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According to the standard model, the proton consists of three quarks (uud). The three quarks are bound together by the strong force. u quarks have a 2/3 charge and d quarks have a -1/3 charge, so the proton carries an equivalent positive charge [1]. The electromagnetic force is realised by exchanging virtual photons and the strong force is realised by exchanging virtual gluons. If we move a proton by electromagnetic force, it is equivalent to moving three quarks bound together by the strong force, and the strong force needs to change at the same time. So how is the electromagnetic force transferred to the strong force, or how is it converted to the strong force?
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References
[1] Schmitz, W. 2019. Particles, Fields and Forces: Springer.
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The content of fresh references here is not actually new, and it may not mean much to professional physicists like you, but it still means something to me, although I don't exactly accept it either.
I agree with you about the current state of physics, ‘fundamental physics is at “complete dead end”’. However, it may not be ‘need to be broken and new ones built’.
We may also differ in our understanding of interacting forces, but I do appreciate the new ideas you bring to the table, after all, we are all on a quest. I look forward to hearing more of your ideas.
Best Regards, Chian Fan
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The concept of mass explained by the Higgs mechanism is able to include all concepts of mass, inertial mass, gravitational mass, mechanical mass, electromagnetic mass [1], kinematic mass, static mass, longitudinal mass, transverse mass [2], bare mass ...... ? Is it the Higgs field that leads to the mass-energy equation? How are coupling relationships established? Do the Couplings Transfer Energy-Momentum?
Although there are many different sub-concepts of mass, a distinguishing feature is that the mass of an object is not reflected, recognisable, or measurable when it is not interacting. We can think of all mass as a property of resistance that only presents itself when an object's state of motion changes§. The so-called "rest mass" can only be regarded as a representation of the amount of static energy, and not vice versa.
Thus, it is clear that masses are essentially the same, differing only in size and form*. This also implies that no matter how many differences there are in the occasions of interaction, as long as the required dimension is the same, they are the same mass. In this way, the Equivalence Principle in GR need not be regarded as a specific condition.
However, mass is not constant, and the magnitude of an object's mass in SR changes according to the Lorentz transformation. This predicts that the mass of an object is related to the increase or decrease in the energy of the object and is bounded by the speed of light.
Higgs physics suggests [3] that the mass of bosons is given by the Higgs mechanism [4]; that the mass of fermions is also given by the Higgs field [10], although this is still an open question [5]; and that Higgs particles themselves give their own mass [3], although this is not a clear-cut conclusion either [6].The Higgs field is a scalar field that pervades space, and is the same as the other elementary particle fields, electron fields, quark fields, etc., co-existing in the vacuum**. They all appear to have the same status, except for the Higgs mechanism.
However, the current Higgs mechanism has some obvious explanation missing.
1) Why does the Higgs field selectively couple to bosons? I.e., how does the Higgs field recognise the bosons W±, Z and γ, g, all of which have energy and perform the same function, and to which the Higgs field selectively assigns mass, or not.
2) The magnitude of the coupling coefficient of the Higgs field determines the mass size of the fermions [10]. Then, the mass hierarchy of the three generations of fermions is determined by the Higgs field.Why should the particles all have different couplings coefficients gj to the Higgs field? and where do these values come from[7][8]? Before there is mass, fermions have exactly the same quantum number and they are indistinguishable [9]. How does the Higgs field recognise these particles? The obvious requirement is that they must have additional parameters, or other physical quantities that do not present . At the same time, The action of the Higgs field on the positive and negative particles (e+,e-; q+,q-; ) is identical. And how does it ignore this difference?
3) If the Higgs field is not coupled to fermions, can fermions really travel at the speed of light like photons without stopping? According to the mass-energy equation E=mc2, are all particles energy before there is mass(or none)? So the coupling of the Higgs field is to energy, do they have to exchange energy between them? What is the energy transfer relation here, E=mc2? If m=0 now, is E fully converted to the raw energy of the particle?
4) If the significance of the existence of inertial mass for fermions, W± can be explained, what is the significance of the Higgs Boson possessing inertial mass itself?Does it really implies the existence of a 'fifth force', mediated by the exchange of Higgs bosons [8]?
5) The shape of the Higgs potential V(Ø) expresses the relationship between the potential and the field strength , V(Ø) ~ Ø [10] . Ø is hidden in the vacuum ††. How do different Ø present themselves at a given spatial location? Do they interact with other particles in one way?
6) How does the mathematical explanation of the Higgs mechanism map reasonably to physical reality? Must the Higgs potential be an external field? ‡‡ Wouldn't it be better if it were the field of the particle itself? [12] Is the Higgs mechanism for mass completely excludes the relation between mass and spin ?[15]
7) Not all mass is caused by Higgs [10], and potential energy (binding energy) gives mass as well. In this case, is mass still consistent? Doesn't mass become a variable?
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Supplement: Can mass have multiple origins? (2024.9.26)
“The Higgs does seem to be the source of the mass of elementary particles, e.g., the electron; but it is responsible for < 2% of the mass of more complex things, like the proton. The mass of the vast bulk of visible material in the Universe has a different source.”[1] “the Higgs boson is almost irrelevant to the origin of the proton mass. ”[2]
Mass is an important particle property. If mass has surprisingly multiple origins, how do we explain their relationship? Do they produce the same results by similar mechanisms, or completely different ones? Do they all rely on external fields? Is the mass-energy equation, m=E/c^2, a clue to determining the uniform origin of mass? Can a mechanism that does not provide energy provide mass?
Does mass obey the superposition principle? Is it a scalar superposition or a vector superposition? Is it a linear or nonlinear superposition? Let us consider a process in which u, d quarks combine to form a proton p. In the early stages of the evolution of the universe, nothing else in particular existed. u and d automatically combine to form p in such a scenario, like a pair of lovers meeting to form a family. The family is a more stable structure, and the ‘quality’ of life of the family (In Chinese, quality and mass are one word, 质量) has increased. The increased ‘quality’ does not come from outside, but from the union itself.
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Notes
§ Mass is usually thought of as resisting a change in the "state" of matter, but what is the "state"? Why does it resist change? Why can it resist change? My personal reference answer is here [12]: Mass originates from damping the superluminal intent of a spinning light ring and as a result is the fundamental property that distinguishes fermions from bosons.
* Mass is somewhat similar to energy in that it exists in various forms, but the two are fundamentally different.
** Physics doesn't know what parameters to use to describe these fields and doesn't seem to be interested.
‡ “One of the most important open questions in Higgs physics is whether the potential written in that equation is the one chosen by nature. ”[8]
‡‡ "Central to all of Higgs physics is the Higgs potential."[8] C. N. Yang[13]: "Symmetry breaking with the introduction of a field will not be the last theory, although for the time being it is a good theory, like Fermi's theory of beta decay." Expresses his scepticism about the Higgs mechanism.
† With no Higgs field, the electron and electron neutrino would be identical particles, and the W and Z particles, and in fact all standard model fermions, would be massless. [9]
†† The vacuum seems to be the all-powerful vacuum, and physics assigns many functions to the vacuum [14].
¶ The hierarchies among fermion masses and mixing angles, however, remain unexplained.[11]
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References
[1] Thomson, J. J. (1881). XXXIII. On the electric and magnetic effects produced by the motion of electrified bodies. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 11(68), 229-249.
[2] Abraham, M. (1902). Principles of the Dynamics of the Electron (Translated by D. H. Delphenich). Physikalische Zeitschrift 4(1b), 57-62.
[3] Ellis, J. (2013). Higgs physics. arXiv preprint arXiv:1312.5672.
[4] Higgs, P. W. (1964). Broken Symmetries and the Masses of Gauge Bosons. Physical Review Letters, 13(16), 508-509. https://doi.org/10.1103/PhysRevLett.13.508
[5] Ghosh, D., Gupta, R. S., & Perez, G. (2016). Is the Higgs mechanism of fermion mass generation a fact? A Yukawa-less first-two-generation model. Physics Letters B, 755, 504-508.
[6] Consoli, M., & Cosmai, L. (2020). The mass scales of the Higgs field. International Journal of Modern Physics A, 35(20), 2050103.
[7] Melia, F. (2021). The origin of rest-mass energy. The European Physical Journal C, 81(8), 707. https://doi.org/10.1140/epjc/s10052-021-09506-w
[8] Salam, G. P., Wang, L.-T., & Zanderighi, G. (2022). The Higgs boson turns ten. Nature, 607(7917), 41-47. https://doi.org/10.1038/s41586-022-04899-4
[9] Lancaster, T., & Blundell, S. J. (2014). Quantum field theory for the gifted amateur. OUP Oxford.
[10] Schmitz, W. (2019). Particles, Fields and Forces. Springer.
[11] Bauer, M., Carena, M., & Gemmler, K. (2016). Creating the fermion mass hierarchies with multiple Higgs bosons. Physical Review D, 94(11), 115030.
[13] C.N.Yang. (2014). 六十八年心路(1945-2012). 三联书店.
[15] C. N. Yang emphasised: in the context of gauge theory, the conjecture of why we need a theory of gravity with spin electrons. Today I remain believing that this is a key to the future conquest of quantum general relativity.
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Dear
Enrico P. G. Cadeddu
I apologise for confusing the name and the paper link. It's been corrected over.
I'd really like to see everyone's different scenarios eventually come together consistently. What is the probability that everyone has the same outcome, but the wrong idea? So it's more likely that everyone started from different locations on a distributed chain of causality and came to the same spot.
Best Regards, Chian Fan
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In his article "More is different", Anderson said that new laws of physics "emerge" at each physical level and new properties appear [1]; Wheeler, when claiming that "law without law" and "order comes out of disorder", argued that chaotic phenomena " generate" different laws of physics [2][3]. What they mean is that the laws, parameters, and constants of the upper level of physics appear to be independent of the laws of physics of the lower level. Is this really the case? Are we ignoring the conditions that form the physical hierarchy, thus leading to this illusion?
Let's suppose a model. The conditions for the formation of new levels are at least two: i. Existence of low-level things A,B ...... , the existence of interaction modes a, b,...... ; two, the existence of a sufficient number of low-level things, NxA, MxB....... Then when they are brought together, there are many possible combinations, e.g., (AA), (AAA), (AAA)', ...... , (AB), (BA), (AAB)', (BAB), ........ Then it escalates to [(AA)(AA)], [(AB)(ABA)], ....... What this actually leads to is a change in the structure of things and a corresponding change in the way they interact. The result of the "change" is the appearance of new physical phenomena, new forces, and so on.
Physics is an exact match for math, so let's use math as an example of this phenomenon. Suppose we have a number of strings (threads) that can be regarded as underlying things, then, when a string is curled into a circle, L=2πR, the law of the relationship between the length of the string and its radius, and the irrational constant π appear; when two strings are in cascade, L=l1+l2, the law that the total length of the string is equal to the sum of the individual string lengths (Principle of superposition) appears; and, when three strings form a right triangle, the law of Pythagoras, c2=a2+b2, the law of sums of interior angles of triangles ∠A + ∠B + ∠C = 180° , and the irrational constant √2 appear ...... ; and the transcendental number e appears when the string length L grows in a fixed proportion (continuous compound interest)[4] ...... ; when the string vibrates, sine waves (sinωt) appear; when two strings are orthogonal, i appears ...... ; and when more kinds of vibrating strings are superimposed under specific conditions, more phenomena appear *.......
All these "qualitative changes" do not seem to be caused by "quantitative changes", but more by the need to change the structure. As mathematical theorems emerge, so must the laws of physics, and it is impossible for physics to transcend mathematics. Therefore, as long as there is a change of structure in physics, i.e. the possibility of symmetry breaking [5]**, new "symmetries", new "laws", new "forces", new "constants", new "parameters" are almost inevitable.
Can we try to attribute all physical phenomena to emergence under hierarchical structural conditions? For example, the fine structure constant‡‡and the Pauli exclusion principle emerge because of the formation of atomic structure; the "nuclear force" emerges because of the combination of protons and neutrons; The "strong interaction force" and "weak interaction force" appeared because of the structure of protons and neutrons. We should pay attention to the causal relationship here. Without structure, there would be no new phenomena; it is the more fundamental interactions that form structure, not these new "phenomena".
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Notes
* e.g. Blackbody radiation law, Bose statistics, Fermi statistics, etc.
** Should there be "spontaneous symmetry breaking"? Any change in symmetry should have a cause and a condition.
‡ What does it mean in physics if e will appear everywhere and the individual mathematical constants appear so simply? They must likewise appear at the most fundamental level of physics.
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2024-07-27 补充
In addition to the structure and statistics generated by the interactions that result in new laws of physics, the expression of the different orders of differentials and integrals of such generating processes is another important way of making the laws of physics emerge.
Typical examples of such expressions can be seen @ Ingo D. Mane: “On the Origin and Unification of Electromagnetism, Gravitation, and Quantum Mechanics“:
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Referencs
[1] Anderson, P. W. (1972). More Is Different: broken symmetry and the nature of the hierarchical structure of science.
. Science, 177(4047), 393-396. https://doi.org/doi:10.1126/science.177.4047.393
[2] Wheeler, J. A. (1983). ‘‘On recognizing ‘law without law,’’’Oersted Medal Response at the joint APS–AAPT Meeting, New York, 25 January 1983. American Journal of Physics, 51(5), 398-404.
[3] Wheeler, J. A. (2018). Information, physics, quantum: The search for links. Feynman and computation, 309-336.
[4] Reichert, S. (2019). e is everywhere. Nature Physics, 15(9), 982-982. https://doi.org/10.1038/s41567-019-0655-9;
[5] Nambu, Y. (2009). Nobel Lecture: Spontaneous symmetry breaking in particle physics: A case of cross fertilization. Reviews of Modern Physics, 81(3), 1015.
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The way the laws of physics emerge
In addition to the structure and statistics generated by the interactions that result in new laws of physics, the expression of the different orders of differentials and integrals of such generating processes is another important way of making the laws of physics emerge.
One of examples of such expressions can be seen in Ingo D. Mane 's paper : “On the Origin and Unification of Electromagnetism, Gravitation, and Quantum Mechanics“:
Preprint On the Origin and Unification of Electromagnetism, Gravitati...
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Hello Everyone,
I am trying to model a representative volume element (RVE) at the mesoscopic scale for yarn-knitted hoses in abaqus. The geometric model looks something like this.
The "matrix" is a rubber compound, and the reinforcement is aramid (Kevlar).
I am stuck here. I would like your suggestions about how I can model the interaction between the reinforcement and the matrix.
1. If I use embedded elements in abaqus, I can easily generate hex meshes and can impose periodic boundary conditions easily on opposite faces. However, I want to know what happens to the volume of the reinforcement when using embedded elements.
2. I dont know if I can use Tie constraints. I really need to think about it.
I would love to hear some valuable suggestions.
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Hi,
What properties have you used for the Yarn ?
Is it Transversely isotropic or Isotropic ?
If you have any literature reference can you please post or send to me ?
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As researchers and academics, we often have a community of followers on RG who engage with and support our work. I’m interested in exploring creative and sincere ways to express our gratitude to them. How do you thank your followers on RG? Let’s share ideas and best practices.
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Dear Doctor
to Express Gratitude to our RG Followers is through reading their novel contributions, answering their questions, and offer support via recommend and follow their works. In addition, we can offer advices as possible among each other. ResearchGate is a nice community and gratitude through these offerings is making this community be like sun beams that shine the academic world.
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Hello
I want to conduct moderation analysis using the DEFINE command, categorical variables, cluster variable, and the WLSMV estimator. However, when I calculate the moderation analysis with these commands, analysis did not work.
I am just wondering whether I can use WLSMV estimator rather than ML or MLR to calculate the moderation analysis using DEFINE command in Mplus.
Thank you in advance.
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Yes, you can use the WLSMV (Weighted Least Squares Mean and Variance adjusted) estimator in Mplus to conduct moderation analysis using the DEFINE command. WLSMV is commonly used for modeling categorical or ordered categorical outcomes. The DEFINE command in Mplus allows you to create new variables or compute interactions and use them in your analysis.
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I've often seen the following steps used to test for moderating effects in a number of papers, and I don't quite understand the usefulness of Model 1 (which only tests the effect of the control variable on the dependent variable) and Model 4 (which only adds the cross-multiplier term of one of the moderating variables with the independent variable). These two models seem redundant.
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Burke D. Grandjean Thank you very much for your patience! I agree with you, though I still don't think there is much value in performing this manipulation in the results section.
If the difference between the two coefficients is not large, I cannot be sure if this is because the risk of confounding is low in my study or because my control variables are null (not eliminating the risk of confounding). Conversely, if the difference between the two coefficients is large, again I cannot determine whether this is because I am controlling for confounding well or because of some other problem (such as the multicollinearity problem you describe).
What is the point of this manipulation, given my difficulty in determining the reason for a particular situation? I think the relevant discussion should focus on the relationship of the control variables to the independent and dependent variables, with a dedicated section for detailed discussion and further testing.
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I'm doing a research on numerical investigation of behavior of steel concrete composite beams. I'm using the Abaqus software in my analysis. In my model, I'm using shell element to model the Steel beam and solid element to model the concrete slab where the reinforcement has embedded in it. The steel beam and the concrete slab is connected using the shear studs which were modelled using solid elements. My question is, If we use a tie constrain in between the steel beam top flange (modelled with shell) and shear studs (modelled with solid element) what would happen to degree of freedom in rotation of the steel beam? Here I have used a tie constrain to simulate the welded connection between the steel beam top flange to the shear studs. Will ABAQUS automatically constrain the degree of freedom in rotation if I use this interaction? If so will it cause any inaccuracy in the final results?
Also, is there any possibility to use shell to solid coupling to simulate the same interaction?
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Akila Dulanjalee Wijethunge Can you share your Abaqus models (.inp format)?
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I am attempting a numerical modeling of a pullout test for reinforced concrete. I have calculated the stiffness parameters following the FIB 2010 standard. I used cohesive contact interaction property for modeling ineraction between embedded length of rebar and concrete. However, for some reason, my bond stress vs. slip curve is not declining as expected. I also tried randomly increasing the stiffness parameters (Knn, Kss, Ktt), but the curve still does not behave as it should. Can you suggest what I should do?"
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Please check, that you extract data at the right location
Make sure, that the steel does not yield
try to validate your input data - you can use differential equation solver for this => check my matlab files
have a look at the phd thesis of Roman Sedlmair from KIT Germany
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I am currently working on a model which has steel beam whose one end is embedded in a concrete wall. The cantilevered end of steel beam is subjected to cyclic shear load. I am struggling to model the interaction between the portion of the steel beam embedded and the concrete. What will be the appropriate way to do it?
I tried by using 'hard' contact in normal direction and using coefficient of friction of 0.45 along tangential direction. The results obtained are different than experimentally observed.
Now, I am thinking of using surface based cohesive interaction, but I don't have necessary parameters which is needed for defining traction-separation and damage. Is there is a rational way to calculate these parameters without doing experiment?
Any suggestions and help will be appreciated.
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Jamal Bidadi Thank you for the response. Do you know any papers/materials related to inverse method for finding the constants for the cohesive zone model?
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A micellar solution of protein (in PBS, pH 7.4) was loaded into the sample cell of the calorimeter. The ligand was prepared in 10% DMSO and was loaded in the injector-stirrer syringe.
I have also performed a control experiment to consider the heat of dilution of the ligand solution. A similar addition of the ligand solution was performed under the same experimental conditions keeping PBS in the sample cell. Before evaluating the data, the control data were subtracted from the actual experimental data.
N = 0.4
Model: One set of sites
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The thermogram shows a huge heat effect, very likely stemming from DMSO dilution. It is not enough performing the control experiment and then subtracting it. You will be subtracting large quantities and the residual amounts may be meaningless.
Imagine you have a scale which is not well adjusted having two unbalanced arms and, to get it even, you have to put a 1 kg weight in one side. Now, you want to detect a 1 g weight placed on any arm. Will you be able to detect that small weight?
I recommend you to follow the suggestions provided by Verna Frasca
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Are annihilation and pair production mutually inverse processes?
p+p− → γ γ'
“Annihilation can happen when all the quantum numbers of two colliding particles add up to zero. It might be electron on positron, proton on antiproton, neutron on antineutron, quark on antiquark etc. The force responsible depends on the possible interactions of the annihilating particles.” “Annihilation does not require the presence of other fields.”[x]
“In particular, one concludes that the two photons resulting from the annihilation of slow positrons in matter always have their planes of polarization perpendicular to each other. This has been pointed out by Wheeler who also proposed a possible experimental verification.”[2]
γ γ' →p+p−
It is often assumed that the concept of pair generation was first introduced by Breit and Wheeler, ω1+ω2→e+e-; however, in their paper [1], "pair generation" appears as an old term and cites the paper by Weizsäcker, CF, Z (1934), and Williams' formula。
Perrin (1933) (in French) was probably the first to introduce the concept of 'pair production'. He had a paper entitled "The possibility of materialization by the interaction of photons and electrons."
Regarding pair production: 1)At first sight light-light scattering seems to be impossible because in classical electrodynamics (linear Maxwell equations) the process does not occur. The resulting wave is everywhere given by the sum of the two incoming waves. 2)In quantum mechanics however the situation is quite different. Due to the uncertainty principle a photon of energy E can fluctuate into states of charged particle pairs (with mass mpair.)Experimentally it is very difficult to collide high energy photon beams. A very elegant way of avoiding this difficulty is again to use virtual particles, this time the quantum fluctuation of an electron into an electron photon state.[3]
The identification of pairs is usually a result of statistical findings[4][5][7][8][9]. e.g.
The identification of γ γ → pp events is mainly based on three artificial neural networks, used to separate antiprotons from e−, µ− and h−, where h− represents either a π− or K−[4]
QCD predictions for large-momentum transfer cross sections of the type ‘γγ→ BB' are given, for B and B' any members of the baryon octet or decuplet, and all possible helicity combinations for photons and baryons[8].
An electron enters the laser beam from the left, and collides with a laser photon to produce a high-energy gamma ray. The electron is deflected downwards. The gamma ray then collides with four or more laser photons to produce an electron-positron pair [9].
My questions:
1) The process of "pair production" and the process of annihilation of positive and negative particles are not mutually invertible. Just as the mass-energy equation is not reciprocal (E=mc^2, which is irreversible for photons), p+p- → γ γ' and γ γ' → p+p- are not γ γ' = p+p-. This is one of the differences between the mathematical equations and the physical equations.
(2) The process of "annihilation" does not require special conditions, while the process of " pair production" must require auxiliary conditions, the presence of other particles being necessary. What is the essential function of these auxiliary conditions? What are the conditions under which photons can "collide" and not just interfere?
3) Is the process of "pair production" one or two processes? Must the " pair of particles" be produced in pairs at the same time, or with equal probability for positive and negative particles? Or is it both. The literature [6] describes pairs of positive and negative particles as being produced simultaneously. This question is very important because it determines the mechanism of the "photon-particle" transition and even their structure.
(4) The colliding positive and negative particles do not necessarily annihilate into photons, but essentially depend on whether the magnitude of the energy reaches the energy value of a certain particle, e+e-→µ+µ-. Here is the root of the problem of the level difference of the three generations of particles implied, just as the energy level difference of orbiting electrons. Can quantum field theory give a concrete, or directional, explanation?
5) Where do the properties of the original positive and negative particles go after annihilation occurs? Charge, spin-magnetic moment, mass, and the spacetime field of the elementary particle. Can the origin of the properties be inferred from this? That is, if the properties are somehow conserved, then by reversibility, do the annihilated photons imply all the properties of the elementary particles. The total charge is conserved after the annihilation of the positive and negative electrons. But where does the positive charge go and where does the negative charge go? The following issues are involved here: https://www.researchgate.net/post/How_Fermions_combine_four_properties_in_one
[1]【Breit, G. and J. A. Wheeler (1934). "Collision of two light quanta." Physical Review 46(12): 1087】
[2]【Yang, C.-N. (1950). "Selection rules for the dematerialization of a particle into two photons." Physical Review 77(2): 242】
[3]【Berger, C. and W. Wagner (1987). "Photon photon reactions." Physics Reports 146(1-2): 1-134】
[4]【Achard, P., O. Adriani, M. Aguilar-Benitez and etl. (2003). "Proton–antiproton pair production in two-photon collisions at LEP." Physics Letters B 571(1-2): 11-20】
[5]【de Jeneret, J., V. Lemaitre, Y. Liu, S. Ovyn, T. Pierzchala, K. Piotrzkowski, X. Rouby, N. Schul and M. V. Donckt (2009). "High energy photon interactions at the LHC." arXiv preprint arXiv:0908.2020.】
[6]【Michaud, A. (2013). "The Mechanics of Electron-Positron Pair Creation in the 3-Spaces Model." International Journal of Engineering Research and Development 6: 2278-2800】* Researchgate Link:
Minimum mass issues are involved here:
[7]【Klein, S. R. and P. Steinberg (2020). "Photonuclear and two-photon interactions at high-energy nuclear colliders." Annual Review of Nuclear and Particle Science 70: 323-354.】
[8]【Farrar, G. R., E. Maina and F. Neri (1985). "QCD Predictions for γγ Annihilation to Baryons." Nuclear Physics B 259(4): 702-720】
[9]【SLAC. (1970). "SLAC Experiment E144 Home Page." from https://www.slac.stanford.edu/exp/e144/.】
【Burke, D. L., R. C. Field, G. Horton-Smith, J. E. Spencer, D. Walz, S. C. Berridge, W. M. Bugg, K. Shmakov, A. W. Weidemann, C. Bula, K. T. McDonald, E. J. Prebys, C. Bamber, S. J. Boege, T. Koffas, T. Kotseroglou, A. C. Melissinos, D. D. Meyerhofer, D. A. Reis and W. Ragg (1997). "Positron Production in Multiphoton Light-by-Light Scattering." Physical Review Letters 79(9): 1626-1629】
【Schwarzschild, B. (1998). "Gamma Rays Create Matter Just by Plowing into Laser Light." Physics Today 51(2): 17-18】
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2023-06-25
For the "pair production" experiment, the 2021 STAR Collaboration collectively published a paper "Measurement of e+ e- momentum and angular distributions from linearly polarized photon collisions" [4].
"At RHIC, scientists accelerate gold ions to 99.995% of the speed of light in two accelerator rings. If the speed is high enough, the strength of the circular magnetic field can be equal to the strength of the perpendicular electric field," Xu said. perpendicular electric and magnetic fields of equal strength is exactly what a photon is-a quantized "particle "So, when the ions are moving close to the speed of light, there are a bunch of photons surrounding the gold nucleus. As the ions pass one another without colliding, two photons (γ) from the electromagnetic cloud surrounding the ions can interact with each other to create a matter-antimatter pair: an electron (e-) and positron (e+) [5]. [The headline of the media report is more interesting [5][6][7]]
The history of the discovery of the physics of particle production and annihilation is presented in paper [1]; paper [3] is an analysis of the experimental phenomena by Anderson, the discoverer of positrons, in which four possibilities are proposed for each result, "pair production" being one of them. He finally determined that "pair production" was the real case. The results provided by André Michaud [9] should be similar [see his replies for details].
Comparing the STAR experiment [5] and the E114 experimental method [8], they produce photon "collisions" in a very different way. These two experiments are in turn different from experiments [2] and [3]. It is commonly believed that there are three possible interactions [4]: the collisions of two virtual photons (as calculated by Landau and Lifshitz, giving the total cross section for e+e- production predominantly at the pair threshold), of one virtual and one real photon (Bethe-Heitler process ), or of two real photons-the Breit-Wheeler process.
Question: Yang[1] and Andeson considered that Chao [2] and Anderson [3] are both electron pair generation processes, so is this a "photon-photon" collision "γγ → e+e- " process? If so, are the photons real or virtual, and what is the difference between them and the experiments [4][8]? If not, then there are no "photon-photon" collisions in the experiments of Chao [2] and Anderson [3], but only "photon-particle" collisions?
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Reference:
[1] Li, B. A. and C. N. Yang (1989). "CY Chao, Pair creation and Pair Annihilation." International Journal of Modern Physics A 4(17): 4325-4335.
[2] Chao, C.-Y. (1930). "The absorption coefficient of hard γ-rays." Proceedings of the National Academy of Sciences 16(6): 431-433.
[3] Anderson, C. D. (1932). "The apparent existence of easily deflectable positives." Science 76(1967): 238-239.
[4] Adam, J., L. Adamczyk and etl. (2021). "Measurement of e+ e− momentum and angular distributions from linearly polarized photon collisions." Physical Review Letters 127(5): 052302:
[5] "Collisions of Light Produce Matter/Antimatter from Pure Energy": https://www.bnl.gov/newsroom/news.php?a=119023
[7] "Scientists Generate Matter Directly From Light – Physics Phenomena Predicted More Than 80 Years Ago": https://scitechdaily.com/scientists-generate-matter-directly-from-light-physics-phenomena-predicted-more-than-80-years-ago/?expand_article=1
[8] SLAC. (1970). "SLAC Experiment E144 Home Page." from https://www.slac.stanford.edu/exp/e144/.
[9] the FERMILAB experiment E632 bubble chamber picture;
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I certainly hope the double tracks are simultaneous, because it looks so beautiful.
However, it is also necessary to do further verification as an adequate scientific study. I think this is your point as well. We look forward to more experimental analyses.
Best Regards, Chian Fan
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I am working on Numerical modelling to simulate tribological interactions while marble cutting. If anyone familiar please guide me little bit on this.
Thanks and Regards,
Bhargav Prajwal
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Hi guys,
Can anyone help me to understand, how shall i simulate the isotropic confining pressure in PFC3D using the wall servo command for triaxial testing in soil?
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If we use the solid elements to model the concrete slab, studs and shell element to model the beam, what would be happen to degree of rotations in the steel beam at the following interactions. solid slab and and shell beam - surface to surface interaction solid stud and shell beam - tie constraint
Will these interactions which doesn't take the all degree of freedom in the beam into account might lead in to imprecise results? please explain
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Are you using Staad/Pro ? I made a small model to validate the interface. I can send, if needed. Advanced software have many options, say, tie constraint (as you had indicated), multi-point-constraints (MPC in NISA software), rigid links in NISA, Floor Diaphragm in Staad/Pro, edge compatibility (in SAP2000, not sure about the word), etc. Each one work in different manner. One needs to be very careful with the modeling and results. Better to validate to understand the behaviour and matrix formulation inside the software. Manuals give some details, but not complete.
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If the two-way interaction of an 2 (A1, A2) * 2 (B1, B2) ANOVA is nonsignificant, can I continue to conduct simple effect analyses (like B1 vs B2 under A1, B1 vs B2 under A2)?
Specifically, previous studies found that this two-way interaction was significant, and further simple effect analyses showed that B1 was significantly larger than B2 under A1 but B1 and B2 did not differ under A2.
In the current case of nonsignificant two-way interaction, if I continue to conduct simple effect analyses and do find that B1 is significantly larger than B2 under A1 but B1 and B2 do not differ under A2, can I trust the results of simple effect analyses?
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You ceratinly can do the simple effects analysis, independent of whether or not the interaction is significant. The significant interaction would allow you to interpret a DIFFERENCE between the simple effects. If the interaction is not significant, you cannot make such a conclusion.
You write "I [...] do find that B1 is significantly larger than B2 under A1"
So you have enough data to conclude that in A1, the expectation for B2-B1 is negative. So far so good.
The you continue: "but B1 and B2 do not differ under A2"
and this sentence is wrong. What you found is that the sample difference of B2-B1 under A2 is not statistically significant. This only means that your data does not warrant a conclusion about the sign of the expected difference (based on what you see in your sample). Just having not enough information about the sign of the B2-B1 under A2 does NOT mean that you showed that the effect of B2-B1 under A2 is different to the effect of B2-B1 under A1.
So, eventally to "can I trust the results of simple effect analyses?": yes (provided the experiments are done correctly and carefully and all that). What you should not trust are your conclusions, because these are note backed up by your data. You can only say that B2-B1 is negative under A1. Not more. Everything else is not (sufficiently) conclusive.
Remember the words of John Cox: "Absence of evidence is not evidence of abscense"!
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I used R studio (Lavaan) to build a moderated-moderation model, everything seems fine, CFI/TLI are all greater than .95. But I don't know why the regressions didn't show me the results of the three-way interaction, only the results of two-way interaction.
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Hello Shujie,
Your syntax for defining the regression of FIC makes it look as if you tried to include the three-way interaction. Do you mind posting at least a portion of your data frame to see if others can find the reason?
If you don't want to do that, then I'd suggest you try the same syntax for the FIC regression in the lm command, and see what happens there. That is, model1 <- lm(FIC ~ Livewith_7 + ..., data = your_data_frame_name)
Good luck with your work.
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To calculate the interaction effect of two variables, the literature recommends following the procedure recommended by Aiken and West (1991) and Jaccard, Turrisi, & Wan (1990) which consists of variables are grand mean centered prior to calculating the interactions. What steps should be followed to calculate the grand mean centered variables?
#multiple linear regression #interactioneffect #moderation
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Miguel, so how should it be calculated when one variable is continuous and the other ordinal?
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Apologises I'm really confused and don't know how to do, appreciate any guidance you can give
Aim
1. To explore if anxiety is predicted by stress and treatment delay and whether this is moderated by Brief Cope strategies (Brief COPE) .
Design
1 continuous outcome variable – anxiety (let’s call this H)
2 continuous predictor variables (let’s call these D, S)
3 Continous Moderator - (lets call these BC - ef, bc pf and bc avoidant). These are inputted into SPSS as 3 separate variables as the questionnaire b-cope does NOT allow you to create a total score (by adding ef + pf + avoidant).
- D – delay
- S – Stress (measured by pss-10)
- BC pf - Brief Cope - 1 (consists of 4 questions with each questions represent a different factor)
- BC ef - Brief Cope – 2 (consists of 9 questions with each questions represent a different factor)
- BC avoidant - Brief Cope – 3 (consists of 4 questions which each questions represent a different factor)
To answer the aim I know i need to complete a hierarchial multiple regression but I don't know what to enter on what model or whether I need to do separate regressions and again what should be entered with what.
Q1. Can you please advise how my regression models would look as I can't work this out given my predictors, moderators and outcome variable listed below.
E.g. Model 1 ...
Model 2 ...
Q2. Do I need to look at interactions? If so which ones, how would this be put into SPSS ie in which models.
Possible Interaction examples ?
Stress x bc ef
Stress x bc pf
Stress x avoidant
Delay x bc ef
Delay x bc pf
Delay x avoidant
Q3. Do I need to run separate hierachial multiple regressions? If so can you please write how the model would look ie. Model 1 ..
Model 2...
To confirm I have completed only parametric tests. I have 1 group completing all predictor /moderators variables.
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It sounds like you would end up with a regression model in which you have a very large number of predictor (independent) variables as well as many interaction terms (since you have so many different COPE variables). My advice would be to first think about meaningful ways to reduce the number of COPE variables to be included in the model, for example, by aggregation (calculation of a summary COPE score), factor analysis, or simply selection of the theoretically most meaningful COPE variables. Otherwise you might run into various problems when entering all individual COPE items into the regression (e.g., potential collinearity, too many individual significance tests, large model with many predictors, loss of power to detect interaction and other effects).
Other than that, you could run a hierarchical regression model with only the main effects (predictors, no interaction terms) in the first model, then add the interaction terms in Model 2 to see if they add anything to the prediction of the outcome. But, once again, I would try to reduce the overall number of predictors first to avoid problems in the analysis.
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I'm working on ABAQUS to study the response of geo-synthetic reinforced soil to dynamic excitation. Geocell provides additional confinement to soil reducing the settlements and lateral movements Here is the work flow :
1) created a soil (deform-able solid) and a geocell (shell homogeneous).
2) assigned material properties and section
3) embedded the geocell at required place in the model
Here there should be interaction between soil and geocell (shell element).
I don't know how to give that interaction. Anyone who can help me would be a great help
I'll attach my model. Screenshot (135) was my actual model, while screenshot (136) give an idea on embedded part
regards,
Aditya Krishna
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Interaction between the surrounding ballast/subballast and the geocell were
modeled with contact elements having “hard” normal contact (no
penetration) and tangential contact was modeled as 2/3 of the
tangent of the friction angle (45), which was applied using penalty
friction algorithm Leshchinsky & Ling, (2013)..... but in my case it is also not working. did you tried that how to give interaction between soil and geocell ?
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I have 10 variables (1-D.V, 8-I.V and 1-Moderarator) in my model and I have 267 no of observations i.e. sample size.
I am newbie in AMOS and want to test the interaction between the variables of this model through AMOS. The measurement model estimates are quite handsome. The results of Hayes process model in SPSS for moderation are also fine. However when I run the Model fit measure plugin or Calculate estimates for interaction. The error "RMSEA is not defined" is shown. May I know the reason and solution please?
Note: The standardized values of IVs and moderator have been used for interaction.
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Nice answer Mr. David Morse, Thanks. Your link is very good. Mr. Muhammad, follow this instruction.
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It is usually argued that when a particle passes a slit (diffraction) it doesn't change energy (only direction of momentum) because the plate with the slit is too heavy.
So imagine a plate which is charged with negative charge. An electron falling on it should be rejected but will it lose energy? Or can it receive energy from the plate?
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Hello Ilian,
If the space between slit and screen has a constant potential, then the energy is conserved. Normally the slit electrode and the screen electrode are set to zero (grounded or another reference potential). But it is important that between both electrodes is no potential difference. Otherwise, a charged particle is accelerated and the energy of such a particle changes.
With Regard
R. Mitdank
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We know from Standard Model that photons mediate electromagnetic interactions.
We also know from Planck's equation that the energy of a photon is linked to its frequency: E = h nu
Now, taking nu = 0 would lead us to an impossibility because zero-energy photons do not exist (a photon stops existing when it is not moving)
However, if I consider a constant electric field, its frequency will actually be nu = 0. This could be for instance the electic field inside a capacitor when applying a constant voltage at its electrodes.
In this case, can I still say photons mediate electromagnetic interactions for such a field?
If not, which particles mediate the electric force of a constant electric field?
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Isn't it always the electrons.?
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I have problems with cross-level data:
a. The main effect of p-value is significant (with Mplus).
b. After adding moderator in the model, the p-value of interaction is significant, but the p-value of main effect is not significant.
So: How to solve this situation? What should I do when performing a simple slope test? Is there an empirical explanation?
Thank you sooooooooo much!!!
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The usual rule is that you need both the simple slopes and the interaction to understand what is going on; that is you should not drop the simple slopes. that is main effects. See case 3 in this and 'concluding remarks'
The reality is you need to appreciate all 4 parameters simultaneously - the estimated intercept, both main effects and slope of the interaction . Personally I find this very hard to do from a table of results whereas the graph does this elegantly and clearly.
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Given the independent variable (x) and two regulating variables (M1 and M2), if we want to plot a three-way interaction diagram, which interaction items need to be calculated? After obtaining the estimated parameters of these interaction items, which software or plug-in can be used to draw the three-way interaction diagram?
Thanks & Regards
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In my lab we use the Jeremy-Dawson excel to plot the three-way interactions. It is an excel template that you only have to introduce the required data.
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While we can find versions of "pilot waves", and "collapsing waves" we learned in any way, that at last two waves must interfere to get the interference pattern. Two!
Also one can read "particles wave is interferencing with it self". As we know by experiment that even if between particles can be a time of any duration, the interference will occur. Mystic was added into quantum mechanics.
While physicians did give up to develope a vividly explanable model, we can present that new one for Your attention:
At the same time it explaines all optical effects too like transparency, reflection, absorption etc..
Very short: the interference is an interaction of two waves too, but the one wave of two is the wave of the atomar and electron mass particles of the surface material in the two splits. Both waves are of electromagentic nature, so we have positive and negative electric fields and nord and south magnetic fields alternating. The size of the slits is therefore to be in tha size range of the photons wave lengths to assure, that photon waves only which are near enough to the surface of material of slits are interacting. All other photon waves will be rejected and so tehy do not distorb the interference pattern. The size of wave lengths of the atomic electrons is very much shorter letsay at factor 500 approximately. The photons wave is attrackting and disattracting by electromagnetic forces and it is for very short time running around the bringe element in the middle of the double slit, but cannot be absorbed into the atomic electrons. This is called an "assiciation interaction", which ends up in a decoppling and this happens on different periodical locations with a slightly chengged moment. While photons are arounding the brindge element, the phases of them are overlapping, and so we get the locations with a periodicity which is shorter then photons wave, but larger then electron waves. The angle of the changed momenta depends on locations by the shape of the edge of bridge element. This explains how it is working with particles sended one by one after a time period which can be as long as one wishes. Only the sender source and the double slit must stay in same position during whole experiment.
Transparency is explained in same association interaction as defraction and reflection too: here instead of macroscopic bridge element atoms are arounded by photons waves many times, this times of interaction on each of atoms is slowing down the summary speed of photons through a transparent media. In between space photons do have a known speed of light in vacuum. So at last photons only have this speed and cannot have a lower one. Also it could be that the bridge element can be build by a group of atoms instead of a singleatom. The difference between optical reflection, defraction and transparency is just, that the association interaction has different durations on different atoms of the optical material.
Also, that explaines how it is possible, that a broad spectra of photons is transparently or reflectively or defractively interactiong on same atoms and how the defraction happens for different wave lengths of photons. There is no explain in no theory about to explain that: that problem was never questioned. The mass particles are interacting in same manner, as they are also electromagnetic waves in the inner struckture.
This is the beginn of a new Quantum Mechanic of association interaction, which unites suddenky all optical effects.
Same article in German:
What is Your first impression?
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Seems that double split phenomena are solved; wave particle duality depends on the emiting source
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Hello,
I am looking for good web-servers and computational tools which can help me identify/study/investigate interactions between two or more intrinsically disordered proteins. A reference to review papers discussing such tools will also be of great help. Thank you.
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Dipanka Tanu Sarmah Thank you for your help. Do you know of any predictors, etc. that help investigate interactions between IDPs?
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For two populations A and B, there are situations in which there is a two way exchange of individuals between the populations, and there are situations in which there is no exchange of individuals between the populations. I have thought before of a situation involving one way isolation between two populations, in which it's easy for individuals from population A to immigrate to population B, but impossible for members of population B to immigrate to population A.
Are there any examples of the one way isolation I mentioned between populations?
Any example for marine populations?
Do you know the names of any of the specific species that would be examples of this?
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Hola estimado Dr. Abdiel.
Tiene un punto muy interesante de discusión. Un caso tan específico como el que mencionas sólo lo he leído en insectos como Prodiplosis longifila y sus hospederos. Puede ver este caso que menciono en este artículo:
Por otra parte, en organismos marinos, el hecho de que los individuos de la población B no migren a la población A, podría ser raro, dado que las corrientes marinas y otros factores naturales (ejm. huracanes, sustratos flotantes, etc.) en teoría deberían facilitar el libre paso de individuos (sobre todo me refiero a macroalgas, que son los organismos con los que he trabajado). Sin embargo, el istmo de Panamá, por ejemplo, es una barrera efectiva que pudo generar vicarianza o especiación alopátrica en muchas de las especies marinas que conocemos, hacia el caribe y pacífico oriental tropical. Al menos, en rodófitas como en Bostrychia y Polysiphonia, entre otras, y sobre todo en los ambientes de manglar en los que prosperan (o incluso en corales), esto podría ser viable, pero requeriría de comprobación filogeográfica. Esto lo menciono, porque he encontrado muchas especies de macroalgas en ambas costas de Colombia, pero no sabemos cómo ocurrió el paso de estas poblaciones de algas y el efecto del istmo de panamá en este proceso. Además, el efecto localizado de la dispersión de esporas en istmos, bahías estrechas, con estuarios poco interconectados, o con impacto antrópico, podría eventualmente limitar la migración de una población B a la A.... Me llamó la atención el tema que propusiste, y ubiqué unos artículos en algunas especies marinas en las que puede existir evidencia sobre tu pregunta. Te dejo por acá los links de los artículos, por si te interesa profundizar el tema. Un saludo cordial, Diego:
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I have the following panel dataset: balanced panel, n = 247 (municipalities), T = 15, N = 3705.
I would like to include interactions between a dummy variable that indicates whether the municipality belongs to a certain geographic area and a continuous variable that indicates the amount received in loans in that municipality. The estimated equation is:
varpc ~ cl_structural + lag(varb_m) + cl_structural:lag(varb_m) +lnptindt_cst + lndemog
where, "cl_structural" is the dummy indicating the geographic area that the municipality belongs to and "varfco_m" is the growth rate of loans received.
My question is, can I put this dummy and the interaction and still make a fixed effects panel "individual effects" or a random effects panel? I ask this because I did the Chow, Husman and Breusch-Pagam tests and they were inconclusive and also the theta of the random effects estimate was zero.
F test for time effects
date: pfm3
F = 14.155, df1 = 14, df2 = 3673, p-value <2.2e-16
alternative hypothesis: significant effects
Lagrange multiplier test - (Breusch-Pagan) for balanced panels
date: pfm3
chisq = 3.1422, df = 1, p-value = 0.07629
alternative hypothesis: significant effects
Hausman test
date: pfm3
chisq = 4.5943, df = 17, p-value = 0.9987
alternative hypothesis: a model is inconsistent
In estimating random effects, the individual error and theta are zeroed:
Effects:
var std.dev share
idiosyncratic 0.06692 0.25869 1
individual 0.00000 0.00000 0
theta: 0
I appreciate any help so I can understand where my mistake is.
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I think I managed to clear my doubts.
As I studied the estimates of the error's variance components further, I found that there are at least four more commonly used methods:
Swamy and Arora (1972)
Amemiya (1971)
Nerlove (1971)
Wallace and Hussain (1969)
So, better understanding the nature of my data, I changed the default method of R (Swamy and Arora) by Nerlove or Amemiya, it was enough to be able to estimate the individual error and theta.
I came back to post this experience of mine and I hope it will be useful for others!
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Hi all. A project I'm working on involves the use of a two-way repeated measures ANOVA. The dependent variable is the transcriptional accuracy of sentences-in-noise (measured in proportions). The independent variables are accents of the sentences (2 accents) and visual primes (2 kinds of primes). The results show that there were significant main effects of primes and accents and a significant two-way interaction between primes and accents (F(1, 30)=9.97, p=0.004). However, as shown in the attached line chart, the two lines are almost parallel. Moreover, post-hoc paired-sample t-tests confirmed that participants' accuracy with accent2(Mean=0.77, s.d.=0.13) is significantly higher than accuracy with accent1(Mean=0.51, s.d.=0.18) in prime 1 condition, and similarly, participants' accuracy with accent2 (Mean=0.68, s.d.=0.13) is significantly higher than accuracy with accent1(Mean=0.31, s.d.=0.12) in prime 2 condition. Does this indicate that the main effects of accent and prime are not dependent on each other? If so, isn't this contradictory with the result suggesting significant interaction? Or is it that the occurrence of a significant 2-way interaction only requires that the difference between the group mean accuracies with accent 1 and 2 was smaller in prime 1 condition than in prime 2 condition, which in this case is true.
Thank you in advance!!!
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This is a good example of why it's important to not place too much importance on p-values.
The significant p-value tells you that the statistical procedure is able to identify an effect of the interaction against the noise of the variability of the data * . Looking at the plot, your eyes, too, tell you that the slopes are different relative to the variability in the data. The error bars on the red points overlap, and those on the blue points do not.
But this significant result does not tell you that the interaction effect is large, nor that it is of any practical importance. The plot is very helpful for the reader to understand the results. The difference in slopes is small relative to the difference in Accents, and probably relative to the difference in Primes. How you interpret these observations for your reader is up to you.
__________
* This description is not technically correct, but hopefully gives you a sense of the point I'm trying to make.
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Hi, i am doing hertzian contact 2D ball-on-flat analysis with axis symmetry elements by imposing concentrated load boundary conditions.
Interactions : surface to surface with small sliding, frictionless and hard contact properties are mentioned. Finite mesh size is used at contact point as shown in Figure.
When i am validating the FEM results with analytical values, my contact pressure (CPRESS) is with in 2 percent error. However, my shear stress(S12) value has 30 percent error. Can anyone help how to resolve this issue?
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Not that this solves your problem, but at MATLAB Central you'll find a script that solves the linear complementarity problem associated with the Boussinesq contact mechanics problem
h = K*p+g, h'*p=0, h,p >=0,
where the unknown h is the gap between two elastic (half-space) bodies and the unknown p is the corresponding contact pressure.
Andreas Almqvist (2021). A pivoting algorithm solving linear complementarity problems (https://www.mathworks.com/matlabcentral/fileexchange/41485-a-pivoting-algorithm-solving-linear-complementarity-problems), MATLAB Central File Exchange. Retrieved July 11, 2021.
Andreas Almqvist (2021). An LCP solution of the linear elastic contact mechanics problem (https://www.mathworks.com/matlabcentral/fileexchange/43216-an-lcp-solution-of-the-linear-elastic-contact-mechanics-problem), MATLAB Central File Exchange. Retrieved July 11, 2021.
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I am studying mobility and socio-cultural interactions among palaeohistorical hunters-fishers-gatherers in the Laurentian part of the Subarctic. My main research hypothesis is that rivers and watersheds are "vectors" that had a structuring role on mobility and social interactions. Since I am interested in examples that has been documented around the world, can you recommend me ethnographies, archaeological publications or researchers that have studied watershed in order to understand cultural or sociological phenomena?
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Dear Olivier,
You may want to have a look on the paper of my former colleagues Tomaž Podobnikar:
Kind regards,
Balázs
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Can anybody explain me the interaction of endophytic fungi and the host in the production of secondary metabolites. I just need to know that if the endophytic fungi and the host plant producing same metabolites means what type of interaction or mechanism is going on between them and if not means what...!
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Endophytic microorganisms may control growth and development of host plants. Importantly, endophytic microorganisms living inside their host can often be cultured and grown after isolation from their host plants.
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Hello, I am trying to describe the expected shape of the interactions in a proposal.
(I have not collected the data yet, so I cannot generate interaction plots based on the data.)
Questions:
1. Does "describing the shapes" mean using graphs to show the expected interaction plots or describe the hypotheses regarding the interactions in words?
2. I have general hypotheses about the interactions. However, with the graphs, am I supposed to describe more elements of the expected interactions graphs in the hypotheses in words (e.g. the starting points, ending points, the changing points, the crossing points)?
3. There are some independent variables that we have predicted their main effects. They can be linear but we are still going to test their interactions, i.e. whether their joints effects are higher than the sum of both main effects. How to show these predicted interactions in a graph?
Thank you very much for your time and help!
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The question is very interesting and very long. I will give my opinion. At this stage, when data has not been collected yet, we will try to justify the interaction effect with the help of literature review. The second thing will be to describe when the relationship between IV and DV will be stronger and when it will not be. The additional things, you have come up with, I don't even try to describe once I have final findings available. To develop the moderation graph, you can use excel filed developed by others.
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In my studies I want to evaluate heterodimer formation of different STAT molecules after stimulation with different cytokines. What buffer can I use since those proteins are phosphorylated and the dimer interaction (non-covalent) needs to remain intact for the Co-IP.
Can you provide recipes for buffers that you have used for similar purposes? Thank you!
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Hello Nils Ott
You need to prepare the lysis buffer such that it will minimize protein denaturation and at the same time does not affect the target and the associated protein. Please refer to the link below for more information on how to go about with the lysis buffer preparation for Co-IP. You need to add protease inhibitors as well as phosphatase inhibitors in the buffer.
Best Wishes.
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Hi all,
It is supposed that we have the following experiment:
A laser emitting monochromatic light placed in front of a double Young's slit and a screen is placed at a given distance behind the double slit. Another laser of the same wavelength is placed perpendicularly to the first laser at the half distance separating the first laser to the double Young's slit. My question: is there a non-negligible probability for the process photon-photon interaction, if so, how it can affected the expected results (wave pattern) as shown in an usual photon double sit experiment.
Thanks,
Jaafar
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Your post illustrates the problem that we all have with wave / particle duality. The concept of wave / particle duality implies that a photon or an electron is sometimes a wave and sometimes a particle. This is most unsatisfactory as we would like it to be just one thing and understand what that one thing really is. This is why I have settled on the description of the photon as being a wave quantum. That is a wave disturbance of spacetime with a specific energy given by E = hf. Light is quantised in this way because that is the way it is emitted not because of some inherent property of light.
So I do think the interference experiment is a very revealing choice to understand what is really going on with these wave quanta, which is why I mentioned it specifically in the video:
I see what you are trying to do when you say "make it think it is being more like a particle" but it really is a wave. Now the problem of understanding how waves interact is a bit difficult to predict.
For example, there is an experiment which fires laser pulses at a hydrogen atom to try to establish the position of the electron. This experiment is designed in such a way that it knocks the electron out of the atom and then detects its trajectory so drawing conclusions about the electron's original position. The result that you get when you run this experiment many times is the predicted probability cloud for the electron around the nucleus. So you go away with the idea that the electron is a particle with a probability wave around the nucleus.
But really what is happening is that the electron is a looped wave in spacetime (see video link above) and when it interacts with the laser pulse ( a non-looped wave disturbance of spacetime), the effect is to knock the electron out of the atom and into the detector system which will always give the result of a particular point in space even though the interaction was between waves.
So in this example between electrons and photons we do have an interaction between waves so it is quite hard to predict the outcome of the experiment that you propose. Best thing would be for someone to try the experiment.
Richard
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I did a product development using Design Expert for planning and evaluation. There is a response with a lot of significant effects shown in the Half Normal Plot. By testing factors A, B, C, D I got all these significant effects in addition to the interactions BCD, AB, AC, AD. Now I loose my head trying to figure out what all the interactions are about and I wondered by the way if there are commonly known mistakes leading to so many significant effects. Is it possible to get many effects, because the response might be very complex and dependent on all the factors? It would be kind of a novelty for this response.
Thanks a lot for brain-support
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Thank you for your answers. Actually, I had a lot of samples. There were 8 variations and two Central points, so 4 replicates per variation and 8 replicates per central point (due to a categoric factor). I produced 48 samples and cutted them into 50 stripes per sample. For the response I described, I only used 5 of each.
The design was a fractional factorial, so I had to deal with many aliases.
Looking at the problem some weeks later, I think the range of factors being too large caused the amount of significant effects.
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  1. Is it possible the interaction of sound and light?
  2. Does sound in the environment have a memory effect and history?
  3. Does every part of the brain have a natural frequency (resonance)? How does the body react in every resonance frequency?
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Tanks Dear Zahra Hosseinpour
I saw your link, and I will express my opinion soon.
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I am doing a hierarchical generalised linear modelling analysis and my interest is to examine the moderation influence of green space (at level 3) on the relationship between SES variables at the individual level 1 (education) and the SES variables at the household level (household wealth) with the probability of chronic illness. The hypothesis is: individuals with lower SES and individuals living in lower SES households who live in areas with better green space have a lower probability of chronic illness compared to their counterparts living in areas with lower greenspace.
My problem is that I have three possibilities of centring variables to test this:
(1) Raw scores or (grand mean centring which has similar interpretation)
yijk=γ000+γ100xijk+γ010hjk+γ001zk+γ101(xijk∗zk)+γ011(hjk∗zk)+V10kxijk+V01khjk+V00k+U0jk+εijkyijk=γ000+γ100xijk+γ010hjk+γ001zk+γ101(xijk∗zk)+γ011(hjk∗zk)+V10kxijk+V01khjk+V00k+U0jk+εijk
However, the argument by Raudenbush, Enders 2007 and followed by a lot of others is that (1) does not lead to pure estimates because the estimates are composed of a within and a between components, whereby cluster mean centring rule out the between components (cluster means) and leads to pure within estimates which are preferable in cross-level interactions.
(2) Cluster mean centring (hierarchically)
yijk=γ000+γ100(xijk−x̅jk)+γ010(hjk−h̅k)+γ001(zk−z̅.)+γ101(xijk−x̅jk)∗(zk−z̅.))+γ011((hjk−h̅k)∗(zk−z̅.))+V10kxijk+V01khjk+V00k+U0jk+εijkyijk=γ000+γ100(xijk−x̅jk)+γ010(hjk−h̅k)+γ001(zk−z̅.)+γ101(xijk−x̅jk)∗(zk−z̅.))+γ011((hjk−h̅k)∗(zk−z̅.))+V10kxijk+V01khjk+V00k+U0jk+εijk
But because my interest is in the moderation effect of green space at level 3 and that individuals and households usually share the same SES, there is another possibility for centring at level 3: (relative SES position in the area)
(3) Cluster mean centring (only at level 3)
yijk=γ000+γ100(xijk−x̅k)+γ010(hjk−h̅k)+γ001(zk−z̅.)+γ101((xijk−x̅k)∗(zk−z̅.))+γ011((hjk−h̅k)∗(zk−z̅.))+V10kxijk+V01khjk+V00k+U0jk+εijkyijk=γ000+γ100(xijk−x̅k)+γ010(hjk−h̅k)+γ001(zk−z̅.)+γ101((xijk−x̅k)∗(zk−z̅.))+γ011((hjk−h̅k)∗(zk−z̅.))+V10kxijk+V01khjk+V00k+U0jk+εijk
These three equations produce different models, my confusion is which one makes more sense and serves better answering my question/hypothesis.
P.s. Kelley et al,.2017 say that (2 and 3) is nonsense and just use 1. Which confused me even more.
Thanks
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There is a major flaw with Kelley et al,.2017 and personally I think it should be withdrawn. The case is made in
One commentator said of this paper : "may the Saints & Angels protect us from ever having a paper this thoroughly dismantled" https://twitter.com/polesasunder/status/931602710876639233/
My recommendation is to fit the within - between formulation at each level as argued in
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For example, I have a 2-level factor variable (diagnose) in interaction with a 6-level factor variable (layer). Intercept by default is the first level of all. In my case intercept is control group, layer 1 (out of 6). Dependent variable is continuous (density).
Does the interaction also compare everything to the reference category (intercept)? SO for example does my significant interaction between diagnose and Layer2 mean, that the two diagnostic groups DIFFER DIFFERENTLY compared to Layer 1?
Or am I overcompliacting it?
I also have a bit of trouble interpreting the effect of covariants on the dependent variable. I mean the coefficients listed in the first column of the output. If someone could give me an example on how to interpret these, I would be veery grateful.
(I work in R, with nlme package, lme function. A model for example:
lme(density ~ diag*Layer+gender+age+pmi, random = ~1|ID, data=sumd)
Thank you in advance!
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Dr Booth, thank you! This post also cleared up a lot about interactions.
Exactly! I suspect that it was asked that way, because SPSS provides an "observed power" value in the output for example a general linear model, for each variable. I am not familiar with how SPSS works, so I did not really understand this. As far as I could read up on it, it is not representative, and is directly linked to the p values? (https://stats.idre.ucla.edu/other/mult-pkg/seminars/intro-power/ ; http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.html)
So...Is it sensible to want to calculate the power of a mixed model with multiple explanatory variables? If not (seeing that post hoc power analysis is not very recommended), how can I account for the validity of the p-values?
Also, as you say, power is generally estimated in regard to sample size before the experiment, while planning.. So why do people often expect it? Is it some sort of "habit"?
I am so grateful for your insight. I really want to get the details right and understand what I'm doing. The internet is helpful but vast; it really helps when people with longstanding experience provide guidance.
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I would like to know how to interpret the result when the interaction term is significant while both of the main effects are not.
For instance, I would like to understand the direct effect of A and moderating effect of B on dependent variable D. The results show that the direct effect of A and B are insignificant. However, the interaction term of A and B is significant.
1. In this case, should I interpret that there are no direct effect of either A or B, yet the B moderates the direct effect of A?
2. Is it okay to plot an interaction chart while there are no direct effects available?
Thank you very much!
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Yes you would be right to report the absence of direct effects. The relationship follows a chain effect for significance to be achieved.
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Jamovi free ware has a built-in interaction function (a product term) in linear regression procedure. Does anybody know if Jamovi automatically mean centres variables included in the product term?
Regards
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I don't think so. But you can easily create centered variables:
"Computed variables can be added to the data set, with the ‘add’ button available on the data tab. This will produce a formula box where you can specify the formula. The usual arithmetic operators are available."
you could use X - VMEAN(X) to get a mean-centered variable, or Z(X) to get the z-scores (mean-centered and saled by the standard deviation).
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Dear all,
I have used a tie constraint in ABAQUS to attach two surfaces.
the meshes of both surfaces are compatible so in front of every node of surface-1 is a node of surface-2.
thus the force is directly transmitted from one surface to another one through their nodes (I mean there is no need for interpolation).
Now I would be grateful if you help me request output for a transmitted force for a specific node?
Thanks in advance,
Mojtaba
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I am currently interested in the question if and how one can make use of Randall Collins' (2004) IRC theory for a better understanding of social dynamics in the digital sphere (e.g. digital activism, digital moral outrage, digital community building) and in the digital society in general (e.g. ideological polarization in western democracies). Following Collins rituals are a mechanism of mutually focused emotion and attention producing a (momentarily) shared reality, which thereby generates solidarity and symbols of group membership. In Collins' understanding rituals are essentially a bodily process, which is why he is very skeptical that digital interactions have sufficient intensity for generating symbols and solidarity. At first sight, Collins reluctance seems to be quite plausible. Do we not all experience during the corona crisis that digital rituals (convivial gatherings online, online lecturing etc.) are not more than lame substitutes of their f2f counterparts? Sure, digital rituals are less intense, but this does not mean that they are empty rituals that do not contribute to the (re)production of moral feelings, feelings of membership, symbols, etc. Although there are a few empirical studies of digital interactions that apply IRC theory (Maloney 2012, DiMaggio et al. 2019), a systematic discussion of the potentials and limits of the theory is missing yet. My intuition is that such a discussion would be fruitful and that IRC theory has the potential to stimulate a mirco-sociological research agenda for the digital society. I am looking for persons who share this intuition and are interested in a discussion.
Here are three topics I would like to discuss:
(1) Digital rituals: Can Collins' ritual model be used to capture digital interactions or do we need a distinctive model for digital rituals?
(2) Hybrid ritual chains: According IRC theory, the social consists out of chains of interaction rituals. Since on- and offline processes are intertwined (“onlife”, Selwyn 2019), we have to deal with hybrid ritual chains. Can types of hybrid chains be identified? How do f2f and digital rituals interact? Do digital rituals primarily have the function of bridging rituals?
(3) Ritual explanation of affective dynamics in the digital sphere: At least as much as the internet is an information and knowledge machine, it is an “affect machine” (Reckwitz) which brings charged symbols into circulation. Can IRC theory contribute to our understanding of affective dynamics like for instance digital moral outrage?
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Dear Matthias,
thanks a lot for your contribution. I haven’t read your paper yet, but I am highly interested in it.
I completely agree with you that digital interactions can be emotionally intense. I would even suspect that for quite a lot of people digital interactions are of higher emotional quality and are more important for their identity formation than f2f interactions. This should be particularly true for those who have a marginalized position in the f2f-interaction order.
Rather, my point is that face2face situations – all other things being equal – offer better conditions for establishing successful rituals (= emotionally intense interactions). Under the condition of physical co-presence, for example, “emotional contagion” among participants evolves easier. One reason for this seems to be that under f2f conditions rhythmic coordination of interactions (e.g. turn takings) usually works faster and more smoothly. But we also have intense rhythms in digital interactions, as you emphasize in your comment (à ping pong e-mail communication). My thesis is that one aspect which determines the ritual quality of digital interactions is their rhythmic structure. We can observe this in the context of hashtag activism on twitter: quick responses and new contributions to a hashtag-discussion can initiate a process of gradually growing excitement and collective effervescence. The crucial ingredient of (digital) rituals are, of course, collectively shared and emotionally charged symbols. Symbols seem to have a double function: we need significant and shared symbols to get a (digital) ritual started and during the (digital) ritual these symbols get emotionally (re)charged. My thesis is that the “narrative character” of a symbol is decisive for whether a digital ritual is successful. The hashtags #BlackLiveMatters or #WhyIStayed represent complex and emotionally charged narratives. Using these hashtags should therefore be a promising starting pointing for initiating digital rituals, for instance on twitter.
Best, Ulf
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One thing I keep hearing from people around me is that social media and smartphone usage, especially for the generations who are in their teens right now, have ruined/worsened the way we interact in real-life and that it has a tendency to encourage the development of social anxiety.
This got me wondering: How does social-media and smartphone-usage actually influence real-life interactions? Have studies been conducted that proof/deny the theories stated above?
I'm really grateful for your help!
Best,
Ben Toepel
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I know studies that indirectly tackle this issue, f.i. analyse personality change or disorders.
Casey, B. M. (2012). Linking psychological attributes to smart phone addiction, face-to-face communication, present absence and social capital. Graduation Project, Graduate School of the Chinese University of Hong Kong.
Pearson, C., & Hussain, Z. (2017). Smartphone use, addiction, narcissism, and personality: A mixed methods investigation. In Gaming and technology addiction: Breakthroughs in research and practice (pp. 212-229). IGI Global.
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Hi all,
I have run a three-factor mixed-design experiment with one between-subjects factor (biological sex: two levels) and two within-subjects factors (having four levels each). I have measured several continuous response variables, each of which I have already analysed with a standard ANOVA. I have also collected the values of a nominal (non-ordinal) categorical response variable. This response variable is non-binary (it takes one out of five possible values).
The question is: How to approach the statistical analysis of a three-factor mixed-design experiment with a non-binary non-ordinal categorical response variable?
In particular, I would like to be able to analyse main effects and interactions as in a standard ANOVA.
Any reference to a related R package would be more than welcome.
Francesco
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Thank you Julio
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I am looking to examine whether BMI (as a continuous variable) at baseline moderates the relationship between mental health symptoms and changes in BMI at follow up. Could you please suggest the best way to examine the interaction effect in multiple regression analysis?
Thank you in advance for taking the time to look at my question!
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Well Neli Pavlova, if you are using Stata as your statistical software and as you have said BMI is continuous and Mental Health Symptoms is categorical (let's suppose) then while fitting the regression model in Stata you can combine these variables by #
For details see to the link below -
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Dear researchers,
after changing my model, I’ve got some new issues and I hope, anyone can help. Thanks in advance!
I’m using amos for my model. I’ve got 2 dummy IVs (treatment 1, treatment 2, control) and 4 continuous latent mediators. I want to moderate the effect of the 2 IVs on the 4 mediators with 2 continuous latent moderators. Yet, if I include the interaction term, my model fit becomes really bad. I calculated the interaction terms as follows: IV1 (1/0)* moderator and IV2(1/0)* moderator. I included the 2 moderator-variables as direct effects, and then also added the interaction terms in the model (4 interaction terms in total). The issue with this is that the items of the interaction terms correlate with the items of the moderator variable. This makes perfectly sense, as they are (at least in part) the same variable. Can I argue that the model fit is bad but I can still accept it as it is caused by the correlation of the moderator IV with the interaction term? Or can I covary the errors of the respective items?
Additionally, I’ve got two more questions:
1) There are in total 8 paths to moderate per moderator-variable. Thus, I tried also using a combined treatment group (treatment 1&2) and moderate its effect on the mediators. The results of this combined moderation make sense theoretically and follow a certain "rule". Yet, if again I split them up and moderate treatment 1 and 2 separately, the significance of the interaction terms changes with the strength of the direct effect (direct effect of treatment 1 or 2 on mediator). In other words, the interaction terms on a strong direct effect, become significant and those on weak direct effects become insignificant. Thus, I would like to argue, that I follow the before mentioned rule of as the slight deviation is attributable to the difference in strength of the direct effect and thus, I want to “ignore” it. Does this make sense? In the best case, does anyone has any literature at hand to justify this?
2) I would like to further explore the moderating effects. I am highly interested, whether the control becomes better in effect than the treatment at very high levels of the moderator. Yet, if I do a spotlight-analysis (mean-split), there is no change in sight. Thus, a more extreme approach is needed. Yet, if I split my data in 3 data sets at 1 std. deviation above/below the mean, the data set is not big enough. I’ve got approx. n=500, and if I include only the data with 1 std. dev. above/below the mean in amos, it is too small (according to amos). I found data sheets to illustrate a moderation (where the unstandardized effects of IV, moderator and interaction term are to include, see: https://www.youtube.com/watch?v=K34sF_AmWio (min 7).). Yet, I feel that those are very inaccurate and I am unsure if I can “trust” the effect and include it in my thesis. Does anyone has any advice on this?
Thank you so much for your support. Any advice is highly appreciated.
Best
Anna
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Hi Anna,
the error variances in these residual indicators will usually be very low/close to zero, thus, leading to Heywood cases. If you the want to force the algorithm into estimating a covariance, this will be too much.
Hence, leave the covariance out, check the model fit and the error variances (if they are positive) and then proceed.
Best,
Holger
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We use digital communication tools in almost every situation. Email is now considered antiquated, but remains the standard communication tool. Erving Goffman has shaped our (sociological) understanding of interaction. Above all, he has shown what an important function everyday rituals have for cooperation and for our identity. My question is to what extent the routine use of digital communication media changes our modes of interaction themselves.
Or maybe it doesn't.
I would like to discuss ideas, results and views here.
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Thank you very much for the exciting suggestions and comments.
It's a little difficult to summarize the different voices, but I'll try.
For one thing, we have a theoretical problem. One that relates less to the question of 'digitalisation' than to the forms of linguistic, symbol-based interaction (where the time dimension remains an open question) that depends on imagination without the physical balancing dimension.
If this is a re-entry of communication into communication - probably. However, this does not say anything about whether "digital interaction" has a quality of its own compared to the physically co-existent 'analogue'.
Does not the machine, the digital processing, require its own translation processes in order to be able to interact in the digital medium?
On the other hand, we have an empirical problem of social relationships and their interdependencies. Where, in my view, the question clearly arises as to whether the common or even remote variations of digital communication platforms and their surfaces take up and process human or social needs. At the crossroads of both sets of questions lies Avi's topic, which is on the trail of the resonance possibilities of digital communication or the lack thereof.
Hopefully, these rough thoughts help to keep the discussion going. I guess, there is still a long way to go.
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Hi,
I have categorized 3 my time points (ftime0, ftime1, ftime2) and treatment type (access_typeAVF, access_typeAVG). Treatment is given after time point 0 (or baseline).
Serials (subject) are nested within sitelocation.
For the time being, If I consider the following model as the final model, how can I interpret the coefficients?
I am especially interested in ftime1:access_typeAVF & ftime2:access_typeAVF .
Please see the attached.
Update: Project Description
It is a kind of Pre (1-time point) - Post (2-time points) design.
I have 98 subjects. Each subject was measured 3 times (Baseline Year, Year 1, Year 2) on treatment efficacy marker Albumin (continuous scale) and they were nested within site locations. There were two types of treatment or access type- AVF & AVG. Initially, all subjects were given an Old treatment "Ot". However, due to some complications from Ot (indicated by a decrease in Albumin. An increase is considered to be an improvement), subjects were assigned to AVF & AVG. This assignment was not randomized.
After collecting the baseline info for each subject, they were given either AVF or AVG. These two treatments were assigned after the baseline year.
87 subjects got AVF and 11 subjects got AVG. There were missing values on the repeated measurements. Missing albumin measurement were more frequent in year 2 (46%) followed by year 1 (13%) and baseline year (6%).
Increased Albumin measurement is an improvement.
My research question-
1) what is the effect of treatment type on the efficacy marker over time.
- Is albumin increased in time point 1 (ftime1) from time point 0 (ftime0) for AVF & AVG groups?
- What is the pattern in time point 2 from time point 1 for AVF & AVG groups?
2) Is there any role of dialysis vintage and sex(not included here)?
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I think it would be good to control for the differences in albumin measurement at ftime0 (time point zero, or baseline); i.e., before the treatments were applied. This can be done by using the albumin measres at that ftime0 as covariates, as I did in the R script. This is a common statistical technique to control for preliminary (unwanted) differences between treatment groups.
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My thesis-partner and I examine context level and individual level predictors effect on populist right-wing voting (i.e., Danish People's Party in Denmark) applying a multilevel logistic regression model with three levels (individuals nested in municipality-elections nested in municipalities). We use a pooled sample encompassing five elections (2001, 2005, 2007, 2011, 2015).
We follow the approach of Fairbrother (2014), separating the context effects into a cross-sectional and a longitudinal component, so change is measured at the upper-level. In this model, we were not able to estimate cross-level interactions - possibly due to low between-cluster variance (our model did not converge).
However, applying a multilevel linear regression model by substituting the binary dependent variable with a scale variable (from 0 - 10, with 0 indicating no sympathy for the party and 10 indicating sympathy for the party), we were able to include two cross-level interactions and an accompanying random slope of educational attainment (this is a robustness check).
Our question is how to interpret the results?
We interact the individual level variable educational attainment (six point scale), with the longitudinal component of the context variable, unemployment rate. The included variables are grand-mean centered.
We find that the conditional slope of the individual level predictor, educational attainment, to be significant at p<0.001 with odds ratio of -0.386.
We find that a one unit increase in the longitudinal component of the unemployment rate causes a decrease in cluster-average levels of support for DPP by -.086 units, at the grand mean of education. The conditional slope of this component is thus negatively associated with support for DPP.
For the cross-level interaction between education and the longitudinal component of the unemployment rate, we find a negative effect size of -.046 significant at p<0.001. This captures that a unit increase in the unemployment rate from the grand mean strengthens the negative association between education and support for DPP, all else being equal. For the municipality that experienced the largest decrease in the unemployment rate from the grand mean, the association between education and support for DPP is -0.2. Oppositely, in the municipality that experienced the highest increase in the unemployment rate from the grand mean, the association between education and support for DPP is -0.61.
However, we do find that for the lower-educated group, the association between the unemployment rate and support for DPP is marginally positive (0.027).
Is this the correct interpretation?
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Three comments that are meant to be helpful!
1 I find it much easier when there are interactions to interpret results by using a predicted value plot of the variables involved in the interaction, keeping all the other variables in the model at say their modal or mean value. It is particularly hard to interpret confidence intervals around the predicted line without some visualization. The SEs for the individuals dummies are of course at some value of the continuous interacting value and will be different at different values, that is teh point of the interaction. To that end we built a tool - the 'customised prediction' facility in MLwiN to do exactly that
see ??
2 On non convergence with logistic,; I usually use MCMC estimation in this situation ;
3 Given the importance of time when it is included, I would have interacted education with time, but you may well have done that and found it not to be important.
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Hello,
I am trying to model a steel bracket fastened with steel nails to two pieces of wood (see pictures below, please). To deal with the nail withdrawing, I am using cohesive elements connecting the wood panels to the nails. I am also using hard contact to connect the nails to the bracket (see the input file, if wanted).
However, the model is not converging during analysis. I have tried to suppress the vertical wood part (along with its nails, cohesive elements, interactions, etc) and run only a bracket load with the remaining elements. In this situation, the model converges just fine. I did the same experiment suppressing the floor wood part (along with its nails, cohesive elements, interactions, etc) and the model also converges just fine. No strange or weird results are present from any of these two tests. However, when I resume the entire model, the not-converging situation happens again.
The loading I want to apply is an upward movement of the vertical wood panel (which should interact with the cohesive elements, which should interact with the nails, which should finally interact with the bracket), but this loading condition does not converge. If I fix the vertical wood panel and apply a vertical load on the bracket itself, then the model converges just fine. It seems like moving the wood, somehow, causes the model not to converge.
So I am wondering, is ABAQUS not converging due to the high amount of interactions present (33 constraints and 36 interactions defined in the model)?
Is there anything else I can try to make this run?
Thanks for the help.
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Interesting... Please share me the final answer...
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Hi,
I am checking the effect of temperature on kidney disease hospital admissions with NO2(an air pollutant). In other words, how the effect of daily temperature on Kidney disease admissions modifies with changing levels of NO2 in air.
I got the significant result with as follows:
Estimate Std. Error z value Pr(>|z|)
templg0:no2lg0 -0.0004774 0.0001964 -2.431 0.015068
if I interpret it as:
An increase in level of No2 is significantly associated with a decrease in the effect of temperature on respiratory admissions. 
OR
The effect of temperature on hospital admissions decreases as NO2 concentration increases.  
Is that the right interpretation or any changes required. Please give your suggestions.
Best,
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Would you like to cite the variables of your study more clearly? Are there two or three variables? In the case of quantitative variables, it could more simply be a problem of correlation between two variables or between two variables taking into account a third one.
Otherwise, the types of interaction encountered in medical
research may be a multiplicative interaction
or an additive interaction.
Multiplicative interaction is measured by relative risk or
odds ratio. The observed effect may be a multiplicative
synergy or a multiplicative antagonism.
Additive interaction is measured by the difference in risk.
the observed effect may be additive synergy or additive antagonism
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I am looking for databases that contain microRNA-drug interactions. Any suggestions or recommendations?
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Dear Ali Akbar Jamali ,
Look the link, maybe useful.
Regards,
Shafagat
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--- QUESTION SOLVED (thanks for your help!)---
I am interested in the effect of different exposures on participants' performance (% correct) in two tasks. Specifically, I would like to compare the effects of different exposures as a function of task. To do so, I build the following model (simplified):
  • performance ~ exposure (4 levels) X task (2 levels)
However, results are difficult to interpret as performance distributions of task 1 and 2 differ from one another. As the attached exemplatory graphs show, participants generally performed better in task 1 than in task 2.
Does anyone have a suggestion how I could reasonably investigate whether there is an interaction between exposure and task? As an example, I am hoping to get to a conclusion like: "compared to the control (exposure A), exposure B had a significantly stronger effect on performance in task 2 than task 1." Z-transformation could be the key, but I don't quite know how to proceed.
Thanks in advance for your suggestions.
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There are many things that need to be considered and discussed to come up with a sensible analysis that would give you interpretable and reliable answers. Some of these questions are about what are between- and within-subject factors in your design, if there was a cross-over or if time-and/training effects need to be considered (and how). Another question is if "exposure" is really categorical or rather metric.
It seems to me that would need some kind of a mixed (multilevel) binomial model adressing the interaction between task and exposure.
A z-transformation is not ok. Using the proportions rather than the (known?) number of trials and successes per subject is already not recommended. If you don't have that information and the response is not binomial (like correct vs. not correct) but measured as a proportion (%), you would use a beta model rather than a binomial model. Although the relationship between predictors and response can be linearized using a logit-transfromation, I would not recommend this. It is better to link the mean via the logit rather then to transform the response values.
These comments should encourage you to follow my main suggestion: seek help from a local statistician.
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I have found a novel peptide with can regulate pollen tube growth.
How can I screen the cell surface receptor for this peptide?
Does Y2H work?
If the peptide interaction with Extracellular domain of its receptor Does split ubiquitin system suitable for screen the interaction outside the cell membrane?
Thank you!
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Check this paper: Development of peptide receptor binding assays: methods to avoid false negatives.
Vergote V, et al. Regul Pept. 2009.
And the attached table.
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Molecular docking analysis
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The easiest way to observe any receptor-ligand interaction in PyMOL is!
(1) Load your complex.
(2) Hide water etc if any for visibility enhancement...
(3) Click the action button (A) and scroll down to presets that will walk you through to another list where you will find "ligand sites".
(4) Select ligand sites and it will open into another list, there click on the option "cartoon".
The whole complex will turn rainbow and you can rotate the structure, zoom in and out to explore the interactions.
Good luck
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Currently, I am writing a research on the impact of the interaction between information systems and Knowledge management on improving quality . Can anyone help me with a questionnaire, model or whatever. Thanks in advance.
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you should go to google scholar and type the topic.You will get a mix of papers both qualitative and quantitiave on this topic.Read the papers which show a pdf as they are free.
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Dear Researchers,
I am looking into the following topic: The impact of the interaction between information systems and knowledge management on improving quality in industrial corporations.
Mostly, I am interested in the impact of the interaction between variables.
I usually use the statistical test of: Univariate of Analysis Variance.
Thus, I need to know how should I test the impact of the interaction between variables using another method.
Thanks in advance.
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Hello Mr. Butler
I want to know how should I measure the interaction between two independent variables.
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Salve, sto lavorando alla mia tesi relativa all'innovazione e sostenibilità ambientale. Ricercavo degli articoli che evidenziano i punti in comune tra i due concetti. Potrebbe essermi utile anche qualcosa circa la sostenibilità ambientale.
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Buon lavoro.