Trending December 2023 # Capitalization In Titles And Headings # Suggested January 2024 # Top 17 Popular

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There are three main options for capitalizing chapter and section headings within your dissertation: capitalizing all significant words, capitalizing only the first word, and a combination of the two.

The three heading capitalization styles

First, you can capitalize every significant word.

Option 1: All significant words capitalized

Chapter 3 Literature Review

Section 3.1 History of Coffee Drinking

Section 3.2.2 Commuting Workers

Section 3.3 Competitors in the Hot Beverage Sector

You may find it easier to instead focus on what usually isn’t considered significant (and thus not capitalized, unless it happens to be the first word in a heading): articles (a, an, the), prepositions (examples: by, for, in), conjunctions (examples: and, or, because).

Option 2: Only first words capitalized

Chapter 3 Literature review

Section 3.1 A history of coffee drinking

Section 3.2.2 Commuting workers

Section 3.3 Competitors in the hot beverage sector

Finally, the third possibility is to use a combination of the other two options. For instance, you could use option 1 for the chapter headings and option 2 for lower level headings.

Option 3: Capitalization varies by level

Chapter 3 Literature Review (level 1)

Section 3.1 A history of coffee drinking (level 2)

Section 3.2.2 Commuting workers

Section 3.3 Competitors in the hot beverage sector

Capitalize proper nouns (names) no matter what

Formal names of people, organizations, and places are capitalized no matter what style you use. For instance, North America is capitalized throughout the above examples.

In this regard, note that specific models, theories, and schools of thoughts are not considered proper nouns. The only component that needs to be capitalized is the scholar’s name, when relevant.

Porter’s Five Forces Model

Einstein’s Theory of Relativity

the Realist school

Porter’s five forces model

Einstein’s theory of relativity

the realist school

Which option should you choose? If you are following the APA style, the rules are clear. Essentially, you should use title case for APA headings level 1 to 5. MLA also has specific requirements for formatting headings.

If you are free to decide, we recommend option 1 or 2. Why? One reason is that it’s easier, you just won’t have to make so many judgment calls about what to capitalize. A second is that using a lot of capital letters may make the text difficult to follow, especially in longer headings.

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Consistency, consistency, consistency

Whatever option you choose, the most important thing is to use effective headings that are capitalized consistently throughout your entire document. This applies not only to the main chapters of your dissertation, but also to any supporting materials that come before and after (including the abstract, table of contents, lists of tables/figures, acknowledgements, reference list, and appendixes).

To make sure that no inconsistencies have snuck through, take a very careful look at your table of contents. Seeing all of the headings together will make any anomalies very apparent. This is especially true if you have used Microsoft Word to automatically generate this list.

Also take care that other aspects of your dissertation layout and formatting are consistent in relation to headings.

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Share And Collaborate In Excel

We’ve been able to share and collaborate in Excel files simultaneously with our co-workers for quite some time. However, recently there have been some significant improvements to the Excel co-authoring experience that will blow you away.

All you need to co-author in Excel is a Microsoft 365 subscription and an internet connection. You can even invite people from outside your organization who don’t have a Microsoft 365 subscription to collaborate with you.

Watch the Share and Collaborate in Excel Video

Download Workbook

If you’d like to see how I built the workbook used in this example, please download it here. Note: it won’t allow you to experience collaboration as this is something you need to instigate by sharing your file with others.

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Share and Collaborate in Excel Step by Step: Sharing Excel Files

To share an Excel file with your co-workers from in or outside your organization:

Make sure you’re signed into your Microsoft 365 account in Excel,

Save your file to SharePoint Online or OneDrive

Here you can specify what editing rights they’ll have (1) and then choose who to share the file with (2):

When someone else is editing the file, you’ll see their initials in the top right:

Note: people from outside your organization who aren’t logged into Microsoft 365 display as a Guest Contributor:

Make sure Autosave is turned on to see changes they make in the workbook within seconds after they’re entered.


In Excel Online you can also assign a task when you at mention someone:

Show Changes

The Show Changes tool, currently only available in Excel Online*, enables you to keep track of edits to your workbooks across any end point: Desktop, Online, Mac, iOS, and iPad. They’re retained for 60 days allowing you to see who changed what, where and when, along with the previous value of the cell.

*[UPDATE] Show Changes is also now available in the Excel desktop app.

Bulk changes are shown in the changes card allowing you to scroll through the list or collapse it via the ‘hide changes’ button:

You can reduce the list of changes to a specific cell, range of cells or sheet via the filter button:

To be clear, you can currently only see the Show Changes tool in Excel Online, but it will display changes made in Excel for the Desktop, Mac, iOS or iPad.

Version History

Show changes gives you the ability to revert to earlier edits on a cell by cell basis. Alternatively, you can use Version History to revert the file back to an earlier version:

This opens the Version History task pane where you can open earlier versions and restore them:

Sheet Views

Sheet Views enables you to create a custom view of the file that allows you to sort and filter the data without affecting what other users currently in the file see. You can save these custom views and quickly switch in and out of them as required.

Excel automatically names your new view Temporary View to indicate the sheet view isn’t saved yet. You’ll also notice the column and row labels are black with white font.

When a Sheet View is applied, an eye symbol is visible in the worksheet tab name and hovering over the eye will display the sheet view name in a tooltip:

You can switch back to the original view by choosing ‘Default’ via the sheet views drop down:

Sheet View Options is where you’ll find tools to rename, duplicate and delete views:

Notes on Sheet View:

You can only use Sheet Views in a document that is stored in a SharePoint or OneDrive location.

Assumptions In Psychological Testing And Assessment

According to the American Psychological Association, about twenty thousand new psychological tests are developed annually. Still, with numerous similar tools available, psychologists must clearly understand the proposition and reason for Testing in any situation. The testing proposition differs from the cerebral propositions of personality intelligence, and exploration shows that a psychologist makes about twelve assumptions in the testing process. The hypotheticals aim to create cerebral tests, establish their theoretical frame, and determine how the interpreted result will be employed in a given setting.

What is Testing or Assessment?

Psychological tests aid in identifying mental problems in a standardized, reliable, and valid manner. A diagnosis can be made using a variety of tests. Psychological evaluation is gathering information about people and applying it to make key predictions and conclusions about their cognition and personality. Psychological exams are used to examine psychological qualities. A psychological exam is simply an objective and standardized evaluation of a sample of behavior. Psychological tests are similar to other scientific tests in that observations are performed on a limited but carefully chosen sample of an individual’s behavior. In this regard, the psychologist works similarly to the biochemist who analyses a patient’s blood.

Psychological tests have a wide range of applications and are utilized in various settings, including therapeutic, counseling, industrial, and organizational settings and forensic settings. It can be used to diagnose psychiatric illnesses in a therapeutic setting, and Beck’s depression inventory, for example, can aid in diagnosing depression.

It may be utilized in counseling to make career selections and understand one’s aptitude and interests. In this context, tests such as the Differential Aptitude Test, Career Preference Record, and Vocational Interest Inventory can be employed. Psychological examinations may also be utilized in industrial and organizational settings for employee selection and to analyze stress-related difficulties, among other things.

In this configuration, job stress scales, organizational citizenship behavior, job satisfaction scales, and so on can be employed. Psychological tests can also be used in forensic psychology to determine an individual’s psychological condition. Thus, psychological tests may be used to assess a variety of psychological entities such as intellect, personality, creativity, interest, aptitude, attitude values, and so on. Psychological tests also assess internet addiction, resilience, mental health, psychological well-being, perceived parental behavior, family environment, and so on.

Why are the Assumptions about Psychological Testing?

Tests aiming to reflect the literacy aptitudes of academy children differ much further than generally is honored. Still, error in assessing similar literacy aptitude inheres much further in the users of the tests than in the tests themselves. Hypotheticals abecedarian to similar assessment, or indeed Testing, are considered. It is particularly important that the assessor, or tester, constantly be sensitive to the relationship between the cerebral demands of test particulars or tests and the literacy demands defying the child.

Indeed, tests that generally are grossly or crudely used can yield psycho-educationally meaningful information if their results are differentially perceived in terms of the light they throw on the cerebral operations abecedarian to literacy, “process,” as varied with that thrown on the results of the functioning of similar operations, “product.”

Assumptions of Psychological Testing and Assessment given by APA

Assumption 1 − Psychological traits and states exist. The trait has been defined as “any distinguishable, fairly enduring way in which one existent varies from another.” States distinguish one person from another but are fairly less continuing. Cerebral particularity covers a wide range of possible characteristics.

Construct is an informed, scientific conception developed or constructed to describe or explain behavior.

Overt conduct refers to an observable action or the product of an observable action, including test- or assessment-related responses.

The delineations of traits and countries we use also relate to how one existence varies.

Assumption 2 − Psychological traits and states can be quantified and measured. Having admitted that cerebral traits and countries do live, the specific traits and countries to be measured and quantified need to be precisely defined.

Assumption 3 − Various approaches to measuring aspects of the same thing can be useful. Decades of court challenges to various tests and testing programs have acclimatized test inventors and druggies to the societal demand for fair tests used fairly. Moment, all major test publishers strive to develop fair instruments when used in strict agreement with guidelines in the tested primer. Test tools are just like other tools; they can be used duly or inaptly.

Assumption 4 − Assessment can give answers to some of life’s most meaningful questions. Considering the numerous critical opinions grounded on testing and assessment procedures, we can readily appreciate the need for tests, especially good ones

Assumption 5 − Assessment can pinpoint marvels that bear further attention or study.

Assumption 6 − A variety of sources of data enrich and are part of the assessment process.

Assumption 7 − Various sources of error are part of the assessment process. Error traditionally refers to a commodity further than anticipated; it is an element of the dimension process. More specifically, error refers to a long-standing supposition that factors other than what a test attempts to measure will impact performance on the test.

Assumption 8 − Tests and Other dimension ways Have Strengths and weakness. Competent test users understand a great deal about the tests they use. For example, they understand, among other effects, how a test was developed and the circumstances under which it is applicable to administer the test. Likewise, competent test users understand and appreciate the tests’ limitations and how those limitations might be compensated for data from other sources.

Assumption 9 − Test-affiliated conduct predicts non-test-related conduct. Patterns of answers to true-false questions on one extensively used test of personality are used in decision timber regarding internal diseases. The task in some tests mimics the factual actions that the test users are trying to understand. For example, the attained conduct sample is used to diagnose unborn behavior.

Assumption 10 − Present-day conduct slice predicts unborn conduct.


Psychological testing is defined as the administration of psychological tests. Psychological tests measure IQ, personality, attitude, interest, accomplishment, motivation, and so on. They may be defined as the standardized and objective measurement of a sample of behavior. Psychological testing is mostly objective, and they are also predictive and diagnostic. A psychological exam is also standardized, which means that the technique for conducting and evaluating the test is consistent.

When it comes to psychological testing, there are several assumptions. In psychological testing, there are four introductory hypotheticals people differ in important trait; we can quantify these traits; the traits are nicely stable; and measures of the traits relate to factual behavior. With quantification, it has meant that objects can be arranged along a continuum. This quantification supposition is pivotal to the conception of measuring.

Difference Between Class And Object In Oops

Key Differences between Class and Object

A class is a template for creating objects in a program, whereas the object is an instance of a class.

A class is a logical entity, while an object is a physical entity.

A class does not allocate memory space; on the other hand, an object allocates memory space.

You can declare a class only once, but you can create more than one object using a class.

Classes can’t be manipulated, while objects can be manipulated.

Classes don’t have any values, whereas objects have their own values.

You can create a class using “class” keyword, while hand you can create an object using “new” keyword in Java.

Class vs Object

What is Class?

A class is an entity that determines how an object will behave and what the object will contain. In other words, it is a blueprint or a set of instruction to build a specific type of object. It provides initial values for member variables and member functions or methods.

What is Object?

An object is nothing but a self-contained component that consists of methods and properties to make a data useful. It helps you to determines the behavior of the class.

For example, when you send a message to an object, you are asking the object to invoke or execute one of its methods.

From a programming point of view, an object can be a data structure, a variable, or a function that has a memory location allocated. The object is designed as class hierarchies.

Class vs Object – Difference Between Them

Here is the important difference between class and object:

Class Object

A class is a template for creating objects in program. The object is an instance of a class.

A class is a logical entity Object is a physical entity

A class does not allocate memory space when it is created. Object allocates memory space whenever they are created.

You can declare class only once. You can create more than one object using a class.

Example: Car. Example: Jaguar, BMW, Tesla, etc.

Class generates objects Objects provide life to the class.

Classes can’t be manipulated as they are not available in memory. They can be manipulated.

It doesn’t have any values which are associated with the fields. Each and every object has its own values, which are associated with the fields.

You can create class using “class” keyword. You can create object using “new” keyword in Java

Understand the concept of Java Classes and Objects with an example.

Let’s take an example of developing a pet management system, specially meant for dogs. You will need various information about the dogs like different breeds of the dogs, the age, size, etc.

You need to model real-life beings, i.e., dogs into software entities.

Moreover, the million dollar question is, how you design such software? Here is the solution-

First, let’s do an exercise.

You can see the picture of three different breeds of dogs below.

Stop here right now! List down the differences between them.

Some of the differences you might have listed out maybe breed, age, size, color, etc. If you think for a minute, these differences are also some common characteristics shared by these dogs. These characteristics (breed, age, size, color) can form a data members for your object.

Next, list out the common behaviors of these dogs like sleep, sit, eat, etc. So these will be the actions of our software objects.

So far we have defined following things,

Class: Dogs

Data members or objects: size, age, color, breed, etc.

Methods: eat, sleep, sit and run.

Now, for different values of data members (breed size, age, and color) in Java class, you will get different dog objects.

You can design any program using this OOPs approach.

Classes and Objects in Java

In the below program, we have declared a class called Dog. We have defined an object of the class called “maltese” using a new keyword. In the last statement System.out.println(maltese.getInfo()); we are displaying dog information like Breed, Size, Age, Color, etc.

class Dog { String breed; String size; int age; String color; public String getInfo() { return ("Breed is: "+breed+" Size is:"+size+" Age is:"+age+" color is: "+color); } } public class Execute{ public static void main(String[] args) { Dog maltese = new Dog(); maltese.breed="Maltese"; maltese.size="Small"; maltese.age=2; maltese.color="white"; System.out.println(maltese.getInfo()); } }


Breed is: Maltese Size is: Small Age is:2 color is: white

Types of Class

Following are the important types of class:

Derived Classes and Inheritance

A derived class is a class which is created or derived from other remining class. It is used for increasing the functionality of base class. This type of class derives and inherits properties from existing class. It can also add or share/extends its own properties.


A superclass is a class from which you can derive many sub classes.


A subclass is a class that derives from superclass.

Mixed classes

A mixed class is one more functionality that helps you to inherit the properties of one class to another. It uses a subset of the functionality of class, whereas a derive class uses the complete set of superclass functionality.

Uses of Class

Here are the important uses of class:

Class is used to hold both data variables and member functions.

It enables you to create user define objects.

Class provides a way to organize information about data.

You can use class to inherit the property of other class.

It can be used for a large amount of data and complex applications.

Use of Object

Here are the important uses of an object

It helps you to know the type of message accepted and the type of returned responses.

You can use an object to access a piece of memory using an object reference variable.

It is used to manipulate data.

Objects represent a real-world problem for which you are finding a solution.

It enables data members and member functions to perform the desired task.

What Is Netback In Oil And Gas?


Used to assess oil and gas company efficiency and profitability

Written by

CFI Team

Published January 14, 2023

Updated July 7, 2023

What is Netback?

Netback is a calculation used to assess companies specifically in the oil and gas industry. This benchmark considers the revenue generated from the sale of oil and gas, and nets it against specific costs required to bring the product to market. Often this is shown as a per barrel measurement. It essentially shows how much the company retains from the sale of a single barrel of oil or oil byproducts. Netback per barrel can be used to assess company efficiency over time or to compare a company to its competitors.

Quick Summary

Netback is a benchmark used in the oil and gas industry to assess the profitability and efficiency of a company based on the price, production, transportation, and selling of their products

This benchmark is calculated by subtracting royalties, transportation, and other operating costs from revenue

Netback/barrels of oil/byproducts is a useful metric for assessing a company over time and comparing the company to its competitors

This benchmark can be found in the management discussion and analysis section of a company’s annual report

How is Netback Calculated?

Netback is calculated by starting with revenue and subtracting the costs of production, transportation, marketing, and other costs of bringing the oil and gas to market.:

Netback = Oil and Gas Revenue – Realized Loss on Financial Derivatives – Royalties – Operating Expenses – Transportation

Netback is often calculated as a per barrel of oil equivalent (BOE) instead, giving a more useful figure to assess the company by:

Netback/BOE = Price – Realized Loss on Financial Derivatives/BOE – Royalties/BOE – Operating Expense/BOE – Transportation/BOE

Why is Netback Important?

Netback is an important benchmark because it is a very useful measure for assessing a company without the bias of non-operating, financing, or other costs. Calculating this metric essentially tells an analyst how efficient the company is at producing and selling its oil and gas products. Taking this number per unit of barrels of oil equivalent gives a type of efficiency ratio. By monitoring netback/BOE over time, the efficiency of the company’s production, transporting and selling can be evaluated. A falling netback/BOE might indicate issues that should be further looked into.

Netback/BOE is also a very useful measure to look at when comparing different companies. Depending on the value, an analyst will be able to judge whether a company can more efficiently produce and market oil, allowing them to retain more profits from the sale of each barrel of oil. A higher netback/BOE will also show how able a company is at dealing with price volatility in the market. A higher netback/BOE suggests that in times of falling prices, the company would still be able to remain profitable.

Netback, however, is not a standardized equation. Different companies may calculate netback and netback/BOE using various methods and may include or exclude different items. Netback/BOE can still be used to look at changes over time for a specific company, however, when comparing the netback of competitors it is important to adjust the equation to ensure they are comparable values.

Netback – Worked Example

Let us consider an example of calculating the netback of a company. Say a company has oil and gas revenues of $11,000,000. They pay royalties of $300,000, transportation costs of $500,000, and have operating expenses of $3,800,000. If they sold 275,000 barrels of oil equivalents, what is their operating netback in dollars, and in dollars per barrel?

To calculate the operating netback, we start with revenues and subtract the costs of bringing the product to market:

Netback = $11,000,000 – $300,000 – $500,000 – $3,800,000 = $6,400,000

Here we see that the total netback is $6,400,000. To make this number more useful for analysts, netback per barrel can be calculated. Netback per barrels of oil can start with price, and each cost can be thought of as a per barrel cost. However, to calculate netback/BOE we can also simply divide the netback by the number of barrels:

Netback/BOE = $6,400,00/275,000 BOE = $23.27/BOE

The calculation for netback is generally found in the management discussion and analysis of a company’s annual report. This benchmark is most often shown in a table. Below illustrates what would generally be seen for a company:

Additional Resource

Thank you for reading CFI’s article on the netback benchmark. If you would like to learn more about related concepts, check out CFI’s other resources:

Artificial Intelligence In 2023: Urgency And Pragmatism

Where is artificial intelligence going in 2023? According to a recent Forrester research report, many companies feel an urgency to reap the benefits of AI. Indeed, artificial intelligence is seen as a propulsive driver of competitive success. If you’re not on the AI train, your competitors are leaving you at the station.

And yet there’s also a growing need to grapple with adopting AI in a pragmatic manner. AI can be wildly expensive, and companies have gotten burned doing “moonshot” projects. The mood is, “okay, we’ve heard that AI is big magic — now prove it to me.”

To shed light on the rapidly AI sector, I spoke with JP Gownder, Vice President, principal analyst, Forrester research. Gownder co-authored a recent report, AI 2023.

This discussion covers:

What is the current state of AI adoption?

Specific predictions for AI’s future.

How companies are purchasing artificial intelligence solutions – from AI companies?

Expectations for AI in 2023

Scroll down to see an edited transcript of highlights from”Data Analytics 2023.”

Download the podcast:

Tech trends come and go, and they have their 15 minutes of fame. Yet it feels like artificial intelligence is more foundational – it’s the Mega trend that will eat all other trends. Agree or is that just hyperbole?

“I think that the future of AI is always bright, and that’s one of the problems we’ve had with AI. In the dawn of the computing, computer science era in the 1940s and ’50s, before we even had proper hardware, some computer scientists thought that we would solve the general AI problem by the 1970s. And of course, that didn’t happen.

“We look forward and then there was what was called an AI winter, which was a period of disillusionment when people realized that practically speaking, certain problems could not be overcome.

“But in the last three or four years, we’ve entered this new phase of AI development where not only have hardware and software become more capable to move to the cloud that you mentioned and other factors making us feel like AI could be done effectively. However, when we come right down to it, we need AI to be in service of something, hopefully it’s in service of improved efficiency or customer obsession or operational effectiveness. And we have fairly mixed record at the moment on that.

“Finally, I want to say, AI is such a plethora of different technologies that it’s all over the place. In some areas, AI will very quickly become table stakes. If you look at what Alibaba has done in the retail sector, or Amazon in terms of personalization and choice and predictive analytics around what people want to buy. Well look, that is becoming really powerful and important, but other areas of AI are quite lagging and are definitely hyperbole today.”

“So our data also shows that about 53% of data and analytics decision makers say that their company is in the implementing phase or they’ve already implemented some AI. But that is to say that within those organizations, that could be a small project.

“So 53% of companies are doing something, but that might be a Tensor Flow model running on one workstation for a data scientist. So it is again, rather variable.

“And I would say that in the grand scheme of things, we remain at an early stage of this, and there’s reasons for that, this is not easy to do well. We don’t necessarily have data hygiene that’s allowed us to tap into the right kind of data, we have these data silos and stuff. So, the foundation of AI being data, that’s a problem.

“We have a lack of governance, most companies don’t know from ethics to explainability to exactly who to participate in the process of overseeing governance. Very few companies have gone deeply down that road.”

How are companies actually purchasing AI solutions?

 “So there’s a wide range. Again, with AI being such a broad area, a couple of important factors here. Number one, it can be very challenging even for a large company to hire AI talent. So I was talking to an insurance company that’s global in span. It’s a huge company, based in the Midwest, however. This is not an area where there’s a lot of local talent. They could choose to hire someone who sits in San Francisco, but it might be a bit of an inhibitor. And it’s also the cost of that talent can grow to the millions of dollars.

“Secondarily, you may not even have the basic organizational capability to set something like this up. And so you may choose to go outside. But there could be other cases where you do have a data science team and what you’re doing is more incremental. You’re building using open-source kind of solutions like Tensor Flow, which is common in the ML and deep learning spaces. And maybe you can start internally.

“So what we’re finding is a distribution, but many companies do turn to external experts, companies that are able to offer data hygiene, data engineering services that are offering a variety of different kinds of analytical techniques. Or they’re working off of a big platform like Microsoft Azure or AWS, which have their own AI tools from which you can build applications.

“Finally, there’s also what you could think of as everyday or embedded AI, which is to say, ‘I am already using software and the vendor of that software has decided to add AI features to make my experience better.’

“When you log into the latest version of PowerPoint, you start building a slide, it actually tells you, ‘Here are some things you can do. Do you wanna do these things?’ And that’s actually powered by AI. So there’s a large span of different ways to do this from systems integrators, who are gonna do big projects, to existing software providers to building off of a platform to maybe a little bit of internal work.”

In closing, I’d like to get your thoughts about where we are in the bigger picture – AI going into 2023 – and if there’s any way for companies to get ahead of that wave as you see it happening.

We’re going to continue to see a move toward, ‘Prove it to me. What are the business results measurement?’ I think that some of the moonshot projects in a year in which we don’t necessarily expect recession, but reasonably slow economic growth, companies are turning pragmatic. That was the theme for 2023. We think that will continue into 2023.

“We also see that AI will be applied to, I think, more specific business problems rather than these broader ones. We will also see an element of AI riding in on certain other technologies. Principally or in the leading case, it’ll be robotic process automation.

“So those are some big trends that we see next year. AI will be a critical part of the conversation for enterprise tech, but it will become a pragmatic part of that conversation.”

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