Categorization - Science topic

14.02.01 - Гігієна та професійна патологія

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The categorization in the attached image is correct. The 12 identified compounds can be classified into the following groups:

Monoterpenes: Camphene, Para-Cymene, 1-Limonene, Gamma-Terpinen

Sesquiterpenes: Trans-Decalone

Aromatic aldehydes: Cuminic Aldehyde

Cyclic ketones: Cyclopentanone

Aromatic alcohols: Acetyl phenyl carbinol

Amines: 1-amino-1-o

Heterocycles: 5-Methyl-2-Phenyl indolizine, 5-nitrobenzofuran-2-carboxylic

Inorganic acids: Silicic acid

Monoterpenes and sesquiterpenes are the largest groups of compounds in the essential oil. They are hydrocarbons that are derived from isoprene, a five-carbon molecule. Monoterpenes have ten carbon atoms, while sesquiterpenes have fifteen carbon atoms.

Aromatic aldehydes are a group of compounds that contain an aldehyde group (-CHO) attached to an aromatic ring. They are often used as fragrances in perfumes and cosmetics.

Cyclic ketones are a group of compounds that contain a ketone group (=O) in a cyclic ring. They are often used as solvents and flavorings.

Aromatic alcohols are a group of compounds that contain an alcohol group (-OH) attached to an aromatic ring. They are often used as fragrances and flavorings.

Amines are a group of compounds that contain an amino group (-NH2). They are often used as solvents and pharmaceuticals.

Heterocycles are a group of compounds that contain a ring with at least one atom that is not carbon. They are often used as pharmaceuticals and agrochemicals.

Inorganic acids are a group of compounds that contain a hydrogen atom that is ionizable in water. They are often used as solvents and catalysts.

The classification of these compounds is important because it can help to predict their properties and applications. For example, monoterpenes are often volatile and have a strong odor, while sesquiterpenes are less volatile and have a more subtle odor. Aromatic aldehydes are often used as fragrances, while cyclic ketones are often used as solvents. Amines are often used as pharmaceuticals, while heterocycles are often used as agrochemicals. Inorganic acids are often used as solvents and catalysts. Mehdi Aghighi Shahverdi

Yordan Epitropov, Kindly see my reply above.

Yes, there is an accepted taxonomy or categorization of environmental issues. Environmental issues can be broadly categorized into several key areas, and these categories help organize and address various challenges related to the environment. Some of the major categories of environmental issues include:

These categories are not mutually exclusive, and many environmental issues are interconnected. Environmental scientists and organizations often use this taxonomy to study and address specific challenges and develop strategies for environmental conservation and sustainability. Additionally, new issues may emerge as our understanding of environmental systems and their interactions evolves.

I am inclined to think that participants in experiments were readily available from pupil- and student-populations. Perhaps pupils were preferred because the students available were already spoiled by psychology courses or participation in other psychological studies. Further, Tajfel used the experimental method. He created different conditions and observed then whether participants in different conditions reacted differently. So the focus was on the effect of particular conditions, not on the difference -- or absence of difference -- between groups or populations. If the manipulation of conditions works well out with pupils, then the experimenter knows that it works. He does not know yet if it would work also with adults, but he can presume that it would. He or other researchers will check this later on, and that later research is possibly not be published in renowned publications because the first experimental studies with pupils formed the breakthrough.

I would suggest that you use the 2 category variable as the DV in a binary logistic regression. Use the remaining variables as predictor variables in your regression. This approach should yield the maximum amount of information in your data. I am attaching some course notes that cover the topic. There many more with examples that you can find with a Google search. Best wishes David Booth

The researchers have to know where to submit and with whom to go for communication before they can act. The same is when you are deciding about the papers.

All around us are communications and everyone gets easily information about everything. It's good that this classification exists, but you should use it by crossing several sets of data (out of Q1, Q2, Q3, and Q4 classification, you check also (1) who wrote a paper – including the team, (2) with using Scopus Author Search (https://www.scopus.com/), (3) Publons (https://publons.com), (4) Research Gate rankings and finally (5) Google Scholar.

Use this classification (Q1, Q2, Q3, and Q4 classification) as a starting point as it is straightforward to approach the web page: Scimago Journal & Country Rank, https://www.scimagojr.com/.

That is a simple and universal comparison and you don't just compare the journals from the next door of your neighbourhood but compare to journals all over the world and with what is being presented as proper to some extent. All of us know that there is no complete and/or perfect classification for all purposes. If the journal is strong, statistically the paper, in principle should follow the quality of the journal.

Using this approach (quartiles Q1, Q2, Q3, and Q4) you are not comparing only the journal with the other journals; but also comparing the same journal with what was the journal yesterday (evolution, continuity). And then, you check the year in the quartile when the paper is published…

Whatever we do in relation to this has to be a bit subjective and with speculations.

In conclusion: I have high respect for this classification (Q1, Q2, Q3, and Q4 classification and Journal Impact factor). The paper should follow more or less the quality of the Journal.

Thank you Madhukar Baburao Deshmukh for your valuable input.

Again, from a statistical perspective, it is best not to categorize/form groups and to rather use the variable/score as is, taking advantage of its (quasi-) continuous (metrical) scale level. Otherwise, you lose information. There are plenty of statistical procedures available for continuous variables and those are typically more powerful than procedures for categorical/ordinal variables.

Most categorization methods are also rather ad hoc/arbitrary (e.g., median or quartile split). A model-based approach that is less arbitrary is latent profile analysis (LPA), which allows you to identify latent groups based on continuous variables. Often, these groups come out as low, medium, high. However, LPA requires multiple continuous indicator variables.

Aha. Thanks for the clarification. I have an article forthcoming on exactly this topic. You need to find articles about representation in qualitative studies. Guba, Denzin, Lincoln, and Onwuegbuzie have all written about representation and legitimation. David L Morgan article "Morgan, D. L. (1993). Qualitative content analysis: a guide to paths not taken. Qualitative health research, 3(1), 112-121." could also be a source.

Check https://courses.lumenlearning.com/boundless-microbiology/chapter/temperature-and-microbial-growth/

Also check https://link.springer.com/article/10.1007/s00216-019-01615-6

Yes, you can use the multiple regression. Don't forget to make a scatter plot to show whether the correlation is linear or not.

Hello Islam,

I, too, have a problem fully comprehending your query.

If your independent variable was continuous, and you subsequently categorized it (and either you or the software appropriately recast it as k - 1 dummy variates, as suggested by David L Morgan ), then the discrepancy is attributable to the loss of information, as suggested by David Eugene Booth . If the categorization was incorrectly done, then the discrepancy is attributable to incorrect recasting of that IV.

However, if your independent variable was genuinely nominal (e.g., 1 = truck, 2 = airplane, 3 = roller skates, 4 = bicycle) and you just treated the numbers as if they were a continuous scale, then any discrepancy between treating such a variable as a continuous variable vs. a categorical variable is meaningless: the continuous version of the scores has no meaning and shouldn't have been evaluated.

Good luck with your work.

An interesting question. Do you think the results are going to be varied? I don't think so.

This article may help

Best wishes

Ying-Xian Goh

As for me, a sequence of proceedings should be as follows. First, stoichiometry, preceded by the development of a chemical model. In turn: Thermodynamic analysis, if necessary preceded by the required tests. Determination of kinetic equations preceded by kinetic tests in a given reactor system and with the elimination, if possible, of undesirable effects and phenomena. Development of a mathematical model whose degree of complexity depends on the adopted assumptions. Finally, there is a validation of the model, although additional checks may be necessary at subsequent stages of its development. This is, in the greatest brief.

Colour psychology is a burgeoning and exciting area, with top researchers in the field providing rigorous overviews of work on colour categorisation, colour symbolism and association, colour preference, reciprocal relations between colour perception and psychological functioning, and variations and deficiencies in colour perception (Elliot, 2015). One of the upcoming research areas that utilise colour psychology is memory research. Memory plays a vital role in our daily activities, responsible in moulding and developing our individual personalities, therefore memory is one of the most essential cognitive functions in an individual’s life. Memory can be explained in the form of a model, part of a sequence of three stages, from sensory to short-term to long-term memory, and this model is known as the modal or multi-store or Atkinson-Shriffin model.  Of undeniable importance, short-term memory is an important element for cognitive tasks including reasoning, planning and problem-solving by storing and interpreting information. The field of short-term memory research is replete with studies that present different pathways to enhance memory performance, recall and retention in order to improve knowledge accumulation, overcome learning disabilities or simply to better process in the learning process. The most notable study done on this area started with the fundamental study of George A. Miller on storage capacity limits, suggesting that the capacity limit on information processing is limited to about seven units (Miller, 1994). Throughout the years, studies on memory processing have expanded to explore the relationship between short-term memory and academic achievement, therefore generating compelling interest to explore factors that contribute to improving performance in memory processes.

If you ask a fake physics :

The latter was not the answer of an ordinary physicist, but the answer of one of the greatest contemporary physicists! And this is a disaster for physics.

It is as if the role of physics has been reduced from a master to a servant.

Technology is now far ahead of knowledge, and physics does not seem to be afraid of this humiliation, and it is still content with its instrumental role.

Please take look at this GitHub repository which most of the techniques are provided.

Vineela Nandam

You could classify the images using Object-based classification by eCognition, or ArcGIS Pro, then do an LCLU analysis using TerrSet. Good luck.

It's really up to you, as long as you don't do something that looks, well, "weird." My only questions would be: (1) how central is age to your research? and (2) what kind of statistical analyses do you intend?

The simplest thing is not to break them into categories at all: just report descriptive statistics (mean, SD, range) and, if you're running correlational or regression analyses, use age as a continuous variable.

But if you want to report results for age groups, and maybe run ANOVAs with age as an IV, then you do want to break age into categories. One easy approach is to go by decades (i.e., 20-29, 30-39, 40-49, 50-59). Another would be to use just two groups ("younger" workers = those under 40, "more mature" workers = those 40 and up). Which is better? Well, more groups gives you a finer-grained analysis. But if your sample is small, then you might end up with groups comprised of just a few people, and that would not be good. In general, you'd like to have at least 8-10 people in each group for a one-way ANOVA, perhaps several times that many if you envision factorial ANOVAs with age as only one IV.

Hi Mohammad,

Could you please provide a more precise question? (Context of research?) What (I think) I see is: you are interested in 'category generation'. Have you read the tons of reinforcement learning books out there how categories are learned? Or the literature on 'category generation' models (e.g. SUSTAIN)? Or the "Bayesian approach" asking/formalizing where category hypotheses come from? (The last one looks like the answer to your question on a superficial level.)

Best, René

Hi,

Please see these attached links:

Best

It depends of your case. Look articles and book of Anil K. Jain

Ambreen Gul Get your output as postscript file. Convert PS to PDF using https://online2pdf.com/convert-ps-to-pdf

I am getting the output.Just verified. Cheers.

Random Forest ensembles the outputs of CART decision trees, CARTs can work with numeric/continuous/integer/float values.

Yes, the algorithm underneath CART uses sorting/categorization techniques to convert continuous values into discrete values, but that's not something the user should be worried about. Generally speaking, Ranfom Forest can work with any real number as attribute values.

The title of the paper is "Using Likert-Type Scales in the Social Sciences" James T. Croasmun Lee Ostrom

Also look at the following link, it may help you:

Thank you

Try this:

1- go to: http://amp.pharm.mssm.edu/Enrichr/

2- paste the gene symbols

3- select Ontologies section

4- select "GO Cellular Component 2018"

5- select "table"

6- select " Export entries to table"

hope this help you.

Thanks, but I need the data for the images you need to process in MATLAB.

This review discusses several potentials and pathways of biomass pyrolysis. Best wishes.

check this paper, it will help you

" Confidence ratio Affinity Propagation in ensemble selection of neural network classifiers for distributed privacy-preserving data mining "

Yes, that's a common problem.

Intersectionality is the overcrossing of categories, as you say, including race, class, and gender but also sexuality, religion, age, disability etc. The categories "black" and "woman" are an example of an intersection that functions as a hybrid category, as does "white male". Specific combinations of categories in different contexts, produce differently textured "regimes of inequality". So what Intersectionality studies should be doing is alerting us to a more diverse and fine-grained view of actual contemporary terrains of domination, discrimination, exploitation and displacement. Such analyses are context specific. It's not that black women, as a category, are losing identity, but that we see that identity more clearly in terms of what's common and what's different, what are their priorities in how they define themselves in terms of their own intersectional cocktail (eg black, woman, Muslim, disabled, lesbian, middle-aged, professional) and what priorities does their cultural context ascribe (or proscribe) in relation to these values.

In a first attempt I would use them all. You can inspect the results of PCA, to figure out whether all of B or all of C are under the most relevant variables. If so, you could reduce the input parameters accordingly. But why are you hesitate to use all available informations?

How do you know this is not following normal distribution ? Histogram does look normal but may be you can run some normality test or plot quantiles to confirm if they are not. Secondly, even if the data is non normal, statistically you still have 97% values falling within 3 sigma limits (chebychef's inequality). So, if you can find out a z score for each project, all with value more than 3.0 are very important. Between 2.5 to 3 are moderately important, and 0 to 2.5 may not be important and less than zero would not be worth exploring. By the way, you can refine the score with some domain knowledge to make it more realistic.

Okay. Thanks Alberto

Dear Yasir,

No clear-cut categorization is present as disciplines are somewhat overlapping in nature

The question is whether readers would agree with law reform to implement the principle that a right to land constitutes an inalienable human right.

The question is not whether the law of this or that jurisdiction presently considers an interest in land to constitute an inalienable human right. As far as I know, the law of no jurisdiction presently incorporates such a principle. The prevailing principle, as Willem notes, is to permit the State compulsorily to acquire land for fair value.

It is predictable that lawyers writing from European and derivative (including U.S.) legal traditions will be inclined to raise objections based on corresponding concepts of property. My own responses fall into that category.

The question is interesting because other perspectives are possible. For example, Paul cited the historical western clerical scholar Thomas Aquinas, whose view to some extent reflects that taken even today by many indigenous peoples.

If one were to consider the perspective of a tribe which understands itself to be charged with custodianship of particular lands, then "right to land" would have a very different meaning. Categorization of such a relationship between a land and its people as an inalienable human right may seem legally awkward, but I cannot see that it obviously would be inappropriate.

From the small amount of information that you have provided, I think that you are operating at a fairly high level of pattern recognition. However, since you are dealing in part with phylogenies, you should be aware that some hominin cladograms are based on single specimens. For example, in the case of the meme "Homo floresiensis" the phylogenies are based on a single skull (LB1) that is developmentally abnormal, so the phylogenies are misleading.

Dear Kelly

Even if we get universal marker one day, DNA barcoding will still remain a tool to aid Linnaean binomial nomenclature or Alpha Taxonomy. Dont get offended if I say, DNA barcode is just like the commercial barcode on the back of a toothpaste packet or tube, but it can't replace the name of the toothpaste on the front of the packet or tube.

Hello Kerner,

Alexander is right in his information.

Hello,

I would suggest to use ArcGIS Spatial Statistics Tools > Modelling Spatial Relationships (geographically weighted regression and OLS, linear regression?). But it is needed all data (explanatory and dependent (response) variables to be in one vector layer.

Dr George Anifandis, Email: rg.htu.dem@finag has studied this topic. You can mail him.

you might try this

Hi Ryan

Take a look at Gauthier T, Tarr MJ Becoming a Greeble Expert etc..., Vision Research 1997, 37, pp 1673-1681.

It may help.

Regards

Michael

Dear Dr. King

Thank you for your recommendation. It seems quite useful.

Do you have a paper about this measurement method, I would like to read and follow ?

It is generally possible to calculate Kappa it situation where two or more codes can be assigned to the same text, since Kappa is simply based on a 2x2 table of whether coders agree or disagree about whether a code should be assigned to an item. So, I don't see any reason to use the more complicated set of codes that you describe.

Hi Ryan,

if you're looking for an easy to use interface in which you input a word (or multiple words) and it returns the closest N semantic neighbors, then I really recommend using snaut. Additionally, if you already have a list of words, the program can tell you how close they are to one another, or even compare the whole list of primes to the respective targets. I found it very useful, also for finding the semantic neighbors I missed when coming up with a list of stimuli.

Here's the link to the website with the interface and all the resources: http://meshugga.ugent.be/snaut-english/

Hope it helps!

PS: I have to thank researcher Giorgio Arcara for suggesting snaut to me in the first place, check him out for some some cool studies on language processing!

Thanks Venkatesh and Suhaib !

A Hurst exponent value between 0 and 0.5 is indicative of anti-persistent behavior.In an anti-persistent time series (also known as a mean-reverting series) an increase will most likely be followed by a decrease or vice-versa (i.e., values will tend to revert to a mean). This means that future values have a tendency to return to a long-term mean.

Typically, if such assumptions aren't met, I see more robust logit models used. For example, non-parametric models or semi-parametric models. Unfortunately, estimates from such models are taken to be "rough" associations; therefore, inferences must also consider this.

I believe most software has options to fit these types of models. My choice, however, would be Limdep for discrete outcome modeling.

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Thanks, Monyer!

Can you provide a bit more context? e.g., what does your output file look like?

Available on BoardEx.

Thank you mr. Franscesco. this very helpful in our work. We will appreciate more of such 

Hi Alexey....

Thanks for ur suggestion. I can see that u are studying in mathematics. Perhaps I can seek more advice from u. I'm not sure if BNS will give a reliable weightage but I've done testing using Chi square, IG Ratio and term frequency, which term frequency so far giving the most accurate result.

I’ve calculated this term freq from 5000+ docs that consists of 2 classes, let say A and B. The weight was calculated as a number of term 'x' occurred in A class divided by a number of term 'x' occurred in both classes. Do u have any idea if there is any other scheme that propose as such. Preferable if the method suggesting; the more the term occurred in the dataset, the higher the value (which it is between 0 – 1, and 1 indicate as mostly occurred). I need to find some other methods for comparative purposes with my existing term frequency findings.

Thank you very much. Appreciate it.

There are no rules for this sort of thing in qualitative research. Instead, I tell my students to think about how they will write the Results section of their article or thesis etc. In particular, if you have too many categories (themes) you will overwhelm the reader, and the same is true if a particular category has too many subcategories.

I suggest that you look at the qualitative publications in your field to see how many categories (themes) are typically reported, and model your own work on that standard.

Dear Martin,

You are very welcome. Unfortunately, classification approaches and relevant taxonomies for technology in general (that is, on one or more highest levels), are quite difficult, if not impossible, to generate and I'm not aware of some "universal" ones. Having said that, there are various approaches and taxonomies that might be of your interest in the context of your planned research. The following links are results of my brief research on the topic (excludes domain taxonomies, such as IEEE and ACM ones on computing). I hope that you will find this is helpful.

- NASA SBIB/STTR Taxonomy: http://sbir.nasa.gov/topic-taxonomy/52896;

- 2014 IEEE Taxonomy: https://www.ieee.org/documents/taxonomy_v101.pdf;

- IF4IT Inventory of Taxonomies (IT-focused, but still useful): http://www.if4it.com/taxonomy.html;

- International Patent Classification: http://www.wipo.int/classifications/ipc/en;

- FARSEEING Taxonomy of Technologies: http://farseeingresearch.eu/resources/taxonomyoftechnologies (see link to PDF at bottom of the page).

- Pavitt's Taxonomy paper: http://www.sciencedirect.com/science/article/pii/0048733384900180;

- Follow-up paper on Pavitt's taxonomy: http://www.danielearchibugi.org/wp-content/uploads/2015/03/Archibugi_Pavitt_Taxonomy.pdf;

- Paper on technological regimes and innovation: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1625823 or http://www.sciencedirect.com/science/article/pii/S0048733310000247;

- Book on taxonomy for technology domain: http://www.igi-global.com/chapter/investigation-into-taxonomy-technology-domain/30053;

- Very interesting MIT working paper / master's thesis on taxonomy generation techniques: http://web.mit.edu/smadnick/www/wp/2010-01.pdf.

Best regards,

Aleksandr

thank sir. I found the answer from Brown et al. (2002) where they talked about mandated vs voluntary technology. :). In mandated technology actual use does not matter only intention to use measurement will be fine.

There is a "WEKA" which is a big library designed and found for data and text mining techniques to be used either with Java platform or as independent tool.

Dear Bin Jiang,

Nested communities provide benefits akin to entanglement in quantum theory, greatly augmenting the utility of spaces and resources, because of the possibility of duality, multiplicity that a, decoupled ,non-nested structure lacks. Nevertheless, nested structures may also increase the fragility of the system exponentially. The crucial issue is whether the nested structure has the right kind of diversity and interconnection/intertwining structures.

I tend to think in metaphorical terms of bloatware versus "goodware" in software. Software by definition consist of nested algorithms, algorithms that run recursively. Bloatware multiplies these nested structures needlessly (approximates 1/f noise?) while "goodware" maintains just the right level of complexity and diversity in it. That is why thinking about cities and social networks from an information-theoretic perspective can make typical problems more tractable. Piece-meal, incremental improvements added to a complex system without understanding the implications of these actions (through simulations- to consider the different possible trajectories) can lead to self-organized criticality (SOC)- Bak et.al sandpile model comes to mind.

Cheers

Most of the remarks in this thread of messages are sharp and deep observations when it comes to the concept of proximity.Distance is a whole different issue and becomes a solid definition concept when ones defines  clearly the objects concerned and the metric used.In the next days allow me to suggest reading an upcoming paper of mine with  a title starting with the "funny" statement "A new proximity definition...". .Funny  in the sense of the timing that  i entered in this thread exactly when  I had taken the affirmative answer of the Jouurnal of Mathematics and Computer Science.

As the old Greek philosophers would  say " ΈΡΩΣΘΕ",

I did find some studies that relate to this, particularly

Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The early epochs of category

learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(6),

1411-36.

and

Smith, J. D., Murray Jr, M. J., & Minda, J. P. (1997). Straight talk about linear separability.

Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(3), 659-80.

One of the topics you filed this question under is "Biostatistics".  In biostatistics, logistic regression is often used when the outcome variable is dichotomous.  You also list "SPSS" as a topic.  A Google search on <SPSS logistic regression example> will no doubt yield many hits, including the UCLA "textbook examples". 

For logistic regression, bear in mind that you should have 15-20 events per model parameter if you wish to avoid over-fitting.  (See the Babyak article linked below for more info on over-fitting.) 

HTH.

I like the SEM idea.  Alternatively, you can compare predictions by model built on baseline data vs. model built based on new data.  I have had very good results using this approach.

1. Capture the empty (only background) screen

2. Capture your image

3. Substract last image from backgroung image 

4. Threshold

Result is your objects

The factors culture media and growth regulators are inter related. At first you try different concentrations of plant growth regulators supplemented to a particular medium and select the best one on the basis of observations and then also try the same with another medium. Selection of best culture medium with suitable plant growth regulator will be based on your observations.