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What is Data?

Data is a raw and unorganized fact that required to be processed to make it meaningful. Data can be simple at the same time unorganized unless it is organized. Generally, data comprises facts, observations, perceptions numbers, characters, symbols, image, etc.

Data is always interpreted, by a human or machine, to derive meaning. So, data is meaningless. Data contains numbers, statements, and characters in a raw form.

What is Information?

Information is a set of data which is processed in a meaningful way according to the given requirement. Information is processed, structured, or presented in a given context to make it meaningful and useful.

It is processed data which includes data that possess context, relevance, and purpose. It also involves manipulation of raw data.

Information assigns meaning and improves the reliability of the data. It helps to ensure undesirability and reduces uncertainty. So, when the data is transformed into information, it never has any useless details.


Data is a raw and unorganized fact that is required to be processed to make it meaningful whereas Information is a set of data that is processed in a meaningful way according to the given requirement.

Data does not have any specific purpose whereas Information carries a meaning that has been assigned by interpreting data.

Data alone has no significance while Information is significant by itself.

Data never depends on Information while Information is dependent on Data.

Data measured in bits and bytes, on the other hand, Information is measured in meaningful units like time, quantity, etc.

Data can be structured, tabular data, graph, data tree whereas Information is language, ideas, and thoughts based on the given data.

Data Vs. Information

Parameters Data Information

Description Qualitative Or QuantitativeVariables which helps to develop ideas or conclusions. It is a group of data which carries news and meaning.

Etymology Data comes from a Latin word, datum, which means “To give something.” Over a time “data” has become the plural of datum. Information word has old French and middle English origins. It has referred to the “act of informing.”. It is mostly used for education or other known communication.

Format Data is in the form of numbers, letters, or a set of characters. Ideas and inferences

Represented in It can be structured, tabular data, graph, data tree, etc. Language, ideas, andthoughts based on the given data.

Meaning Data does not have any specific purpose. It carries meaning that has been assigned by interpreting data.

Interrelation Information that is collected Information that is processed.

Feature Data is a single unit and is raw. It alone doesn’t have any meaning. Information is the product and group of data which jointly carry a logical meaning.

Dependence It never depends on Information It depended on Data.

Measuring unit Measured in bits and bytes. Measured in meaningful units like time, quantity, etc.

Support for Decision making It can’t be used for decision making It is widely used for decision making.

Contains Unprocessed raw factors Processed in a meaningful way

Knowledge level It is low-level knowledge. It is the second level of knowledge.

Characteristic Data is the property of an organization and is not available for sale to the public. Information is available for sale to the public.

Dependency Data depends upon the sources for collecting data. Information depends upon data.

Example Ticket sales on a band on tour. Sales report by region and venue. It gives information which venue is profitable for that business.

Significance Data alone has no signifiance. Information is significant by itself.

Meaning Data is based on records and observations and, which are stored in computers or remembered by a person. Information is considered more reliable than data. It helps the researcher to conduct a proper analysis.

Usefulness The data collected by the researcher, may or may not be useful. Information is useful and valuable as it is readily available to the researcher for use.

Dependency Data is never designed to the specific need of the user. Information is always specific to the requirements and expectations because all the irrelevant facts and figures are removed, during the transformation process.

DIKW (Data Information Knowledge Wisdom)

DIKW is the model used for discussion of data, information, knowledge, wisdom and their interrelationships. It represents structural or functional relationships between data, information, knowledge, and wisdom.


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What Is The Difference Between Data Science And Machine Learning?

Introduction  Data Science vs Machine Learning

AspectData Science Machine Learning DefinitionA multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.A subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that allow computer systems to learn and make predictions or decisions without being explicitly programmed.ScopeBroader scope, encompassing various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and interpretation.Narrower focus on developing algorithms and models that enable machines to learn from data and make predictions or decisions.GoalExtract insights, patterns, and knowledge from data to solve complex problems and make data-driven decisions.Develop models and algorithms that enable machines to learn from data and improve performance on specific tasks automatically.TechniquesIncorporates various techniques and tools, including statistics, data mining, data visualization, machine learning, and deep learning.Primarily focused on the application of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning.ApplicationsData science is applied in various domains, such as healthcare, finance, marketing, social sciences, and more.Machine learning finds applications in recommendation systems, natural language processing, computer vision, fraud detection, autonomous vehicles, and many other areas.

What is Data Science? 

Source: DevOps School

What is Machine Learning? 

Computers can now learn without being explicitly programmed, thanks to the field of study known as machine learning. Machine learning uses algorithms to process data without human intervention and become trained to make predictions. The set of instructions, the data, or the observations are the inputs for machine learning. The use of machine learning is widespread among businesses like Facebook, Google, etc. 

Data Scientist vs Machine Learning Engineer

While data scientists focus on extracting insights from data to drive business decisions, machine learning engineers are responsible for developing the algorithms and programs that enable machines to learn and improve autonomously. Understanding the distinctions between these roles is crucial for anyone considering a career in the field.

Data ScientistMachine Learning EngineerExpertiseSpecializes in transforming raw data into valuable insightsFocuses on developing algorithms and programs for machine learningSkillsProficient in data mining, machine learning, and statisticsProficient in algorithmic codingApplicationsUsed in various sectors such as e-commerce, healthcare, and moreDevelops systems like self-driving cars and personalized newsfeedsFocusAnalyzing data and deriving business insightsEnabling machines to exhibit independent behaviorRoleTransforms data into actionable intelligenceDevelops algorithms for machines to learn and improve

What are the Similarities Between Data Science and Machine Learning?

When we talk about Data Science vs Machine Learning, Data Science and Machine Learning are closely related fields with several similarities. Here are some key similarities between Data Science and Machine Learning:

1. Data-driven approach: Data Science and Machine Learning are centered around using data to gain insights and make informed decisions. They rely on analyzing and interpreting large volumes of data to extract meaningful patterns and knowledge.

2. Common goal: The ultimate goal of both Data Science and Machine Learning is to derive valuable insights and predictions from data. They aim to solve complex problems, make accurate predictions, and uncover hidden patterns or relationships in data.

3. Statistical foundation: Both fields rely on statistical techniques and methods to analyze and model data. Probability theory, hypothesis testing, regression analysis, and other statistical tools are commonly used in Data Science and Machine Learning.

4. Feature engineering: In both Data Science and Machine Learning, feature engineering plays a crucial role. It involves selecting, transforming, and creating relevant features from the raw data to improve the performance and accuracy of models. Data scientists and machine learning practitioners often spend significant time on this step.

5. Data preprocessing: Data preprocessing is essential in both Data Science and Machine Learning. It involves cleaning and transforming raw data, handling missing values, dealing with outliers, and standardizing or normalizing data. Proper data preprocessing helps to improve the quality and reliability of models.

Where is Machine Learning Used in Data Science?

In Data Science vs Machine Learning, the skills required for ML Engineer vs Data Scientist are quite similar. 

Skills Required to Become Data Scientist

Exceptional Python, R, SAS, or Scala programming skills

SQL database coding expertise

Familiarity with machine learning algorithms

Knowledge of statistics at a deep level

Skills in data cleaning, mining, and visualization

Knowledge of how to use big data tools like Hadoop.

Skills Needed for the Machine Learning Engineer

Working knowledge of machine learning algorithms

Processing natural language

Python or R programming skills are required

Understanding of probability and statistics

Understanding of data interpretation and modeling.

Source: AltexSoft

Data Science vs Machine Learning – Career Options

There are many career options available for Data Science vs Machine Learning.

Careers in Data Science

Data scientists: They create better judgments for businesses by using data to comprehend and explain the phenomena surrounding them.

Data analysts: Data analysts collect, purge, and analyze data sets to assist in resolving business issues.

Data Architect: Build systems that gather, handle, and transform unstructured data into knowledge for data scientists and business analysts.

Business intelligence analyst: To build databases and execute solutions to store and manage data, a data architect reviews and analyzes an organization’s data infrastructure.

Source: ZaranTech

Careers in Machine Learning

Machine learning engineer: Engineers specializing in machine learning conduct research, develop, and design the AI that powers machine learning and maintains or enhances AI systems.

AI engineer: Building the infrastructure for the development and implementation of AI.

Cloud engineer: Builds and maintains cloud infrastructure as a cloud engineer.

Computational linguist: Develop and design computers that address how human language functions as a computational linguist.

Human-centered AI systems designer: Design, create, and implement AI systems that can learn from and adapt to humans to enhance systems and society.

Source: LinkedIn


Data Science and Machine Learning are closely related yet distinct fields. While they share common skills and concepts, understanding the nuances between them is vital for individuals pursuing careers in these domains and organizations aiming to leverage their benefits effectively. To delve deeper into the comparison of Data Science vs Machine Learning and enhance your understanding, consider joining Analytics Vidhya’s Blackbelt Plus Program.

The program offers valuable resources such as weekly mentorship calls, enabling students to engage with experienced mentors who provide guidance on their data science journey. Moreover, participants get the opportunity to work on industry projects under the guidance of experts. The program takes a personalized approach by offering tailored recommendations based on each student’s unique needs and goals. Sign-up today to know more.

Frequently Asked Questions

Q1. What is the main difference between Data Science and Machine Learning?

A. The main difference lies in their scope and focus. Data Science is a broader field that encompasses various techniques for extracting insights from data, including but not limited to Machine Learning. On the other hand, Machine Learning is a specific subset of Data Science that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions.

Q2. Are the skills required for Data Science and Machine Learning the same?

A. While there is some overlap in the skills required, there are also distinct differences. Data Scientists need strong statistical knowledge, programming skills, data manipulation skills, and domain expertise. In addition to these skills, Machine Learning Engineers require expertise in implementing and optimizing machine learning algorithms and models.

Q3. What is the role of a Data Scientist?

A. The role of a Data Scientist involves collecting and analyzing data, extracting insights, building statistical models, developing data-driven strategies, and communicating findings to stakeholders. They use various tools and techniques, including Machine Learning, to uncover patterns and make data-driven decisions.

Q4. What is the role of a Machine Learning Engineer?

A. Machine Learning Engineers focus on developing and implementing machine learning algorithms and models. They work on tasks such as data preprocessing, feature engineering, model selection, training and tuning models, and deploying them in production systems. They collaborate with Data Scientists and Software Engineers to integrate machine learning solutions into applications.


Difference Between Bacon And Ham

One key distinction between bacon and ham is the source of the meat. Many cultures rely heavily on pig farming as a primary means of animal protein production. Many people are thrown off by the fact that it is referred to as “pig flesh” in several distinct contexts. You can get ham and bacon. Pork is also available. Both gammon and muscle are present. In reality, there are dozens of distinct names for various pieces of meat from the same animal, and as bacon and ham tend to be the most perplexing, we’ll focus on those here.

To begin, pork is the most used term worldwide for pig flesh. But how exactly does pork end up as bacon or ham? Well, let’s find out.

What is Bacon?

Bacon originates from the side of the pig that remains after the head and feet have been removed. But it doesn’t happen until after the meat has been cured for quite some time. The cut of flesh from the hog loin is also used to produce bacon. Bacon is commonly made from pig bellies in the United States. Because of this, bacon from the United States and Canada is commonly referred to as “American style” “fatty” or “streaky” bacon outside of these countries. This is because pig belly is solely used in the United States and Canada to make bacon.

Green bacon is the name given to the hog leg after it has been salted and left out for a long time. In the south of England, this cured beef is smoked at low heat using oak wood. The leg−wrapped version of this bacon is known as gammon, whereas pork loin is used to make traditional back bacon.

To prepare bacon for consumption, it is often fried. They crisp up well, and bacon is a popular snack. They are a staple of the Western morning. Bacon can be prepared in a variety of ways, including boiling, smoking, and grilling.

What is Ham?

Ham is cut from the upper thigh, buttock, or the area where the thigh meets the knee. According to etymologists, the term “ham” originates from the verb “hom,” which means “to bend the knee.” Ham can be wet−cured or dry−cured. Cured refers to the several methods used to prolong the shelf life of foods such as meat, fish, and produce. Ham can be wet−cured or dry−cured. Meat is “dry cured” by rubbing it with a mixture including salt and several additional substances. After that, the meat is dried and aged for some time. Wet−cured beef is either submerged in brine or given an injection of the brine mixture.

Ham is commonly used to make sandwiches and other snacks. There’s no way you haven’t heard of a ham sandwich before. Ham slices are used to fill the space between the bread in these. Ham is also a popular topping for pizza and other pies.

Differences: Bacon and Ham

The following table highlights how Bacon is different from Ham −

Characteristics Bacon Ham

Cut of the meat Bacon is cured pork flesh, often cut from the ribs or rear of the pig. The pig belly is also used to create bacon in the United States. Ham is cut from the upper thigh, buttock, or the area where the thigh meets the knee.

Preparation Bacon is often smoked after being cured. Ham can be dry cured or wet cured, depending on the method used.

Eating Some common methods of eating bacon include frying, smoking, boiling, and grilling. The typical method of consuming ham is slicing it.

Taste The bacon’s crispiness is what makes it so delicious. Comparatively, ham has more moisture than bacon.

Shape Bacon is available in paper−thin slices. You may also eat ham by slicing it very thinly.


Bacon and ham are both generic terms for pig flesh, and the designation “bacon” or “ham” depends on which portion of the pig the meat was originally taken. Bacon and ham are two of the most popular cuts of pork among meat eaters. In addition, bacon from both is commonly consumed with toast and eggs for breakfast. Ham is a popular sandwich filler and pizza topping substitute. Many people enjoy eating both of them because of their high quality and popularity.

Difference Between Anonymity And Confidentiality

Humans go through different stages in their personal and professional lives during which they undergo situations or experiences after which privacy and information security becomes a vital requirement for them. For example, a person approaching someone from the medical profession would much appreciate his medical data remain protected from others so that he/she doesn’t face discrimination at home or work.

Similarly, a company tasked with conducting surveys and running research activities is obliged to maintain strong anonymity and confidentiality measures in regard to their survey pool. In some sectors– such as medicine, religion, and politics, to mention a few—many participants deciding to participate in studies, experiments, or public outreach initiatives may choose to remain anonymous for various reasons.

Confidentiality, anonymity, and other forms of privacy protection can help with this concern. However, before we move on, there’s a need to know the difference between these two terms, “confidentiality” and “anonymity”, because these two are some of the most widely confused terms in the world of data privacy. As a student or user of data privacy, it’s important for readers to build a clear understanding of difference between these two terms before you participate in any such surveys or employ such services.

What is Anonymity?

This is a situation in which the researchers do not collect personally identifying information about the participants. When the statistics are aggregated, they should not include any personal information that may be used to reveal anyone’s identity. Although a person’s age is not a unique identifier on its own, it may be combined with other information to help pinpoint their location and identify the sort of research they participated in.

What is Confidentiality?

Confidentiality occurs when the collected data is kept secret from anybody other than the researchers or investigators who gathered it. Data are not supplied in a form that would permit respondents to be recognized, and unique identifiers are not made public. Data such as names, ages, addresses, and phone numbers can be collected from participants. Therefore, the researcher should take measures to protect the confidentiality of the material, including the use of passwords and encryption, as well as the secure locking of any physical copies.

Differences − Anonymity and Confidentiality

Both of these concepts are extremely significant in research and data collecting. The following table highlights how Anonymity is different from Confidentiality −

Characteristics Anonymity Confidentiality

Definition The term “anonymity” is used to describe situations in which participants’ names, addresses, phone numbers, identification numbers, emails, images, and other personally identifying characteristics are not collected by the researchers. “Confidentiality” describes a circumstance in which the collected data is kept secret from anybody other than the researchers and investigators involved in the study.

Participants identity Researchers maintain participant anonymity if they do not collect any information that might be used to identify them, such as names, addresses, phone numbers, identification numbers, email addresses, photographs, or any other information that may be used to identify them. Researchers and investigators are the only people who are privy to the information gathered from participants in a covert study. In addition, the information is not made public or presented in a way that may be used to track down the individuals who provided their responses.

Type of research studies Identity concealment is standard procedure in quantitative research. The notion of confidentiality is often used in both qualitative and quantitative research.

Common use The anonymity feature is widely utilized in a variety of research settings, including interviews, content analysis, and online surveys. Confidentiality is commonly preserved when carrying out public activities, including conducting surveys, interviews, medical research, or even just talking to the general public.


Researchers must ensure that participants understand the importance of maintaining their anonymity and the confidentiality of their data. When participants are assured of their anonymity, researchers do not record any information that may be used to identify them, including their names, addresses, phone numbers, email addresses, or other identifying characteristics.

The term “confidentiality” is used to describe when the obtained data is shielded from view by any other parties other than the researchers who gathered it. Separate from the idea of anonymity is the idea of confidentiality.

Difference Between Kw And Kva

In any electric circuit (AC or DC), the electric power is one of the major electrical quantity. The electrical power is broadly classified into three types namely active power, reactive power and apparent power.

These three types of electric powers are measured in different units. Where, the active power is measured in Watts (W), the reactive power is measured in Volt Ampere Reactive (VAR) and the apparent power is measured in Volt Ampere (VA). But in practice, these powers are measured in larger units, i.e., kW (kilo-Watt), kVAR (kilo Volt Ampere Reactive), and kVA (kilo Volt Ampere).

Read through this article to understand how kW is different from kVA. Let’s start with some basics of kW and kVA, the relation between kW and kVA, so that it becomes easier to understand the differences between them.

What is kW?

kW is short for kilo Watt. It is the unit of measurement of active power or real power or true power. kW gives the measure of how much average power an electrical device consumes. 1 kW of electric power is equivalent to 1000 watts. Thus, kW can be converted into Watt simply by multiplying the numerical value in kW by 1000, i.e.


In DC circuit, the Watt or kW is used to measure the average DC power, whereas in an AC circuit, it is used to measure the active or real part of the total AC electric power.

kW is the unit of power which is used to define the load type electrical devices such as motors, lamps, irons, heaters, etc.

What is kVA?

kVA stands for kilo Volt Ampere. kVA is the unit of measurement of apparent electric power in the AC circuit. kVA is a large unit of apparent power, its base unit is VA (Volt Ampere). kVA is the term which is related to AC circuit only. There is no such thing in a DC circuit, because in a DC circuit, the total power is measured in watts or kW.

Just like kW, kVA may also be converted into VA by multiplying it by 1000, i.e.

$$mathrm{1:kVA:=:1:times:1000:VA:= :1000:VA}$$

Generally, kVA is used to represent the rating of such electrical devices that act as source such as alternators, generators, transformers, inverters, etc.

Difference between kW and KVA

Both kW and kVA are the units of measurement of electrical power, but they are different from each other. The major differences between kW and kVA are given in the following table −

Basis of Difference kW kVA

kW stands for kilo-Watt. kVA stands for kilo Volt Ampere.

Description The kW is the unit of measurement of average power in DC circuit and active (or real) power in AC circuits. kVA is the unit of apparent power in AC circuits.

Relevant circuit kW is term related to both AC and DC circuits. kVA is only relevant in AC circuits. It is not used in DC circuits.

What it is? kW is the measure of active power consumed by an electrical load. kVA is the measure of total power delivered by a source (ex. generator) to the load.

Dependency on power factor kW is directly proportional to the load power factor. kVA is independent of the power factor of load.

Expression kW is given by the following expression, $$mathrm{kW:=:1000:times:V:times:A:times:pf}$$ kVA is given by the following expression, $$mathrm{kVA:= :1000:times:V:times:A}$$

Uses kW is used to represent the ratings of electrical load devices such as electric motors, electric heaters, iron, lamps, etc. kVA is used to represent the rating of electrical source devices such as generators, alternators, inverters, UPS, etc.


The most significant difference that you should note here is that kW represents the amount of electric power consumed by an electric load, whereas kVA represents the total amount of power supplied by a source to an electrical load.

Difference Between Bronchitis And Bronchiectasis

Bronchitis and bronchiectasis are two respiratory disorders that affect the airways, particularly the bronchi, which are the tubes that carry air into the lungs. Although both conditions share some similarities, they differ in their causes, symptoms, and treatments. In this essay, we will discuss the difference between bronchitis and bronchiectasis in detail.

What is Bronchitis?

Bronchitis is a common respiratory disorder that occurs when the bronchial tubes become inflamed and produce excess mucus. There are two types of bronchitis: acute bronchitis and chronic bronchitis. Acute bronchitis is usually caused by a viral infection and is characterized by a sudden onset of cough, chest congestion, and difficulty breathing. The symptoms of acute bronchitis usually last for a few days to a week and can be managed with over-the-counter medications and plenty of fluids.

Chronic bronchitis, on the other hand, is a long-term condition that is caused by smoking, air pollution, or other irritants. It is characterized by a persistent cough that produces sputum and can last for several months. Chronic bronchitis can lead to complications such as respiratory failure and pneumonia if left untreated.

Symptoms − Bronchitis typically begins with a cough that eventually may produce sputum. There is also often a pain in the chest and a feeling of tightness and dyspnea is often present. The breathing passages are often very congested. In addition, the condition usually lasts for over a week and may even take up to 21 days for a person to fully recover from.

Diagnosis and causes − A clinician can diagnose bronchitis in a person by doing a physical exam and noting the symptoms. Listening to the chest and also doing a chest X-ray may be helpful in ruling out other possible causes of the chest problems. Most often the condition is the result of a viral infection particularly associated with influenza type A and B, and rhinovirus. There are other viruses that can lead to bronchitis, including the parainfluenza and coronavirus. Environmental factors such as smoking can result in bronchitis as can having the illness cystic fibrosis.

What is Bronchiectasis?

Symptoms − Having a cough that will not go away and also coughing up pus-filled sputum are symptoms of bronchiectasis. Some patients may also experience difficulty breathing and have a fever.

Diagnosis and causes − A physical exam in conjunction with CT scans can help in diagnosing bronchiectasis in a patient. It is important that the scans are done at a high resolution in order to detect the condition. The condition may occur in one or a few spots in the lungs or throughout the lungs. The illness can be caused by allergic bronchopulmonary aspergillosis, cystic fibrosis, and problems with the cilia and disease of the connective tissues. In some cases of focal bronchiectasis, the cause is thought to be pneumonia.

Differences: Bronchitis and Bronchiectasis

The treatment for bronchitis usually involves relieving the symptoms with over-the-counter medications, rest, and plenty of fluids. Antibiotics may be prescribed if the bronchitis is caused by a bacterial infection. In the case of chronic bronchitis, quitting smoking and avoiding irritants is crucial to prevent further damage to the airways.

Bronchiectasis, on the other hand, is a long-term condition that requires ongoing treatment to manage the symptoms and prevent complications. This treatment may include antibiotics, bronchodilators, and pulmonary rehabilitation.

The following table highlights the major differences between Bronchitis and Bronchiectasis −





Bronchitis is the disorder in which the tracheal passage and bronchi become inflamed.

Bronchiectasis is the disorder in which bronchi in the lungs widen and become destroyed.

Permanent change


The earliest symptom of bronchitis is usually a cough, which may or may not result in sputum being produced. The chest is also painful and feels tight and there is difficulty in breathing (dyspnea).

The bronchiectasis often has symptoms of dyspnea, a chronic cough, and production of sputum which is full of pus.


The diagnosis of bronchitis is usually by physical examination and a chest X-ray.

The diagnosis of bronchiectasis is done by looking at a high-resolution CT scan of the chest


Bronchitis can be caused by smoking and from viral infections such as those caused by influenza A or B, parainfluenza or coronavirus.

Bronchiectasis can be caused by bacterial infections such as pneumonia, and chronic infections such as cystic fibrosis, allergic bronchopulmonary aspergillosus, and HIV.

Risk factors

Cigarette smoking, having the flu, various viruses, and cystic fibrosis are risk factors for developing bronchitis.

Having cystic fibrosis, HIV, or allergic bronchopulmonary aspergillosus are risk factors for developing bronchiectasis.


In conclusion, bronchitis and bronchiectasis are two respiratory disorders that affect the bronchial tubes. While they share some similarities in their symptoms, they differ in their causes and treatments.

Bronchitis is usually a short-term condition that can be managed with rest and over-the-counter medications, while bronchiectasis is a long-term condition that requires ongoing treatment to manage the symptoms and prevent complications. It is important to consult a healthcare professional for an accurate diagnosis and appropriate treatment plan for either condition.

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