Mathematics - Science topic

Arani Biswas Deconvolution of a composite peak into its individual peaks plays an important role in the interpretation of many types of graphs including XRD, XPS, FTIR, and PL etc. In this video, I have discussed how to deconvolute simple combined peaks, composite peaks and how to correct missing data in a given peak with the help of deconvolution. In the case you want to further ask about it, please do comment on the specific video, I'll respond to it shortly. I have provided the practice files (OriginLab) here. Thanks

Assuming you have a strong join (no delamination issues etc.) then you have continuity of the displacement and the stresses across the join. However, strains will *not* be continuous across the join. The weak form of the elasticity equations will still hold, and energy minimization (for static problems) also holds. Using FEM, make the element boundaries conform with the join to avoid the problems of trying to approximate discontinuous functions (e.g., strains) with the discontinuity inside an element.

It is not hard to see that for each real number t which is not algebraic over integers, C(X) has a maximal subring R_t which contains t but not t^-1, which immediately implies that the first question is not correct

I recommend for you to read: "Situated Learning: A Legitimate Peripheral Participation in Learning Mathematics in Public Schools"; Author: Zoncita D. Norman

Kwadwo Boakye RE: "Can you just assume a situation with no figures or data to back it ? Is it reasonable?"

Yes to both questions. If you are giving a reductio ad absurdum argument, your assumption is formulated solely for the purpose showing that it is untenable because it entails a logical contradiction or absurdity.

A reductio ad absurdum argument that the square root of two is not rational has been attributed to Aristotle.

Dear Carsten Knoll. I developed ThingFO two y. ago, so I'm not aware so far that other researchers used it for abstract mathematical concepts like polynomial or complex number as you are looking for. My interest in ontologies started in early 2000. At that moment, we developed a process ontology, and an ontology for metrics and indicators. In the last years we have harmonized semantically those ontologies with ThingFO in the context of the four-layered ontological architecture. Obviously that for indirect metrics and elementary and derived indicators, we use mathematical constructs (formulas, aggregation models), but that is all. These ontologies are specified at the low-domain level.

For MetricsLDO

For IndicatorsLDO

if you don’t have the original model that this error term come from then you can’t get the exact answer you’re after. Though you could approximate it by fitting a regression model. E.g. fit a least-squares model to find the best set of parameters a,b,c,d to the equation error = a*temp + b*humid + c*pressure +d.

im=imread('GIS.jpg');

%imshow(im);

BW = im2single(im);

numclust=5;

[L,Centers] = imsegkmeans(BW,numclust); % matlab inbuilt

temp_mask = L==1;

figure,imshow(temp_mask);

We have the same dilemma. how did you interpret your research? Nik Smidt

Quite a few methods are listed here:

or here

root_matrix.pdf (lambda-11235.github.io)

Dear Hartwig Baumgaertel,

I suggest you to see links and attached files on topic.

Best regards

The largest such numbers I know of come from combinatorial problems, e.g., Ramsey Theory

You can check the equation 2.11 and 2.12 in the paper attach

Regards,

K

Filippo Maria Denaro The reference is Bulletin of the American Mathematical Society, volume 45, p. 135-152 (2008); the quote appears on page 143.

I don't know why you say "I am used to approach differently the issue". This is precisely what I described and where I start. However, In the case of the discrete equations, it is not necessary to proceed to the limit, which is ill-posed. That is one of the advantages of the discrete formulation. Much of this is discussed in my paper "Finite scale theory: The role of the observer in classical fluid flow" (2014). In any case, it is mathematical nonsense to say "as dx --> 0". dx is a length scale, and so can only be described as being small with respect to some other length scale.

I believe the CFL limit is well defined. But operationally, for a given problem just start to increase the explicit time step. The simulation will tell you where instability begins; since the CFL (advective) instability grows exponentially, there is little ambiguity.

As for hyperbolicity, I have had several discussion with my young colleague @Ilya Peshkov who convinced me that hyperbolicity has been given several definitions, none of which seem to apply to the discrete equations. Perhaps Ilya will comment.

as long as you need only information, yes.

Borrowing from Mathematical Association of America (MAA) Homepage: There are many excellent reasons to study the history of mathematics. It helps students develop a deeper understanding of the mathematics they have already studied by seeing how it was developed over time and in various places. It encourages creative and flexible thinking by allowing students to see historical evidence that there are different and perfectly valid ways to view concepts and to carry out computations.

I would suggest that you skim through conference proceedings papers from ICME (International Congress on Mathematical Education) topic study group number 39 and CERME (Congress of European Research in Mathematics Education) thematic working group number 9 and then search for more journal articles with authors from these two groups. Also, I highly recommend the book "Speaking mathematically: communication in mathematics classrooms" by David Pimm and the series of papers published in "for the learning of mathematics" by Dave Hewitt called "Arbitrary and Necessary" Part 1, 2 and 3 (accessible online)

Dear Christine Böckmann

How much time does it take to accept or publish a paper in special issue?

Thank you

Dear Abderrahmane Senoussaoui,

I suggest you to see links and attached files on topic.

Best regards

See the attached paper which is useful

Borrowing from Stefanie Reichert: The constant e appears practically everywhere in science: popping up in the definition of the standard normal distribution; allowing us to decompose a time-dependent signal into its frequencies via Fourier transformation; telling us how to calculate the half-life of radioactive elements; playing a crucial role in the growth of bacteria; and governing temperature-activated chemical reactions.

see

If you want to use the RANS formulation you should assume your flow field is totally developed. That means you can set the inflow profile according to a statistically mean velocity in a channel

see the following papers

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Lv, Dongdong, Yuan, Shuhan, Li, Meizi, Xiang, YangJournal:Mathematical Problems in EngineeringYear:2019

A Proposal on Machine Learning via Dynamical Systems

E, WeinanJournal:Communications in Mathematics and StatisticsYear:2017

A machine learning approach to ornamentation modeling and synthesis in jazz guitar

Giraldo, Sergio, Ramírez, RafaelJournal:Journal of Mathematics and MusicYear:2016

I am developer of Uyamak. Please look at Uyamak. You can use it to simulate a mathematical model in real time. However, communication with a data acquisition card is not yet available.

No need to install and it is free to use.

Home page: https://umk.skgadi.com/

App: https://umk.skgadi.com/app/latest/

simply 1xQ; where Q is product of dimensions of tensor at the input. Further help can be taken at https://deeplizard.com/learn/video/fCVuiW9AFzY

You need to work under a set of hypotheses allowing you to apply the

Inverse function theorem for f ( ⋅ , ⋅ ) in the first case, and for the composition f∘f ( ⋅ , ⋅ ) in the latter case.

Perhaps only near the peak of stress-concentration tips and Grain boundary/precipitates or near-dislocation regions. If one tries to correlate Creep relaxation and strain, one might fit the data (usually viscoelastic time-dependent+instantaneous deformation) with standard viscoelastic models (Kelvin, Voigt, Maxwell) and/or creep models (Nabarro-Herring, Coble, Harper-Dorn) to find out the parameters, and do some dimensional analysis to represent the data in reduced-dimensional form (Common in Fluid Mechanics, Heat and Mass transfer, CIvil Engg.) using standard method (like Rayleigh, Buckingham-Pi), then even significantly different data may be compared.

To see the strength of scaling laws, see it!

@Alisson Figueiredo.

The viscocty of blood varies with temperature invivo or invitro. In low Ts, the viscocity increases and in accordance with Hagen-poiseillue equation on flow, as flow is inversely proportional to viscocity , this suggests a reduced blood flow. In addition, physiologically, in low temperatures, peripheral blood vessels constrict redirecting flow to vital organs. If you apply the same equation where flow is proportional to the radius of a vessel( which is reduced when vasoconstricted), still the flow should reduce. Vive versa, in hyperthermia.

As a follow up to the above discussion, a MATLAB file that can give the numerical value of Euler's number accurate to 10,000 significant digits is now available via https://www.mathworks.com/matlabcentral/fileexchange/77046-euler-s-number. You can download the file and add it to MATLAB path. To call the function just type en in the Command Window. To show Euler's number for 2 <= d <=10,000 digits, type vpa(en,d).

انا لا أعلم اي مجلة من التي تبحث عنها

The Zeta function is a very important function in mathematics. Because he was able to prove an important relationship between its zeros and the distribution of the prime numbers. hi.zeta(s=a+ib)=0

PS. The LaTeX file from my previous post after processing

Yes. You can see the website:

Oke ... Thank

rez-ord.docx

Mathematics usually helps to develop better thinking among different subjects, including mathematical issues of a creative nature

You can use google or bing.com to search "how to get the pseudo inverse laplacian/generalized inverse laplacian" or just some words

This is also interesting. However difficult if the graph is not specified.

Dear Boris Menin ,

Golden ratio is a geometric mean or average . it could be apply to every aspect of life even in which amount we have to inhale and exhale to getting the perfectness that also explain by the meditation. The perfectness can achieve by the meditation that reflects in many Indian temples architecture those follow the Fibonacci series pattern in the temple architecture without knowledge of the series but due the devotion towards the god temple construction they constructed temple with meditation. There are also the statue of Gautam Buddha in which we found the golden ratio without knowledge of golden ration . So some where you can see the golden ration and meditation and devotion are the synonyms .

By personality style

On the construction of minimal surfaces from geodesics

By: panelJ.Sánchez-Reyes

Hello Ana-Lyn,

You could try:

1. Using released items from NAEP for the high school level (https://nces.ed.gov/NationsReportCard/nqt/help/NQT_Help/!SSL!/WebHelp_Pro/NQT_Help.htm);

2. Using previous versions of PISA items (https://www.oecd.org/pisa/pisaproducts/pisa-test-questions.htm)

3. Using previous versions of TIMMS items (https://nces.ed.gov/timss/released-questions.asp), though these are generally only for as high as grade 8.

4. Using ACT practice test items (https://blog.prepscholar.com/printable-act-practice-tests-5-free)

Some combination of these sources would likely serve to give you a good starting point for assessing teachers' command of a specific discipline, at least at a level commensurate with that of high school training. Of course, if the teachers you assess are very strong in the discipline (and good at test-taking!), then you may end up with a lot of very high scores (so-called "ceiling effect").

Short of that, you could obviously assemble a group of subject-matter experts, a test-development expert, and attempt to: (a) define the relevant domain of skills that you believe a teacher should possess; and (b) develop and try out items designed to measure whether a given examinee sufficiently possesses each given skill.

Good luck with your work.

Dear Roberto Bruzzese ,

Look the link, maybe helpful.

Regards,

Shafagat

If anyone read my articles and has recommendations or comment, please inform me of these comments. Any comments or suggestions would be very helpful.

Also if anyone knows someone that would be interested in my articles please do recommend it to them.

Douglas Vinson Nance

You are correct. Causality and the arrow of time are being consistently ignored as both Quantum probabilities and General relativity seem to develop math models at the expense of little regard for causality. Then there are the many complex papers on how to determine causality after the models have made liberal use of probability and shifting parameters from one side to the other of equations with only the number relations of math as a justification. This is the reason for the question.

The FQXi site seems to be devoting more to causality, sometimes called correlation.

Thanks for your insight. Perhaps, the comment should extend to fundamental principles.

@ Subir Datta Sir did you got the answers, if yes then please share the designing with me I knew many equations regarding this but I am confused how to calculate these parameters.

Thanking you sir.

Yes, you must shift all the conditions. All the problem has to be changed: The bounds, the conditions. Finally, you must come back the first problem: the first condition and the first bounds. To interpret the results, you need to return.

There are many theories, but most popular one is (Behavioral theory).

Please refer to the link below:

I hope the review process would be fast and how many weeks will it take to index accepted paper in web of science?

Dear Peter Breuer

The operator f{*} is defined as follows

f{g(x)}=1/g(x)^\ (the reciprocal of the derivative)

f{x}=1/1

f{x²}=1/2x

f{sinx}=1/cosx

etc,..

We find that this operator works as a solution for the given equation.

You can see

f(a ln x²)/f(x)= (1/2a(1/x))/(1)= x/2a.

You can also replace x with any other function, and the relationship works.

Here f is considered as an operator.

Best wishes

Bitte erklären Sie das Problem klar. Gruß

Actually, the idea in the case of a functional is the same as in the ordinary calculus: you have to resort to the second variation of the functional, and then to study several conditions depending on the weak or strong character of the critical point (Jacobi condition for weak extrema, Weierstrass condition for strong ones). You can find a readable treatment in the classic book "Calculus of Variations", by Gelfand and Fomin (Dover's edition is very affordable), chapters 5 and 6.

I agree with Mehdi Mesrizadeh

You are correct Omran, as usual. Thank you!

THENNAVARAJAN SUBRAMANIAN you may want to employ LSTM. Attached is the paper with the concept of Hochreiter and Schmidhuber (1977).

Hi,

I suggest you to see this link i hope it's in the topic.

Best regards

It is absolutely fine and fair to get values above 1.25 as you are using a surface reflectance imagery and sine you mentioned that the area is heavily vegetated, the values reflect the colour composite and type of strata so the number and the work is acceptable.

Best wishes and good luck !

Elements of Parametric Design Book by Robert Francis Woodbury can be useful especially in parametric modelling.

see

It may be useful to make a distinction between 'global' optimisation (where one is given an explicit, non-convex objective function) and 'black-box' optimisation (where the function is not given explicitly). The methods for them are very different.

We're all faced throughout our lives with decisions and choices, some on a grand scale, most of them on lesser points. We define ourselves by the total sum of decisions and choices we have made.

Yes, your assumption is right. I am using a Likert- Scale. The same participants rated the same statements for both learning context. The number of respondents of my study is more than 30. Well, thank you for responding my question Anna-Gesina Hülemeier .

Long and boring solutions are attached. If anyone can provide a nicer argument, please share.

A not too complicated solution, please see attached.

Excellent Omran, your construction solves my inquest, with the minimal continuity set for f.

Utkarsh Singh There is an esthetic reason in why a mathematical method is of interest in signal processing:

-a beautiful algorithm is well articulated, says what it does in few instructions, and does it in a stable and reliable manner

-this hints to the underlying algebra

With powerful and minimal computation, we go deep into algebra structures: group, rings, fields (see references on Evariste Galois as the inventor of "group" as we know it)

-Fourier transform is an interesting invention: it allows to decompose a signal into resonating modes (as for piano music: you produce a sound at frequency F, but also its harmonic NxF...). Naturally there is the aliasing question and the Nyquist theorem for reconstruction

There are many more time-frequency representations: Fourier, Laplace, discrete or continuous, cosine transform, wavelet transform, etc.

The interesting feature of discrete algorithms for those transforms is that you can implement a butterfly structure.

The key idea is to replace a very large number of multiplications (in brute force "non-esthetic" programming) by a smaller number of additions.

This idea worked for me for developing a codec system using underlying GF(n) properties.

See this patent:

The regularity in the processing and the efficiency of the representation go hand in hand.

Let me go back to a very basic mathematical method: the Gram-Schmidt decomposition: take a sequence of n vectors v(1),..., v(n), and the matrix of cross-products m(i,j)=<v((i),v(j)>. The Gram-Schmidt method diagonalises this matrix. It extracts eigenvalues, and eigen vectors. In frequency terms, it extracts modes (resonating modes present in the signal).

This algorithm highlights the efficiency side of the representation: it's projecting the signal onto something found "in itself", call it principal components if you want.

There are only two reasons for choosing a technique in engineering:

-(i) it addresses the problem completely

-(ii)it's economically implementable.

Both criteria are equally important and a good way to find these is to look for elegant, esthetic solutions (minimal and complete at the same time).

Does it help?

I am surprised that a known scholar with a long experience in the transportation domain maintains such a hard stance on heuristic search. Obviously, we live in a world where extreme opinions are those which are the most echoed. Truth is, assuming that all practical optimization problems can be solved to optimality (or with approximation guarantees) is essentially wishful thinking. Given this state of art, better integration of exact and heuristic algorithms can largely benefit the research community. At the risk of repeating myself, here are some important remarks to consider:

• CPLEX and Gurobi (the current state of the art solvers for mixed integer programming optimization) rely on an army of internal heuristics for cut selection, branching, diving, polishing, etc... Without these heuristic components, optimal solutions could not be found for many problems of interest. CPLEX has even recently made a new release permitting a stronger heuristic emphasis (https://community.ibm.com/community/user/datascience/blogs/xavier-nodet1/2020/11/23/better-solutions-earlier-with-cplex-201). MIP solvers also heavily depend on the availability of good (heuristic) initial solutions to perform well. For many problems, cut separation is also done with heuristics. In the vehicle routing domain, we have a saying: heuristics are the methods that find the solutions, exact methods are those that finally permit to confirm that the heuristics were right (sometimes many decades later, and only for relatively small problems with a few hundred nodes despite over 60 years of research on mathematical models)...

• The machine learning domain is quickly taking over many applications that were previously done with optimization. Among the most popular methods, deep learning applies a form of stochastic gradient descent and does not guarantee convergence to optimal parameters. Neural networks currently face the same scrutiny and issues as the heuristic community, but progress in this area has still brought many notable breakthroughs. Decision-tree construction and random forests are also largely based on greedy algorithms, same for K-means (local improvement method) and many other popular learning algorithms.

• Even parameter tuning by the way is heuristic... I'm sorry to say that, but most design choices, even in the scientific domain, are heuristic and only qualify as good options through experimentation.

Hi, you can directly use the following MATLAB function : fminsearch that uses the Nelder-Mead simplex (direct search) method instead of trying to implement your own version. Best

Ruqayah Al-Ani The digital elevation model (DEM) is a good component in the field of remote sensing and GIS. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). The ASTER GDEM needs further error-mitigating improvements to meet the expected accuracy specification. The RMSE values can be used to represent the DEM errors, in addition to mean error and standard deviation (stddev).

Vinayak - this is just a preprint, the authors are allowed to write whatever they like and how they like it; I would follow the printed version (in a journal, conference proceedings etc) - usually it gets better when reviewed & printed.

An Euler brick is a cuboid that possesses integer edges a>b>c and face diagonals. If the space diagonal is also an integer, the Euler brick is called a perfect cuboid, although no examples of perfect cuboids are currently known.

What actually happens is that, closure property is a common one to almost all structures (systems). Therefore, authors who drop it in their texts assume that it is automatically embedded in the structure. Others who include it want to be vivid in their texts for clarity sake.

So, those who drop this important property do not truncate it entirely or cancel it.

I don´t understand you. It´s no trivial to find two prime numbers whose sum +1 will be other prime number. In fact my formula have a success of the 80%

Please see attached my take on this problem.

As a mathematician & statistician, I can say that, when modeling reality, we make compromises; and, when validating the models, we have lots of surprises.

I was thinking of defining k(n) := sup { k ≥ n : x_k - x_n < c }. Then

x_k(n) - x_n < c ≤ x_k(n)+1 - x_n , and the length of the interval containing c is x_k(n)+1 - x_n , which → 0 as n → ∞ by hypothesis.

Is this OK for all c > 0?

Design optimization, fabrication, and performance evaluation of solar parabolic trough collector for domestic applications

Attached for your kind perusal@Miss. A.M

Dear Dagnachew Jenber

The first 11 theorems in your article provide a limit family of numbers that obey the Collatz conjecture and the number of steps needed to reach 1.

Unfortunately, in theorem (12), you have assumed that Collatz conjecture is true!!

In fact, your assumptions of the existence of b₁ , b_{2 ,} ....b_k-1 where k is finite

is exactly the Collatz conjecture, and the rest is an elementary computation of the number of steps to reach 1.

Can you prove that k is finite?

Obviously, if one assumes k is finite, then he assumes that Collatz conjecture is true.

Anyway, you have determined a nice family of numbers that satisfy the Collatz conjecture.

I wish you good luck to show k is finite.

Best regards

Ajit - if you are not interested in statistical analyses (regressions etc), a polynomial interpolation will do just fine.

No

consult

see

Anh C.T., Hung P.Q., Ke T.D., Phong T.T.: Global attractor for a semilinear parabolic equation involving Grushin operatot. Electron. J. Differ. Equ. 32, 1–11 (2008)

D’Ambrosio L.: Hardy inequalities related to Grushin type operators. Proc. Am. Math. Soc. 132, 725–734 (2004)

Shallow Water Hydrodynamics: Mathematical Theory and Numerical Solution for a Two-dimensional System of Shallow Water Equations

ElsevierTan Weiyan (Eds.)Year:1992

Lattice Boltzmann Methods for Shallow Water Flows

Springer-Verlag Berlin HeidelbergDr. Jian Guo Zhou (auth.)Year:2004

Numerical Methods for Shallow-Water Flow

Springer NetherlandsC. B. Vreugdenhil (auth.)Year:1994