biofuel deals with the conversion process itself from the microalgae to biofuel. The fourth generation of biofuel concept deals with development of microalgal biotechnology via metabolic engineering to maximize biofuel yield. Fourth generation biofuel uses genetically modified (GM) algae to enhance biofuel production. In comparison with third generation in which the principal focus is in fact processing an algae biomass to produce biofuel, the main superior properties of the fourth are introducing modified photosynthetic microorganisms which in turn are the consequence of directed metabolic engineering, through which it is possible to continuously produce biofuel in various types of special bioreactors, such as photobioreactors.

Biomass has the highest potential for small scale business development and mass employment. Characterized by low-cost technologies and freely available raw materials, it is still one of the leading sources of primary energy for most countries. With better technology transfer and adaptation to local needs, biomass is not only environmentally benign, but also an economically sound choice. Bio-based energy can be expected to grow at a faster pace in the years to come.

On the Biomass Energy sector, the India government committed to increasing the share of non-fossils fuel in total capacity to 40% by 2030. India produces about 450-500 million tonnes of biomass per year. Biomass provides 32% of all the primary energy use in the country at present. A total capacity of 10145 MW has been installed in the Biomass Power and Cogeneration Sector. The Installed Capacity of Biomass IPP is 1826 MW together with the Installed Capacity of Bagasse Cogeneration is 7547 MW and the Installed Capacity of Non-Bagasse Cogeneration is 772 MW.

The eco-friendly business has lots of benefits, by going green with your business you’re promoting the Earth’s safety from potential environmental catastrophe, you support innovation and concomitantly producing green energy.

The Government of India has been constantly bound on increasing the use of clean energy sources. This does increase a better future and at the same time creates employment opportunities too. According to The Ministry of New and Renewable Energy (MNRE), India’s total installed capacity of renewable energy is 90 GW excluding hydropower. Also, it states that 27.41 GW will be added. Renewable Energy in India is a great asset to Energy Contribution, yet India still needs to work a lot in Renewable Energy Sectors.

THE NANO SCIENCE AND ITS CONTRIBUTION IN TREATING CANCER

Nanoscience involves the study of the control of matter on an atomic and molecular scale. This molecular level investigation is at a range usually below 100 nm. In simple terms, a nanometer is one billionth of a meter and the properties of materials at this atomic or subatomic level differ significantly from properties of the same materials at larger sizes. Although, the initial properties of nano materials studied were for its physical, mechanical, electrical, magnetic, chemical and biological applications, recently, attention has been geared towards its pharmaceutical application, especially in the area of drug delivery. According to the definition from NNI (National Nanotechnology Initiative), nanoparticles are structures of sizes ranging from 1 to 100 nm in at least one dimension. However, the prefix “nano” is commonly used for particles that are up to several hundred nanometers in size. Nanocarriers with optimized physicochemical and biological properties are taken up by cells more easily than larger molecules, so they can be successfully used as delivery tools for currently available bioactive compounds.

Cell-specific targeting can be achieved by attaching drugs to individually designed carriers. Recent developments in nanotechnology have shown that nanoparticles (structures smaller than 100 nm in at least one dimension) have a great potential as drug carriers. Due to their small sizes, the nanostructures exhibit unique physicochemical and biological properties (e.g., an enhanced reactive area as well as an ability to cross cell and tissue barriers) that make them a favorable material for biomedical applications. It is difficult to use large size materials in drug delivery because of their poor bioavailability, in vivo solubility, stability, intestinal absorption, sustained and targeted delivery, plasma fluctuations, therapeutic effectiveness etc. To overcome these challenges nanodrug delivery have been designed through the development and fabrication of nanostructures. Nanoparticles have the ability to penetrate tissues, and are easily taken up by cells, which allows efficient delivery of drugs to target site of action. Uptake of nanostructures has been reported to be 15–250 times greater than that of microparticles in the 1–10 um range. Nanoparticles can mimic or alter biological processes (e.g., infection, tissue engineering, de novo synthesis, etc. These devices include, but not limited to, functionalized carbon nanotubes, nanofibers, self-assembling polymeric nano constructs, nanomembranes, and nano-sized silicon chips for drug, protein, nucleic acid, or peptide delivery and release, and biosensors and laboratory diagnostics. Various polymers have been used in the design of drug delivery system as they can effectively deliver the drug to a target site and thus increase the therapeutic benefit, while minimizing side effects. The controlled release (CR) of pharmacologically active agents to the specific site of action at the therapeutically optimal rateand dose regimen has been a major goal in designing such devices. The drug is dissolved, entrapped, encapsulated or attached to a NP matrix and depending upon the method of preparation, nanoparticles, nanospheres or nanocapsules can be obtained. Nanocapsules are vesicular systems in which the drug is confined to a cavity surrounded by a unique polymer membrane, while nanospheres are matrix systems in which the drug is physically and uniformly dispersed. Biodegradable polymeric nanoparticles have attracted considerable attention as potential drug delivery devices in view of their applications in the controlled release of drugs, their ability to target particular organs/tissues, as carriers of DNA in gene therapy, and in their ability to deliver proteins, peptides and genes through a per oral route of administration. Recent advances in the application of nanotechnology in medicine, often referred to as nanomedicine, may revolutionize our approach to healthcare. Cancer nanotechnology is a relatively novel interdisciplinary area of comprehensive research that combines the basic sciences, like biology and chemistry, with engineering and medicine. Nanotechnology involves creating and utilizing the constructs of variable chemistry and architecture with dimensions at the nanoscale level comparable to those of biomolecules or biological vesicles in the human body. Operating with sub-molecular interactions, it offers the potential for unique and novel approaches with a broad spectrum of applications in cancer treatment including areas such as diagnostics, therapeutics, and prognostics.

Nanotechnology also opens pathways to developing new and efficient therapeutic approaches to cancer treatment that can overcome numerous barriers posed by the human body compared to conventional approaches. Improvement in chemotherapeutic delivery through enhanced solubility and prolonged retention time has been the focus of research in nanomedicine. The submicroscopic size and flexibility of nanoparticles offer the promise of selective tumor access. Formulated from a variety of substances, nanoparticles are configured to transport myriad substances in a controlled and targeted fashion to malignant cells while minimizing the damage to normal cells. They are designed and developed to take advantage of the morphology and characteristics of a malignant tumor, such as leaky tumor vasculature, specific cell surface antigen expression, and rapid proliferation.

Nanotechnology offers a revolutionary role in both diagnostics (imaging, immune-detection) and treatment (radiation therapy, chemotherapy, immunotherapy, thermotherapy, photodynamic therapy, and anti-angiogenesis). Moreover, nanoparticles may be designed to offer a multifunctional approach operating simultaneously as an effective and efficient anticancer drug as well as an imaging material to evaluate the efficacy of the drug for treatment follow-up. In recent years, nanomedicine has exhibited strong promise and progress in radically changing the approach to cancer detection and treatment.

Post COVID Career Prospects of M.Sc. Tech (Statistics and Data Science)

In this current scenario i.e. post COVID period data science becomes a new era. Data science has played a vital role in making the policies or decision making in real life world. It is one of the trendiest jobs across the globe in terms of future scope and career stability. Data science is an interdisciplinary subject that includes the use of statistics, big data analytics, machine learning and related aspects in order to understand the problem or phenomena with respect to a set of real-world data. The thrust areas of data science are fraud and risk detection, healthcare, internet search, targeted advertising, advanced image recognition, speech recognition, airline route planning etc. Under health care sector it is having different applications such as medical image analysis, genetics & genomics, drug development, virtual assistance for patients and customer support. Thus, data science has major demand in many organization around the globe. In today’s career-oriented world, students are confused on choosing the right subject after completing graduation that will help them to get a good placement in the job enterprise. After graduation, numerous options like master degree in the general subjects, or in various professional courses confuse the students to take the right decision. Today, both students and their parents are seeking for job-centric programs, though general study programs are mostly preferred as their first choice. A good choice can be a program that is a combination of both general and professional courses. It is always better to choose a program that is a natural progression of the existing skills and qualifications along with some professional development skills.

The Role of Statistics and Data Science in Today’s World:
The pursuit of a career in Statistics is in high demand today. With a degree in Statistics, career opportunities are boundless. Statisticians have been known as Economists, Scientists, Mathematicians, Field Investigator or Qualitative Researcher. The ‘data-hungry’ modern world now calls them data analyst, business analysts, data scientists, quality and risk analysts. Data Science has become an integral contributor to success in career opportunities. Data Scientists and Data Analytics are in high demand in today’s job world. Data Science based enterprises are the largest companies in the entire world. The famous websites like Google, Amazon, and Facebook, use data science to create algorithms that improve customer satisfaction, which in turn maximizes the profit. Thus, with a degree in Data Science, one can work with high-tech companies like Google, Amazon, LinkedIn, Facebook, banking and financial companies like ICICI Bank, Axis Bank, or research firms like McKenzie, Deloitte.

So, according to the trend of the modern job world, the best option is to choose a program in Statistics or Data Science. But, can one pursue both Statistics and Data Science at the same time? Yes, the Department of Mathematics, Adamas University is offering such a program which is a combination of both Statistics and Data Science. The program name is ‘M.Sc. (Tech.) in Statistics and Data Science’. This program is also a combination of both general and professional courses, Statistics, being a general subject and Data Science, a professional course.

M.Sc. Tech (Statistics and Data Science) program is a two years (four semesters) post-graduate degree course which combines Statistics, Mathematics and Computer Science with applications to Data Science and Data Analysis to meet the demand of today’s job world. From Probability Theory and Statistics to Statistical Inference, from Applied Statistics to Statistical Modeling, from Problem Solving to Number Theory, from Computer Programming to Data Mining, the program is also offering a number of optional papers, a few of which are Big Data Analytics, Cryptography and Network Security, and Artificial Intelligence. Besides these, the program also offers summer internship and Project/Dissertation. In summer internship, a student may choose to visit relevant institute or industry according to the availability. The project/Dissertation helps the students to explore and strengthen the understanding of fundamentals through practical application of theoretical concepts.
On completion of the program, a student will
• Be acquainted with the various Statistical tools useful for Data Analysis
• Develop programming skills
• Acquire knowledge on Data Analytics and Data Mining
• Learn the concepts of Data Structures
• Develop a conceptual understanding on Network Security
Eligibility Criteria for the Program:
Graduate student having Statistics/Mathematics/Economics/Physics as compulsory subject, or graduate students in Data Science, or students having a B.Tech. degree in IT/CSE/ECE or BCA or other relevant stream with at least 50% marks are eligible to apply for this course.

Career Prospects:
From careers in IT sector to technological companies, Data Science professionals can choose their career in a numerous field including business, industry, agriculture, government and private sectors, computer science, and software development.
A few job roles available for a student after completion of the program are:
(i) Data Scientist: Data scientists also called analytical experts utilize their skills in both social science and technology to manage all kinds of data. A data scientist involves in arranging and analyzing disorganized and unstructured data, from numerous sources like smart devices, social media feeds, emails, industry, health science, environmental data.
(ii) Data Analyst: The role of a Data Analyst is to figure out a market trend. The data analyst serves as a caretaker for an organization’s data and as such shareholders are able to understand data and use it to make tactical business decisions.
(iii) Statistician: A Statistician deals with gathering, analyzing and interpreting to aid in many businesses and decision-making process. The Statisticians apply statistical models and methods to real-world problems. They analyse, gather and interpret data to help draw valid conclusions.
(iv) Forecasting Analyst: The task of a Forecasting Analyst includes tracking, analyzing, and evaluating operations in order to provide accurate forecasts. Forecasting analysts use current data of the company to predict future level production and sales. By examining inventory levels, demand for products or services, and speed of production, they ascertain a company’s optimal production levels and possible future sales.
(v) Data Manager: Data Manager are involved in making and implementing policies for effective data management, framing management techniques for quality data collection to confirm adequacy, accuracy and validity of data. They are also involved in planning and executing efficient and secure procedures for data management and data analysis with attention to all procedural aspects

Conclusion:
From above discussion we can see that a student with master degree in Statistics and Data Science has numerous career opportunities and so this program is recommended to graduate students seeking for a good career opportunity in the present scenario of the job world.

Green Hydrogen – A New Fuel of the Future

India is witnessing the multitude of benefits of renewable energy, including increased access to electricity, reduced local air pollution and carbon dioxide emissions, and lower energy imports. There are easy ways to boost renewable energy’s position in the grid as well as end-use industries like transportation and industrial. Nonetheless, there remain economic, technological, and feasibility challenges to fully electrifying all existing energy usage, limiting the extent to which renewable power may directly replace fossil fuels. Hydrogen is already widely employed, particularly as an industrial feedstock in the production of ammonia-based fertilizers. Most of the hydrogen is produced through methane reforming, which results in large carbon dioxide emissions. Carbon capture and storage (CCS) technologies can collect these emissions, but they are yet undeveloped in India.

Hydrogen produced using electrolysis powered by renewable energy—green hydrogen—and its use in fuel cells has a long history of promising a pathway to a global clean energy economy yet failing to deliver. Electrolysis, where water (H₂O) is split into its component parts using electricity, is an alternate means of processing. While there is important research activity on electrolysis, photolysis and biogenic hydrogen production methods, these low-carbon emission technologies have not yet been implemented on a scale. This is partially due to today’s low-carbon hydrogen production costs that are higher than fossil fuel-based hydrogen or other fossil-fuel alternatives. However, it is possible that these costs could achieve equilibrium in the future with green hydrogen in desirable regions undercutting grey hydrogen. This is made more possible in India, where tariffs on renewable energy are already among the lowest in the world and natural gas supplies are low and expensive. The capital cost of electrolysers, along with energy costs, is another significant factor for reducing the cost of green hydrogen. With a ramp-up in implementation, these are likely to continue to decline, since most electrolysers are produced on a relatively small scale today.

Fresh water resources make up around 2.5 percent of the total amount of water on the planet. As shown in figure 1, fortunately, the accessible seawater resource is 39 times that of fresh water. Water use due to electrolysis should, however, not be viewed as gradually using up the water resource, because when green hydrogen is oxidized (by combustion or via a fuel cell) it yields the same amount of water as was originally electrolyzed. This may enter the atmosphere as water vapor or be condensed at the point of use and recovered as liquid water. Moreover, the production of green hydrogen simultaneously produces oxygen in the exact amount required to oxidize the hydrogen, this is an important characteristic, because atmospheric oxygen depletion is contributing to global warming.

Figure 1: Annual water requirement of Green Hydrogen production relative to Earth’s water resources. [Source: https://www.sciencedirect.com/science/article/pii/S1464285921006581] — Figure 1: Annual water requirement of Green Hydrogen production relative to Earth’s water resources.
[Source: https://www.sciencedirect.com/science/article/pii/S1464285921006581]

Green hydrogen provides India with major opportunities to grow into a new field of renewable energy technology, building domestic manufacturing expertise to supply the Indian market as well as overseas. Electrolysers would be the principal technology of significance. There are currently no major Indian producers of this technology, with electrolysers being imported from Germany, Norwegian or Japanese companies currently in use in India.

Globally, the momentum for hydrogen and fuel cell technology is rising, with market forecasts ranging from $2.5 trillion to $11.7 trillion by 2050. India has the capacity to manufacture more than its domestic demand, large quantities of low-cost, low green hydrogen. Significant economic value could be produced by exploiting the country’s diverse range of hydrogen production feedstocks to produce hydrogen for export. To manufacture hydrogen for sale, India has many strategic advantages, as depicted in the figure 2 below:

Figure 2: Strategic Advantages of Green Hydrogen in India; Source: Self-evaluation

Green hydrogen is therefore widely viewed as the ‘net zero’ fuel for our future energy system, with green oxygen replenishing the associated consumption of atmospheric oxygen. However, it should be noted that some of the hydrogen will be required as a feedstock (e.g., for ammonia and methanol production) rather than as a fuel, and some of the green oxygen will be applied to industrial processes and water oxygenation as opposed to being vented to the atmosphere. For instance, hydrogen and nitrogen will be carried into plants in the form of ammonium, and oxygen will be used by the steel industry. It is therefore important to identify synergies between the electrolysers’ need for water and the use of both green hydrogen and oxygen, because these could accelerate the deployment of electrolysis in the limited period, we have left to combat climate change.

As a result, green hydrogen and its derivatives are projected to play a crucial role in global decarbonization at scale due to their adaptability, which allows them to be used in a variety of applications and decarbonizes hard-to-abate sectors.