Everything goes in a planned way in life, but the most beautiful unplanned thing is having a best friend. because, it something that we can’t say the actually day the month the time, we become best friends. It just happen all unexpected. maybe on the worst day of life, maybe in the worst place.

it is said right, that, somthing after a particular time we understand, that why certain thing take place, why that has to be me? but, becassue there was somthing good coming, somthing better that we whould never expected, best friend. Sooner or later everyone will get best friend, but, till than doesn’t stop being nice. doesn’t stop paying attendtion who are helping to you.

To deserve a best friend you also have to be responsible, you want you best friends to help you guide you, become happy if you are happy, instead of jealous . Than these are the things first you have to lean in yourself, because friendship is from both sides. otherwise everyone would have all the friends. But why there are not, because of these very same reason.

Just never give up in becoming the nice person. other nice person you will surly find. friends doesn’t only limit to talk everyday, or to make fun everyday, both have to understand, there are some bad day, some busy days. understanding bond should be strong. if one is feeling low, other be always be head up to support him. encourage him. Just our believe is everything. plus its not compulsory, that we would be always together, no not, we are not, but that doesn’t mean we are no longer friends.

Realty, if you have stronger bonds and if you are not together the friendship grows. it never decay. just believe and support at every path

NEW CHEMICAL PROCESS TURNS SINGLE USE PLASTIC INTO FUELS

New research may offer a way to keep single-use plastics such as grocery bags, bottles, straws and food containers out of landfills. But instead of transforming these hard-to-recycle products back into useful plastics, the new technique reduces the plastics back down to the petroleum oil used to make them in the first place, reports Molly Taft for Gizmodo. The resulting oil can then find a new life as fuels or lubricants.

Apart from keeping single-use plastics out of landfills, if implemented at a large scale the new method could reduce the world’s oil demands. Per Gizmodo, plastic manufacturing is projected to comprise half of the world’s oil demand by 2050.

Researchers say their process, described in a paper published last week in the journal Science Advances, works best on plastics called polyolefins, which are used to make products we are often told can’t be recycled, such as plastic bags.

The basic concept is to heat the plastics to break their chemical bonds and reduce them down to their constituent parts. What’s new is that the team behind this research has found a way to achieve this at much lower temperatures than ever before, which makes the process more energy efficient and cost effective.

“This is the first technology that’s able to take the most difficult plastics and recycle them into something really useful,” Dionisios Vlachos, a chemical engineer at the University of Delaware and co-author of the paper, tells Jordan Golson of Inverse. “It’s the best way to recycle single-use plastics and packaging like polyethylene and polypropylene.”

Using heat to break down plastics is called pyrolysis, and Vlachos tells Gizmodo that most prior research focuses on processes that require temperatures ranging from 752 to 1,472 degrees Fahrenheit to work. Vlachos says his team’s technique can get the job done at just 437 degrees, and that the end result is “nearly ready-to-use fuels for cars, trucks, or airplanes and lubricants.”

The special sauce in this new method is its catalyst, which is a combination of zeolites (minerals mostly made up of aluminum and silicon) and metal oxides including platinum and tungsten, per the paper.

“Alone these two catalysts do poorly. Together, the combination does magic, melting the plastics down and leaving no plastic behind,” says Vlachos in a statement.

According to Inverse, the method can convert up to 85 percent of the original material into useful oil. Vlachos tells Inverse he estimates around 300 half-liter water bottles could produce enough oil to make a gallon of gasoline, and two pickup truck beds full of plastic bottles might fill a car’s gas tank.

Vlachos and his co-authors have filed a provisional patent on their technique and its catalyst, but he says more work is needed to translate the method to industrial-scale use.

“We need to take action on the plastics problem and develop technologies and policies to eliminate it from the environment,” Vlachos tells Gizmodo. “Research takes 10-plus years before it becomes useful. Investing in this field now is a priority.”

INSITU CONCRETE MATURITY SENSORS SAVE CONSTRUCTION TIME AND MONEY

Researchers at Purdue University have developed in situ piezoelectric concrete sensors that can monitor in real time the strength of concrete as it cures. This development has the potential to significantly accelerate construction schedules and reduce spending on infrastructure projects.

The sensors work by converting electrical energy into mechanical energy before sending a mechanical wave into the concrete and measuring its propagation and speed using a resonator. When concrete is initially placed, it has little resistance to such waves, explains Luna Lu, Ph.D., the American Concrete Pavement Association professor of civil engineering at Purdue. However, as concrete cures, its resistance to the propagation of the waves grows. Using mathematical and physical models, the researchers established the relationship between the wave impedance and the concrete strength.

These sensors have the potential to replace the two most used methods of judging concrete’s strength: cylinder testing and maturity curves, both of which test a specific concrete mix in the laboratory. Because the sensors test concrete on-site rather than in a lab, they can work on any mix and under any temperature or humidity conditions.

This development has the potential to significantly accelerate construction schedules and reduce spending on infrastructure projects.

In cylinder testing, concrete is placed in a cylinder in a lab, then tested to destruction to establish the mix’s strength. But specimens in the lab may have lower break values than in the field, leading mix designers to compensate by adding roughly 10%-15% more cement into the mix than might otherwise be necessary, Lu says. Because the new Purdue sensors work on-site, such guesswork is eliminated. Less cement can be used, cutting costs and reducing the carbon footprint of the project.

Maturity curves, the other commonly used method, measure the temperature of concrete as it cures as a proxy for strength. “The temperature is a direct representation of the hydration process,” Lu explains. But in the field, many factors can affect that expected curing time. If water, an accelerator, or a decelerator is added, for example, those maturity curves are rendered inaccurate. Extreme temperatures can also impact the curing rate. By working on-site and in real time, the new sensors avoid these issues.

Currently, Lu and her team are developing a way for the sensors to automatically and wirelessly generate, store, and transmit data.

The sensors were first developed in 2017 for the Indiana Department of Transportation, which wanted to accelerate its roadway construction times. The sensors were deployed in three highways in Indiana in 2019. It was estimated at the time that the process could save millions of dollars annually. (In March 2021, this project was named an ASCE Gamechanger, an innovation that helps solve the problems identified in ASCE’s 2021 Report Card for America’s Infrastructure.)

The sensors are now being tested in a building as well. In late 2020, 12 sensors were placed on the third floor of the five-story Engineering and Polytechnic Gateway Complex project at Purdue, six in columns and six in decks. The building is under construction by F.A. Wilhelm Construction Co. Inc., with an expected completion date in fall 2022. 

Currently, Lu and her team are developing a way for the sensors to automatically and wirelessly generate, store, and transmit data. Her team is also working on more robust packaging. “As you know, the construction industry is not the most gentle or elegant,” she says. The team is also working with six states and the Federal Highway Administration to complete additional sensor tests so that the method can be accepted for use nationwide, she says. 

The sensors are being developed as part of Purdue’s Center for Intelligent Infrastructure, which focuses on research in sustainability and resiliency, smart sensing technology, autonomy and adaptivity, and artificial intelligence applications. It is led by Lu, who is the center’s director, and associate director Shaoshuai Mou, Ph.D., an assistant professor in the university’s school of aeronautics and astronautics. 

“The vision we have for the future of infrastructure is that it really has to be sustainable,” Lu says. “The next generation of infrastructure needs to be more adaptive. It needs to be more resilient, and it needs to have autonomous features.”

It’s okay to be sad. It’s necessary. It acts you to look at our emotions closely. As there are seasons and phases in life, Sadness is one of them. Avoiding sadness can lead to anxiety and distress. But if choose to accept it and do through it you will experience a positive of it.

I am not saying one should take sadness lightly. It should be distinguished from ordinary sadness. We think we are not happy all the time, our desire to be happy keeps leveling up. It is a common emotion that every next person goes through when they face rejection, lose someone, despair, grief, etc. It’s okay to get overwhelmed by it.

Friendly reminder🔔
Whenever you feel low or sad just remind yourself that it’s okay whatever you are feeling. Nobody can monopolize happiness they have to go through sadness some days. Facing your emotions and acknowledging them will bring you mental well-being.

Data visualization is the graphical portrayal of data and data. By utilizing visual components like diagrams, charts, and guides, data visualization instruments give an available method to see and get patterns, exceptions, and examples in data. 

In the realm of Big Data, data visualization apparatuses and innovations are fundamental to break down monstrous measures of data and settle on data-driven choices.

Advantages

Our eyes are attracted to tones and examples. We can rapidly distinguish red from blue, square from circle. Our way of life is visual, including everything from workmanship and commercials to TV and motion pictures. 

Data visualization is another type of visual craftsmanship that gets our advantage and keeps our eyes on the message. At the point when we see an outline, we rapidly see patterns and exceptions. On the off chance that we can see something, we disguise it rapidly. It’s narrating with a reason. On the off chance that you’ve at any point gazed at a gigantic accounting page of data and couldn’t see a pattern, you realize the amount more compelling a visualization can be.

As the “time of Big Data” gets going, visualization is an undeniably key apparatus to figure out the trillions of lines of data created each day. Data visualization assists with recounting stories by curating data into a structure more clear, featuring the patterns and anomalies. A decent visualization recounts a story, eliminating the commotion from data and featuring the helpful data. 

In any case, it’s not just as simple as sprucing up a chart to make it look better or slapping on the “information” part of an infographic. Compelling data visualization is a fragile difficult exercise among structure and capacity. The plainest diagram could be too exhausting to even think about getting any notification or it makes an amazing point; the most staggering visualization could totally fizzle at passing on the correct message or it could say a lot. The data and the visuals need to cooperate, and there’s a workmanship to joining extraordinary investigation with incredible narrating.

Why data visualization is important 

It’s difficult to think about an expert industry that doesn’t profit by making data more justifiable. Each STEM field profits by getting data—thus do fields in government, money, advertising, history, purchaser products, administration businesses, instruction, sports, etc. 

While we’ll generally wax gracefully about data visualization (you’re on the Tableau site, all things considered) there are commonsense, genuine applications that are certain. Furthermore, since visualization is so productive, it’s likewise quite possibly the most helpful expert abilities to create. The better you can pass on your focus outwardly, regardless of whether in a dashboard or a slide deck, the better you can use that data. 

The idea of the resident data researcher is on the ascent. Ranges of abilities are changing to oblige a data-driven world. It is progressively important for experts to have the option to utilize data to settle on choices and use visuals to recount accounts of when data illuminates the who, what, when, where, and how. While customary training normally defines a particular boundary between imaginative narrating and specialized investigation, the cutting edge proficient world additionally values the individuals who can cross between the two: data visualization sits directly in the center of examination and visual narrating.

Types

At the point when you consider data visualization, your first idea most likely quickly goes to straightforward reference diagrams or pie outlines. While these might be an indispensable piece of envisioning data and a typical standard for some data illustrations, the correct visualization should be matched with the correct arrangement of data. Basic charts are just a hint of something larger. There’s an entire choice of visualization strategies to introduce data in successful and fascinating manners.

• Charts
• Tables
• Graphs
• Maps
• Infographics
• Dashboards

1.Machine Learning Algorithms

Comprehend the fundamental hypothesis behind directed, unaided, and support learning algorithms.

There are various ways a calculation can show an issue dependent on its collaboration with the experience or climate or anything we desire to call the information. 

It is famous in machine learning and computerized reasoning course books to initially consider the learning styles that a calculation can embrace. 

There are a couple of principle learning styles or learning models that a calculation can have and we’ll go through them here with a couple of instances of calculations and issue types that they suit. 

This scientific categorization or method of getting sorted out machine learning calculations is helpful on the grounds that it constrains you to consider the jobs of the information and the model planning measure and select one that is the most suitable for your concern to get the best outcome.

• linear regression:Direct relapse is a straight model, for example a model that expects a direct connection between the info factors (x) and the single yield variable (y). All the more explicitly, that y can be determined from a direct mix of the info factors (x).
• neural network:A neural organization is a progression of calculations that tries to perceive fundamental connections in a bunch of information through a cycle that emulates the manner in which the human cerebrum works. In this sense, neural organizations allude to frameworks of neurons, either natural or fake in nature.

2.Statistics & Math

Stats and math are the structure squares of information science, particularly in AI and AI, including key information.

• linear algebra
• calculus
• probability distribution
• hypothesis testing: t-test, ANVOA, correlation

3. SQL

SQL is the language used to speak with the data set and determine experiences through information concentrates and questions, a few basic strategies.

• CRUD — create, read, update, delete
• filter, sort, aggregate
• date, string, number manipulation
• join and union
• subquery

4. Programming

There are some simple to start yet powerful programming dialects like Python and R. Rather than zeroing in on the coding linguistic structure, most importantly, is to learn the programming rationales just as the developer attitude.

• loop structure: for loop, while loop
• conditional structure: if … else statement
• data structure and complexity
• object-oriented programming

5. Data Visualization

Data Visualization is inserted all through the data science venture, from the exploratory data examination before all else to the last announcing and expectations. Some normally utilized devices.

• Tableau
• PowerBI
• seaborn (Python package)
• ggplot2 (R package)

YouTube Channels

1. Ken Jee

His channel is very venture centered and novice well disposed. It’s an incredible spot to begin with building information science projects, particularly Kaggle projects, and not threatened by the math or measurements behind the unpredictable calculations. Ken Jee likewise gives helpful vocation tips and profitability hacks.

2. Joma Tech

 It has the enchantment that makes you continue to watch his recordings. Joma Tech portrays information science from a software engineer’s viewpoint. For instance, he has an arrangement called “If Programming Was An Anime” which arrives at a large number of perspectives. His video blog styled substance will most likely leave you alone engaged and instructed simultaneously.

3. StatQuest with Josh Starmer

This channel centers around representing AI ideas and calculations through enlivened visuals. It is astounding how the maker separates complex ideas (for example Stochastic Gradient Descent, Support Vector Machine) into absorbable pieces. It is the go-to channel at whatever point I need to gain proficiency with another ML model.

4. 3Blue1Brown

3Blue1Brown is an incredible mix of expressions and science. The maker Grant Sanderson portrays the narrative of the mathematical world through shocking visual outlines from a special point of view.

5. Nate at StrataScratch

It gives exhaustive walkthroughs of SQL inquiries from enormous tech organizations, for example, Microsoft, Facebook and so on For those that are planning for information science specialized meetings, you might need to look at it. The activities help to combine the SQL execution through the interaction of dynamic review.

6. Art of Visualization

What makes this channel stand apart is that it covers a scope of custom graphs that are not natural to make in Tableau, including Sankey outline, Sunburst diagram. Also, there are arrangements of instructional exercises encompassing the subject of information perception utilizing Python, R, and past.

In early March, India’s health minister Harsh Vardhan declared the country was “in the endgame” of the Covid-19 pandemic. Mr. Harsh Vardhan also lauded Prime Minister Narendra Modi’s leadership as an “example to the world in international co-operation”. From January onwards, India had begun shipping doses to foreign countries as part of its much-vaunted “vaccine diplomacy”.

At the end of February, India’s election authorities announced key elections in five states where 186 million people were eligible to vote for 824 seats. Beginning 27 March, the polls would stretch over a month, and in the case of the state of West Bengal, be held in eight phases. Campaigning had begun in full swing, with no safety protocols and social distancing. In mid-March, the cricket board allowed more than 130,000 fans, mostly unmasked, to watch two international cricket games between India and England at the Narendra Modi stadium in Gujarat.

India was in the grips of a devastating second wave of the virus and cities were facing fresh lockdowns. By mid-April, the country was averaging more than 100,000 cases a day. On Sunday, India recorded more than 270,000 cases and over 1,600 deaths, both new single-day records. If the runway infection was not checked, India could be recording more than 2,300 deaths every day by first week of June.

Meanwhile, almost in a parallel universe, away from the death and despair, the world’s richest cricket tournament was being played behind the closed doors, every evening, and tens of thousands of people were following their leaders to election rallies and attending the festival Kumbha Mela.

India’s second wave was fuelled by people letting their guard down, attending weddings and social gatherings, and by mixed messaging from the government, allowing political rallies and religious gatherings. With infections declining, fewer people were taking the jabs, slowing down the vaccination drive, which had aimed to inoculate 250 million people by the end of July. 

Experts now say that crowing about India’s exceptional in “beating” the epidemic – younger population, native immunity, a largely rural population – and declaring victory on the virus turned out to be cruelly premature. “As is typical in India, official arrogance, hyper-nationalism, populism and an ample dose of bureaucratic incompetence have combined to create a crisis.

Experts say this rapid increase shows that the second wave is spreading much faster across the country. Dr A Fathahudeen, who is part of Kerala state’s COVID taskforce, said the rise was not entirely unexpected given that India let its guard down when daily infections in January fell to fewer than 20,000 from a peak of over 90,000 in September.

Many Indian cities are reporting a chronic shortage of hospital beds. It’s also evident in the desperate cries for help on social media platform.

The world is God’s creation
With many attractive location
And a perfect aeration
For people to go on vacation
Leaving their vocation
To give up their frustration
One can take aviation
For a good migration
And have positive vibration
Don’t just live in imagination
Of travelling different nations
Must take precaution
Or will be under probation

Naruto is generally a very simple minded, easy going, cheerful person. He often rushes things, and misses obvious things such as Hinata’s constant shyness around him.

In the beginning of the series, Naruto is very fun loving, often pulling pranks on fellow villagers. This usually ends in a scolding from Iruka Umino. Naruto later reveals he did these pranks because he liked the attention, as he grew up with no parents or friends. He’s also very beginning when he’s young, despite being largely unskilled in comparison to his peers.

As the series progresses and Naruto gets older, he eventually starts to become more serious and wises up.

As a Jinchūriki bearing the Nine Tailed Fox, Kurama, Naruto was often jeered at and avoided by most villagers. Because of this, Naruto was largely alone in the early parts of his life, and would often seek out attention by pulling pranks. This would become Naruto’s main reason want to become Hokage, so everyone in the village would acknowledge and respect him. Eventually, Naruto’s actions towards saving the village (And even the whole world) lead to many villagers respecting and even admiring him. In the movie, Naruto: The Last, Naruto has fans who come from all different villages just to see him.

Naruto has a strong dedication to his friends, often willing to put his life on the line to protect them. Despite his treachery towards the Hidden Leaf Village, Naruto has always considered childhood rival Sasuke Uchiha to be his best friend. In the end of the series, Naruto reveals that he cared for Sasuke so much because he felt they knew the same pain of being alone, and he constantly would chase after him because Naruto did not want to see him hurt when there is no need to.

As Hokage, Naruto seems to have grown even more mature. When Boruto was pulling pranks, Naruto knew this was because he didn’t have a father figure, and spent time with him. This shows that Naruto cares deeply for his children.

Harry Potter In the past couple years, there has been a growing phenomenon in the world of children’s literature, this phenomenon is Harry Potter. J.K. Rowlings series of novels about a young wizard and his years at “Hogwarts School of Wizarding and Witchcraft,” has become one of the most successful children’s book series of all time. Before reading any of the now four novels, one may find it hard to believe that a children’s novel may be so entertaining. But once one starts reading any of the four books, it is plain to see why these books are so popular.

Before first reading a Harry Potter novel, I was skeptical that a children’s book about an orphan wizard going to school would be entertaining. 

Whether it is a fight for his life, winning a quidditch match, or just staying out of trouble, these novels are anything but boring. This I believe, is one of the biggest reasons why these books have been such a success, the author knows how to keep the story going at all times in order to maintain the readers attention. Another factor which I believe has led to the novels success is that while reading, the reader wishes that they could live in this fantasy world created by Rowling where life is anything but ordinary. The novels make the reader somewhat desire a life like the one led by the characters of the novels, like going to a school to learn wizarding, always having extraordinary things happen and just being at Hogwarts seems like an unequaled joy for some. These novels have the ability to reignite the long lost imaginations of adults, and keep the imaginations of children strong. 

Although I am a strong supporter of this book series, I do find one thing wrong with the books. All the novels have endings which are almost the same, Harry saves the day and Voldemort the major antagonist gets defeated in one way or another

“A real friend is one who walks in when the rest of the world walks out.”
Friendship Day Quotes by: Walter Winchell

Having a true friend is one of the most precious gifts one can ever have in their life. A person should consider himself/herself one of the luckiest persons on the earth if he/she has a true and sweet friend in their life. Real friends give us any sweet and pleasurable memories to rejoice for. None of us will ever want to lose a true friends as that’s where we find our self most comfortable. A friend is somebody who will without exaggerating will keep appreciating you to help you keep going with a positive frame of mind. The will keep on thriving forever for all those years you will be there on the earth. Keeping sane balance between two people in friendship is in fact not an easy task and you need to be transparently honest to be able to maintain the balance.

A friend is always devoted to you in helping you out on various issues of life and that develops a sense of feeling protected with a friend. Friendship day is observed around the World in order to give people an opportunity to express their feeling of love. One can express their feeling with International friendship day essays, speeches, wishes, messages and various other means.

it is only this bond of friendship that gives us a person in life that will never become greedy for anything. They will always be active on finding something that can benefit their friends in one way or the other. We do not have boundations to select our friends on any ground including caste, age, sex, creed or any such differentiation that exit within the society. It is that a friend knows all the realities of his/her friend and helps each other satisfactorily on various issues they may be feeling stranded in. In general a friendship blooms when both the partners are of the same age, background and character but that does not mean people from variant background cannot be best friends. There will always the bond of love between two people no matter what age group they fall in or which society they are from.

The term Revenge Bedtime procrastination means staying up late at night just because your entire day was preoccupied with chores, kids, work, or other responsibilities. This is the time that you provide to yourself and regain all your lost time.

When we feel like we can’t control our days we try to monopolize our nights. However, this is not healthy behavior. As we need an adequate amount of sleep to function properly and be mentally healthy. So if you find yourself stuck in the loop of Revenge bedtime procrastination,
Try to set boundaries to your work, read a book, allot a little time for yourself in the day this would surely change your lifestyle. As Rome was not built in a day, you have to be patient in this process.

Knowledge management is how we manage our knowledge. This includes our creativity, skills, leadership and competencies. The management of our knowledge is commonly referred to as knowledge management. In the business world, the definition of knowledge management also includes the maintenance of a knowledge base or portal where specific knowledge related to the company is housed. Only a few initiatives are able to truly transform how an organization operates, and knowledge management is one of them.

Types of knowledge management:

The types include,

• Explicit knowledge management
• Implicit knowledge management
• Tacit knowledge management

Explicit knowledge management: Explicit knowledge means the knowledge which covers topics that are easy to systematically document, and share out at scale: what we think of as structured information. Explicit knowledge includes things like frequently asked questions (FAQs), instructions, raw data and related reports, diagrams, one-sheets.

Implicit knowledge management: Implicit knowledge is what is gained when you learn the best way to something. You can then take that experience and synthesize it with other learned information in order to solve an entirely new problem. This is very much different from the explicit management. Generally implicit knowledge management is been excluded from traditional because it is very much difficult.

Tacit knowledge management: Tacit knowledge is intangible information that can be difficult to explain in a straightforward way, such as things that are often “understood” without necessarily being said, and are often personal or cultural. Tact knowledge is very informal and is applied only for a specific situation. Knowledge which is gained from personal experience and is more difficult to express.

Knowledge life cycle:

There are 4 stages in knowledge life cycle, they include

• Creation
• Mobilization
• Diffusion
• Commoditization

Creation: The first stage in knowledge life cycle is creation. It is about creating the knowledge, how it is going to be applied.  In fact, no one can fully understand the idea or emerging knowledge, even if person creating it. In doing business, the suitable strategy in this early stage is to test the idea on its commercial viability.

Mobilization: It is the improved state of creation, the value of knowledge will be more creative in mobilization stage.  In fact, no one can fully understand the idea or emerging knowledge, even if person creating it. In doing business, the suitable strategy in this early stage is to test the idea on its commercial viability.

Diffusion: Diffusion is the third stage in knowledge life cycle. The organizations will accept the leakage of knowledge, and no longer try to keep the knowledge under wraps. They will spread out the knowledge by selling it to outsiders.

Commoditization: The last stage in knowledge creation life cycle is the commoditization stage. The organizations concentrate on managing knowledge that is already well known. The basic knowledge has been completely diffused. However, there are many opportunities to extract value from current knowledge to generate one in this stage

A leader is a person who resembles as a role model to others. A leader is a person who should work on behalf of the people. He should understand the feeling of his employees and their thinking capacity. He is the person who voices out on their behalf. A person is said to be a leader, only if he satisfies the basic criteria for leadership. A leader is a person who is extraordinary in his work, skills, knowledge and attitude. Leadership helps him and also others to the correct things at the right time.

Leadership means different things to different person around the world, and different things in different situations.

There are different styles of leadership models. They include:

Autocratic leader

Democratic leader

Transactional leader

Transformational leader

Laissez-Faire leader

Autocratic leader: An autocratic leader is a leader who do involve others in his decision. He will not let the employees according to their will ness. He is a person who is very arrogant towards his employees. Autocratic leadership is the inverse of democratic leadership. Employees are neither considered nor consulted prior to a direction, and are expected to adhere to the decision at a time and pace stipulated by the leader.

Democratic leadership: A democratic leadership style is entirely an opposite of autocratic leadership style. A democratic leader always cares for his employees. He always motivate them in doing the work. He always engage them in decision making process. He is a person with friendly nature and makes relationship with others.

Transactional leader: Transactional leader is more or less similar to autocratic leadership style. These managers reward their employees for precisely the work they do. Transactional leadership helps establish roles and responsibilities for each employee, but it can also encourage bare-minimum work if employees know how much their effort is worth all the time.

Transformational leader: Transformational leader is similar to democratic leadership style. This is a highly encouraged form of leadership among growth-minded companies because it motivates employees to see what they’re capable of. But transformational leaders can risk losing sight of everyone’s individual learning. They always listen to their employees in each and every step, they are going to take. They discuss among themselves. A good style of bonding with the leader and employees will occur in this type of leadership.

Laissez-Faire leader: leadership can empower employees by trusting them to work however they would like, it can limit their development and overlook critical company growth opportunities. A good leadership style which could be executed in the organization. Laissez faire leadership is exactly the opposite of autocratic leadership style. This would be similar to democratic style of leadership. The team would be involved in decision making process with the leader. If this type of leadership exists within an organization, the goal of an organization can be easily achieved and the employees would also work hard to reach their goals.

1. Google’s Machine Learning Crash Course

This course altogether and successfully brings anybody — regardless of whether it’s a lesser DS or a total novice — into the universe of machine and profound learning while at the same time covering significant ideas like slope drop and misfortune works and introducing primary calculations from straight relapse to neural organizations. The course material comprises readings, activities, and scratch pad with genuine code executed with Tensorflow and running on google Colab which implies no establishment is needed for you to have the option to run it. 

Other than the brief training there is a tremendous measure of materials in this site in regards to information science and AI, separated as follows: 

– Courses — here you’ll discover further plunges into explicit points like bunching, suggestion frameworks and then some. 

– Practica — instances of “how Google utilizes AI in its items”. 

– Guides — bit by bit educated answers for normal ML issues. 

– Glossary — my number one asset here, I figure each information researcher ought to have this in her work area.

2. IBM’s Machine Learning with Python

This course begins with presenting the contrasts between the two fundamental sorts of learning calculations — managed and solo and afterward gives a decent survey of all fundamental calculations in Machine picking up including a (captivating to my assessment) part on proposal frameworks. Every section closes with a code the student can run on her program, in addition to — when you complete every one of the necessities you will get an identification that can be subsequently shared on LinkedIn, Twitter, and so forth

3. AWS’s the Elements of Data Science

A year ago Amazon opened to the public courses that have run in the organization for their designers and the outcome is a gigantic corpus of information on different subjects from ML essentials through acquaintances with their administrations (for instance Neptune, ElastiCache) to explicit applications utilizing AWS apparatuses (for instance — Computer Vision with GluonCV, Visualizations with QuickSight). 

4. Fast.AI

I should concede that I never completed this course, yet I cherished the parts I did in addition to it’s extremely famous, so I felt committed to remember it for this rundown. Here you’ll discover since quite a while ago recorded talks (~1.5 hours each) joined by code tests. They have various courses as well and cover further developed points in contrast with different courses in this rundown, including Computer Vision and Natural Language Processing. I will say that on the first occasion when I needed to begin this course I got debilitated by the specialized necessities referenced in the initial segment of it yet then I discovered that everything note pads can be run on Google Colab which implies no establishment required and a free GPU.

When I arranged for DS interviews I discovered their Elements of Data Science course profoundly valuable for two principle reasons, first — it covers the down to earth phases of a standard ML pipeline from information planning to show preparing and evaluation which was useful, second — every subject has been introduced and clarified with real code models which gave an incredible look to the useful part of the field and furthermore uncovered extraordinary functionalities of Pandas and Scikit Learn I wasn’t familiar with (did you know there are underlying datasets in the SKlearn bundle you can load and use for your own practica? I do because of this course). Here, as well, you’ll get an accreditation while finishing every one of the necessities.

5. Kaggle Learn

Kaggle is notable as the best spot to get useful involvement with information science because of its tremendous measure of coordinated datasets and numerous rivalries, however there is another incredible part to it and that is their ‘Learn’ segment. What you’ll discover here isn’t actually courses at the same time, as named by Kaggle themselves, Micro courses with numerous intuitive activities intended to instruct and progress required abilities from Python for DS through explicit libraries right to profound learning, SQL and progressed Machine Learning. This is most likely the most ideal approach to grow your insight with insignificant responsibility and time.

Yes it is necessary to know which is more important, according to me knowledge is the most important. Because marks do not decide your future, knowledge does

Knowledge is how much you can retain knowledge. It’s a never ending process, and there is always so much to learn. Knowledge is abundant, and knowing it equips you with so much out there. Our world is a fascinating place with a lot of things you can learn and attain in terms of knowledge so get the best out of it.

Knowledge is more important than marks but again you need marks to enter a place where you can earn knowledge. contrary, you don’t need a school or college to earn knowledge. … People with good knowledge always get good marks. BUT KNOWLEDGE WINS THE BATTLE AGAINST MARKS EVERYWHERE.

Today the world has become very competitive. Competition is everywhere. It is in studies, sports, job and almost all activities of mankind. But, is competition really giving benefit in the field of education? Let us think about it for a while.

Educational competition is everywhere and is growing day by day. Friends compete each other for better marks. Different Schools compete with each other for better result in Board Examinations. Nowadays, we are focusing very much on more marks and good grades. Frankly speaking I am not an exception to it. And how can I be ?

If I start neglecting marks, I won’t get admission in good colleges. Nowadays it is not odd for a college to set its cut off marks very high.

But does high marks ensure that a student have got good knowledge ? Here lies my question and it was in my brain for the last two three days. There are many factors which can lead to high marks without good knowledge.

With the ascent in notoriety of Data Science and AI, everybody needs to embrace a couple of practices to achieve better outcomes in these perspectives. Fortunately for us all, with every one of these consistent movements, there are a lot of free devices that we can use to improve the nature of the models we fabricate and the ventures we develop. 

Every one of these fast movements in AI and Data Science has additionally led to the ascent of some incredible free AI devices. These are devices that each Data Science or AI wannabe should utilize to get out on the ball and get significantly more successful outcomes. 

In this article, our primary concentration and destinations will be to take a gander at the seven best free AI and Data Science apparatuses that each individual hopeful and aficionado of the subjects should use to get substantially more successful outcomes. Not exclusively will these apparatuses assist you with accomplishing higher proficiency, however they will likewise empower you to arrive at these elevated requirements considerably more rapidly because of their effortlessness and a more significant level of performing calculations.

1.Anaconda

The Anaconda stage is a totally free apparatus and maybe probably the best asset that are accessible to Data Scientists. This stage is accessible with different appropriate bundles on pretty much each and every dispersion, including Windows, Linux, and macOS. With the assistance of the Anaconda climate, you will actually want to achieve the advancement of a large number of Data Science projects without any problem. 

The Anaconda conveyance gives you different apparatuses and hardware to take care of various sorts of issues. They offer Jupyter Notebooks, Spyder conditions, Visual Studio Code, thus substantially more for the disentanglement of assignments. They likewise give a simplicity of establishment to complex libraries, for example, the GPU establishment of the TensorFlow library with a basic grammar and code for their introducing system. 

The Anaconda climate, alongside its different highlights, additionally empowers you to handily get to and download different libraries. In general, it improves on the whole intricacy behind a portion of the intricate mechanics that an individual would need to manage while overseeing bundles, libraries, or some other components in Data Science. It is effectively “The World’s Most Popular Data Science Platform.”

2. Kite

Kite turns out to be truly outstanding and most impressive free devices that can be utilized by a Data Scientist or an AI lover. It adds the capacity for software engineers to profit by the high velocity AI-fueled consumption highlights to make the designers program at high rates. The best part about Kite is it has support highlights for around sixteen diverse programming dialects like Python, Java, and numerous others. Close to these astonishing highlights, it likewise upholds sixteen code editors, including Jupyter Notebooks, Visual Studio Code, Sublime content, Pycharm, and considerably more. 

On the off chance that you are a flat out novice in the field of Data Science and AI, I would recommend that you stand by a smidgen prior to investigating this instrument. The primary justification for me expressing this point is on the grounds that I accept that each individual should initially begin rehearsing plain code on a Python IDLE (attempt to stay away from autocomplete highlights in content managers). When you have a decent act of dealing with your code squares and sorting out the ideal arrangements with your coding experience, it is strongly suggested that you begin using the Kite apparatus to constantly explore your code and tasks in a like manner.

3.GitHub and Git Bash

GitHub is quite possibly the most noticeable stage for information researchers to share their codes and work on a few parts of Data Science, Python coding, and a few different undertakings all together. It is totally allowed to join and gives various advantages like expanding intelligence with the local area and other speculative highlights. You can undoubtedly share code scraps with the making of another significance or make another archive to exhibit the undeniable level undertakings you assemble. 

To deal with an asset like GitHub, you have the alternative of using the Git Bash application for the Windows climate. While on a stage like Linux, you can unreservedly utilize Git orders to perform explicit tasks, on a windows stage, you will require an imitating layer. This copying is given the assistance of the Git Bash interface. When you have Git introduced on your Windows stage, you can utilize the slam orders to control the GitHub site by performing activities like cloning or other a few comparable functionalities.

Dubai didn’t say Good Bye to India
France is not being Trance to India
Greece’s love is Freezed in India
Germany gives it’s Harmony to India
Saudi wishes for healthy India
Emirates Generates it’s care to India
Taiwan prays for Shawn of India
Russia rushes it’s Sputnik V to India
United States sent its O2 to Indian States
Arch Rival Pakistan doesn’t want Pawn to India
Mongolia gives a heart warming gesture to India
Niagra (falls) in tricolour for India
UNO says “No” to help India
WHO gives hope for India
Microsoft is being Soft heart to India
Google was not frugal to India
“You are not alone India
We will overcome together”
            STAY STRONG INDIA
           WE SHALL OVERCOME.

Failure in life is inevitable. Everyone experiences some kind of failure at some point in their life. The biggest failure of life is having the fear of failure. Failure is a lesson that makes you learn which part should not be repeated or done again. It allows you to grow and enhance yourself.

To think failure is the opposite of success is futile. Failure should be perceived positively. Each failure teaches you something. When you overcome the fear of failure you reach the best version of yourself.

Failure paves you a way to success. People who have perseverance and dedication even after failure achieves the summit with their sheer hard work. Potential failure should not deter you from trying and persisting. So after confront failure review your action and learn from your errors and begin again.

Now-a-days work life balance is very much important for our life. Work life balance is balancing both our life and work. Balancing the work and life is a hectic task for every one of us. Everyone should keep a balance of work and life. Many people always work had, and forget to balance the life. This leads to poor balancing. The balancing should be strong and should create a positive environment among the people around us. Working is not the only vital task in our life, there are many more. All can be practices and achieved only there is a good balance among each other.

Actually the concept of work from home also emerged before COVID-19. But, it became very popular during the COVID. Due to COVID, everyone started to work from home. Many organizations are also allowing the employees to work from home. Now-a-days the people like to continue to work, only from home, because they are very used to work from home during COVID. Especially during work from home, balancing of work life is very vital. Because for the whole day we work in home and do not have time to spend with our family. Due to many distractions, stress etc.

Work-life balance is an important aspect of a healthy work environment. Maintaining work-life balance helps reduce stress and helps prevent burnout in the workplace. Chronic stress is one of the most common health issues in the workplace. It can lead to physical consequences such as hypertension, digestive troubles, chronic aches and pains and heart problems. Chronic stress can also negatively impact mental health because it’s linked to a higher risk of depression, anxiety and insomnia.

When you are considering your work-life balance, planning begins before you look for a job and accept a new position. First, take the time to determine your real-life needs from the broadest perspective. When you plan your work first, it can accordingly be executed. Planning helps in achieving work life balance. For example, you may be surprised to discover that a lower-paying job with proximity to great day care for your children is preferable to another option that takes you an hour away. Work life balance will be achieved easily if the family supports them. The working people should listen to the family what they are about to say. Only if it happens, the family will run smoothly. Otherwise it leads to distractions within the family with arising a lot of problems.

During your interview for a new job, keep your ears open to hear the company’s view on telecommuting, work culture, time flexibility, and so on. All of these aspects of employment will affect your ability to pursue a work-life balance. If they’re not mentioned during the interviews, you’ll want to ask specific questions to assess the workplace’s compatibility with your work-life balance needs.

Deforestation is cutting down trees. The facts of deforestation,

Forests cover more than 30% of the Earth’s land surface, according to the World Wildlife fund. These forested areas can provide food, medicine and fuel for more than a billion people. Worldwide, forests provide 13.4 million people with jobs in the forest sector, and another 41 million people have jobs related to forests. 

Today, most deforestation is happening in the tropics. Areas that were inaccessible in the past are now within reach as new roads are constructed through the dense forests. A 2017 report by scientists at the University of Maryland showed that the tropics lost about 61,000 square miles (158,000 square kilometers) of forest in 2017 — an area the size of Bangladesh.

The World bank estimates that about 3.9 million square miles (10 million square km) of forest have been lost since the beginning of the 20th century. In the past 25 years, forests shrank by 502,000 square miles (1.3 million square km) — an area bigger than the size of South Africa. In 2018, The guardian report that every second, a chunk of forest equivalent to the size of a soccer field is lost.

Often, deforestation occurs when forested area is cut and cleared to make way for agriculture or grazing. Reports that just four commodities are responsible for tropical deforestation: beef, soy, palm oil and wood products. UCS estimates that an area the size of Switzerland (14,800 square miles, or 38,300 square km) is lost to deforestation every year. 

Natural fires in tropical forests tend to be rare but intense. Human-lit fires are commonly used to clear land for agricultural use. First, valuable timber is harvested, then the remaining vegetation is burned to make way for crops like soy or cattle grazing. In 2019, the number of human-lit fires in Brazil skyrocketed. As of August 2019, more than 80,000 fires burned in the Amazon, an increase of almost 80% from 2018. 

Many forests are cleared to make way for palm oil plantations. Palm oil is the most commonly produced vegetable oil. It’s cheap, versatile and can be added to both food and personal products like lipsticks and shampoo. Its popularity has spurred people to clear tropical forests to grow more palm trees. Growing the trees that produce the oil requires the levelling of native forest and the destruction of local lands — on the ecosystem. According to a research report, the global palm oil market was valued at $65.73 billion in 2015 and is expected to reach $92.84 billion in 2021. 

Forests can be found from the tropics to high-latitude areas. They are home to 80% of terrestrial biodiversity, containing a wide array of trees, plants, animals and microbes, according to The World Bank, an international financial institution. Some places are especially diverse — the tropical forests of New Guinea, for example, contain more than 6% of the world’s species of plants and animals. 

Perhaps the most essential parts of handling your ideal job in data science is building a solid, powerful, attractive portfolio that demonstrates your skills and shows that you can deal with enormous scope projects and play pleasantly in a group. Your portfolio needs to demonstrate that you invested the energy, exertion, and assets to sharpen your skills as a data researcher. 

Demonstrating your skills to somebody who doesn’t have any acquaintance with you, particularly in a brief timeframe outline — the normal time a scout spends on a resume or a portfolio is 7~10 seconds — isn’t simple. In any case, it’s certainly feasible by the same token. 

A decent portfolio ought to incorporate different sorts of projects, projects about data gathering, examination, and representation. It ought to likewise contain projects of various sizes. Managing little projects is altogether different than managing enormous scope ones. In the event that your portfolio has the two sizes, it implies you can pursue, deal with and investigate all size programming, which is an expertise needed for any data researcher.

That may lead you to think about how you would discover great open-source data science projects that are not difficult to get into and look incredible on your portfolio. Furthermore, that is an incredible inquiry, however with the detonating number of data science projects out there, discovering great ones that could be what lands you the work isn’t the most straightforward of errands. 

At the point when you take a stab at looking into data science projects to add to, you will frequently go over the large ones, similar to Pandas, Numpy and Matplotlib. These goliath projects are extraordinary, however there are less known ones that are as yet utilized by numerous data researchers and will look great on your resume.

1.NeoML

Machine learning is presumably the core of information science applications, so I needed to have in any event one open-source project exclusively for machine learning. NeoML is a machine learning system that permits the client to configure, assemble, test, and convey machine learning models for free with a collection of in excess of 20 customary machine learning algorithms. 

It includes materials that help common language processing, computer vision, neural organizations, and picture classification and processing. This system is written in C++, Java, and Objective-C and can run on any stage from Unix-based ones, macOS, and Windows.

2.Kornia

We’ll finish up our rundown with Kornia. Kornia is a supporting PC vision library for PyTorch. It incorporates different schedules and differentiable features that can be utilized to take care of some nonexclusive PC vision issues. Kornia is based upon PyTorch and intensely relies upon its effectiveness and CPU ability to register complex capacities. 

Korina is something other than a bundle; it is a bunch of libraries that can be utilized together to prepare models and neural organizations and perform picture change, picture separating, and edge discovery.

3.Google’s Caliban for Machine Learning

How about we kick this rundown off with a task from the tech monster, Google. Regularly when building and creating information science projects, you may think that it’s hard to assemble a test climate that will show you your undertaking in a genuine circumstance. You can’t foresee all situations and make a point to cover all edge cases. 

Google offers Caliban as a possible answer for that issue. Caliban is a trying apparatus that tracks your natural properties during execution and permits you to imitate explicit running conditions. Specialists and information engineers built up this device at Google that plays out this errand consistently.

                                                       So you endured the labyrinth that is information science work chasing, you figured out how to interpret the work job’s names and sort out what job accommodates your abilities better and you might want to do, it’s an ideal opportunity to consider how to make your portfolio land you that work with no postponement. 

You have likely gone through numerous activities during your information science learning venture, from more modest ones with a couple of lines of code to moderately huge ones with many lines. However, to sincerely demonstrate your abilities and information level, you need to have a few commitments that will make you hang out in the candidates’ pool.

One of the confounding inquiries that you need to reply to before you get into a task that requires managing data is, which profession would it be advisable for me to pick? Which one will accommodate my character and desire most? 

Addressing these inquiries is troublesome on the grounds that a few terms are difficult to recognize from others, so on the off chance that you don’t have a clue about the distinction, how might you settle on a choice? As I would see it, the most troublesome jobs to recognize are a data researcher and data examiner. 

For a very long time, back when I began my excursion in data science, I thought they were exactly the same thing however told in an unexpected way. The way that data science is a dubious, expansive term didn’t assist with my disarray. After huge loads of perusing and exploration, I could at long last handle the unobtrusive distinction between data science and data analytics.

In all actuality, data science and data analytics are interconnected terms; there is a great deal of cover between the two terms. All things considered, every way requires a fairly extraordinary learning way and will give various outcomes. 

To assist you with keeping away from additional disarray, I chose to compose this article, getting out the contrasts between the two terms, in definition, required abilities, and job obligations. With no further ado, how about we get into it… 

Data Science 

Data science isn’t only one job, and it is, indeed, an umbrella term covering various terms and sub-branches, similar to characteristic language handling, PC vision, machine learning, profound learning, and so forth 

In any case, on the off chance that we need to put what a data researcher does in words, it will be a nearby thing; a data researcher is an individual with an inquisitive brain that loves to pose inquiries to tackle some issue. They depend on data to plan algorithms, create code and construct models to arrive at noteworthy experiences from this crude data. 

The fundamental objective of any data science project is to investigate data, discover examples and patterns, and utilizing this data to foresee future examples and patterns utilizing various devices and procedures that the center of is regularly machine learning algorithms.

Skills required 

Since data science is an interdisciplinary field, with the end goal for you to be a fruitful data researcher, you should dominate a few specialized and delicate skills. Be that as it may, dominance requires quite a while; you can launch your profession on the off chance that you are OK with the crucial information expected to fabricate any undertaking. These major skills are: 

Maths and measurable information. 

Programming and programming improvement. 

Data assortment, cleaning, and investigation. 

Data representation and narrating. 

Knowledge of the center algorithms of machine learning. 

An essential understanding of plans of action and how they are created.

Job responsibilities 

As a data researcher or a specialist in any of its subfields, you will be relied upon to tackle complex issues utilizing gathered data to investigate, clean, investigate, model and test. Your job will fundamentally be to utilize various algorithms or plan new ones to tackle the issue at hand effectively and rapidly. 

The experiences gathered from your model will be utilized to improve or fabricate new plans of action. In this way, your job will be basic for the accomplishment of certain organizations and how much benefit they may get. 

Data Analytics 

Like data science, the term data analytics likewise covers distinctive subfields, like databases examiner, business investigation, deals examination, valuing expert, statistical surveying investigator, and so on 

As a data investigator, your fundamental objective will be to utilize the data given to you to respond to various business questions, similar to, what item sold best and why? In the event that there was a drop in income, for what reason did it occur and how might the organization conquer it, and so on. 

To arrive at a response for these inquiries, the data examiner should have the option to genuinely break down datasets, make instruments to gather data and put together it and concentrate comparable data from it later on. To put it plainly, a data investigator’s job is to respond to inquiries with obscure answers dependent on the present status of data and drive prompt activities.

NITROGEN PLANTS INTO OXYGEN GENERATORS

Experts from IIT – Bombay have come up with a novel solution to the country’s oxygen shortage caused by the COVID-19 Corona virus. In collaboration with Tata Consulting Engineers (TCE) , the institute has come up with a device as part of a pilot project that can convert a standard nitrogen generator into a functioning oxygen generator.

This solution could appeal to a large section of the Indian Healthcare System given that it only takes three to four days to convert existing nitrogen plants. By comparison, setting up a new oxygen plant from scratch can take at least 45 days.

The conversion is done by replacing the molecular filters , people involved in the project said. Moreover , Pressure Swing Adsorption (PSA) Nitrogen plants are available in several industries across India, making the idea more feasible. Another crucial factor here is that conversation of an existing nitrogen plant will only cost around 10 to 15 percent of the amount required to construct an oxygen plant.

Although IIT – Bombay has validated a proof of concept it is yet to be scaled up to accommodate the large potential demand. Professor at the Institute , Milind Atrey explained the process saying, “If the molecular carbon sieves (filters) in these plants are replaced with zeolite (another mineral) and the settings are modified a bit, we can do the reverse. Instead of rejecting oxygen from the atmosphere , this conversion process will help in its production.”

Oil refineries, food industries and a handful of other sectors make use of nitrogen plants. Atrey added that bringing the resources of all these plants under one regulatory body and purchasing zeolite from other sources will be instrumental in the mass production of oxygen.

Spantech Engineers which specializes in PSA nitrogen and oxygen plants , is also involved in this project. The company helped in installing the parts of the plant at IIT – Bombay’s refrigeration and cryogenics laboratory . TCE , IIT and Spantech have signed an MoU recently to scale up the technology for mass production.

As per the tests conducted at the site, oxygen production was achieved at 3.5 atmospheric pressure with a purity level of 3-6 percent . This is deemed suitable for use in COVID-19 hospitals.

Managing Director at TCE , Amit Sharma said , ” The center has directed setting up over 500 PSA oxygen plants. But , till then, this approach of conversion may be used under emergency needs. Post conversion of an existing nitrogen plant, the oxygen produced is approximately one third of the capacity of the original nitrogen plant output”.

Sharma was also wary of the hurdles they could face with the procurement of zeolite. “Procuring zeolite could be a challenge . But both the private and government entities have been procuring such zeolite material for the new oxygen plants and for industrial use and hence the same material could be used for this conversion”, he said.

After an existing plant has been converted into an oxygen generation facility, it will require requisite approvals from the Central Pollution Control Board as well as the local administration. Such plants will also need to be tested for quality and safety.

What’s more important in determining life success—book smarts or street smarts?

So let’s understand what is actually IQ and EQ.

IQ only measures our intelligence potential, which has no direct relationship to our present intelligence, as psychologist Robert Sternberg suggested. But even with a high IQ, you cannot succeed if you are not determined enough. You also need to be sociable and creative.

IQ represents abilities such as:

• Knowledge of the world
• Quantitative reasoning
• Analysis
• Memory

EQ is the person’s ability to control, evaluate or express emotions.

• Controlling one’s own emotions
• Evaluating how others feel
• Identifying your emotions

Research has found that individual with strong leadership potential tens to be more emotionally intelligent. high EQ is an important quality for leaders.

Both IQ and EQ play role in success or happiness. But, EQ holds more weight. This includes the ability to perceive emotion, use emotions to facilitate thought, understand emotions, and manage emotions. EQ is something that can be improved over time without the limitation of age or circumstance.

Every human has a right to vote
But it’s like a sinking boat
Politicians dote of note(money)
And treat people like a beaten goat
Instead of sloat
They are safe in moat
And float in gloat
If one voices his throat
He will be a smote

There is no doubt that colorism is widely present in India. Light-skinned has a higher preference over dark in every field.

1.Marriage and Makeup
Light-skinned domestic Indian women have become the desired choice for wives, while poorer, darker, lower caste and Dalit women often experience fewer options in the marriage market. Often, with many fairness creams and other beauty products being highlighted in the media user, particularly girls, believe they do wonders to the complexion.

2.Career opportunities.
As a community, It is imperative that we understand colorism and how it can affect an opportunity for a job, Perhaps a great career. If we can all be aware of this phenomenon and how it just. not only affects those in Hollywood but regular working people that want to make an honest living . We can change the definition of competence and skills that are just not skin deep, what it means in the workplace and beyond that space.

3.Mental health.
Colorism can critically affect one’s self-esteem and they and get body image issues, anxiety, and many more psychological difficulties.

SQL vs NoSQL

The Language 

Think about a town – we’ll call it Town A – where everybody communicates in a similar language. The entirety of the organizations are worked around it, each type of correspondence utilizes it. To put it plainly, it’s the lone way that the inhabitants comprehend and associate with their general surroundings. Changing that language in one spot would be mistaken and problematic for everybody. 

Presently, think about another town, Town B, where each home can communicate in an alternate language. Everybody interfaces with the world in an unexpected way, and there’s no “widespread” understanding or set association. In the event that one home is extraordinary, it doesn’t influence any other person by any stretch of the imagination. 

This represents one of the key contrasts between SQL (social) and NoSQL (non-social) databases, and this qualification has enormous ramifications. How about we clarify: 

SQL databases: SQL databases utilize structured question language (SQL) for characterizing and controlling information. On one hand, this is amazingly incredible: SQL is quite possibly the most adaptable and generally utilized alternative accessible, settling on it as a protected decision and particularly extraordinary for complex questions. Then again, it tends to be prohibitive. SQL necessitates that you use predefined patterns to decide the structure of your information before you work with it. What’s more, the entirety of your information should follow a similar structure. This can require critical direct front readiness, and, similarly as with Town A, it can imply that an adjustment of the structure would be both troublesome and problematic to your entire framework. 

NoSQL databases: NoSQL databases, then again, have dynamic constructions for unstructured information, and information is put away from multiple points of view: They can be segment situated, archive arranged, diagram based or coordinated as a KeyValue store. This adaptability implies that: 

You can make reports without having to initially characterize their structure 

Each record can have its own extraordinary structure 

The punctuation can fluctuate from one information base to another, and 

You can add fields as you go. 

The Scalability 

By and large, SQL databases are vertically versatile, which implies that you can expand the heap on a solitary worker by expanding things like CPU, RAM or SSD. NoSQL databases, then again, are evenly versatile. This implies that you handle more traffic by sharding, or adding more workers in your NoSQL data set. It resembles adding more floors to a similar structure as opposed to adding more structures to the area. The last can eventually expand and all the more remarkable, settling on NoSQL databases the favored decision for huge or consistently changing informational indexes. 

The Structure 

SQL databases are table-based, while NoSQL databases are either archive based, key-esteem sets, diagram databases or wide-section stores. This makes social SQL databases a superior alternative for applications that require multi-line exchanges – like a bookkeeping framework – or for heritage frameworks that were worked for a social structure. 

A few instances of SQL databases incorporate MySQL, Oracle, PostgreSQL, and Microsoft SQL Server. NoSQL data set models incorporate MongoDB, BigTable, Redis, RavenDB Cassandra, HBase, Neo4j and CouchDB.

Perhaps the most regularly referred to disadvantages of NoSQL databases is that they don’t uphold ACID (atomicity, consistency, seclusion, strength) exchanges across numerous archives. With suitable construction configuration, single record atomicity is worthy for bunches of utilizations. Notwithstanding, there are as yet numerous applications that require ACID across various records. 

To address these utilization cases MongoDB added support for multi-report ACID exchanges in the 4.0 delivery, and stretched them out in 4.2 to range sharded bunches. 

Since information models in NoSQL databases are normally upgraded for inquiries and not for lessening information duplication, NoSQL databases can be bigger than SQL databases. Capacity is as of now so modest that most think about this as a minor disadvantage, and some NoSQL databases likewise support pressure to diminish the capacity impression. 

Contingent upon the NoSQL information base sort you select, you will most likely be unable to accomplish the entirety of your utilization cases in a solitary data set. For instance, diagram databases are fantastic for dissecting connections in your information however may not give what you need to ordinary recovery of the information, for example, range questions. While choosing a NoSQL data set, consider what your utilization cases will be and if a universally useful information base like MongoDB would be a superior alternative.

 

More unfortunate nations can’t get vaccines. So a second as well as third wave is essentially tearing through them. These nations have committed numerous errors

In any case, there’s a bigger truth at work here. More unfortunate nations can’t get vaccines. 

Why would that be? Since the West didn’t let them — and still will not. That is not simply ethically hostile, since it’s in a real sense going to cause a great many unnecessary passings. It’s dumb, in light of the fact that new variations will return to contaminate the West, as well. 

For what reason can’t helpless nations get vaccines? Since the rich West has in a real sense made it incomprehensible for them to. Canada and America obstructed medications rights waivers at the WTO for Covid vaccines. So now there is extremely, restricted creation — rather than vaccines being open-source that can be delivered across the world in numerous areas. 

How restricted would we say we are talking? The West left the helpless world to fundamentally be provided by one spot: the Serum Institute of India. That is one significant maker for something like a large portion of the globe. That is around 4 billion individuals, on the off chance that you were pondering. 

How did the Serum Institute of India wind up delivering an antibody? It lucked out. It marked an arrangement with Oxford University. That was back when the specialists at Oxford said they needed to make vaccines free or extremely minimal effort — as in monetarily free, however giving the rights to any drug organization. 

And afterward something dim, odd, and odd occurred. As per all that I’ve perused, on the counsel of the Bill Gates Foundation, Oxford altered its perspective. It offered its privileges to AstraZeneca. Who, obviously, made a monster imposing business model. 

That is the way a large portion of the world wound up in the outlandish issue of expecting one establishment in India to make every one of its vaccines. 

That expectation was in every case excessively. Since when India’s subsequent wave hit, because of Modi’s patriot indiscretion, it spread quickly. What’s more, India at that point fundamentally said: “We’re keeping every one of these portions implied for you all, since now there’s not even enough for us.” 

Do you see how underhanded and frightful this story truly is? 

Allow me to attempt to summarize it as evidently as possible. The greater part of the world needs more vaccines. Not on the grounds that it must be that way. But since the West liked it as such. Amazing establishments and figures in the West. Every one of those “moderators” who impeded admittance to antibody licenses at the WTO, for the sake of huge Pharma organizations, with the consent of their Presidents and Prime Ministers. The strange outrage of Oxford privatizing its antibody in the wake of promising not to and offering it to AstraZeneca. 

What is the exercise here? 

Antibody free enterprise has bombed the world. In an epic, amazing, awful style. Can anyone explain why one Western establishment seems to have been equipped for impeding the whole world from getting limitlessly more vaccines quicker? 

Since free enterprise. Can anyone explain why Western countries have more than once obstructed opening antibody licenses? Since free enterprise. 

Think Joe Biden’s a particularly extraordinary person? Shouldn’t something be said about Justin Trudeau? Brilliant people, isn’t that so? Why are they allowing a gigantic, huge human calamity to unfold across the world? That will cause passing on a terrible scale? Which Arundhati Roy has effectively depicted as an unspeakable atrocity? 

My inclination, and my bet, is that the normal Westerner couldn’t care the slightest bit about the story above. They’ve gotten narrow minded, materialistic, individualistic, vain, idiotic, oblivious — at any rate Anglo countries, similar to America and Britain have been. As there’s no objection in the West to inoculate the world. 

Pause, for what reason would it be a good idea for us to do that at any rate? Is it accurate to say that you are a numbskull? 

Sorry to sound angry, I presume. Yet, you should know the response to this inquiry. 

One, we ought to immunize the world since it’s a good activity. That is the way we demonstrate our own ethical fiber, and keep ourselves ethically solid. Else, we deteriorate into countries of Nietzschean narcissists, similar to America, past great and fiendishness, where nothing matters. We lose our spirits by allowing outrages to continue. 

Two, we should immunize the world for a prominently common sense explanation. The more we stand by and the more it takes, the more new variations will change and spread, which will “escape” the vaccines that even we rich Westerners have, and cause destruction once more. Need to spend another colder time of year like the final remaining one, on account of Covid-20? I didn’t think so. It will occur, however, going on like this. 

Third, we ought to immunize the world since free enterprise has privatized a public decent. How about we be thoroughly clear: Covid vaccines are public products. Every one of them. They were financed and created with public assets, at public foundations, on the public dime, for public purposes. The “Oxford AstraZeneca” antibody? It’s simply the Oxford antibody: it was envisioned, made, and made in research labs — AstraZeneca simply makes it. The mRNA vaccines? All made with public assets, in research labs, at colleges. 

The West, all in all, made vaccines as a public decent. And afterward it let free enterprise tag along and privatize the increases. That is the reason helpless nations are following through on incomprehensibly greater expenses than rich ones, coincidentally — a horrifying reality which Pharma organizations rationalize with corporate language like “immunization costs will fluctuate by area.” It’s simply exploitative. Of the most indecent and corrupt kind. Exploitative on a human misfortune, a notable calamity, on death of incredible magnitude. 

Vaccines were a public decent made by the West, and public products are made to be shared. Why would that be? Since they have “positive externalities,” also known as great overflows for us all. Take a recreational area. I’m in an ideal situation when you’re fitter, saner, kinder, on the grounds that you go to the recreation center each day. We put resources into public products accurately in light of the fact that they advantage us all together more than they advantage every one of us exclusively. 

Put another way, how are you best off? At the point when everybody on the planet suffers a heart attack, quickest and least expensive. That is the point at which your odds of getting Covid-20 are limited. Yet, we are currently living in the contrary world. The world isn’t getting vaccines quickest and least expensive — yet slowest and generally costly. Why? 

Since free enterprise benefits most when there’s fake shortage. That is the reason the greater part of mankind is currently in the awful situation of depending on one establishment — one — to make every one of their vaccines. The one establishment that lucked out and struck an arrangement before the exploitative started. But then one establishment can’t give every one of the vaccines the world necessities. So the world presently should pay the payoff the Pharma organizations request. However, its greater part can’t do that, as it’s looking frantically, to China. 

What a story. It fills me with outrage, rage, disdain, of a specific white-hot kind. Immunization private enterprise is a gigantic, monstrous disappointment. Didn’t anybody with a functioning mind realize it was continually going to be? 

This methodology — bound to fizzle — uncovered the ethical false reverence and scholarly idiocy of the West, both. It is aggravating itself off eventually, as well — yet I guess that is OK, as long as the remainder of the world endures massively. 

What a misfortune. In any case, you understand what consumes me the most? The way that the normal Westerner won’t ever know. Never care the slightest bit. Never instruct themselves. About the straightforward, cursing realities above. They’ll continue living in their air pockets of vain fancy, never asking: “Why doesn’t the world have vaccines?” And so they’ll never hear the appropriate response, by the same token. 

Since, for the wellbeing of cash — the cash of the generally really ultra super rich, for the most part — the West didn’t allow the world to have vaccines, the very ones it made for the public premium explanation of offering them to a whole world, just to backtrack once the decision must be made. The West picked demise on a stunning, shocking worldwide scale all things being equal. Furthermore, that, my companions, makes me wish there was a hellfire.

When my life was cool
In joy of pool
There came a rule
Of going school
I heard a bell
Which made me dull
It’s really not a hell
But it is to tell
My success to swell
Like a glowing shell
For my life to dwell
When there’s a knell
I will only spell
“I lived well”

India falls below many Asian countries on the human development index (HDI). There are multiple reasons which hinder growth in India.
Why is India still a developing nation?

1. Inequality is the primary reason stopping India to grow.

• Economic Inequality. The distribution of wealth in India is unequal. Either people are very rich or extremely poor.
• Social and caste status. The inequality in society for Dalits and lower Caste has been too much in India. They get treated unpleasantly.

2.The government Divide and rule strategy. The Indian Government is very infamous for creating rift among religions or divide them based on religion to collect votes during the election. And people easily get fooled by those leaders.

3.Unproductive population. High population is not a substantial problem. The real problem is unproductivity among the people. China has a high population too but still, they are a developed country in every field.

4.Corruption.The corruption in India is spreading its roots day by day . If no action is taken against it, being a developed nation would be just a dream for India.

Stars in this journey
Bloomed at night
The footprints in time
Became colours in me
Was it a path drenched in rain?
Or was it a path
That was covered in blooming flowers?
It was endless
In this journey to find oneself
Is it true?
Is truth a burning fire?
Time washes away all habits
It’s a never ending journey to perfection

The COVID-19 pandemic has unleashed ruin on the eatery business and has been a distinct advantage for food wellbeing controllers here in Chicago. 

At the point when the pandemic began, many started zeroing in on the impact of commanded closures on eateries: in-person feasting terminations and worker cutbacks. 

As indicated by the National Restaurant Association, in excess of 110,000 eating and savoring foundations the United States had to incidentally or for all time close for business from March 2020 to January 2021. 

In Illinois, it has been over a year since all food and cooking foundations were requested to end in-entryway eating, be that as it may, Gov. J.B. Pritzer has revealed a way toward completely returning bars and eateries. 

During a March 18 media preparation, Pritzer presented a, “connect stage,” pointed toward Phase 5 — the last phase of the state’s COVID-19 returning arrangement. 

This scaffold stage will start when 70% of the populace, matured 65 and more established has gotten at any rate one portion of the COVID-19 antibody, and to Phase 5 when half of the populace matured 16 and more seasoned follows after accordingly. As per Pritzer, 58% of those matured 65 and more seasoned have effectively been regulated by immunization.

As foundation closures turned out to be more obvious, it set off apprehensions of vulnerability. Eatery proprietors became worried about excess just getting started, and how to acquire income from their restricted menu and inhabitants. 

Little and neighborhood organizations specifically were attempting to endure the pandemic. As per Womply Research, 55% of little and neighborhood entrepreneurs conceded their business wouldn’t endure if deals halted for one to a quarter of a year, and 21% said they wouldn’t endure one month. 

Eatery proprietors must be inventive, started promoting their business to get deals, and used online media to acquire footing and contact more individuals. They additionally needed to advocate for themselves, and hence got down on Pritzer and requested activity for guaranteed help. 

Alexandra Vargas, 20 who functions as a store agent at Weber’s Bakery, said the business needed to execute new changes to keep maintaining their business. 

“Since COVID-19, we have begun offering curbside pickup, and we’ve done countless various forms of it, to discover what turns out best for us, since we are so occupied,” she said. “Generally I’m simply a store agent, however at whatever point a client comes in and needs something, we simply get it for them, and box it up.” 

Numerous eateries didn’t have the proper assets or strength to stay just getting started. As per Time Out Magazine, there have been 65 Chicago eateries and bars for all time shut as of February.

In the midst of the turmoil welcomed on by COVID-19, eateries, all things considered, areas, and specialties needed to adjust and carry out new security measures to keep working. 

This implied a few foundations could presently don’t stand to give their full menu, needed to modify store or eatery hours, and many endured a hard shot monetarily subsequently. 

“So we just had a restricted menu … we just had our essential doughnuts, and stuff that way, and our hours were abbreviated,” Vargas said. “So we just had what we would call, COVID hours, since we would simply attempt to close early, and attempt to get the client in and out as quick as could be expected.” 

Toward the start of the pandemic, wellbeing monitors were not top of brain. In March, the FDA briefly delayed face to face assessments, however now they’re returning, yet the prerequisites are marginally unique. 

Prepared to forestall the spread of foodborne ailments, wellbeing investigators are entrusted with guaranteeing that cafés are conforming to the rules to help forestall the spread of COVID-19. 

Isabelle Campa, 20, an associate senior supervisor at Taco Bell, has encountered what a regular investigation resembles, and the repercussions for the eatery neglecting to meet the necessities to stay open.

The originally recorded instance of Covid-19 in the United States was accounted for a large portion of a year prior, days before early alerts from the U.S. Communities for Disease Control and Prevention (CDC) that a “intense general wellbeing danger” lingered. However wellbeing authorities had just a harsh thought of how the novel Covid spread, who the infection influenced most, and how to best battle transmission and give treatment. 

Public informing on the reality of the infection was on occasion clashing and befuddling, including the early exhortation not to wear covers. A half year later, researchers have a strong handle on how the infection spreads and how ought to be dealt with to get the pandemic leveled out. 

Here are nine things we think about Covid-19 since we didn’t know at that point.

At that point: Early exhortation from the CDC underlined hand-washing, cleaning surfaces, and sniffling into your elbow, with the understanding that the Covid spread for the most part through handshakes, contact with contaminated surfaces, and through close contact with irresistible individuals (inside six feet). 

Presently: After long periods of logical conversation and study, and some confounding correspondence to general society, the specialists concur: The infection can get airborne — inside minuscule, suspended drops called pressurized canned products — and taint individuals past six feet, particularly in inadequately ventilated indoor spaces, where the mist concentrates are caught and develop. The World Health Organization, following a half year of mounting proof, at last concurred with researchers on this point. The danger outside is lower, the specialists actually say, yet not zero. 

What it implies: Covid-19’s numerous methods of spreading vex everything except the most severe endeavors to control transmission, especially inside. This is the reason wellbeing specialists beg individuals to stay away from huge groups, notice physical separation, wear veils inside and outside, and proceed with careful hand-washing.

Face masks are crucial to control the pandemic

At that point: In the pandemic’s initial months, wellbeing authorities focused close by washing and social removing, while at the same time debilitating covers, for three reasons: There was a limit deficiency of clinical evaluation veils for medical care experts; the essential methods for spread hadn’t been decisively decided; and U.S. flare-ups existed distinctly in pockets, having not yet spread to all states or districts. 

Presently: The study of how to moderate or stop the pandemic has been agreed to for months, and it’s protected to say that each wellbeing master currently suggests face covers. Past face covers, wellbeing specialists prompt: Prevent huge indoor get-togethers, particularly at superfluous settings like bars; furnish significantly more far and wide testing with faster outcomes, matched with contact following; command physical removing for public places that stay open; direct this in a planned style from the government level. “We genuinely have incredible information on how we can handle the infection,” says Yonatan Grad, MD, an associate teacher of immunology and irresistible sicknesses at Harvard T.H. Chan School of Public Health.

Covid-19 affects the whole body, not just the lungs

At that point: For half a month, the CDC held firm to the idea that the three unmistakable side effects of Covid-19 were fever, hack, and windedness. However every week it appeared specialists were perceiving another Covid-19 indication. 

Presently: By February, contemplates showed that the infection caused body hurts, sickness, and looseness of the bowels in certain individuals. At that point came information on anosmia, the deficiency of smell. We learned of Covid toe, conceivable mind diseases creating tipsiness and turmoil, and an extreme response by the safe framework prompting blood clusters, coronary episodes, and other organ disappointments. All the more as of late, researchers say it looks as though veins are being tainted. Hardly any illnesses cause a wide assortment of side effects. “It’s been extraordinary from multiple points of view,” says Robert Salata, MD, an educator of medication in the study of disease transmission and worldwide wellbeing at Ohio’s Case Western Reserve University. “Regarding the inconveniences we’re seeing, it’s mind blowing.”

Despite the pandemic unleashing in full force, the issue over holding or postponing board examinations is being debated.

• While there are valid concerns about the future, which include admissions to higher educational institutions, the possibility of holding exams in person poses a further threat to the lives of people.
• The National Curriculum Framework of 2005 affirms the importance of embracing the emotional, social, physical and intellectual growth of children within a framework of human values.
  1. Therefore, a question to consider is whether academic performances can continue to be the sole representation of student growth.
  2. Alternatively, student success can be redefined based on social, emotional and spiritual development benchmarks.
• Raising the quality of educational assessments and evaluations by involving higher educational institutions may even prevent a mass exodus to international universities so that young leaders can be nurtured to engage with underlying national challenges and add value by sustaining the fabric of a diverse and non-stratified India.

For betterment:

1. Virtual educational committees must be organised to rethink approaches to assessing student learning.
2. Question papers must be designed in a way that encourages students to critically engage with the material, contest perspectives and build opinions.
3. Right to education that affirms the importance of formative assessments, teachers could be invited to engage in evaluating student’s performance across the year.
4. If there are concerns around the tendency of schools to self-bolster their performance, reports, portfolios, samples of responses and grades could be shared across a pool of schools so that teachers can anonymously assess and provide insightful feedback on student performance, until a sense of self-accountability and trust can be cultivated.
5. Evaluating students on their performance through the year will contribute towards making evaluation and learning much more holistic.
6. Inviting higher educational institutions to facilitate online entrance exams could be another option. Eventually, the goal could be to involve students in self and peer evaluations.

Wind-up:

The pandemic presents an opportunity to redefine meaningful education even though it requires a concerted change across the curriculum in K-12 schools, the entrance criteria determined by higher educational institutions. Viewing this crisis as a signal for urgent change, core issues can be engaged with and re-evaluated to prevent students from being trapped in the current system of cramming, rote learning and anxiety.  The government needs to give complete autonomy to educational committees composed of students, teachers, educational leaders, scholars and researchers who can advocate, organise and implement this change nationally and internationally. Re-imagining educational assessments and evaluations, exploring alternatives is the need of the hour.

With the slow pace of the government’s vaccination drive, this article talks about other active interventions and measures for the prevention of a third wave of COVID-19 infections.

• The United States and Brazil have had three distinct waves.
• The United Kingdom had a small first wave, after which the virus seemed to be disappearing. This was followed by two explosive waves, which only subsided after a lockdown and an aggressive vaccination campaign in which 95% of all those over the age of 50 have been vaccinated.
• South Africa has also seen two waves.

Given the pattern, a second wave in India was almost a given. And once this wave recedes, it is highly likely that a third wave will build up, unless active measures are taken to stop it from building up. Now is the time to think ahead and find ways to prevent the next wave.

Vaccine as an option for preventing the third wave

• Vaccines are the best option. However, this might not be the most feasible option to blunt either this or future waves due to:
  • The huge population of the country
  • The slow pace of vaccination
  • Inelastic vaccine supplies both in India and globally
  • Limited finances with State governments which have now been given the responsibility of vaccinating the bulk of the country’s population.

Methods that work

• Along with vaccination, it is important to practise the full methods that have been shown to slow the spread of COVID-19 in different parts of the world: wearing a mask, physical distancing, hand hygiene and a ban on mass gatherings.
• While these measures may not be as effective as mass vaccination, but in the absence of vaccines, they are perhaps the only way to reduce community transmission and slow the spread of the virus.
• The near-universal adoption of nonmedical masks when out in public, in combination with complementary public health measures could reduce community spread, provided the measures are sustained.

Conventional wisdom in India has it that wearing a mask only works when it is imposed as a police measure. However, public health measures that work best are those that the people voluntarily adopt, drastically reducing transmission.

A special bench of the Supreme Court questioned the Centre, the States and the authorities on the various aspects of COVID management in a suo motu hearing called, ‘In re- distribution of essential supplies and services during COVID-19’.

• The Supreme Court said that it would not interfere in the work done by the various High Courts to monitor life-saving COVID-19 management amid a second wave of the pandemic.
• It asserted that the High Courts are best suited to make an assessment of ground realities in each State and find flexible solutions for problems faced by citizens.
• However, it said that it would examine issues that travel beyond the boundaries of States and have national repercussions.

The adoption of articial intelligence (AI) and machine learning (ML) in dierent sectors has transformed the conventional approach for dierent applications. Although the terms AI and ML are used interchangeably, the former aims at the success of a task, whereas the latter ensures accuracy. 

From marketing and retail to healthcare and nance, adoption of articial intelligence (AI) and machine learning (ML) in these sectors is drastically transforming the conventional approach for dierent applications. AI makes it possible for systems to sense, comprehend, act, and learn for performing complex tasks such as decision making that earlier required human intelligence. Unlike the regular programming, where action needs to be dened for every situation, AI in conjunction with ML algorithms can process large data sets, be trained to choose how to respond, and learn from every problem it encounters to produce more accurate results. 

This has not only impacted how we use data but also how we design and fabricate hardware chips or integrated circuits (ICs) for enabling next-generation devices, thereby opening new opportunities. The growth of AI shifts the core of innovation from the software back to hardware. For better performance, for example, AI needs more memory as compared to traditional requirements to process and transfer large data sets. Consider the case of virtual assistants that are increasingly being used in homes. Without reliable hardware for such functionalities as those associated with memory and logic, these cannot work properly. 

According to Accenture Semiconductor Technology Vision 2019, from the annual report by the Accenture Labs and Accenture Research, the semiconductor industry is highly optimistic about the potential of AI in their work in the coming years when compared to that in technologies like extended reality, distributed ledgers, and quantum computing. Three-quarters (77 per cent) of semiconductor executives surveyed for the report claimed that they either had already adopted AI within their business or were piloting the technology. 

The concept of AI and ML 

Although the terms AI and ML are used interchangeably, the former aims at the success of a task, whereas the latter ensures accuracy. So, solving a complex problem is done through AI 

training, but maximizing efficiency by learning from the data and already performed tasks is the concept of ML. ML relies on large datasets to nd common patterns and makes predictions based on probability. 

Applied AI is more commonly seen in systems that are designed for nancial market predictions and autonomous vehicle control. Generalised AIs to handle dierent general situations is where ML, usually considered the subset of AI, comes into play. In what is called supervised learning ML algorithm, the relationship model between input data and target output is established to make predictions, whereas unsupervised learning does not involve categorising data for training. In situations like competing with humans in complex computer games where information needs to be gained from the environment continuously, reinforcement learning is implemented. 

Sub-branches of ML like deep neural networks have already been applied to elds, including speech recognition, social network ltering, computer vision, natural language processing, and so on. These technologies take and examine thousands of users’ data for precision and accuracy applications like face recognition. This is contributing to the rapid development of innovations that are considered magical right now, but with hardware advancements, its place might be taken by much more advanced innovations in the coming decades. 

The process (Credit: https://cooltechnospy.com) 

How AI and ML applications are redefining the traditional systems 

AI and ML could grow to the current extent due to advancements in not only algorithms but also in storage capabilities, computation capacity, networking, and the like, which made it possible to make advanced devices accessible to the masses at an economical cost. Traditionally, logic was usually hard-wired in the design of electronic systems. But in the light of high manufacturing costs presently and the growing complexity of chip development, AI driven processor architectures are redening traditional processor architectures to suit new demands. 

Computation is mainly done on the central processing unit (CPU), the brain of the computer. With the emergence of computationally demanding applications that apply AI and ML algorithms, additional processing choices through combinations of graphics processing units (GPUs), microprocessors (MPUs), microcontrollers (MCUs), programmable gate arrays (FPGAs), and digital signal processors (DSPs) are coming up to meet the optimum feature

requirements. The options, considered distinct categories, are gradually becoming heterogeneous processing solutions like system-on-chips (SoCs) and custom-designed application-specic integrated circuits (ASICs).

Unlike traditional storage methods to store parameters while tuning a neural network model during training and inference, high-bandwidth RAM is required not only at data centres but also for edge computing. The increased amount of volatile memory for proper functioning causes an excessive rise in power consumption levels. This is driving the need for evolving memory interfaces to ensure that the tasks get executed at high speeds. Although new processor architectures are helping reduce the load, other mechanisms like new memory interfaces and processing into memory itself are also being researched upon and implemented. 

UK-based startup Graphcore’s IPU is a novel processor with high on-chip memory capacity specically designed for dealing with complex data structures in machine intelligence models. Outside memory sources take much more time to return a result than on-chip ones. According to a report from IHS Markit, worldwide revenue from memory devices in AI applications will increase to 60.4 billion dollars in 2025 from 20.6 billion dollars in 2019, while the processor segment will grow from 22.2 billion dollars in 2019 to 68.5 billion dollars in 2025. 

Graphcore’s AI chips used in MS Azure cloud (Credit: http://www.eetindia.co.in) 

Thus, semiconductors provide the necessary processing and memory capabilities required for all AI applications. Improving network speed is also important for working with multiple servers simultaneously and developing accurate AI models. Measures such as high-speed interconnections and routing switches are being examined for load balancing. 

The answer to improving chips for AI applications lies with the technology itself. AI and ML are being leveraged to improve performance, and as design teams become more experienced in this eld, they will enhance how chips are developed, manufactured, and tweaked for updates. Complicated issues related to the implementation, signo, and verication of chips that cannot be solved and optimised with traditional methods can be solved with AI and ML. 

Using ML-based predictive models inside the existing EDA tools, US-based company Synopsys claims to have achieved results like a five times faster Prime Time power recovery and a hundred times faster high sigma simulation in HSPICE. All this requires a focus on R&D and

accurate end-to-end solutions, creating opportunities for possibly entirely new markets with value-creation in dierent segments of semiconductor companies. 

Overcoming the challenges 

The main focus is on data and its usage. This requires more than just a new architecture for the processor. Dierent chip architectures work for dierent purposes, and factors like the size and value of the training data can render AI useless for some applications. 

AI makes it possible to process data as patterns instead of individual bits and works best when memory operations are to be done in the form of a matrix, thereby increasing the amount of data being processed and stored and hence the eciency of the software. For instance, spiking neural networks can reduce the ow of data as the data is fed in the form of spikes. Also, even if there is a lot of data, the amount of useful data to train a predictive model can be reduced. But the issues still exist. Like in chip design, the training of ML models happens in dierent environments independently at dierent levels. 

There needs to be a standardised approach to the application of AI. For ecient utilisation of AI, chips designed for AI, as well as those modied to suit AI requirements, need to be considered. If there is a problem in the system, there is a need for tools and methodologies to quickly solve it. The design process is still highly manual despite the growing adoption of design automation tools. Tuning the inputs is a time-consuming yet highly inecient process. Even just one small step in a design implementation can be an entirely new problem itself. 

There has been widespread misuse by companies claiming to use AI and ML to gain the advantage of the trend and increase sales and revenue. Although the cost of compression and decompression is not so high, the cost of on-chip memory is not so cheap. AI chips also tend to be very large. 

To build such systems that store and process data, the collaboration of experts on different teams is necessary. Major chipmakers, IP vendors, and other electronics companies will be adopting some form of AI to increase efficiency across their operations. The availability of cloud compute services at a reduced cost of computing can help in boosting evolution. Technology advancements will force semiconductor companies to empower and reskill their workforces, enabling the next generation of devices in the marketplace. 

JLSR Wellness, a startup in the healthcare and wellness space, has launched a rst-of-its-kind Wi-Fi enabled smart oxygen generator with an aim to bring a practical solution to the nation’s dire need to breathe in the fresh air. 

Air pollution is not a hidden fact; we are all aware of it. Yet deteriorating air quality continues to be a huge problem worldwide, with India being among the worst aected. 

According to the Air Quality Life Index (AQLI) report by The Energy Policy Institute, the University of Chicago, air pollution will reduce life expectancy in the country by 5.2 years if pollution is not reduced. 

While reduced particulate matter (PM) 2.5 and 10 levels were observed during the lockdown, the pollutant levels are rising again with the unlock guidelines in action, which can aggravate the condition of Covid-19 patients. 

Giving a boost to Atmanirbhar Bharat, JLSR Wellness, a startup in the healthcare and wellness space, recently launched a rst-of-its-kind Wi-Fi enabled smart oxygen generator under the brand name UrbOx (Urban Oxygen) India with an aim to bring a practical solution to the nation’s dire need to breathe in the fresh air. The Made In India product was designed in partnership with leading technology companies from across the world. Anand Krishnan, COO, UrbOx, was the brainchild behind this product. He has had extensive experience in clinical research, medical devices, and drug development in the UK. 

He says, “In 2019, with the spark of an idea, a group of women entrepreneurs put together a robust R&D team for the further vision of the product, and thus I got introduced to the project as the lead scientist. The women possess strong background and skills from business and trade, technology development, nance, and accounting.” 

The articial intelligence (AI), machine learning (ML), and Internet of Things (IoT) congured solution has been created by ne-tuning and improvising an open source technology that was developed by NASA for its critical space missions to suit commercial and residential needs. The technology has been previously used in Apollo spacecraft. 

The oxygen generator comes with oxygen-sensing air-quality monitor. The oxygen sensor, procured from a Switzerland-based company, is RoHS compliant based on the European Union’s Directive 2002/95/EC. The indoor unit can continuously measure the concentration of oxygen, besides other gases in a place. So, once you switch it on, it switches on and o automatically depending on the oxygen requirements in a room in real time. A mobile app, available on both iOS and Android, can be used to control and monitor the oxygen levels. 

MCERTS-certied optical PM sensor, CMOSens Technology for IR detection during carbon dioxide measurement, and NIST-traceable ISO/IEC 17025 accredited calibration technology ensure high accuracy and credibility. Temperature and humidity sensors together form a single unit. According to the company, the precision levels are ±3 per cent accuracy for humidity, ±1 hPa accuracy for pressure, and ±1.0°C accuracy for temperature. 

Krishnan says, “Developing world-class cutting-edge technology with the global partners was the key to success, and we had to overcome many hurdles during the corona times, but the team worked hard to make it available during these dicult times. Our product has exceptional long-term stability and is easy to install and operate. The sleek design perfectly complements the superior technology.” 

The device uses a process called Pressure Swing Adsorption (PSA) to generate a high concentration of oxygen gas from ambient air. This technique uses high pressure to separate a gas from a mixture based on properties like molecular characteristics and anity for an adsorbent material that dierentiates one from another. Once the pressure is reduced, the adsorbed gas—here oxygen—gets pumped back into the environment. In simple terms, the device captures atmospheric air and lters it to provide pure and fresh oxygen. 

The eco-friendly innovation works well for homes, workplaces, classrooms, operation theatres and clinics, and public places such as restaurants, airports, multiplexes, and gyms. These indoor areas can gain the maximum benet from the generator when the doors and windows are kept closed. In summers, it can be used along with air-conditioners. 

“Registered under the Make in India programme and Startup India programme, the need of the hour product has attracted many segments and audience. With India having more than 21 cities out of thirty cities which are most polluted in the world, UrB.Ox Smart Sensible Oxygen Generator becomes an important device to analyse, monitor, and generate your own oxygen for your enclosed indoors. Air puriers cannot generate oxygen and are limited to an area. Patients with asthma, breathing, and respiratory disorders can feel at ease without any tubes around their noses,” adds Krishnan. 

The oxygen generator was launched on 28th September, 2020 and comes in three variants. Urb.Ox Eco (two air dispensers, one indoor unit (AQI)) and Urb.Ox Premium (four air dispensers, one indoor unit (AQI)) are priced at ` 180,000 and ` 250,000, respectively,

excluding eighteen per cent GST. Urb.Ox Booster can be ordered for commercial establishments or other large spaces by making a payment of ` 500,000+ 18% GST. 

The benet is that it can be used 24/7 without any side eects. Both outdoor and indoor units are noiseless and consume low power. The units do not require large space for installation either—the indoor unit can even be tted on a room wall. Fireproof tubing ensures safety, and the oxygen is delivered in a controlled and optimised way. 

The products will be available on the company’s website as well as popular e-commerce sites. To adapt to the constantly evolving market, the team plans to launch more futuristic products at aordable costs. Krishnan says, “The company is in talks with potential collaborators from 

countries such as the UK, Europe, UAE, Dubai, Qatar, Saudi Arabia, Thailand, Singapore, Vietnam, Cambodia and Africa, and is all set to foray into Sri Lanka in due course.” 

Global media has called inventor and author Ray Kurzweil everything from ‘restless genius’ and ‘the ultimate thinking machine’ to ‘rightful heir to Thomas Edison’ and ‘one of the 16 revolutionaries who made America.’ His list of inventions is long—from at-bed scanners to synthesisers and reading machines for the visually-impaired. He has over 20 honorary doctorates, and has founded a string of successful companies. 

There is an interesting story about how this genius, who was not known to work for any company other than his own, joined Google as director of engineering in 2012. He wanted to start a company to build a truly-intelligent computer and knew that no company other than Google had the kind of resources he needed. When he went to meet Larry Page about getting the resources, Page convinced Kurzweil to join them instead. Considering that Google was already well into deep learning research, Kurzweil agreed. It is said that it is the aura of deep learning that closed the deal. 

What is this deep learning all about, anyway? It is machine learning at its best—machine learning that tries to mimic the way the brain works, to get closer to the real meaning of articial intelligence (AI). 

Today’s neural networks have several layers. The lowest layer takes raw data and its 

neurons store some information about the data these encounter. Each neuron sends 

information up to the next layer of neurons, which learn a more abstract version of the 

data below it. Higher layers learn more and more abstract features. The neural network 

in the image has five layers, of which three are hidden (Image courtesy: NVIDIA) 

Taking machine learning a bit deeper 

Machine learning is all about teaching a machine to do something. Most current methods use a combination of feature extraction and modality-specic machine-learning algorithms, along with thousands of examples, to teach a machine to identify things like handwriting and speech. The process is not as easy as it sounds. It requires a large set of data, heavy computing power and a lot of background work. And, despite tedious eorts, such systems are not fool-proof. These tend to fail in the face of discrepancies. For example, it is easy for a machine-learning system to get confused between a hurriedly written 0 and 6, or vice versa. It can understand brother but not a casually-scribbled bro. How can such machine learning survive in the big, bad, unstructured world? 

Deep learning tries to solve these problems and take machine learning one step ahead. A deep learning system will learn by itself, like a child learns to crawl, walk and talk. It is made of multi-layered deep neural networks (DNNs) that mimic the activity of the layers of neurons in the neocortex. Each layer tries to go a little deeper and understand a little more detail. 

The rst layer learns basic features, like an edge in an image or a particular note of sound. Once it masters this, the next layer attempts to recognise more complex features, like corners or combinations of sounds. Likewise, each layer tries to learn a little more, till the system can reliably recognise objects, faces, words or whatever it is meant to learn. 

With the kind of computing power and software prowess available today, it is possible to model many such layers. Systems that learn by themselves are not restricted by what these have been taught to do, so these can identify a lot more objects and sounds, and even make decisions by themselves. A deep learning system, for example, can watch video footage and notify the guard if it spots someone suspicious. 

Google has been dabbling with deep learning for many years now. One of its earliest successes was a deep learning system that taught itself to identify cats by watching thousands of unlabelled, untagged images and videos. Today, we nd companies ranging from Google and Facebook to IBM and Microsoft experimenting with deep learning solutions for voice recognition, real-time translation, image recognition, security solutions and so on. Most of these work over a Cloud infrastructure, which taps into the computing power tucked away in a large data centre. 

As a next step, companies like Apple are trying to gure out if deep learning can be achieved with less computing power. Is it possible to implement, say, a personal assistant that works o your phone rather than rely on the Cloud? We take you through some such interesting deep learning eorts.

Brain is Google’s deep learning project, and its tech is used in many of Google’s products, ranging from their search engine and voice recognition to email, maps and photos. It helps your Android phone to recognise voice commands, translate foreign language street signs or notice boards into your chosen language and do much more, apart from running the search engine so eciently. 

Google also enables deep learning development through its open source deep learning software stack TensorFlow and Google Cloud Machine Learning (Cloud ML). The Cloud oering is equipped with state-of-the-art machine learning services, a customised neural network based platform and pre-trained models. The platform has powerful application programming interfaces (APIs) for speech recognition, image analysis, text analysis and dynamic translation. 

Another of Google’s pets in this space is DeepMind, a British company that it acquired in 2014. DeepMind made big news in 2016, when its AlphaGo program beat the global champion at a game of Go, a Chinese game that is believed to be much more complex than Chess. 

Usually, AI systems try to master a game by constructing a search tree covering all possible options. This is impossible in Go—a game that is believed to have more possible combinations than the number of atoms in the universe. 

AlphaGo combines an advanced tree search with DNNs. These neural networks take a description of Go board as an input and process it through 12 dierent network layers containing millions of neuron-like connections. A neural network called the policy network decides on the next move, while another network called the value network predicts the winner of the game. 

After learning from over 30 million human moves, the system could predict the human move around 57 per cent of the time. Then, AlphaGo learnt to better these human moves by discovering new strategies using a method called reinforcement learning. Basically, the system played innumerable games between its neural networks and adjusted the connections using a trial-and-error process. Google Cloud provided the extensive computing power needed to achieve this. 

What made AlphaGo win at a game that baed computers till then was the fact that it could gure out the moves and winning strategies by itself, instead of relying on handcrafted rules. This makes it an ideal example of deep learning. 

AI world has always used games to prove its mettle, but the same talent can be put to better use. DeepMind is working on systems to tackle problems ranging from climate modelling to disease analysis. Google itself uses a lot of deep learning. According to a statement by Mustafa Suleyman, co-founder of DeepMind, deep learning networks have now replaced 60 handcrafted rule based systems at Google. 

Some trend-watchers claim that it is Watson, IBM’s AI brainchild, which transformed IBM from a hardware company to a business analytics major. Watson was a path-breaking natural language processing (NLP) computer, which could answer questions asked conversationally.

In 2011, it made the headlines by beating two champions at the game of Jeopardy. It was immediately signed on by Cleveland Clinic to synthesise humongous amounts of data to generate evidence based hypotheses, so they could help clinicians and students to diagnose diseases more accurately and plan their treatment better. 

Watson is powered by DeepQA, a software architecture for deep content analysis and evidence-based reasoning. Last year, Watson strengthened its deep learning abilities with the acquisition of AlchemyAPI, whose deep learning engines specialise in digging into Big Data to discover important relationships. 

In a media report, Steve Gold, vice president of the IBM Watson group, said that, “AlchemyAPI’s technology will be used to help augment Watson’s ability to identify information hierarchies and understand relationships between people, places and things living in that data. This is particularly useful across long-tail domains or other ontologies that are constantly evolving. The technology will also help give Watson more visual features such as the ability to detect, label and extract details from image data.” 

IBM is constantly expanding its line of products for deep learning. Using IBM Watson Developer Cloud on Bluemix, anybody can embed Watson’s cognitive technologies into their apps and products. There are APIs for NLP, machine learning and deep learning, which could be used for purposes like medical diagnosis, marketing analysis and more. 

APIs like Natural Language Classier, Personality Insights and Tradeo Analytics, for example, can help marketers. Data First’s inuencer technology platform, Inuential, used Watson’s Personality Insights API to scan and sift through social media and identify inuencers for their client, KIA Motors. The system looked for inuencers who had traits like openness to change, artistic interest and achievement-striving. The resulting campaign was a great success. 

Quite recently, IBM and Massachusetts Institute of Technology got into a multi-year partnership to improve AI’s ability to interpret sight and sound as well as humans. Watson is expected to be a key part of this research. In September, IBM also launched a couple of Power8 Linux servers, whose unique selling proposition is their ability to accelerate AI, deep learning and advanced analytics applications. The servers apparently move data ve times faster than competing platforms using NVIDIA’s NVLink high-speed interconnect technology. 

IBM is also trying to do something more about reducing the amount of computing power and time that deep learning requires. Their Watson Research Center believes that it can reduce these using theoretical chips called resistive processing units or RPUs that combine a central processing unit (CPU) and non-volatile memory. The team claims that such chips can accelerate data speeds exponentially, resulting in systems that can do tasks like natural speech recognition and translation between all world languages. 

Currently, neural networks like DeepMind and Watson need to perform billions of tasks in parallel, requiring numerous CPU memory calls. Placing large amounts of resistive random access memory directly onto a CPU would solve this, as such chips can fetch data as quickly as these can process it, thereby reducing neural network training times and power required. 

The research paper claims that, “This massively-parallel RPU architecture can achieve acceleration factors of 30,000 compared to state-of-the-art microprocessors—problems that currently require days of training on a data centre size cluster with thousands of machines can be addressed within hours on a single RPU accelerator.” Although these chips are still in the research phase, scientists say that these can be built using regular complementary metal oxide semiconductor technology. 

Speaking of reducing processing power and learning times, can you imagine deep learning being performed locally on a mobile phone, without depending on the Cloud? Well, Apple revealed at 2016’s Worldwide Developer’s Conference (WWDC) that it can do precisely that. The company announced that it is applying advanced, deep learning techniques to bring facial recognition to iPhone, and it is all done locally on the device. Some of this success can be attributed to Perceptio, a company that Apple acquired last year. Perceptio is developing deep learning tech that allows smartphones to identify images without relying on external data libraries. 

Facebook intends to understand your intent 

Facebook Articial Intelligence Research (FAIR) group has come out with innumerable innovations, which are so deeply woven with their products that we do not even realise that we are using deep learning every time we use Facebook. FbLearner Flow is Facebook’s internal platform for machine learning. It combines several machine learning models to process data points drawn from the activity of the site’s users, and makes predictions such as which user is in a photograph, which post is spam and so on. Algorithms that come out of FbLearner Flow help Facebook to identify faces in photos, select content for your news feed and more. 

One of their recent innovations is DeepText, a deep learning based text understanding engine that can understand the textual content posted on Facebook in 20-plus languages. Understanding text might be easy for humans, but for a machine it includes multiple tasks such as classication of a post, recognition of entities, understanding of slang, disambiguation of confusing words and so on. 

All this is not possible using traditional NLP methods, and makes deep learning imperative. DeepText uses many DNN architectures, including convolutional and recurrent neural nets to perform word-level and character-level learning. FbLearner Flow and Torch are used for model training. 

But, why would Facebook want to understand the text posted by users? Understanding conversations helps to understand intent. For example, if a user says on Messenger that “the food was good at XYZ place,” Facebook understands that he or she is done with the meal, but when someone says, “I am hungry and wondering where to eat,” the system knows the user is looking for a nearby restaurant. Likewise, the system can understand other requirements like the need to buy or sell something, hail a cab, etc. This helps Facebook to present the user with the right tools that solve their problems. Facebook is also trying to build deep learning architectures that learn intent jointly from textual and visual inputs. 

Facebook is constantly trying to develop and apply new deep learning technologies. According to a recent blog post, bi-directional recurrent neural nets (BRNNs) show a lot of promise, “as these aim to capture both contextual dependencies between words through recurrence and position-invariant semantics through convolution.” The teams have observed that BRNNs achieve lower error rates (sometimes as low as 20 per cent) compared to regular convolutional or recurrent neural nets for classification. 

Big Sur is designed around operational efficiency and serviceability (Image courtesy: 

If Google open-sourced its deep learning software engine, Facebook open-sourced its AI hardware last year. Known as Big Sur, this machine was designed in association with Quanta and NVIDIA. It has eight GPU boards, each containing dozens of chips. It has been found that deep learning using GPUs is much more ecient compared to the use of traditional processors. GPUs are power-ecient and help neural nets to analyse more data, faster. 

In a media report, Yann LeCun of Facebook said that, open-sourcing Big Sur had many benets. “If more companies start using the designs, manufacturers can build the machines at a lower cost. And in a larger sense, if more companies use the designs to do more AI work, it helps accelerate the evolution of deep learning as a whole—including software and hardware. So, yes, Facebook is giving away its secrets so that it can better compete with Google—and everyone else.” 

Deep learning can deeply impact our lives 

Deep learning’s applications range from medical diagnosis to marketing, and we are not kidding you. There is a fabulous line on IBM’s website, which says that we are all experiencing the benets of deep learning today, in some way or the other, without even realising it. 

In June 2016, Ford researchers announced that they had developed a very accurate approach to estimate a moving vehicle’s position within a lane in real time. They achieved this kind of sub-centimetre-level precision by training a DNN, which they call DeepLanes, to process input images from two laterally-mounted down-facing cameras—each recording at an average 100 frames/s. 

They trained the neural network on an NVIDIA DIGITS DevBox with cuDNN-accelerated Cae deep learning framework. NVIDIA DIGITS is an interactive workow based solution for image classication. NVIDIA’s software development kit has several powerful tools and libraries for developing deep learning frameworks, including Cae, CNTK, TensorFlow, Theano and Torch. 

The life sciences industry uses deep learning extensively for drug discovery, understanding of disease progression and so on. Researchers at The Australian National University, for example, are using deep learning to understand the progression of Parkinson’s disease. In September, researchers at Duke University revealed a method that uses deep learning and light based, holographic scans to spot malaria-infected cells in a simple blood sample, without human intervention. 

Abu Qader, a high school student in Chicago, has created GliaLab, a startup that combines AI with the ndings of mammograms and ne-needle aspirations to identify and classify breast cancer tumours. The solution starts with mammogram imaging and then sifts Big Data to build predictive models about similar tumour types, risks, growth, treatment outcomes and so on. He used an NVIDIA GeForce GT 750M GPU on his laptop along with TensorFlow deep learning framework. 

Deep learning is supposed to be the future of digital personal assistants like Siri, Alexa and Cortana. Bark out any command, and these personal assistants will be able to understand and get it done. Deep learning is also going to be the future of Web search, marketing, product design, life sciences and much more. 

Once the Internet of Things (IoT) ensnares the world in its Web, there is going to be Big(ger) Data for deep learning systems to work on. No wonder companies ranging from Google, Facebook, Microsoft and Amazon, to NVIDIA, Apple, AMD and IBM, are all hell-bent on leading the deep learning race. A year down the line, we will have a lot more to talk about! 

When someone does not behave according to our expectations our brain automatically, mindlessly starts reacting to it by getting angry, anxious, or end with bad terms. We get irritated and annoyed by others’ actions because we expected better from them. But you know that you can’t control the way others behave. But you can control your actions and respond to them.

People try to create turmoil around us. But the only thing we can do is be compassionate towards them. This can happen through Mindfulness. Mindfulness is when you take a pause and let the wave of mindless reaction pass and then respond to the situation.

3 ways to Mindfulness:

1. Ask for a change. If you think the person is capable of changing you can ask for it. But while you ask make sure you don’t put the other person on the defensive. Asking him with kindness and gracefully rather than doing it with criticism. Criticism is futile and it does not work well in such a situation. Do not forget to appreciate the efforts of the person if he is changing for you.
2. Set limits and Boundaries .When you can’t control someone else behavior. One should learn to limit their responses to it. One should maintain boundary which means refusing some behavior if you feel it’s inappropriate.
3. Acceptance. This means mindfully accepting that the person would never change putting efforts would be ineffective. You decide to be ok with it. Acceptance doesn’t mean you shouldn’t try it. One should try but if it’s not hitting any change, stop and accept with compassion.

I was deserved to be preserved
But neither reserved nor served
I was measured as a pressure
Rather than being treasured
Should I now assure?