#but that's more of a scale than qualitative categories

[legendaryvermin]

So my home ttrpg group is between longform games right now, and I have been planning to bring a bunch of games to them this weekend as options for what we might play next. However, I have been trying to figure out how to talk about the games in a way that doesn't rely as much on me explaining the vibes to them.

I know that people have a bunch of qualitative categories for how they explain games, but I find the idea of saying things like Dark Fantasy OSR, or Lesbian Goofball PBTA less helpful when talking about how games actually play, especially when two games in the same category are like, wildly different in the way they use their frameworks.

So I invented a 6 axis, 1 to 5 star rating scale for TTRPGs that you are free to borrow when talking to groups, or whatever.

TTRPG 5 Star Rating Matrix

Width

What is the scope of this game? Is it narrowly about one thing or does it encompass many types of play? (Credit to friend of the blog @ostermad-blog for this one, they came up with it from my draft)

Weight

How much cognitive load does the player need to bear? Do rules often need to be referenced verbatim? Can those rules fit on a handout?

Wargame

Is the player expected to apply tactical acumen? Is movement tracked tightly or loosely? Does a bad build punish a player?

Writers Room

How much are players expected to make narrative choices and drive the story without the rules scaffolding them? Does this game fall apart without excellent improvisational storytellers?

(Prep)Work

Does this game require a lot of pre-planning by the facilitator? Are there intricate systems to attend to outside of table play? Can I put in the same amount of time as other players and still have everyone leave happy? 

Whimsy

Expected tone of the game. Does this game have difficult thematic elements baked in? Is the core subject or role in the game high or low risk?

Here are some games I know well and how I calibrated them:

I have breakdowns of what each star rating means below the cut if you're curious. Happy Gaming!

Width

⭐ - As written, the game has basically one mode of play, or one thematic core that it meditates on. May have phases, but textural difference is minimal.

⭐⭐ - As written, there are at least two modes of play, but the scope of that play is highly thematically focused or highly dependent on using the game’s own lore. Might have only one kind of character (e.g. Mech Pilot) that it supports. Has limited tools outside of the primary mode of play.

⭐⭐⭐ - Has a variety of modes of play, but may be rigid in their execution. Might encompass multiple kinds of characters (e.g. Doctor, Lawyer, fighter) or character options. The narratives that this game tells within its setting are narrowed, a three word description tells you what kind of stories it can tell with consistency.

⭐⭐⭐⭐ - Loose framework, but with some kind of thematic grounding. Describing the framework in 3 words doesn’t tell you the kind of stories that the game tells (e.g. Dark Fantasy, Star Wars Romp). 

⭐⭐⭐⭐⭐- As written, this game is designed in such a way that it doesn’t put specific limits on what sorts of stories that it is meant to tell. It might ask players to define abilities or stats for themselves. The Facilitator is going to pitch a thematic grounding on top of the rules set.

One Star Examples: For the Queen, Dialect, Honey Heist Five Star Examples: Fate Core, Savage Worlds, GURPS

Weight

⭐ - It is reasonable for a player to be able to recite the rules from memory. The game may be prompt based, or driven by a flow of rules that are read aloud as played.

⭐⭐ - Players can hold most of the most important information about the game in their heads, with a page or less of rules reference needed to play smoothly. This reference could all fit neatly on the character sheet if one is present.

⭐⭐⭐ - Everything a player needs to know about the game is visible on less than 3 sheets of reference. Players are more or less expected to know exactly how their own abilities work in precise detail, and are unlikely to make a mistake in executing them.

⭐⭐⭐⭐ - Players make extensive use of multiple reference sheets to keep rules moving smoothly. No external tools are needed, but players memorizing the details of all of their abilities is taxing. 

⭐⭐⭐⭐⭐- Players and facilitators will prefer to make extensive use of external tools or reference to keep play moving smoothly. Expecting a player to have the exact details of their abilities memorized is not reasonable.

One Star Examples: For the Queen, Stewpot, Mobile Frame Zero: Firebrands Five Star Examples: Dungeons and Dragons 3-5e, Lancer, Edge of the Empire

Wargame

⭐ - As written, this game does not treat combat as mechanically different from any other aspect of play, or does not include narrative violence at all.

⭐⭐ - While players may engage in combat, it is minimally different from regular play. There may be tools or abilities for players to use to conduct a fight, but the texture of those fights is thematic, not mechanical. Narrative and consequence drive the action, not hit points.

⭐⭐⭐ - As written, combat has its own set of rules. This game may have some elements of buildcrafting, but either it is difficult to build something that doesn’t work, or the player may meaningfully invest in other modes of play and still find a commensurate level of satisfaction. If combat occurs, spacing is kept in mind, but is tracked in relative terms (range bands) or highly simplified (zone based combat).

⭐⭐⭐⭐ - This game has buildcrafting that is somewhat mandatory if players wish to survive a fight, but there is still a meaningful choice in choosing a non-combat role. It may use a grid or a spacing system to help players visualize the combat. Fights are driven by mechanics, not by narrative.

⭐⭐⭐⭐⭐- To enjoy this game, players must spend time buildcrafting. If a player’s build is suboptimal, there may be significant parts of the intended experience that will either feel tedious, or that the player will not have meaningful access to. This game is played on a grid.

One Star Examples: Wanderhome, Dialect, Belonging Outside Belonging Five Star Examples: Lancer, Dungeons and Dragons 3-5e, Valor

Writers Room

⭐ - Players in this game are not expected to provide much in the way of narrative substance. Story is something that is driven by external input or tools, and players are there to imagine and react. The player need not separate the self from the character they play in any meaningful way.

⭐⭐ - The mechanics of this game drive most of the narrative, or else the narrative is set for the players by an external source or player. Players are encouraged to play optimally rather than dramatically, but do have room for expressing the identity of their character within the game’s mechanical frameworks.

⭐⭐⭐ - While the game does provide strong scaffolding to tell a story, the players present are expected to drive the story within those frameworks. The game’s systems create and resolve conflict on their own, but works best when the players are willing to choose the dramatically interesting option even if it mechanically non-optimal.

⭐⭐⭐⭐ - The game provides some mechanical tools that create and resolve drama, but there is a significant expectation that the players are buying into and driving the game’s thematic concepts. Players are the ones deciding what the scenes should be and when to end them, but mechanics still help determine outcomes.

⭐⭐⭐⭐⭐- The players are expected to drive the narrative at all times. Tools for deciding what scenes to do and when to end them are limited, optional, or vague. There is no meaningful scaffolding that creates conflict or resolution, it is incumbent on those present to manifest those things.

One Star Examples: Alice is Missing, Ribbon Drive, For the Queen Five Star Examples: Wanderhome, Systemless RP

(Prep)Work

⭐ - Facilitators are not expected to do work outside the time at the table. All rules can be read while the game is played. No memorization is needed.

⭐⭐ - This game expects the facilitator to have read the rules in advance, but the rules are so few that they can be run from a single reference sheet. At times, the facilitator must think about and potentially advance and adjust the narrative of the game behind the scenes. Prep is qualitative; answering questions about where the narrative is going to go, who will be there etc. The game can be run smoothly predominantly as improv.

⭐⭐⭐ - This game expects the facilitator to not only know the rules, but to imagine scenarios where the group must play. However, the scope of the scenario design is limited and qualitative. It takes a bit of pondering and perhaps a sketch and a few words of notes. Alternatively, the facilitator must design simple foes or track a simple background system. The work is trivial, and can be done with a bit of time before session.

⭐⭐⭐⭐ - The facilitator of this game is expected to have run systems between games, or created usable maps or scenarios. Generally, games at this level have some reduced wargaming component. The facilitator might need to engage in enemy design, but the work is limited or imminently reusable. The work is non-trivial, and failing to do it will somewhat impact the quality of play.

⭐⭐⭐⭐⭐- The facilitator of this game puts in significant time between sessions engaging in game design activities. They are expected to plan narratives, write NPCs, draw maps, run significant background systems, and design enemies and combat encounters. The work is significant outside of play, and failing to do it beforehand will result in a worse table experience.

One Star Examples: For the Queen, Alley-Oop, Lasers and Feelings Five Star Examples: Lancer, D&D 3-5e, Stars Without Number, Edge of the Empire

Whimsy

⭐ - This game’s thematic core is considered dark, taboo, or difficult, and separating the game’s mechanical features from this subject matter is next to impossible. Games with horror elements almost certainly fit within this category. These games encourage extensive pre-play safety talks.

⭐⭐ - This game is designed to look at dark subject matter, but doesn’t expect the player to spend all of their time there. Players explore difficult topics, but may get to choose what topics to explore, or when to explore them. Games with political messaging/commentary tend to fit this category. These games encourage pre-play safety talks.

⭐⭐⭐ - This game may have dark aesthetics, but doesn’t enforce them mechanically. Alternatively, there are mechanics that address difficult topics in broad strokes, but players are given leeway in the rules with how any difficult topics are approached. These games may encourage safety talks. 

⭐⭐⭐⭐ - This game may have the option to explore dark topics, but none of the mechanics are tied to such topics. This game may have violence in its aesthetics, but players may choose to adjust the aesthetics at the table to suit their comfort. These games tend not to talk about safety in their text.

⭐⭐⭐⭐⭐- This game is designed to focus on thematic material that is considered to be relatively safe. The game is unlikely to tread into violence or trauma without effort.

One Star Examples: Trophy Dark, Dungeon Bitches, Vampire the Masquerade Five Star Examples: Honey Heist, Princess World, Beach Episode

The system here isn't about what's good or bad, to be clear. I think there are good and bad games at every level of these categories, but when I think about what my game group is good at and comfy with, I don't think we go in for things at like the 5 end of the Writers Room scale. It's too much work, and most of them aren't pro improvisers.

Similarly, if we play another game that is a 4 or 5 on the PrepWork category, I don't have time to run it these days. So this helps me make practical choices about our next game.

[flamebringer0]

More About Nightwing Powers

I decided I had more to say about this post, so I reblogged it and wrote some more. It seems like when you do that Tumblr doesn't put the post into tag searches, so nobody can actually find it. I'm just going to copy the full text into a new post. I'm sorry if this spams anyone's feed, I just don't understand how this site works yet. I've never actually had a blog before.

-----

Ohhhh damn I just had another thought about this. This is my problem, I think about this stuff all the time and I keep coming up with new things I think are cool, but now that I've written all this out and published it for the whole world to see I can't change it so easily.

It's actually fine though. These thoughts are kind of a mix of things I think are canon but sufficiently non-obvious to be worth stating (All Nightwings have powers at hatching and then lose them), things that are definitely not canon but I feel like it doesn't mess with the story too much to change them (Prophets have a silver scale on their foreheads), and things that are ambiguous and I'm just filling in the blanks (fake Nightwing magic is based on their sense of smell). But there's a fourth category, which is things that I don't like about canon but changing them would basically entail writing a whole new story. I'm pretty sure this new idea falls into the latter category, so I wouldn't really have put it in that post anyway.

But let's explore the hypothesis that Tui T Sutherland and Scholastic collectively lose their minds and give me the rights to Wings of Fire, and also I have infinite time, resources and motivation to make my own adaptation of the story. Then what happens? Well, the first thing is obviously to change animus magic to be something other than total omnipotence, because omnipotent characters are the kind of albatross (tee hee) you don't want around your neck when you're trying to write a coherent story. And the second thing is to make it so the terrible Rainwing queens in book 3 are hereditary royalty and Glory is not, because I've never liked the valorisation of hereditary royalty in WOF (or in general), and that would be an easy way to subvert that. And THEN I would turn scavengers into lizard people, because I am a furry and I think that's cuter than making them humans. Lol.

But somewhere down the list would be the fact that I don't actually vibe with the idea that normal Nightwing seers/mind readers apparently have a built-in power limiter that varies randomly according to the specific individual, and the special gift you get for hatching under three moons is just to have that limiter set to 0. I don't like this because, first of all, the fact that it works like that just doesn't fit into my brain in a satisfactory way. Hatching under zero, one, or two full moons each results in a qualitatively different outcome, so in my mind, the third full moon should also do something qualitatively different. But it doesn't, it's just the same as two full moons but better. And then I'm left to assume that there are probably very rare cases of twice-moonborn Nightwings who have all the power, just because whatever secondary factors there are happened to line up in their favor? Weird.

But the more important reason is, I don't really like the idea of Clearsight as someone who has extraordinary power because she just hatched that way. Nor do I like the fact that the reasons for this are, on the one talon, unexplained (maybe inexplicable?), and on the other, not a function of who she is as a character. I think it would make for a much stronger motif if run-of-the-mill seer Clearsight were able to take down once-in-a-generation chosen one Darkstalker because she's diligent and doesn't believe in destiny while he's entitled and sure of his glorious future. At its core this feeling is actually the same thing as what I said about the Rainwing queens above: I just don't find it satisfying when the hero has some kind of special trait that makes them naturally better than everyone else. A villain can have that, but a hero I want to prove themselves through their own efforts. But that's explicitly not what happened in canon: Clearsight IS naturally more powerful than all the other seers, that's an essential part of her story, and changing it requires basically rewriting the books. I don't love it!

So anyway in the universe where I'm rewriting the books, here's a thought about how Nightwing powers might work. This partially contradicts what I wrote above, but I've decided I'm cool with that. I love contradicting myself actually, and maybe next week I'll contradict all of this again. There are no laws.

Some Nightwings are seers, some are mind readers, a few are both, and most are neither. But aside from the fact that an individual might or might not have these powers, they don't vary in strength from dragon to dragon. All seers have equally strong abilities, and all mind readers have equally strong abilities. But the strength of the abilities does vary: not per individual, but over time. Specifically, the current state of the moons affects the abilities of empowered Nightwings. On a hypothetical "darkest night" with three new moons, all Nightwings are effectively powerless. On the brightest night, empowered Nightwings experience the full strength of their abilities. Seers can easily look down many different paths into the distant future, and mind readers can easily examine any information in the mind of another dragon. Of course, the phases of the three moons are not synchronous. Most of the time, the moons are all in different states, and empowered Nightwings experience abilities somewhere in between those extremes.

Above, I said that the "strength" of Nightwing abilities doesn't really vary per dragon. Strength, as I use the term here, only refers to the raw potential to look into the future or into another dragon's mind. But different individuals do have different levels of adeptness when it comes to applying their abilities. On the brightest night, any seer can look with relative ease into the far future, down multiple timelines, examining subtle ripples of possibility. The rest of the time, most seers can't use their abilities on that level, but a particularly adept one can get closer. What makes one seer more adept than another? Well, it's really just training. If you're someone who, for whatever reason, keeps looking into the future, over time you'll get better at it, just like anything you keep doing. This is what sets Clearsight apart from her peers. She isn't more blessed by the moons than them, because no seer is more blessed by the moons than any other. Clearsight just looks into the future all the time, to a degree nobody else does. She works harder on her visions than any other seer, so her abilities are more advanced than those of any other seer.

There is one exception to the maxim "no seer is more blessed by the moons than any other". The special gift of the thrice-moonborn is that they are exempt from the cyclical waning and waxing of power with the phases of the moons. They hatch on the brightest night, and its power soaks into them and becomes permanently part of them. And so, they live as if every night is a brightest night. They always have the potential to see the ripples that spread into many distant futures, and they can always pluck any information they want out of another dragon's mind. What other Nightwings may train and train for, these dragons do as easily as they breathe.

There are few Nightwings who never dreamed as dragonets about what it would be like to have hatched under three full moons. How different their lives could have been from those of the common powerless Nightwing, or even ordinary seers and mind readers! And not just directly because of what they could see: the thrice-moonborn are almost inevitably beloved by their tribe, showered with attention, and elevated to the highest strata of society. Wouldn't that be nice, think the dragons who could have walked that path but for something as meaningless as the sky under which they hatched.

It's unsurprising that many covet that life, but it's not entirely as pleasant as they imagine. The adulation that surrounds the most powerful Nightwings is instrumental. As laid out in excruciating detail just behind the eyes of smiling dragons, few love them for who they are; many more love them for what they are. They will never have any interaction with friends or family that isn't fundamentally shaped by the asymmetrical power dynamic implied by their abilities. It's easy for a dragon in that situation to become profoundly isolated, especially without support. But the brightest night comes so rarely that most Nightwings who receive its gift will grow up with neither peers, nor mentors who can relate to these experiences. That many ultimately respond in unhealthy, self-destructive ways is a given; that the self-destruction of such powerful dragons often also destroys those around them is a recurring tragedy in the history of the tribe.

But it's not inevitable. No dragon's fate is actually written in the stars or sealed by the moons. It's always possible to choose a different future.

[flowres921]

Understanding QDA Software: A Comprehensive Guide

·

Just now

In today’s data-driven world, qualitative research is more important than ever. Researchers, analysts, and academics often grapple with vast amounts of textual data, from interviews and focus groups to open-ended survey responses. This is where Qualitative Data Analysis (QDA) software comes into play. If you’ve been exploring ways to manage and analyze qualitative data more effectively, you’ve likely come across QDA software. Let’s dive into what it is, why it matters, and how it can revolutionize your research.

What is QDA Software?

Qualitative Data Analysis (QDA) software is designed to assist researchers in organizing, analyzing, and interpreting non-numeric data. Unlike quantitative data analysis, which deals with numbers and statistical methods, qualitative analysis focuses on understanding concepts, themes, and patterns within textual or visual data.

QDA software provides tools for coding data, identifying themes, and visualizing relationships within the data. It helps streamline the process of sifting through large volumes of text, making it easier to draw meaningful insights and conclusions.

Key Features of QDA Software

Data Coding and Categorization: One of the fundamental features of QDA software is the ability to code data. Coding involves labeling sections of text with specific tags or categories to identify patterns or themes. This process is crucial for organizing data and making sense of complex information.

Thematic Analysis: QDA software enables users to perform thematic analysis, which involves identifying and analyzing themes or patterns within the data. This can be particularly useful for understanding recurring ideas or sentiments.

Data Visualization: Visual tools such as charts, graphs, and mind maps help researchers see patterns and relationships more clearly. These visualizations can make complex data more accessible and easier to interpret.

Querying and Searching: With QDA software, you can run queries to find specific information or cross-reference data. This feature helps in pinpointing exact data points or themes across large datasets.

Collaboration Tools: Many QDA software solutions offer collaborative features, allowing multiple users to work on the same project. This can be invaluable for research teams working together on qualitative studies.

Integration with Other Tools: Some QDA software can integrate with other tools and platforms, such as survey software or reference managers, providing a seamless research experience.

Why Use QDA Software?

Efficiency: Managing and analyzing qualitative data manually can be time-consuming and prone to errors. QDA software automates many aspects of the process, saving researchers valuable time and effort.

Enhanced Accuracy: By reducing the risk of human error and offering systematic approaches to coding and analysis, QDA software enhances the accuracy and reliability of research findings.

In-depth Insights: The ability to uncover themes and patterns in qualitative data can lead to richer, more nuanced insights. This depth of understanding is often crucial for making informed decisions or developing new theories.

Scalability: QDA software can handle large volumes of data, making it suitable for complex research projects. Whether you’re working with a small dataset or a massive collection of text, QDA software can scale to meet your needs.

Reproducibility: The structured approach to data analysis provided by QDA software helps ensure that research findings can be replicated and verified, which is essential for the credibility of qualitative research.

Popular QDA Software Options

Several QDA software solutions are available, each with its own strengths and features. Some of the most popular options include:

NVivo: Known for its comprehensive set of tools for coding, thematic analysis, and data visualization, NVivo is widely used in qualitative research.

Atlas.ti: This software offers powerful coding and querying capabilities, along with robust visualization tools to help researchers interpret complex data.

MAXQDA: MAXQDA is praised for its user-friendly interface and strong support for various data formats, including text, audio, and video.

Dedoose: Ideal for mixed-methods research, Dedoose provides tools for both qualitative and quantitative data analysis, making it versatile for different research needs.

Getting Started with QDA Software

Choosing the right QDA software depends on your specific needs and preferences. Most software providers offer free trials or demo versions, allowing you to explore the features before committing. Additionally, many platforms provide training resources and support to help you get up to speed quickly.

In summary, QDA software is a powerful tool for anyone involved in qualitative research. By offering a systematic approach to data analysis, it helps researchers uncover valuable insights and make informed decisions. Whether you’re new to qualitative research or looking to enhance your current practices, investing in QDA software could be a game-changer for your research endeavors.

So, if you’re ready to take your qualitative analysis to the next level, exploring QDA software might just be the key to unlocking deeper insights and achieving more impactful results.

[spookysaladchaos]

Global Top 9 Companies Accounted for 58% of total Industrial Inkjet Printheads market (QYResearch, 2021)

Inkjet heads also call inkjet printheads, inkjet printhead is the core of inkjet printer. It is used on the industrial and commercial and office inkjet printer. Inkjet print head can be divided into two categories by printing method: continuous print head and drop-on-demand print head. As continuous print head is waste of ink and print dots are large, the major manufacturers use the drop-on-demand print head. In this report, industrial inkjet printheads are mainly used in document printing, textile printing, label & packaging, ceramic & décor, etc.

The global Industrial Inkjet Printheads market was valued at US$ 1000.19 illion in 2021 and will reach US$ 2236.72 illion by the end of 2028, growing at a CAGR of 12.42% uring 2022-2028.

The data for 2022 is an estimate, based on the historical data and the integrated view of industrial experts, manufacturers, distributors, and end users, etc.

Top-down and bottom-up approaches are used to validate the global Industrial Inkjet Printheads market size and to estimate the market size for company, regional division, product type and application (end users). In the complete market engineering process, both top-down and bottom-up approaches along with several data triangulation methods have been extensively utilized to perform market estimation and market forecast for the overall market division and subdivision listed in this report. Extensive qualitative and further quantitative analysis has also been done from all the numbers arrived at in the complete market engineering process to list key information throughout the report.

According to the new market research report “Global Industrial Inkjet Printheads Market Report 2023-2029”, published by QYResearch, the global Industrial Inkjet Printheads market size is projected to reach USD 2.51 billion by 2029, at a CAGR of 12.5% during the forecast period.

Figure.   Global Industrial Inkjet Printheads Market Size (US$ Million), 2018-2029

Figure.   Global Industrial Inkjet Printheads Top 9 Players Ranking and Market Share (Ranking is based on the revenue of 2022, continually updated)

The global key manufacturers of Industrial Inkjet Printheads include Fujifilm, Konica Minolta, SII Printek, Kyocera, Toshiba Tec, etc.

In 2022, the global top four players had a share approximately 58.0% in terms of revenue.

About QYResearch

QYResearch founded in California, USA in 2007.It is a leading global market research and consulting company. With over 16 years’ experience and professional research team in various cities over the world QY Research focuses on management consulting, database and seminar services, IPO consulting, industry chain research and customized research to help our clients in providing non-linear revenue model and make them successful. We are globally recognized for our expansive portfolio of services, good corporate citizenship, and our strong commitment to sustainability. Up to now, we have cooperated with more than 60,000 clients across five continents. Let’s work closely with you and build a bold and better future.

QYResearch is a world-renowned large-scale consulting company. The industry covers various high-tech industry chain market segments, spanning the semiconductor industry chain (semiconductor equipment and parts, semiconductor materials, ICs, Foundry, packaging and testing, discrete devices, sensors, optoelectronic devices), photovoltaic industry chain (equipment, cells, modules, auxiliary material brackets, inverters, power station terminals), new energy automobile industry chain (batteries and materials, auto parts, batteries, motors, electronic control, automotive semiconductors, etc.), communication industry chain (communication system equipment, terminal equipment, electronic components, RF front-end, optical modules, 4G/5G/6G, broadband, IoT, digital economy, AI), advanced materials industry Chain (metal materials, polymer materials, ceramic materials, nano materials, etc.), machinery manufacturing industry chain (CNC machine tools, construction machinery, electrical machinery, 3C automation, industrial robots, lasers, industrial control, drones), food, beverages and pharmaceuticals, medical equipment, agriculture, etc.

[shireen46]

Text Analytics: Unlocking the power of Business Data

Due to the development in the use of unstructured text data, both the volume and diversity of data used have significantly increased. For making sense of such huge amounts of acquired data, businesses are now turning to technologies like text analytics and Natural Language Processing (NLP).

The economic value hidden in these massive data sets can be found by using text analytics and natural language processing (NLP). Making natural language understandable to machines is the focus of NLP, whereas the term “text analytics” refers to the process of gleaning information from text sources.

What is text analysis in machine learning?

The technique of extracting important insights from texts is called text analysis.

ML can process a variety of textual data, including emails, texts, and postings on social media. This data is preprocessed and analyzed using specialized tools.

Textual analysis using machine learning is quicker and more effective than manually analyzing texts. It enables labor expenses to be decreased and text processing to be accelerated without sacrificing quality.

The process of gathering written information and turning it into data points that can be tracked and measured is known as text analytics. To find patterns and trends in the text, it is necessary to be able to extract quantitative data from unprocessed qualitative data. AI allows this to be done automatically and at a much larger scale, as opposed to having humans sift through a similar amount of data.

Process of text analysis

Assemble the data- Choose the data you’ll research and how you’ll gather it. Your model will be trained and tested using these samples. The two main categories of information sources are. When you visit websites like forums or newspapers, you are gathering outside information. Every person and business every day produces internal data, including emails, reports, chats, and more. For text mining, both internal and external resources might be beneficial.

Preparation of data- Unstructured data requires preprocessing or preparation. If not, the application won’t comprehend it. There are various methods for preparing data and preprocessing.

Apply a machine learning algorithm for text analysis- You can write your algorithm from scratch or use a library. Pay attention to NLTK, TextBlob, and Stanford’s CoreNLP if you are looking for something easily accessible for your study and research.

How to Analyze Text Data

Depending on the outcomes you want, text analysis can spread its AI wings across a variety of texts. It is applicable to:

Whole documents: gathers data from an entire text or paragraph, such as the general tone of a customer review.

Single sentences: gathers data from single sentences, such as more in-depth sentiments of each sentence in a customer review.

Sub-sentences: a sub-expression within a sentence can provide information, such as the underlying sentiments of each opinion unit in a customer review.

You can begin analyzing your data once you’ve decided how to segment it.

These are the techniques used for ML text analysis:

Data extraction

Data extraction concerns only the actual information available within the text. With the help of text analysis, it is possible to extract keywords, prices, features, and other important information. A marketer can conduct competitor analysis and find out all about their prices and special offers in just a few clicks. Techniques that help to identify keywords and measure their frequency are useful to summarize the contents of texts, find an answer to a question, index data, and generate word clouds.

Named Entity Recognition

NER is a text analytics technique used for identifying named entities like people, places, organizations, and events in unstructured text. It can be useful in machine translation so that the program wouldn’t translate last names or brand names. Moreover, entity recognition is indispensable for market analysis and competitor analysis in business.

Sentiment analysis

Sentiment analysis, or opinion mining, identifies and studies emotions in the text.

The emotions of the author are important for understanding texts. SA allows to classify opinion polarity about a new product or assess a brand’s reputation. It can also be applied to reviews, surveys, and social media posts. The pro of SA is that it can effectively analyze even sarcastic comments.

Part-of-speech tagging

Also referred to as “PoS” assigns a grammatical category to the identified tokens. The AI bot goes through the text and assigns each word to a part of speech (noun, verb, adjective, etc.). The next step is to break each sentence into chunks, based on where each PoS is. These are usually categorized as noun phrases, verb phrases, and prepositional phrases.

Topic analysis

Topic modeling classifies texts by subject and can make humans’ lives easier in many domains. Finding books in a library, goods in the store and customer support tickets in the CRM would be impossible without it. Text classifiers can be tailored to your needs. By identifying keywords, an AI bot scans a piece of text and assigns it to a certain topic based on what it pulls as the text’s central theme.

Language Identification

Language identification or language detection is one of the most basic text analysis functions. These capabilities are a must for businesses with a global audience, which in the age of online, is the majority of companies. Many text analytics programs are able to instantly identify the language of a review, social post, etc., and categorize it as such.

Benefits of Text Analytics

There is a range of ways that text analytics can help businesses, organizations, and event social movements:

1. Assist companies in recognizing customer trends, product performance, and service excellence. As a result, decisions are made quickly, business intelligence is improved, productivity is raised, and costs are reduced.

2. Aids scholars in quickly explore a large amount of existing literature and obtain the information that is pertinent to their inquiry. This promotes quicker scientific advancements.

3. Helps governments and political bodies make decisions by assisting in the knowledge of societal trends and opinions.

4. Search engines and information retrieval systems can perform better with the aid of text analytics tools, leading to quicker user experiences.

5. Refine user content recommendation systems by categorizing similar content.

Conclusion

Unstructured data can be processed using text analytics techniques, and the results can then be fed into systems for data visualization. Charts, graphs, tables, infographics, and dashboards can all be used to display the results. Businesses may immediately identify trends in the data and make decisions thanks to this visual data.

Robotics, marketing, and sales are just a few of the businesses that use ML text analysis technologies. To train the machine on how to interact with such data and make insightful conclusions from it, special models are used. Overall, it can be a useful strategy for coming up with ideas for your company or product.

[iaeeducation]

Importance of NAAC Accreditation: A Guide for Higher Education Institutions

If you have not yet understood the importance of Higher Education Institutions NAAC Accreditation, here is a short primer. Once an academic institution adheres to a specific standard quality of education, it is ‘Accredited.’ In 2022, it has become necessary for all colleges and universities to go for it. It recognises the learning process of leading universities and colleges in various states in India.

Institute of Academic Excellence, throws light on its value for colleges to rank better. If a college needs grants, funding or any kind of financial aid, Higher Education Institutions NAAC Accreditation is extremely useful.

The entire process of getting the NAAC Accreditation is not an easy task. If you are interested, our team will offer experienced advisory. Run through this content to understand how the process works and what it is all about.

Why NAAC?

NAAC stands for National Assessment and Accreditation Council and is an autonomous body supported by University Grants Commission from 1994. The grading scales determine the status of the academic institutions.

For a long time, India has been delivering differing systems for education for students. There is no single parameter that helps one to decide if one system is better than the other. Now a few specifications have been formulated and are essential for identification for a college or university. The higher education institutions NAAC Accreditation is considered as a framework as a measuring potential and provides a standard quality assurance.

Benefits for students

To be recognised as an academic institution of excellence this certification ensures that more students take admission. It gives them an opportunity to have good job and career prospects. Before taking admission they can figure out college details like:

Quality of education they offer.

Teaching -learning ratio.

Research and labs for live projects.

Infrastructure and assessment values.

Compliance procedures and grades

For an overall requirement, universities and colleges have to follow certain procedures.  If a college requires financial aid or grants from UGC some criteria of NAAC have to be fulfilled. They include performance indicators and grading systems which are important for higher education institutions NAAC Accreditation in India.

Assessments are done for:

Institutions and best practices and values.

Governance and leadership

Learning resources for students for all subjects.

Infrastructure for academic studies.

Research and Innovation growth

Curriculum and real-world adaptation for students.

Management and organisation.

Student support, placement and progress.

Evaluation and teaching methodology.

Trained faculty with in-depth knowledge.

The grading system for higher education institutes is done in 4 categories of A, B, C, D. An overall score is essential and all parameters are weighed before the categories are given. Cumulative GPA is used to prepare the final result. The grades provide benefits for reliable judgement like:

Qualitative measurements with points.

Scope of normalising scores.

Conversion of letter grades to numerical values of the grading system.

Evaluation to minimise variances or standard deviations.

Inter-peer variables are reduced significantly.

The performance chart looks like the table below.

Institute of Academic Excellence offers several ways to get higher education institutions NAAC Accreditation. Contact our team to understand how we can make the process easier for your college or university.

[tagxdata22]

Text Analytics: Unlocking the power of Business Data

Due to the development in the use of unstructured text data, both the volume and diversity of data used have significantly increased. For making sense of such huge amounts of acquired data, businesses are now turning to technologies like text analytics and Natural Language Processing (NLP).

The economic value hidden in these massive data sets can be found by using text analytics and natural language processing (NLP). Making natural language understandable to machines is the focus of NLP, whereas the term “text analytics” refers to the process of gleaning information from text sources.

What is text analysis in machine learning?

The technique of extracting important insights from texts is called text analysis.

ML can process a variety of textual data, including emails, texts, and postings on social media. This data is preprocessed and analyzed using specialized tools.

Textual analysis using machine learning is quicker and more effective than manually analyzing texts. It enables labor expenses to be decreased and text processing to be accelerated without sacrificing quality.

The process of gathering written information and turning it into data points that can be tracked and measured is known as text analytics. To find patterns and trends in the text, it is necessary to be able to extract quantitative data from unprocessed qualitative data. AI allows this to be done automatically and at a much larger scale, as opposed to having humans sift through a similar amount of data.

Process of text analysis

Assemble the data- Choose the data you’ll research and how you’ll gather it. Your model will be trained and tested using these samples. The two main categories of information sources are. When you visit websites like forums or newspapers, you are gathering outside information. Every person and business every day produces internal data, including emails, reports, chats, and more. For text mining, both internal and external resources might be beneficial.

Preparation of data- Unstructured data requires preprocessing or preparation. If not, the application won’t comprehend it. There are various methods for preparing data and preprocessing.

Apply a machine learning algorithm for text analysis- You can write your algorithm from scratch or use a library. Pay attention to NLTK, TextBlob, and Stanford’s CoreNLP if you are looking for something easily accessible for your study and research.

How to Analyze Text Data

Depending on the outcomes you want, text analysis can spread its AI wings across a variety of texts. It is applicable to:

Whole documents: gathers data from an entire text or paragraph, such as the general tone of a customer review.

Single sentences: gathers data from single sentences, such as more in-depth sentiments of each sentence in a customer review.

Sub-sentences: a sub-expression within a sentence can provide information, such as the underlying sentiments of each opinion unit in a customer review.

You can begin analyzing your data once you’ve decided how to segment it.

These are the techniques used for ML text analysis:

Data extraction

Data extraction concerns only the actual information available within the text. With the help of text analysis, it is possible to extract keywords, prices, features, and other important information. A marketer can conduct competitor analysis and find out all about their prices and special offers in just a few clicks. Techniques that help to identify keywords and measure their frequency are useful to summarize the contents of texts, find an answer to a question, index data, and generate word clouds.

Named Entity Recognition

NER is a text analytics technique used for identifying named entities like people, places, organizations, and events in unstructured text. It can be useful in machine translation so that the program wouldn’t translate last names or brand names. Moreover, entity recognition is indispensable for market analysis and competitor analysis in business.

Sentiment analysis

Sentiment analysis, or opinion mining, identifies and studies emotions in the text.

The emotions of the author are important for understanding texts. SA allows to classify opinion polarity about a new product or assess a brand’s reputation. It can also be applied to reviews, surveys, and social media posts. The pro of SA is that it can effectively analyze even sarcastic comments.

Part-of-speech tagging

Also referred to as “PoS” assigns a grammatical category to the identified tokens. The AI bot goes through the text and assigns each word to a part of speech (noun, verb, adjective, etc.). The next step is to break each sentence into chunks, based on where each PoS is. These are usually categorized as noun phrases, verb phrases, and prepositional phrases.

Topic analysis

Topic modeling classifies texts by subject and can make humans’ lives easier in many domains. Finding books in a library, goods in the store and customer support tickets in the CRM would be impossible without it. Text classifiers can be tailored to your needs. By identifying keywords, an AI bot scans a piece of text and assigns it to a certain topic based on what it pulls as the text’s central theme.

Language Identification

Language identification or language detection is one of the most basic text analysis functions. These capabilities are a must for businesses with a global audience, which in the age of online, is the majority of companies. Many text analytics programs are able to instantly identify the language of a review, social post, etc., and categorize it as such.

Benefits of Text Analytics

There is a range of ways that text analytics can help businesses, organizations, and event social movements:

1. Assist companies in recognizing customer trends, product performance, and service excellence. As a result, decisions are made quickly, business intelligence is improved, productivity is raised, and costs are reduced.

2. Aids scholars in quickly explore a large amount of existing literature and obtain the information that is pertinent to their inquiry. This promotes quicker scientific advancements.

3. Helps governments and political bodies make decisions by assisting in the knowledge of societal trends and opinions.

4. Search engines and information retrieval systems can perform better with the aid of text analytics tools, leading to quicker user experiences.

5. Refine user content recommendation systems by categorizing similar content.

Conclusion

Unstructured data can be processed using text analytics techniques, and the results can then be fed into systems for data visualization. Charts, graphs, tables, infographics, and dashboards can all be used to display the results. Businesses may immediately identify trends in the data and make decisions thanks to this visual data.

Robotics, marketing, and sales are just a few of the businesses that use ML text analysis technologies. To train the machine on how to interact with such data and make insightful conclusions from it, special models are used. Overall, it can be a useful strategy for coming up with ideas for your company or product.

[hourlymbti]

can cognitive functions become scientific? is mbti?

If anything, Scientific research is primarily about statistics, and thus trends (i.e. general rules). I’ll start by saying while there is some subjectivity on how much is enough evidence to call something scientifically true, this is largely a matter of data. Science doesn’t care about subjective/qualitative aspects of the world. To make something scientific, it needs to have a lot of data to back it up across multiple rigorous studies. Without data, it would merely be a theoretical paper at most. Qualitative data, on the other hand, is mainly viable as a supplement to explain the quantitative results. Science needs to quantify everything, which would make it difficult to do studies on cognitive functions and a big portion of psychology (since it's hard to quantify all of it), but something like the dichotomies or big 5 will be simpler for empirical research. Overall, science is statistical discrimination. There will most often be cases in which the general trend doesn’t apply, because anomalies will exist.

Mbti was looking for what traits -correlated- the most with the dichotomies they came up with (which, while having the same terms as how Jung titled or described the functions, went far from the actual concept because they associated them with certain traits). Just to be clear, MBTI does actually use cognitive functions (they have their own simplified descriptions you can find in some of their resources), just that they are left in the background. They diluted them down into letters based on what they believe to be common behavior/traits. Mbti is founded on Jung's theory, it would've gone something like "oh based on my interpretation of Jung's descriptions, as well as my surface behaviors and mannerisms of the people around me, I think this guy is this type and that guy is that.", Then later on "based on those anecdotes, I think this trait is correlated with this letter". Based on what they have read and associated with the types, they'll use the letters to make questions that give you scores (one large goal of theirs is to make the theory quantifiable in order to have potential for scientific backing). MBTI designed a self-input type indicator so that people can be typed quickly and on large scales. To make a concept catch the interest of the general public, it often needs to be made simple. Not just the test, but often the theory as well.

A more noteworthy reason for correlation-seeking is that for a personality theory to seem reliable, it needs to seem “scientific”, which in psychology (and general research) tends to mean statistically true. Reasoning alone does not matter without any quantitative data to back it up. Statistics seek to quantify (though not necessarily with full certainty, especially in research). Psychology is naturally subjective, many categories will be qualitatively distinct and it is difficult to extract abstract experiences without simplifying them. Only focusing on general truths (e.g. majority, average) and quantitative data would be a limited view of the human mind (then again, typology is meant to simplify), but that’s necessary. And to be fair, as a categorization system for personality, most applications and discussions will utilize generalizations. We're speaking about a type, not every individual within that type. Of course, many will take generalizations to an absolute, but always keep in mind that whenever the theory is used or talked about, people are referring to a concept based on what they perceive to be the general rule — they're following a probabilistic mindset. You can argue if they're taking the generalization too far or if the generalization can be improved, but you shouldn't expect it to represent all individuals (sometimes the majority may even be just 30% in cases where there are more than 2 options)

While published scientific studies naturally do not encompass the whole being of truth (as they need to form rules rather than absorb the data as it is), it is arguably (one of) the most acceptable form of evidence or validation (besides your -own- direct observation and personal experience) in a sense that the word “scientific” holds weight great enough to make concepts seem -completely- good or bad. After all, proper procedures and peer review for the data and methods will be more likely to bring us to the right conclusions (in theory, it is a delicate process) as compared to the blabbering of anyone with an opinion. Common & specialized knowledge today is built on layers of scientific research from the past (not to say that scientific research alone is what constitutes these). It has gradually supported the development of society in fields such as medicine, infrastructure, and technology. Misinformation is a large issue, and the title of being "a study" is extremely valued. While there are sometimes other motives/interests involved (from the researchers/writers, reviewers, and/or the publisher), for quality and integrity, scientific journals have the responsibility to be strict with what they publish and ensure that the paper provides substantial details about their methods (for reproducibility and also critique/ to assess limitations). Mentioning the scope, limitations, or potential conflicts of interest of the study is also common procedure. But rather than look at science before forming conclusions, a similarly common occurrence is when people form conclusions (that are not grounded scientifically; internal reasoning or personal experience/assumption/belief) and in the event that it happens to be somewhat backed by any research, they are quick to portray that as absolute evidence… that their claim is indisputably true, then use the value of science to easily claim that the other side is preposterous…rarely acknowledging limitations unless the study contradicts their personal conclusions (“science” has become a word people use to say “my claim is true and indisputable, anything else is completely false”).

In other words, people are often nitpicky about data, and concepts that are not backed up by science are rarely convincing to those whose initial conclusions/perspectives are opposing or undecided. If something is commonly considered as scientific, it will be seen as common sense to believe it is true. If it is commonly considered as unscientific, it will be seen as basic intelligence to deem it false (you are not allowed to question whatever people claim as science, even though science is fluid and the development of which relies on exploring other/new ideas). The importance of scientific validity is one reason correlations are often sought by typologies (there’s also theory development). Although MBTI acknowledges that there is more to people than their type, many tests have been conducted by Myers to show what types are best for certain careers, this is to help institutions assess who to hire with the use of her type indicator. Is research worth considering when assessing the typology framework? Yes. Is it the only thing worth considering? No. Is it a simple matter when it comes to whether it is scientific or not? As a general rule, confidence should scale with the quantity and quality of existing studies. Scientific right or wrong is not always black-and-white. Statistics do not seek to claim absolutes. Also, the process of validating typology is not as straightforward as you'd imagine. This problem requires some form of correlation with type, so the question becomes what can/should you use as the variable to compare the typology's personality types with? If you test enough variables, you'd at least have a few unless your classifications are random. So what variables would be considered relevant?

Now: Could cognitive functions be well-supported by research? Do note that for something to be well-supported, there needs to be a decent amount of publications on it (as a general rule, a single or few studies is not sufficient for science), especially ones with rigour.

It's not simple for something to become scientific. Scientific communities are supposed to be skeptical, even to things they agree with or things that do not trigger their internal alarm. Aside from the many physical limitations (e.g. resources, nuisance/extraneous variables, sample vs population, quantitative measurements to act as simplifications for subjective or qualitative entities), research is conducted and reported by mere humans. It can often be problematic. A few issues with the study can be detrimental. These include: Methodology: limits of sample collection method (e.g. how are people found, selected, and asked to participate), sample size & demographics, data collection method (e.g. experimental design, research instruments, variables, leading questions) Analysis/Interpretation: biased or narrow attention to details (overlooking or overemphasizing), overinterpretations which lead to conclusions not necessarily implied by the data Documentation/Report: lack of fullness/transparency/ poor or biased writing/methods (e.g. p-hacking, limited statement of: details of method, contradictory data, and study limitations). It's not exactly simple for peer reviewers to hold people accountable, to monitor the researchers' every move in the process to fix minor errors (e.g. miscalculation or mistabulation, misleading or vague wording), and to ensure that the report is complete and accurate to what actually occurred and was found. Dont expect it to be easy to detect and prove bias and any sort of misconduct. Even when it comes to fabrication, falsification, and modification. Seemingly minor issues with the study can have significant influence in the reported findings and conclusions. For obvious reasons, individual studies will also have to be compared to other studies. The findings need to be consistently reproducible and applicable to various contexts. Meta analyses and systematic reviews are valuable for a reason.

It seems unrealistic as of the moment for there to be published studies on cognitive functions theory with scientific rigour. Aside from the fact that proper research literature won't necessarily be published in journals, it is not simple to type people using cognitive functions. Self-input tests (e.g. big five, the perceived stress scale) are the easiest for research since they lead to faster sample collection plus require no interpretation for the test result to be final (If we're purely relying on the questionnaire, then the results, assuming they present the entire population, will reflect the reliability of the questionnaire. If we're relying on a person, there's the matter of potential bias, limited view, or the unknown extent of how much of the results are due to the person and how much due to the theory itself). The other option is for there to be some sort of ‘expert’. How do we choose the expert/s? Especially in a way that is convincing on paper. Those who subscribe to the theory have various contradicting interpretations of it. People like to say that they're ways are completely aligned with MBTI or Jung, but the reality is that each person's system differs and is a mix of various things they've read alongside their own thoughts. The lack of consensus is one of the reasons it’s a bit less simple to assess the theory…because how the theory works is not solid but fluid (this is mainly referring to cognitive functions). We need to know what exactly we are proving in order to test its validity. We probably can't get something fully consistent, but perhaps it would be enough to avoid the theory's developments (since it has branched out a lot) and for the supposed basis to be Jung's works (not that they're perfect). If we were to use an expert, we need some sort of status that is "universally" valid (at least good enough for those without much knowledge of the theory). The most probable solution seems to be to gather a certain number of certified Jungian Analysts (for additional credibility, hopefully with a degree on psychology, neuroscience, etc.), who will type people based on the 8 psychological types in Jung’s book (even Jung acknowledges the importance of the auxiliary but we likely cannot go for 16types unless you want to test MBTI. Unless, of course, you prefer the other option: a self-input cognitive functions questionnaire which is probably a simpler option that acts as the first step in validating the theory scientifically). This way, they are following the same version of the theory and are also credible in this aspect (though they may not necessarily know how to type people since theory differs from application, plus the psychological types is only one of many ideas/theories of Jung. Not to say that they don't go through any sort of training or application, just that it may be unlikely for psychological types in particular since it is not as meaningful as other teachings).

Now, assuming the typing method is reliable and credible, how do you prove that the categories hold meaning and that these types are quantitatively distinct (also qualitatively but this is more difficult to prove and not as important in terms of scientific validation)? This can be done in many ways. The best would likely be to compare the brain scans of each type (e.g. consistent brain patterns in function groups). Other things you could compare the typings to are well-supported questionnaires that measure traits. If the researchers only care about proving its validity, it may be compared to the big 5 (which is considered somewhat scientific). Something you may or may not consider an issue is that these parts will overlook the unique qualities within each type, and is likely to lead to more generalizations based on type (e.g. ascribing behavior to function). If enough research is published regarding the original theory, it may become a stepping stone for cognitive functions to be a more acceptable topic, thereby letting people be able to test the validity of also 16-type cognitive functions. Now…do I believe all of this will occur? No. Chances are, it'll never be scientific, but does it actually matter outside of using it as proof? Can this be beneficial when it comes to the self-discovery aspect of the theory? No, at least not during the validity testing phase, beyond that, it is still likely to encourage generalizations. But it may also provide new insights to develop the theory and better understand type differences (especially when neurology is involved).

[pranalipawarshinde]

Pea Protein Market is expected to witness incredible growth and forecast 2021-2031 | Roquette (France), Ajinomoto (Japan), Kerry (Ireland), Bunge (US), Ingredion (US)

Global Pea Protein Market report from Global Insight Services is the single authoritative source of intelligence on Pea Protein Market .The report will provide you with analysis of impact of latest market disruptions such as Russia-Ukraine war and Covid-19 on the market. Report provides qualitative analysis of the market using various frameworks such as Porters’ and PESTLE analysis. Report includes in-depth segmentation and market size data by categories, product types, applications, and geographies. Report also includes comprehensive analysis of key issues, trends and drivers, restraints and challenges, competitive landscape, as well as recent events such as M&A activities in the market.

To Remain ‘Ahead’ Of Your Competitors, Request for A Sample – https://www.globalinsightservices.com/request-sample/GIS20149

Pea protein is a type of protein that is derived from peas. Pea protein is considered to be a complete protein, meaning that it contains all of the essential amino acids that the body needs. Pea protein is also a good source of iron and fiber. Pea protein is often used as a vegan or vegetarian alternative to animal-based proteins, such as whey protein. Pea protein powder can be used to make shakes, smoothies, and baked goods.

Key Trends

The key trends in pea protein technology are the increasing demand for plant-based proteins, the development of new protein sources, and the improvement of protein quality.

The demand for plant-based proteins is increasing due to the growing awareness of the health benefits of plant-based diets. Plant-based proteins are lower in saturated fat and cholesterol than animal-based proteins, and they are a good source of fiber and antioxidants.

The development of new protein sources is driven by the need for more sustainable and environmentally friendly protein sources. Pea protein is a sustainable protein source that requires less water and land than animal-based proteins.

The quality of pea protein has been improved through the development of new processing techniques. Pea protein isolate, for example, is a high-quality protein that is nearly free of carbohydrates and fat.

Key Drivers

The rising demand for plant-based protein is driven by the growing awareness of the health benefits of plant-based diets, the increasing number of vegans and vegetarians, and the growing popularity of flexitarian diets. Pea protein is a good source of plant-based protein and is a viable alternative to animal-based proteins for vegans, vegetarians, and flexitarians.

The health benefits associated with pea protein include its high protein content, its low fat and carbohydrate content, its high digestibility, and its high levels of essential amino acids. Pea protein is also a good source of iron, magnesium, and potassium.

The increasing popularity of sports nutrition and functional foods is also driving the growth of the pea protein market. Sports nutrition products such as protein powders and bars are increasingly being used by athletes and bodybuilders to improve their performance and recovery.

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Restraints & Challenges

The key restraints and challenges in Pea Protein market are:

 The high cost of pea protein relative to other protein sources.

The lack of large-scale production facilities.

The lack of consumer awareness of pea protein as a viable protein source.

The limited availability of pea protein-based products.

Market Segments

The Pea Protein Market is segmented into type, form, application, and region. By type, the market is divided into pea protein isolate, pea protein concentrate, and textured pea protein. Based on the form the market is bifurcated into dry and liquid. Whereas on the application, the market is segmented into dietary supplements, bakery & confectionery, meat products & alternatives, beverages, and others. Region-Wise the market is segmented by North America, Europe, Asia-Pacific, and the rest of the world.

Key Players

The Pea Protein Market report includes players such as Cargill (US), Roquette (France), Ajinomoto (Japan), Kerry (Ireland), Bunge (US), Ingredion (US), A&B Ingredients (US), Axiom Foods Inc. (US), Burcon Nutrascience Corporation (Canada) and Cosucra Groupe (Belgium) among others.

Buy Now – https://www.globalinsightservices.com/checkout/single_user/GIS20149

With Global Insight Services, you receive:

10-year forecast to help you make strategic decisions

In-depth segmentation which can be customized as per your requirements

Free consultation with lead analyst of the report

Excel data pack included with all report purchases

Robust and transparent research methodology

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About Global Insight Services:

Global Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

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[squarestarheart]

I'm sure that other people have done this with a more scientific process, but I was chewing on the big question of What Is Important To Me In Relationships while hiking among some Very Large Trees and ended up writing down a framework for judging relationship quality in my own nonmonogamous life. Maybe it'll feel useful for someone else too.

1. Reciprocity

This is a measure of how well matched each person in the relationship is with each other. Highly reciprocal relationships match energy input levels on average over time, require intentionality and reflection from relationship participants, and include both time and effort when determining balance.

Examples:

Labor split on domestic responsibilities

Labor and time split on child, pet, and other family care

Emotional support, including taking turns

Qualitatively, shouldn't feel lopsided to any people in the relationship

2. Complementary-ness

This is a measure of how well the reciprocal inputs match the needs of the people in the relationship. You could have a relationship where people are both putting in the same amount of effort/time, but people aren't necessarily getting what they need or want. Highly complementary relationships should shore up each person's weaknesses, such that the relationship is more than the sum of its parts, and require that all participants pay attention to each other's needs.

Examples:

Getting to know a person well enough to offer the assistance that is needed, even when it's not explicitly requested (and being able to gracefully take no for an answer)

Helping with course correction if you notice that a partner is not moving towards the personal goals that they've stated to you

Noticing each other's needs and wants, and asking if those are not clear

Keeping partners in mind when decision making

3. Synchronicity

This is about timing- when the reciprocal and complementary actions above happen matters a lot. Perfectly symmetrical relationships could end up summing to a net zero, so it's important to sync positive moments in time so the people in the relationship can experience moments of joy.

Examples:

Matching schedules when possible and making time for each other when those schedules match up

Being intentional about including each other in moments of joy and sharing those positive feelings actively

4. Positivity

This one seems obvious but there should be a net positive when all of the above categories are considered together. This is an inherently qualitative measure, but the question to reflect on is: does this relationship make me happy?

In my case, it was helpful to write down each of these categories as a linear scale and then draw a point on each scale to show where I felt the relationship was currently sitting. Then I drew an arrow if I felt that it was moving in one direction or the other. Abstracting things like this felt to me like a helpful way of reflecting on a relationship. If you decide to give this a shot, please let me know how it goes and if you have other additions!

[globalinsightsservices]

Rooftop Solar Market To Deliver Prominent Growth & Striking Opportunities During 2031 - SunPower, First Solar, Vivint Solar, Sunrun, Jinko Solar.

New York, Global Rooftop Solar Market from Global Insight Services is the only authoritative source for intelligence on the Rooftop Solar Market. The report will provide you with an analysis of the impact of the latest market disruptions such as the Russo-Ukrainian War and Covid-19 on the market. The report provides a qualitative analysis of the market using various frameworks such as Porters Analysis and PESTLE. The report includes in-depth segmentation and market size data by categories, product types, applications, and geographic regions. The report also includes a comprehensive analysis of key issues, trends and drivers, restraints and challenges, competitive landscape, as well as recent events such as mergers and acquisitions activities in the market.

Download Free Sample of Report : https://www.globalinsightservices.com/request-sample/GIS10069

Solar photovoltaic (PV) panels are devices that convert sunlight directly into electricity. A typical PV panel is a collection of many solar cells. Solar cells are made of semiconductor materials, such as silicon. When sunlight hits a solar cell, it knocks electrons loose from their atoms. If solar cells are connected together in a PV panel, the electrons can flow through the panel to form an electric current.

Rooftop solar is typically used to generate electricity for the building it is installed on, while utility scale solar is used to generate electricity for the grid. Rooftop solar is installed on the rooftops of buildings, while utility scale solar is installed on large tracts of land

Key Players

SunPower,

First Solar,

Vivint Solar,

Sunrun,

Jinko Solar.

Canadian Solar

Trina Solar

Hanwha Q CELLS

Panasonic

LG

Yingli Solar

Key Trends

There are a number of factors driving rapid growth of rooftop solar market. One is the continued decrease in the cost of solar panels. In the past decade, the price of solar panels has fallen by more than 90%. This has made solar an increasingly attractive option for homeowners and businesses looking to reduce their energy costs.

Another factor driving the growth of rooftop solar is the increasing availability of financing options. In the past, most solar installations were paid for outright by the homeowner or business. However, with the introduction of solar leases and power purchase agreements (PPAs), it is now possible for homeowners and businesses to go solar with little or no upfront cost.

Lastly, the industry has benefited from a number of technological advances. In particular, the development of more efficient solar panels has made it possible to generate more electricity from a given amount of rooftop space. Additionally, new mounting systems have made it easier to install solar panels on rooftops, further reducing the cost of going solar.

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Market Segments

By Technology

Thin Film

Crystalline Silicon

Others

By Grid Connectivity

On Grid

Off Grid

By Application

Residential

Commercial

Industrial

By Region

North America

U.S.

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia Pacific

China

India

Japan

Rest of Asia Pacific

Central & South America

Brazil

Rest of CSA

Middle East & Africa

GCC Countries

South Africa

Rest of MEA

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Key Drivers

The most important driver of the rooftop solar market is the cost of PV panels. The cost of solar has fallen dramatically in recent years, making it an increasingly attractive option for homeowners and businesses.

The other key driver of the solar market is government incentives. In many countries, the government offers financial incentives for installing solar, which can make solar a very attractive option.

The third driver of the solar market is the increasing availability of financing. Solar leases and power purchase agreements (PPAs) have made solar more accessible to homeowners and businesses by making it possible to finance the upfront cost of solar.

The fourth driver of the solar market is the increasing awareness of the benefits of solar. As more people become aware of the environmental and economic benefits of solar, the more likely they are to install solar on their rooftops.

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[eventindustrynews]

8 Tools and Metrics to Measure an Event’s Success

Success is a relative term. Everyone involved in organizing an event punch in days or months of hard work and dedication to deliver a smash-hit event. And, that is why assessing event performance is so crucial and requires analysis, based on specific metrics or performance indicators. Evaluating the bygone event is a post-event requisite that helps in introspecting and evolving for the next event. Although there is no straightforward way of determining an event’s success, measuring certain KPIs can surely give you answers to help improve and scale.

The Purpose of Evaluating an Event’s Success An event’s performance and outcome cannot only be measured in absolute numbers. To create a comprehensive success report, it is essential to identify the purpose of this evaluation and understand what defines a successful event. It is also equally important to decide the factors that are critical for aligning business objectives with the event’s goal.

Defining an event’s purpose or goal often requires correctly answering a set of questions. However, the dilemma is that as an organizer, you must also prepare the questions yourself!

Broadly, an event’s purpose, and hence success evaluation, can be derived from the following areas

a) Finance b) Marketing c) Branding d) Sales e) Attendees f) Sponsors

Tools and Metrics for Measuring Event Success Each purpose of an event can be linked to several metrics and tools that cumulatively determine the success of an event. Interestingly, each metric fits in like an element on the moodboard to deliver a complete overview when put together. These metrics are often divided into qualitative and quantitative categories to relate them with event-specific activities.

1. Attendance (Registrations or Tickets Sold) — The first metric is the number of registrations or tickets sold. This response and financial marker indicate a pre-event statistic that has several uses. Time-mapping the RSVPs or tickets sold before the event date can directly be linked to the performance of marketing campaigns and, of course, the ROI. Depending on when the RSVPs were made or tickets were sold (more on weekends or workdays) can also help identify the demography of the prospective attendees. If an event has ticket/entry categories, it is critical to notice and correlate the number of tickets booked to the categories. The output then must be further processed and compared with the attendee turnout numbers collected, based on actual check-ins.

a) If the turnout to tickets ratio is small it confirms that promotion, invites, content, and communication were on-point, and future events can follow the track. b) If the turnout to tickets ratio is significant it red flags factors like marketing, reminders, speaker panels, or even incorrect demography targeted. Thus begins the quest to find the answer to why?

2. Post-Event Survey — Undeniably, post-event surveys from attendees paint an accurate picture of everything that went right or wrong in an event. To understand the event experiences of the ultimate consumers, making the survey interesting is fundamental. If the survey isn’t heavy on the negative side, it indirectly indicates that the event was a success. The reason is universal — an unhappy or unsatisfied attendee is always more motivated to respond than a happy or neutral one. Negative surveys must be analyzed with a keen eye to figure out the issues that need correction in the future.

3. Social Media Actions — All the actions on social media before/during/after-event is a great event-feedback tool that can shed light on popularity and engagement metrics. Non-attendee participation during the event’s live streaming, likes, mentions, and shares on social media, can draw a quick success graph for the event. Also, it is essential to ensure that a relevant, easy-to-imprint hashtag is used in all promotions across social channels to obtain an accurate assessment of event performance.

4. Speaker Engagement and Attendee Interaction — Analyzing qualitative metrics can give long-term insights about executing future event agendas. Reporting tools that capture during-event details are paramount like a) Duration of attendance b) Time spent in event rooms or booths c) Specific room check-ins and checkouts

These reports can be linked to success metrics such as a) Speaker engagement b) Preferred topic/product discussions c) Attendee interactions d) Request/submission of networking details

5. NPS (Net Promoter Score) — The net promoter score makes things little exciting instead of point-blank asking attendees emotionally driven questions in a survey. NPS asks attendees to rate their experiences on a scale of 1 to 10 giving them the feel of power and being the decision-maker. The feedback inherently becomes even more specific and focused. Based on the ground-level analysis, it is pretty simple to set an NPS score before the event and compare actual scores to determine attendee-affirmed success.

6. Returning Attendees — Attendee data is hyper-important. In case of a company that frequently organizes events, the data of past event attendees is even more crucial. Simply comparing recurrent attendee data on a YOY basis can conclude the events’ actual growth. If the returning attendee number slows downward, delving into engagement factors becomes imperative.

7. Sales and Leads — An event does not magically escalate sales figures and funnels. The aftershock of events sometimes takes months to show results. A significant share of the increase is dependent on networking opportunities entwined throughout the event cycle. Also, quantifying sales and allocating lead generation to processes like speaker panels or marketing can help build a budget foundation for future events. Comparing increase in sales in every quarter is the real check on the success of events based on sales and leads KPIs.

8. Sponsor Feedback — Isn’t it one of the most significant post-event activities to get the accurate picture straight from the horse’s mouth? Approaching the sponsors for event feedback through surveys, evaluation meetings, and interviews provides honest and ground-breaking inputs. It is equally important to respect their feedback and own them constructively for continuing professional terms in future events.

These are some important KPIs for measuring an event’s success. But like events, the metrics or KPIs can also differ based on event type, industry, goal, or overall management’s vision. The tools mentioned here are just the tip of the iceberg, but are certainly enough to get you through reasonable measurement of an event’s success and set the course for diving deeper into more crucial insights.

Gain the edge with 360-degree event management platform and forward-thinking tools for your next event with some Fireworks!

Source — Eventcombo — Event Management Technology

[imr1234]

Global Vegan Cosmetics Market Size 2022: Future Demand, Emerging Trends and Latest Technology, Forecast Research Report 2028

https://introspectivemarketresearch.com/reports/vegan-cosmetics-market/

Global Vegan Cosmetics Market size was valued at USD 17.42 Billion in 2021 and is projected to reach USD 25.51 Billion by 2028, growing at a CAGR of 5.6% from 2022 to 2028.

Vegan cosmetics are free of any animal product and its derivatives. The traditional components of animal origin such as honey, beeswax, lanolin, collagen, elastin, etc. They are replaced by others of vegetable or mineral origin. Vegan populations have a great need and popularity for vegan cosmetics. The vegan skin care, hair care, makeup, and color cosmetics come in a wide variety of designs and are readily available on the market.Since the 1970s, animal testing has been a major ethical concern for the public when it comes to the use of animals in the cosmetics business. Large-scale public awareness campaigns have been ongoing for many years and have contributed to product reviews, development of new methods, and preservation of animal life. Consumers need to worry about more than just the animals used for testing, as cosmetic and toiletries supplies often contain items made from animals, such as sheep, insects, beavers, and even whales. However, globally, there was a 175% growth in vegan cosmetics launches between 2014 and 2019. According to estimates, more than 80% of new vegan products introduced in the UK in 2020 were in the beauty area.

To recap, the report's writers examined the Vegan Cosmetics market through several segmentations, followed by an in-depth study of the industry supply and sales channel, including upstream and downstream fundamentals, to help firms efficiently roll out their goods and solutions to the market. This in-depth market analysis employs a variety of technologies to examine data from numerous primary and secondary sources. It can assist investors to find scope and possibilities by providing insight into the market's development potential. The report also goes over each section of the global Vegan Cosmetics market in detail. The analysts thoroughly researched the market and created key segmentation such as product type, application, and geography. The market share, growth potential, and CAGR of each segment and its sub-segments are also examined. Each market category provides in-depth quantitative and qualitative market perspective information.

Read More:

https://introspectivemarketresearch.com/reports/vegan-cosmetics-market/

[bropiner]

Image inpaint

However, it is usually difficult to restore the complex structure and rich details because no semantic information of image is utilized. The second one is realized by searching the similar image blocks in the information area and copying them to the damaged area. The first one is proposed to deal with the small object removal, such as noise, rain, and scratch, which is realized by expanding the information in the existing region to the damaged region, so it may fail to restore the image with large damaged regions. The image inpainting algorithms can be divided into three categories: propagation-based algorithm, search-based algorithm, and learning-based algorithm. Image inpainting technology has been proposed for several decades. The quality of filling damaged region determines the final image inpainting result, and the main challenge of image inpainting is to generate the feasible structure and realistic texture. It is an important part of many image editing operations, such as image target removal, image restoration, and image denoising. Image inpainting is to restore the complete visual effects by generating the alternate structures and textures in the missing areas of images. Besides, the visual results on three datasets also show that our network can produce visual plausible results compared with the recent works. We trained our network on three popular datasets on image inpainting separately, both Peak Signal to Noise ratio (PSNR) and Structural Similarity (SSIM) between our results, and ground truths on test images show that our network can achieve better performance compared with the recent works in most cases. This is beneficial to focus on the regions of interest (ROI) on different scales and tends to produce more realistic structural and textural details. Moreover, we proposed a global and local PatchGANs (GAN means generative adversarial network), named GL-PatchGANs, in which the global and local markovian discriminators were used to control the final results. The use of our two-stage loss function in coarse network helps to focus on the image structure, while the use of it in refinement network is helpful to restore the image details. The loss function consists of different Gaussian kernels, which are utilized in different stages of network. To solve this issue, this paper proposes an image inpainting approach using the proposed two-stage loss function. However, existing inpainting networks may fail to reconstruct the proper structures or tend to generate the results with color discrepancy. We provide qualitative and quantitative evaluations along with an ablation analysis to show the effectiveness of our approach, which outperforms state-of-the-art inpainting methods.Image inpainting networks can produce visually reasonable results in the damaged regions. Our zoom-in, refine and zoom-out strategy, combined with high-resolution supervision and progressive learning, constitutes a framework-agnostic approach for enhancing high-frequency details that can be applied to other inpainting methods. To assist training the refinement network on large upscaled holes, we propose a progressive learning technique in which the size of the missing regions increases as training progresses. By introducing high-resolution images to the refinement network, our framework is able to reconstruct finer details that are usually smoothed out due to spectral bias - the tendency of neural networks to reconstruct low frequencies better than high frequencies. In this paper, we propose applying super resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.

[ceescedasticity]

Suppose EVERY Greek letter wanted its own ludicrous fanfic biology category

Because if you just said alpha and omega, fine, you're naming categories after first and last letters. Alpha and beta, fine. Alpha, beta, and gamma, you've got three categories. But alpha, beta, and then jumping all the way to omega -- now you've raised some questions about all the other letters.

Consider, a possible alpha/beta/gamma/delta/epsilon/zeta/eta/theta/iota/kappa/lambda/mu/nu/xi/omicron/pi/rho/sigma/tau/upsilon/phi/chi/psi/omega-verse:

alpha — as usually defined.

beta — as usually defined.

gamma — physiologically similar to betas, but pheromone chameleons; can smell like anything depending on what their subconscious thinks is the most useful.

delta — can transition between effectively-alpha and effectively-omega based on pheromone exposure, but it's mostly involuntary, it takes about six months to finish, and it's about as dangerous as a beta pregnancy.

epsilon — strictly infertile; 'supposed' to bond with mated pairs (often but not always ones they're related to) to help with kids; NOT always asexual, and they do have sex organs, just no gametes.

zeta — physiologically similar to alphas, but their instincts are 'set' on submission, not dominance.

eta — physiologically similar to omegas, but their instincts are 'set' on dominance, not submission.

theta — physiologically similar to betas, but their pheromones put everyone else on reproductive standby. Tend to be dominant in an implacable way, rather than an aggressive one.

iota — can transition between effectively-epsilon and effectively-beta.

kappa — what the hell, lays eggs.

lambda — sex organs internal when not in use; can either carry offspring or fertilize.

mu — sex organs internal when not in use; can carry but not fertilize.

nu— sex organs internal when not in use; can fertilize but not carry.

xi — physiologically similar to alphas with identical sex organs, but pheromonally and behaviorally very similar to betas. That way they can sneak around behind a dominant alphas back.

omicron — superficially physiologically similar to omegas, but: generally reproduce parthenogenetically, so mostly clones of themselves and sometimes mutants of other dynamics; non-omicron parents will sometimes throw out an omicron, but it's rare; almost indistinguishable from omegas without modern science.

pi — superficially physiologically similar to omegas. Unlike omicrons they CAN reproduce with others, and they aren't parthenogenetic, but they can self-fertilize.

rho — ummmmmmmm like betas but just make every situation tenser???

sigma — fully functional alpha and omega sexual organs.

tau — superficially physiologically similar to betas, but with universally attractive pheromones; technically infertile, but through a mechanism still not understood, anyone who has had ANY kind of sex with a tau has a vastly increased chance of tau offspring. (It goes from almost zero to maybe about 0.5% after a single tau sexual encounter and increases with additional encounters until leveling off at about 10%.)

upsilon — look like an epsilon, smell like an epsilon, but NOT infertile.

phi — produces eggs, but has no functional uterus; instead, after acquisition of sperm from someone, hopefully-fertilized eggs are deposited in someone else's uterus — phis are pheromonally drawn to chis, but that's not the only possibility. Omegas instinctively don't want to tolerate their presence unless the phi submits.

chi — has a functional uterus, but no eggs. However, the developing embryo IS subject to a sort of retroviral bath to append fragments of the carrier's DNA. Chis are pheromonally drawn to phis, but if they get an embryo some other way that works too. There's some superficial similarity to omegas, but they smell very different.

psi — physiologically similar to omegas with identical sex organs, but pheromonally and behaviorally very similar to betas.

omega — as usually defined.

Do with this what you will.

[pranalipawarshinde]

Cloud Computing Market Revenue Share Detailed Analysis Report 2022-2032 | Rackspace (US), Salesforce (US), Workday (US) and Zoho (India)

Global Cloud Computing Market report from Global Insight Services is the single authoritative source of intelligence on Cloud Computing Market. The report will provide you with analysis of impact of latest market disruptions such as Russia-Ukraine war and Covid-19 on the market. Report provides qualitative analysis of the market using various frameworks such as Porters’ and PESTLE analysis. Report includes in-depth segmentation and market size data by categories, product types, applications, and geographies. Report also includes comprehensive analysis of key issues, trends and drivers, restraints and challenges, competitive landscape, as well as recent events such as M&A activities in the market.

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Cloud computing is a type of computing that relies on sharing computing resources instead of having local servers or personal devices to handle applications. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

Key Trends

In the past decade, cloud computing has become an increasingly popular option for businesses and individuals alike. The appeal is clear: cloud-based services are typically more affordable and easier to scale than on-premises solutions, and they offer users the ability to access their data and applications from anywhere in the world. Here are the key trends in cloud computing industry:

The rise of hybrid cloud: A hybrid cloud is a mix of on-premises and cloud-based infrastructure and services. In the past, businesses that wanted to use the cloud had to choose between public cloud providers, such as Amazon Web Services (AWS) or Microsoft Azure, and private cloud solutions, which are usually more expensive and difficult to set up. However, hybrid cloud has emerged as a popular compromise, giving businesses the best of both worlds. With hybrid cloud, businesses can keep sensitive data on-premises while taking advantage of the cost savings and flexibility of the public cloud for less critical workloads.

The growth of containerization: Containerization is a method of packaging applications so that they can be run in isolated environments, such as on a public or private cloud. This approach has several advantages over traditional virtualization, including improved portability and greater resource efficiency.

The rise of serverless computing: Serverless computing is a cloud-based computing model in which the cloud provider manages the underlying infrastructure and resources, and the customer only pays for the resources they use.

The growth of artificial intelligence: Artificial intelligence (AI) is a branch of computer science that deals with the design and development of intelligent computer systems. AI has a wide range of applications, including speech recognition, image classification, and decision-making. In recent years, AI has become increasingly popular in the cloud, as cloud-based AI services make it easier and more affordable for businesses to get started with AI.

Key Drivers

There are various key drivers of cloud computing market which are as follows:

Increased adoption of cloud services by small and medium enterprises (SMEs): Small and medium enterprises (SMEs) are increasingly adopting cloud services due to the numerous benefits offered by cloud computing such as reduced costs, scalability, and flexibility.

Growing demand for cloud-based applications and services: There is a growing demand for cloud-based applications and services such as cloud-based email, storage, and collaboration tools. This is due to the benefits offered by cloud computing such as pay-as-you-go pricing, on-demand scalability, and improved collaboration.

Increased awareness of cloud computing: There is an increased awareness of cloud computing among businesses and individuals. This is due to the growing number of media coverage and articles on cloud computing.

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Restraints & Challenges

One of the key challenges is the lack of standardization. There is no one-size-fits-all solution in the cloud computing market, which can make it difficult for customers to compare and select the right service provider. Furthermore, the lack of standardization can also lead to compatibility issues between different cloud platforms.

Another challenge facing the cloud computing market is the issue of security. As more businesses move their data and applications to the cloud, there is a greater risk of data breaches and cyber-attacks. Service providers need to ensure that their systems are secure and that their customer’s data is protected.

Finally, the cloud computing market is also facing the challenge of pricing. Although the pay-as-you-go model can offer significant cost savings, customers are still reluctant to commit to long-term contracts. Service providers need to find the right balance between pricing and features in order to attract and retain customers.

COVID-19 Impact

The outbreak of COVID-19 has had a significant impact on the cloud computing industry. The demand for cloud services has increased significantly as businesses look to move their operations online. This has led to a shortage of cloud services and an increase in prices. Additionally, the outbreak has disrupted the supply chain for cloud services, making it difficult for businesses to obtain the services they need.

Market Segments

The Cloud Computing Market is segmented into Service Model, Deployment Model, Organization Size, Vertical, and Region. Based on Service Model, the Cloud Computing Market is segmented into Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Based on Deployment Model, the market is segmented into Public Cloud, Private Cloud, and Hybrid Cloud. Based on Organization Size, the market is bifurcated into Small and Medium-sized Enterprises (SMEs) and Large Enterprises. Based on Vertical the market is segmented into BFSI, IT and Telecommunications, Government and Public Sector, Retail and Consumer Goods, Manufacturing, Healthcare and Life Sciences, and Others. Region-wise, the market is segmented into North America, Europe, Asia-Pacific, and the Rest of the World.

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Key Players

Some of the key players of Cloud Computing Market are Amazon AWS (US), Google Cloud (US), Microsoft Azure (US), IBM Cloud (US), Oracle Cloud (US), Alibaba Cloud (China), Rackspace (US), Salesforce (US), Workday (US) and Zoho (India).

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10-year forecast to help you make strategic decisions

In-depth segmentation which can be customized as per your requirements

Free consultation with lead analyst of the report

Excel data pack included with all report purchases

Robust and transparent research methodology

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About Global Insight Services:

Global Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

Contact Us:

Global Insight Services LLC 16192, Coastal Highway, Lewes DE 19958 E-mail: info@globalinsightservices.com Phone: +1-833-761-1700 Website: https://www.globalinsightservices.com/