Science topics: Human-Computer InteractionInteraction

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# Interaction - Science topic

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Questions related to Interaction

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

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.

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

[6] "Colliding photons were spotted making matter. But are the photons 'real' ? ": https://www.sciencenews.org/article/colliding-photons-matter-particle-physics#:~:text=In%20a%20demonstration%20of%20Einstein%E2%80%99s%20E%3Dmc%202%2C%20collisions,colliding%20particles%20of%20light%20create%20matter%20and%20antimatter.

[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;

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

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

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?

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?

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.

Hello,

There are methods such as DPM, DEM, Eulerian, and MP-PIC to model fluidized beds with particles moving within the system. For systems with a very high number of particles moving within them, some of these models such as DEM are time-taking methods and in some other methods that have some simplifications, accuracy may not be enough. I was wondering what is an appropriate method for modeling fluidized beds with a very high number of particles moving within the system?

Thank you,

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

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.

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

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.

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?

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?

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!!!

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

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?

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.

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?

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.

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!!!

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?

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?

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...!

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!

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!

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

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

- Is it possible the interaction of sound and light
**?** - Does sound in the environment have a memory effect and history
**?** - Does every part of the brain have a natural frequency (resonance)
**?**How does the body react in every resonance frequency**?**

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

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!

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!

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

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

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?

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

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

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!

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

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.

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)?

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?

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.

+2

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,

I am looking for databases that contain microRNA-drug interactions. Any suggestions or recommendations?

--- 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.

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!

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.

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.

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.

I would like to test for mediated moderation as in the attached path diagram.

I obsered that the effect of binary treatment (X) on behavioral outcome (Y) depends largely on the moderator (Mod). I want to test whether the moderation is (partly) explained by subjects perceptions of the intervention (Med).

Any idea how to perform this in SPSS, R, or Stata?

Any publications concerning these methods are highly appreciated.

Hello there, i am using a model of the form:

Y=a+β1 X1+β2X1*X2+X1t-10+X1t-10*X2+...

where :X2 is an index that takes values between 0 and 1

X1t-10 is 10-year lagged version of X1

I want to estimate the marginal effect of X1 at different values of X2 in STATA. For example let's assume that X2 represents a proxy for intelligence, i want to calculate the marginal effect of X1 for people with intelligence at the 25th, 75th percentile of the intelligence distribution in the short run and long-run. I experimented with the command -margins- without success.

Thank you for your time.

In testing moderating effect of a moderating variable on relationship between IVs and DV (all the latent variables are with reflective indicators; and sample size over 200) using SmartPLS 3, firstly it obtained a significant moderating effect by using the product indicator approach. However, the test again using the two stage approach found no moderating effect.

As referred by Henseler & Chin (2010), the two-stage approach has the higher statistic power, i.e. it is the most likely approach to detect a significant interaction. But the results here showed the opposite!!

I would appreciate any suggestions regarding this.

Dear everyone,

I am performing a cox proportional hazard regression on survival, in a sample in which almost everyone dies in the follow up period. I am far from an expert, and want to be sure to thoroughly check the PH assumption. I can get some help from a statistician, but it takes 2 weeks til i can get an appointment (I will!)

I found 2 methods for checking the PH assumption that i can easily perform in SPSS: visually I can inspect stratified log minus log plots (and scatterplots of residuals for continuous variables). My question is about the statistical method: checking if the product of time and my variable becomes significant in the cox regression (if yes, not fulfilling PH assumption). I havenoticed that it is quite common to first make an univariable cox regression for each covariate. I have been reading on the subject but see that different methods are used when it comes to check individual Time dependent covariates.

Some people check the product of time*variable (T_COV) univariable in the cox-regression, others put both the T_COV and the original variable in the cox regression (example for age: T*Age and age would both be taken into the cox-regression). In the second method, one does noet acquire univariable cox regression for the T_COV.

Why is it important to me? There is one T_COV variable that is not significant in univariable cox- regression, but becomes significant in the regression with only the T_COV and the original variable that it is a product of.

I hope you want to give me your thoughts on the topic. I am extra happy with reading recommendations/reliable sources!

If you have other remarks, questions, or if my methods are all wrong: please comment!

Hi there,

I have recently conducted a moderation analysis using the PROCESS Macro plug-in for SPSS. I have tested the moderation effect of W (categorical using dummy variables) on the relationship between X (scale) and Y (scale).

I am trying to interpret the results, and I am looking for clarification (I have pasted the results at the bottom of this post).

For this model, the R-square change of the interaction is not significant - p = .055. However, for W categories 3 and 4, the p value of the conditional effects is significant, at .0002 and .0009 respectively.

I have interpreted this as the interaction between W and X only being significant in predicting Y for those in categories 3 and 4.

Is this correct, or have I interpreted this incorrectly?

Many help in advance

Test(s) of highest order unconditional interaction(s):

R2-chng F df1 df2 p

X*W .0446 2.3599 4.0000 163.0000 .0556

Focal predict: X

Mod var: W

Conditional effects of the focal predictor at values of the moderator(s):

W Effect se t p LLCI ULCI

1.0000 .0865 .1300 .6656 .5066 -.1701 .3432

2.0000 .0807 .1319 .6119 .5415 -.1798 .3412

3.0000 .4775 .1303 3.6638 .0003 .2201 .7348

4.0000 .5028 .1484 3.3879 .0009 .2098 .7959

5.0000 . 2005 .1165 1.7206 .0872 -.0296 .4306

Hi,

I am looking to test the the extent to which variable M (scale) moderates the relationships between X (categorical) and y1 (scale) and y2 (scale).

I was hoping to use PROCESS, but it only has one box to drag DVs into. I was wondering if it is possible to somehow test this model using PROCESS?

Any help would be greatly appreciated!

Many thanks

There are some useful methodologies such as ISM and DEMATEL in order to reach the mentioned purpose. Could you suggest me a novel and more accurate methodology in this way?

Best regards,.

I am running analyses for gender as a moderator between groups with a GLM. The F-statistic for gender is not significant in my GLM,

*p*= .069, however, when I run independent samples t-tests for gender, there is a significant difference betweens males and females on my DV,*p*= .011. Can someone enlighten me on why this might be? Thank you!!