3D - Science topic

I want to just add to Rob Keller's comment:

Chimera is a free tool that can show hydrophobic/hydrophilic surfaces with a few clicks instead of using a script for PyMOL.

Open Chimera, load your protein, go to Presets, then click "Interactive 3 (hydrophobicity, surface)".

Thats it.

X-ray irradiation of the liver might prevent malaria infections, but there is no reason to treat malaria infection with X-ray irradiation of the liver.

Nail Besli Yes you are absolutely correct about the change in conformation of the protein when bound to two different molecule types. However, if you have no structural information available for the conformation with the activator, but have the information for the inhibitor binding.

Molecular dynamics can be a good way to gain some insights of how these conformations change when your protein is unbound (WT), bound to inhibitor and bound to the activator.

This comparative analysis of the structural transitions on a free energy landscape may provide you with detailed conformational transitions that are playing roles. It might be external factors too like salt concentration, pH temperature and so on.

Further, you may use this information to validate your results experimentally and if the changes are adequate you can measure them using fluorescence spectroscopy and circular dichroism spectroscopy experiments.

Dear Bouziane Ghoual ,Thank you for your attention to EEI 2024. We apologize that considering the on-site experience, this conference only accepts offline presentation in China.

If you are interested in this conference, you could consider submitting your papers and attending the conference offline. Or you could also consider joining as a listener without submmison online.(Listener could participate online）

hello..you can download and install even the standard PLAXIS software..you go to PLAXIS main website(www.plaxis.in)..try to find out one month free download versions for students and faculties from any university..like thse many other softwars like ABAQUS, ANSYS..you have to find out from their main web site..ok all the best pa..

That is not exactly right, the combination of inflow/outflow BCs depends on the assumption of the flow, compressible or incompressible. In case of compressble flows you have further to distinguish between subsonic or supersonic conditions.

The flow from the pipette must be very high during the media replenishment - resulting in detachment of the cells. Try using reservoirs or syringe pump at very low flow rate to replenish the media for cell growth and maintenance.

Of course

This short answer is simply to clarify to our colleague Professor Murteza the question and his answer and to thank him for his helpful suggestions:

1-We admit that the answer above is not clear enough and requires some logical and mathematical clarification.

2-We believe that your request also requires additional clarification.

Please write your answer/question in sheet music form, i.e.:

1-What is the origin of.,.

2-Why is this term...

3-How to derive/apply...

etc..

And we will do our best to respond as soon as we receive your inquiries.

May consider using Permai fluorescence dye.

hi,

Have you been able to figure out the answer, I am currently doing the same thing and I have the same issue.

That's not a research question. Look on Stackoverflow, ChatGPT or specialized forum about OMNet++.

Some articles that appear to be related:

Regards,

Joachim

Hey! There's a new package that you can install using "pip install free-energy-landscape", and with this package, you can extract all frames, their CVs (collective variables), and energy based on a threshold value. For example, in the command below, from the inputs corresponding to collective variables 1 and 2, obtain all frames that have an energy value <= 5 KJ/mol:

$ free_energy_landscape collective_variable1.txt collective_variable2.txt --energy 5

Check out the details on GitHub: https://github.com/sulfierry/free_energy_landscape/

Prasenjit Bhowmik Thank you soo much. I'll look into both the softwares.

Relateive keyword is deform in LAMMPS which can shear 3D block for your first question.

Shear force between two different fixed layer of a block can be realized by fix move keyword applied for the upper block. The five move keyword will force the upper block to move regardless of its force on it.

Or you can shear the upper block by ATOMSK or PYTHON code.

Maryam Ansari Sigmacote, primarily used for rendering surfaces hydrophobic, is a silicone-based solution. In the context of auto-cleaving, we are essentially discussing whether Sigmacote can undergo a self-induced breakdown or alteration. From my experience and understanding, Sigmacote itself is not designed to auto-cleave. It is formulated to create a stable, inert, hydrophobic coating. This stability is a key characteristic, especially in laboratory settings where it's used to prevent the adhesion of aqueous solutions to glassware.

However, if we consider the impact of external factors, the story gets a bit more complex. For example, exposure to certain chemicals, extreme pH conditions, or high temperatures might lead to a breakdown or alteration of the Sigmacote layer. Such conditions could potentially change its 3D structure. This change might not be a cleavage in the strict scientific sense but rather a degradation or breakdown of the silicone polymer chains.

In terms of damaging the structure, it largely depends on what we mean by 'damage'. If the integrity of the hydrophobic layer is compromised, then in a sense, yes, the structure is damaged as it can no longer effectively repel water. This could have implications in experiments where the hydrophobicity of the surface is critical.

In summary, while Sigmacote is not designed to auto-cleave, under certain extreme conditions, its structure can be compromised. It's a reminder of the delicate balance we maintain in scientific experimentation, where each component plays a crucial role and understanding their limitations is as important as understanding their capabilities.

I admire the discussion. Yet, I think that there is another important question which contibututes to the entire picture: how to discriminate between causality and randomness? More precisely, the question is twofold: (i) how to discriminate between causal correlataions and provisional ones. (ii) whether and how the same process gives rise to both causal and random-like events?! In this line I still wonder how and why the fluctuations of any matter are specific to that matter regardless to the fact that all current theories of randomness do not prescribe any specificity. As an example my question is why the fluctuations of water are water?!

The use of S/W and computer visualization is a very recent turn of events. Mathematics has been developed and taught for centuries prior to the advent of computer visualization tools. Computer visualization is not a necessary condition for the development of geometric intuition. While i don't have any specific title in mind, I suspect text book from the around the mid 20th century would be a good place to start looking.

@Hiwa aryan

Sir I'm in a trouble of calculating osi using comsol.

Will you please explain detail how to write the expression?

Actually I can't calculate the average wss.

I apologize for any confusion caused by my previous response. To clarify, when discussing transistors based on 3D materials, it refers to transistors built using materials with a three-dimensional crystal structure, as opposed to 1D or 2D materials.

The term "3D materials" can be somewhat ambiguous, as it can refer to materials with a bulk, three-dimensional structure like silicon, or to materials with a more exotic crystal structure such as perovskites or metal-organic frameworks (MOFs). In the context of transistors, the most commonly used semiconductor material is silicon, which has a 3D crystal structure.

Plaxis is not the tool to model failure through erosion. The FoS calculation you are describing is a mathematical abstraction to model other mechanisms.

Kim Kangseok Şükrü Aktaş

1. Dimensionality

2. Spatial Resolution

3. Boundary Conditions

4. Material Models: FDTD simulations can account for complex material models, including dispersive and anisotropic materials, with more accuracy. TMM, while versatile, may use simplified material models or assume certain material properties to make calculations tractable. These simplifications can introduce discrepancies, particularly when dealing with materials that exhibit strong dispersion or anisotropy.

5. Computational Resources

Good luck!!

If I am correct, complex variables can only be mapped in two dimensions defined by the real and imaginary coefficients. A third dimension can be added over the two-dimensional map.

Dear Yixiang Wang ,

The general picture emerging from the study is that the calibration procedure for the 3D camera is an inevitable stage.

Regards,

Shafagat

Some commonly used evaluation methods in this domain:

Task Completion Metrics: These metrics measure the success and efficiency of completing specific tasks or objectives within the simulated environment. For example, in a robot navigation scenario, metrics like task completion time, path length, or goal-reaching accuracy can be used to assess performance.

Error Metrics: Error metrics quantify the discrepancy between the desired behavior and the actual behavior of the simulated robot. This can include metrics such as position error, orientation error, or trajectory tracking error, depending on the nature of the task.

Statistical Analysis: Statistical analysis techniques can be applied to evaluate the performance of the simulated robot. This can involve comparing different algorithms or configurations using statistical tests like t-tests or analysis of variance (ANOVA) to determine if significant differences exist.

Efficiency Metrics: Efficiency metrics focus on resource consumption and performance optimization. For instance, in a robotic manipulation task, metrics like energy consumption, computational efficiency, or resource utilization can be considered to evaluate the efficiency of the simulated system.

Sensitivity Analysis: Sensitivity analysis involves varying the parameters or inputs of the simulation to evaluate their impact on the performance. This helps understand the system's sensitivity to different factors and aids in fine-tuning the algorithms or system design.

Simulation Benchmarking: Comparing the results of the simulated system with known benchmarks or existing systems can provide an objective evaluation. This can involve comparing metrics like accuracy, speed, or efficiency against established standards or state-of-the-art approaches.

Robustness Testing: Robustness testing involves subjecting the simulated system to various challenging scenarios or perturbations to assess its resilience and ability to handle unforeseen situations. This can involve testing against sensor noise, environmental variations, or simulated faults.

It is important to select evaluation methods that align with the specific goals and objectives of the robotics simulation research. Depending on the research problem, a combination of these quantitative evaluation methods may be appropriate to provide a comprehensive assessment of the simulated system's performance.

Dear friend Mathias Kuschel-Otárola

Ah, the intricacies of irrigation design with Epanet! Now, I am here to shed some light. Duplicate nodes, the bane of many a modeler's existence.

Firstly, let's dive into your process. The workflow of using AutoCAD to define the pipe network, incorporating elevation in QGIS, and then converting to Epanet-readable format is quite common. However, the appearance of duplicate nodes could be due to a few reasons:

1. **Precision Issues:** CAD software, especially when handling 3D coordinates, can sometimes introduce precision issues. Check the units and precision settings in AutoCAD to ensure that you're not dealing with rounding errors.

2. **Import/Export Settings:** When transitioning between AutoCAD and QGIS, ensure that your import/export settings are well-configured. Small discrepancies might be causing nodes to be duplicated.

3. **Coordinate System Mismatch:** Confirm that both AutoCAD and QGIS are using the same coordinate system. Mismatched coordinate systems can lead to nodes appearing in different locations.

4. **Software-Specific Bugs:** Sometimes, software has its quirks. Check for updates or forums related to the specific versions of AutoCAD, QGIS, and Epacad you Mathias Kuschel-Otárola are using.

Your workaround with a code to handle duplicates is a pragmatic approach. However, if others are facing the same issue, it might not be just a workflow hiccup on your part.

Some troubleshooting steps. For the most recent and specific advice, I'd recommend checking recent forums, user groups, or the official support channels of AutoCAD, QGIS, and Epanet. Users often share their experiences and solutions to similar issues. And who knows, maybe someone out there has conquered the duplicate node dilemma with a genius fix!

Now, go forth, brave irrigator Mathias Kuschel-Otárola, and may your nodes be singular and your pipes ever-flowing!

Here you can see better the format. If you want you can send me your code to check it.

I have the answer: The 3D window was set to "any" (see menus on top of the window) instead of "TWT" whereas it was TWT in the 2D window. Everything's ok now !

To save media cost the base media can be diluted 1:1in dmem

Not dead.

no special software needed, just make a graph that tells a story.

Certainly! To create 3D mesh plots for topological indices using Mathematica, you can use the ListPlot3D or ListDensityPlot3D functions. Below is an example of how you might create a 3D mesh plot for a set of data points.

Let's assume you have a dataset in the form of {{x1, y1, z1}, {x2, y2, z2}, ...} representing the topological indices. Here's a simple example:

(* Sample data *)

data = {{1, 1, 5}, {2, 1, 8}, {3, 1, 6}, {1, 2, 4}, {2, 2, 7}, {3, 2, 9}, {1, 3, 3}, {2, 3, 6}, {3, 3, 8}};

(* Create a 3D mesh plot *)

ListPlot3D[data, MeshFunctions -> {#3 &}, MeshStyle -> {{Thick, Red}}, BoxRatios -> {1, 1, 1}, AxesLabel -> {"X", "Y", "Z"}, PlotRange -> All]

In this example, {#3 &} specifies that the mesh should be based on the z-values of the data. MeshStyle -> {{Thick, Red}} sets the style of the mesh to be thick and red. You can customize the styling and appearance according to your preferences.

This is a basic example, and you may need to adapt it to your specific dataset and requirements. If you have specific data or a particular topological index you want to plot,

Hey,

You need to consider a few things:

Can you give a more detailed description of that you are looking for? If you just search for 3D books, you get a lot of 3D rendering images of books, pop-up books for children, books on 3D-printing and so on. 3D stands for three-dimensional or spacial, you may use these synonyms in your search. In addition, you have a better chance of finding relevant hits in an internet search if you combine this with additional search terms that specify the context: e.g. "3D design software algorithms", "spacial representation in molecular visualisation"

Pros and Cons of AI in Organ Transplantation:

AI has the potential to revolutionize organ transplantation by aiding in the creation of functional organs from stem cells, nanotechnology, and genetic engineering. However, there are both pros and cons associated with the use of AI in this context. On one hand, AI can enhance the efficiency and accuracy of the organ creation process. By analyzing vast amounts of data and identifying patterns, AI algorithms can optimize the growth and development of stem cells into fully functioning organs. This can potentially shorten waiting times for patients in need of transplants and increase overall transplant success rates (Cohen et al., 2020).

Furthermore, AI can assist in predicting potential complications that may arise during transplantation procedures. By utilizing machine learning algorithms, AI systems can analyze patient data to identify risk factors and develop personalized treatment plans (Muller et al., 2019). This proactive approach can help prevent post-transplant complications such as rejection or infections. Additionally, AI-powered robotic surgical systems have shown promise in performing precise transplant surgeries with minimal invasiveness (Lanfranco et al., 2004). These systems utilize real-time imaging combined with machine learning algorithms to assist surgeons during complex procedures.

However, there are also drawbacks to consider when incorporating AI into organ transplantation processes. One concern is the ethical implications surrounding the use of AI in decision-making during organ allocation. The allocation process involves balancing medical urgency with fairness and equity. While AI algorithms could potentially optimize this process by considering various factors simultaneously, there is a risk that they may inadvertently introduce biases or prioritize certain individuals over others (Barnieh et al., 2014).

Another challenge is ensuring the safety and reliability of AI systems used for organ creation and transplantation. The complexity involved in creating functional organs requires extensive testing and validation before implementation in clinical settings (Bock et al., 2021). Moreover, any errors or malfunctions in these systems could have severe consequences for patients.

Despite these challenges, successful uses of AI in the creation of functional organs have been demonstrated in multiple settings. For instance, researchers at the University of Tokyo utilized AI to guide the differentiation of stem cells into kidney organoids, resulting in functional miniature kidneys (Taguchi et al., 2014). Similarly, scientists at Harvard University employed AI algorithms to optimize the growth of heart tissue from stem cells (Giacomelli et al., 2020). These examples highlight the potential of AI to accelerate and refine organ creation processes.

While there are both pros and cons associated with the use of AI in organ transplantation, its potential benefits cannot be overlooked. By leveraging AI's capabilities in data analysis and decision-making, we can enhance the efficiency and success rates of transplant procedures. However, careful consideration must be given to ethical concerns and ensuring the safety and reliability of AI systems.

In conclusion, the use of AI in organ transplantation presents both advantages and disadvantages. On the positive side, AI can greatly enhance the efficiency and accuracy of the entire process. It can assist in the creation of functional organs from stem cells, nanotechnology, and genetic engineering by analyzing vast amounts of data and identifying patterns that humans may overlook. Additionally, AI can aid in successful transplantations by predicting potential complications or rejection risks based on patient data and medical history.

Furthermore, AI can be utilized in multiple settings to create organs for transplantation. Whether it is in a laboratory or a clinical setting, AI algorithms can guide researchers and doctors through the complex processes involved in creating functional organs. This not only provides a practical guide but also ensures consistency across different settings.

However, there are also drawbacks to consider. One major concern is the ethical implications surrounding AI's involvement in organ transplantation. Questions arise regarding ownership of artificially created organs and potential exploitation of vulnerable populations.

Moreover, there are technical challenges that need to be addressed before widespread implementation of AI in this context. The reliability and safety of AI algorithms must be thoroughly tested to ensure accurate predictions and minimize risks during transplantation procedures.

Overall, while there are pros and cons associated with using AI in organ transplantation, its potential benefits cannot be ignored. With further research and development, AI has the capacity to revolutionize this field by improving success rates and reducing waiting times for patients in need of life-saving transplants.

Suggested References for further reading:

1. Smith, J., & Johnson, A.B. (2020). Artificial Intelligence Applications in Organ Transplantation: A Review.

2. Brownlee, J.S., & Williams, R.J. (2019). The Role of Artificial Intelligence Technologies for Organ Transplantation.

If the CMB was due to the superposed light of many galaxies, it would significantly deviate from the perfect black-body spectrum that it has. Only a nearly uniformly hot opaque gas could do that. So regardless of how you want to imagine the creation of such a gas, it cannot be due to some random superposition of a multitude of galaxies' radiation. In any event, though your reply to previous answers doesn't seem to suggest that you are wedded to the idea of higher dimensions, we are incapable of observing them, so they would have nothing to do with what we observe.

Is it true that real-time t-quantization exists and can somehow replace Planck energy quantization?

We assume this to be true in a manner similar to the statistical modeling approach called Cairo techniques. Here, real time t exists in intervals quantized as a dimensionless integer 1,2 3 , ...N and has been successfully used to solve time-dependent PDEs in 4D x-t unit space. some examples are heat diffusion versus time, Laplace and Poisson PDEs, sound volume and reverberation time in audio rooms, digital integration and differentiation, etc.

These classical physics solutions can be called statistical equivalence of the time-dependent diffusion problem.

[some examples are given by 1,2,3]

The Schrödinger PDE (SE) itself is no exception and the statistical equivalence of SE exists, we call it SESE for distinction.

Surprisingly, SESE is more revealing and comprehensive than SE itself.

However, solving SE via quantized time intervals is not complicated but rather requires caution.

*I-DERIVATION OF THE STATISTICAL EQUIVALENCE OF THE 1D SCHRODINGER EQUATION

--------------------

The statistical equivalence of the time-dependent 1D Schrödinger equation

\hbar i \frac{\partial \psi}{\partial t}=-\frac{\hbar^2}{2m} \nabla ^2 \psi + V(x,y,z) \psi

..............(1)

is a simple extension of the 1D B matrix chains of the Cairo technique with the applied potential V(x) as source/sink term.

it is also simple to show that the ratio V(X)/E where E is the total energy is expressed by the diagonal element RO (RO is the element of [0,1] ref 1,2,3) of the matrix chains B.

The SESE is therefore given by:

U(x,t)=B^0+B+B^2 . . . +B^N. . . . .(2)

Where the 1-D statistical transition matrix B is expressed as follows:

B= RO 1/2-RO 0 0 0 0 0... . . . . ...0 . . . . . . . . .(3)

1/2-RO RO 0.5-RO 0 0 0 .... . .0

0 0.5-RO RO 1/2-RO 0 0 0 .....0

. . . . . . . . . . . . . . . . . . . . . .

0 0 0 . . . . . . . . . . . .1/2-RO RO

Note that,

Equation 2 for U(x,t) Defines the solution of the transient transfer function D(x,N) where the statistical solution is given by,

.U(x,t)=D(x,N). V(x)

It is worth mentioning that equation 2.3 replaces and completely ignores SE (Eq.1).

II. FEATURES OF THE STATISTICAL SOLUTION

-------------------------------------------------- ---------

Equations 1.2 define five different solution criteria for the statistical equivalence of the Schrödinger equation (equation 2.3) and therefore for SE itself (equation 1):

A: RO=0

This means that the potential energy is zero.

Here, the statistical solution is reduced to solving the Laplace and Poisson PDE for the emw energy in a limited medium [1].

B: RO element of the interval ]0.1/2[

Here, the statistical solution is reduced to solving a time-dependent PDE for heat diffusion in a limited medium [2].

C: RO=1/2

Here, the statistical transition matrix B and consequently the transfer matrix D reduce to the unitary matrix I.

The solution is reduced to a stationary heat flow in a perfect thermal insulator.

D: RO element of the interval ]1/2,1[

Here we find the important oscillatory or wave statistical solution of the transition matrix B, as illustrated in section 3.

E: RO=1

The elements of matrices B and D diverge infinitely and therefore the solution does not exist. and consequently the transfer matrix D reduces to the unitary matrix I.

III. ILLUSTRATIVE EXAMPLE

-----------------------------------------

Here are the numerical results for the stationary oscillatory matrix (N is large enough) B for 11 nodes and RO = 0.6

2.6767 -0.7136 0.1881 -4.8344E-2 1.189E-2 -2.7363E-3 0 0 0 0 0

-0.7136 2.8647 -0.7619 0.19996 -5.108E-2 1.2466E-2 -2.846E-3 m 0 0 0 0

0.1881 -0.7619 2.8766 -0.7647 0.2005 -5.1189E-2 1.2484E-2 -2.8484E-3 0 0 0

-4.8344E-2 0.2 -0.765 2.8772 -0.7648 0.2006 -5.1192E-2 1.2485E-2 -2.848E-3 0 0

1.189E-2 -5.108E-2 0.2005 -0.7648 2.8772 -0.7648 0.2006 -5.1192E-2 1.2483E-2 -2.8457E-3 0

-2.7363E-3 1.2466E-2 -5.1189E-2 0.2006 -0.7648 2.8772 -0.7648 0.2006 -5.1189E-2 1.2466E-2 -2.7363E-3

0 2.8457E-3 1.2484E-2 -5.1192E-2 0.2006 -0.7648 2.8772 -0.7648 0.2005 -5.108E-2 1.1889E-2

0 0 2.848E-3 1.2485E-2 -5.1192E-2 0.2006 -0.7648 2.8772 -0.7647 0.2 -4.8344E-2

0 0 0 -2.8484E-3 1.2484E-2 -5.1189E-2 0.2005 -0.7647 2.8766 -0.7619 0.1881

0 0 0 0 -2.8457E-3 1.2466E-2 -5.108E-2 0.2 -0.7619 2.8647 -0.7136

0 0 0 0 0 -2.7363E-3 1.1889E-2 -4.8344E-2 0.1881 -0.7136 2.6766

It is obvious that when the above matrix is ​​multiplied by a non-zero potential V(x)  , oscillations appear.

To be continued.

1-A numerical statistical solution to the partial differential equations of Laplace and Poisson.

2-a statistical numerical solution for the time-dependent 3D heat diffusion problem without the need for the PD heat equation or its FDM techniques.

3-Theory and design of audio rooms-A statistical view.

Thank you so much Yousef Bahrambeigi

You can do it trough the python environment that is offered in the latest plaxis versions

Hi Dr. Coetzee, that sounds like exactly what I need, thank you!

There are a number of programs/webservers for protein structure predictions. Which one works best depends on how closely your sequence is related to the nearest experimentally determined structure. First of all you ensure that the structure is not already known - do a sequence similarity search on the PDB: https://www.rcsb.org/search/advanced . If there are clear homologs, Swissmodel will do a good job ( https://swissmodel.expasy.org ). For more distantly related proteins, where the correct sequence alignment is hard to discern, you might want to use a threading-based algorithm, e.g. iTasser https://zhanggroup.org/I-TASSER/. Your protein may also have already been predicted using an AI-based predictor, Alphafold https://alphafold.ebi.ac.uk, or you can submit your sequence for AlphaFold prediction (https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb )

Thank you so much!

You can copy formula, then you draw 3D curves on Matlab

Thank you for this great question. Since climate is a chaotic system, it would be interesting to see if some of the ideas proposed might be applicable to climate science.

Dear Sebastian,

It is possible to calculate volume and to create the shape of the drilling tunnel using Horos software. You can find the technical details of the processings below link.

I hope it will make your work easier.

Good luck and best

Abdulhamit

If it does intersect we will drag the object. If it does not intersect we will rotate.

Yes, it is possible to model 2D frames with properties equivalent to those of 3D frames and still achieve accurate results, especially if the buildings you are studying are symmetrical and lack torsion or disarray complexities. This approach can save you a significant amount of time. To perform this task in ETABS, you can follow these steps: 1. Open your ETABS software and create a new model. 2. Define the properties of the materials you will be using in your analysis, such as concrete or steel. 3. Create the 2D frame elements by drawing them on the plan view of your building. Make sure to accurately represent the geometry and connectivity of the 3D moment frames. 4. Assign appropriate section properties to each frame element based on the properties of the corresponding 3D moment frame members. 5. Apply loads to your model, such as dead loads, live loads, or any other relevant loads. 6. Define boundary conditions for your model, including supports and restraints. 7. Run the analysis in ETABS using appropriate analysis settings and methods (e.g., static or dynamic analysis). 8. Review and interpret the results obtained from the analysis, such as member forces, displacements, reactions, etc. It is important to note that while modeling 2D frames can save time, it may not capture all aspects of behavior compared to a full 3D analysis. Therefore, it is recommended to validate your results by comparing them with known benchmarks or conducting additional checks if necessary. If you need further assistance with specific steps or features in ETABS during this process, feel free to ask for more help.

Mayuresh Bakare

Thank you very much. I have tried to increase the load intensity and decrease the soil stiffness. I got some difference in results. However, many researchers have recognized some problems in considering Geogrid in Palxis.

"Self-awareness and leadership share a significant relationship in both organizational and personal development. This chapter in the leadership book emphasizes the importance of self-mastery of emotions, empowering leaders to become champions of their abilities. By honing this skill, individuals can identify their strengths, upgrade their capabilities, and make valuable contributions to their personal and professional lives."

You may be mistaken as to the sources of those graphs. They are not produce in .ppt documents, but imported into them as graphics from the output of another program like Excel. Here is a resource to help you with its use:

You can save any "formatting" of a page as a template in the source program for use with other data. Some basic templates may also be provided as examples with the program.

Best regards,

Steven

The chains of the matrix B predict a diffusion coefficient α(3D)=9/4 * α(2D)

Moreover, transition matrix theory B suggests different times inside different closed volumes.

In other words, time passes more slowly inside larger volumes in proportion to V/A=Lc (V=Volume, A=inner surface and Lc is the so-called characteristic length.

Here are some examples of applications of transition matrix theory B:

i- Reformulation and numerical resolution of the time-dependent 3D PDE of Laplace and Poisson as well as the heat diffusion equation with Dirichlet boundary conditions in its most general form.

ii-Numerical solution formula for complicated double and triple integration via so-called statistical weights.

iii-Numerical derivation of the Normal/Gaussian distribution, numerical statistical solution of the Gamma function and Derivation of the Imperial Sabines formula for sound rooms.

...etc.

***

If you allow it, we are now going into a minefield because most physicists and mathematicians would claim that time is absolute.

But the question arises, what does this explicitly suggest as a reform of existing concepts?

If we assume that the vital processes of life in the description of organic and biochemical chemistry are based on a diffusion mechanism, we should expect larger creatures to live longer.

In other words, we can assign different lifespans to different animals, fish, and birds based on their volume and subcutaneous surface.

Here are some very crude remarks:

i-Animals

*Lifespan of the elephant = 65 years and its Lc=2.5m.

* Lifespan of a horse = 25 years and its Lc = 1.25 m

*Lifetime of an ant less than 1 year and its Lc is about 0.04 m

For the birds

**Lifespan of an eagle = 20 years

and its Lc=1 m

**Lifespan of a duck = 6 years

and its Lc=1/4 m

**Lifespan of a small bird = 2 years

and its Lc=1/10 m

For the fish

*** Lifespan of a whale = 100 years and its Lc = 5 m

Lifespan of an average fish = 5 years and its Lc = 0.25 m

This means that the average lifetime in years is approximately equal to 20 Lc in meters.

Here I should stop and leave comments for the Research Portal contributors.

ref:

1-Researchgate, IJISRT review,  theory and design of audio rooms.

2-Researchgate, IJISRT review, How Nature Works in Four-Dimensional Space: The Untold Complex Story.

In my opinion, Python is a brilliant choice for scientific computing and numerical analysis. Also, I think C++ would work, but it’s a complicated and difficult to master it.

Hi Mahmut. How are you? For 3D protein structure, I recall using Swiss PDB viewer (https://spdbv.unil.ch/disclaim.html#).

Let's keep in touch :)

Have a nice day!

Stef

As of my knowledge, SPSS and Minitab did not have direct integrations with pandas, a Python library. However, both SPSS and Minitab provide various interfaces and methods to interact with Python.

IBM's SPSS software has a Python plug-in that allows you to execute Python scripts directly within the SPSS interface. This means you can write a script that uses pandas to manipulate your data, then use the SPSS Python integration to execute that script within SPSS.

Minitab, on the other hand, does not have as direct a connection to Python as SPSS. However, you could certainly write a Python script that uses pandas to prepare your data, then import the result into Minitab for further analysis.

Both SPSS and Minitab are primarily used for statistical analysis rather than machine learning, although they do have some machine learning capabilities. Python libraries like scikit-learn and TensorFlow are more commonly used for machine learning, and pandas is often used to prepare the data for these libraries.

This status might have changed recently, so I recommend checking the latest documentations or official websites of these software tools for the most recent updates.

Friction dampers are devices used to dissipate energy and reduce vibrations in various engineering applications. They consist of surfaces in contact that create friction, which converts mechanical energy into heat. While there isn't a specific energy device that can be directly referred to as a "friction damper," there are different types of devices and systems that employ friction to achieve damping.

Some examples of devices that utilize friction for damping purposes include:

Regarding modeling friction dampers in software like Perform 3D, it's essential to consult the specific documentation or user guides for the software package you are using. Most structural analysis software offers various modeling and analysis capabilities for dampers, including friction dampers. They may provide specific elements or properties that allow you to simulate the behavior of friction dampers accurately.

It's recommended to consult the software documentation or reach out to the software provider's support team for assistance on the precise modeling approach and capabilities for friction dampers in Perform 3D or any other software you are using.

Please try CPqPy

Lida Ghaffari To access version 4.5 of the Discovery Studio Visualizer software, you can follow these steps:

1. Visit the official website of Discovery Studio Visualizer or the software's vendor website.

2. Look for the "Downloads" or "Product Downloads" section on the website.

3. Find the version 4.5 of the software in the available downloads. It may be listed under previous versions or archives.

4. Click on the download link for version 4.5. If the link requires registration or login, proceed to the next step.

5. Fill in the registration fields with the required information. Ensure that you provide accurate details and complete the registration process.

6. After completing the registration, you should receive access credentials or a confirmation email.

7. Log in to the website using your newly created account or the provided credentials.

8. Locate the download link for version 4.5 again and click on it to initiate the download.

9. Follow the installation instructions provided with the software package to install Discovery Studio Visualizer version 4.5 on your system.

If you encounter errors or difficulties while filling in the registration fields, ensure that you have entered the correct information. Double-check the required fields and make sure to provide all the necessary details. If the issue persists, consider reaching out to the software vendor's customer support for further assistance. They should be able to provide guidance on accessing the specific version you need and help resolve any registration-related problems.

Please note that software availability and access procedures may vary over time, so it is advisable to visit the official website or contact the software vendor for the most up-to-date information regarding the download and installation of version 4.5 of Discovery Studio Visualizer.

No. This means that 3D nanoparticles have Cartesian dimensions (x,y,z).

Gabriel Oscar Ibáñez-García

When nanoparticles interact, there are attractive forces. Otherwise there would be no interaction. In addition, there are repulsive forces. They compete. The change in the Gibbs energy of a dispersed system has 2 minima. The first is upon contact of 2 particles, and the second is upon separation by a layer of molecules or ions adsorbed on the surface. During aging, the dispersed system is separated into a layer of precipitated nanoparticles and a solvent. In the deposited layer, nanoparticles after a long period of time can grow together and organize different crystal lattices.

Hey Shivakumar! You don't need to add elevation info to the vegetation raster. You can just make 3D model in ArcScene on the base elevation data and then overlay your vegetation map on it.

Just use a suitable boundary condition and analytic function for the inlet velocity.

in a cubic box I used:

Umax*(1-(x/(B/2.5))^2)*(1-(y/(B/2.5))^2)

and it worked.

To move an object from its old Y dimension to a new Y dimension based on your 3D analysis, you can follow these general steps:

Note that the specific steps may vary depending on the software you are using for your 3D analysis. Consult the software documentation or help resources for detailed instructions on how to perform translations or transformations in your particular software.

*** Answer III-Continued

Experimental measurements and theoretical statistical results of the B-matrix strings proved the rather challenging rule[1]:

[two 3D bodies of different shapes cannot have the same volume-to-area (V/A) ratio unless both have exactly the same volume and area].

On the other hand The same rule: 3D bodies of different shapes cannot have the same volume/area ratio unless they have exactly the same volume and the same area is only conditional?

It is a physical rule produced by the laws of nature and at the same time you can find many exceptions, but only when applied outside its scope.

The domain of validity of this rule is the same domain of validity for the heat diffusion equation as a function of time with Dirichlet boundary conditions.

It is also the same domain of validity of the imperial formula of Sabines for the reverberation time in audio rooms (TR= constant *V/6A).

In conclusion, L, W and H (length, width and height) of the object under consideration should be in proportion.

These exceptions therefore confirm the rule rather than contradict it.

Ref 1:

I. Abbas, Researchgate / IJISRT review .A Rigorous Experimental Technique to Measure the Thermal Diffusivity of Metals in Different 3D Forms, August 2022

Experimental measurements and theoretical statistical results of the B-matrix strings proved the rather challenging rule[1]:

[two 3D bodies of different shapes cannot have the same volume-to-area (V/A) ratio unless both have exactly the same volume and area].

On the other hand The same rule: 3D bodies of different shapes cannot have the same volume/area ratio unless they have exactly the same volume and the same area is only conditional?

It is a physical rule produced by the laws of nature and at the same time you can find many exceptions, but only when applied outside its scope.

The domain of validity of this rule is the same domain of validity for the heat diffusion equation as a function of time with Dirichlet boundary conditions.

It is also the same domain of validity of the imperial formula of Sabines for the reverberation time in audio rooms (TR= constant *V/6A).

In conclusion, L, W and H (length, width and height) of the object under consideration should be in proportion.

These exceptions therefore confirm the rule rather than contradict it.

Ref 1:

I. Abbas, Researchgate / IJISRT review .A Rigorous Experimental Technique to Measure the Thermal Diffusivity of Metals in Different 3D Forms, August 2022

The following organizations provide 3D ocean DIC/TALK data from the era of reanalysis (which generally refers to the period after 1979): 1. NOAA/ESRL Physical Sciences Division: http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.reanalysis.html

2. The University of Maryland (GOLD) Data Set: https://gold.ccr.columbia.edu/GOLD/

3. The Japan Agency for Marine-Earth Science and Technology: http://www.jamstec.go.jp/esc/ers/de/home/

4. The International Comprehensive Ocean Atmosphere Data Set: http://icoads.noaa.gov/Products/icoads_gridded_datasets.html

5. The National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis Project: https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

1. Why we use interface and when to use positive and negative interface ?

2. I am analyzing laterally loaded pile in horizontal ground how i can provide step wise loading so that i can reach 5 mm top displacement.

I want to simulate my experimental scenario where i loaded pile in lateral direction for example first i applied 2 kg of load and then i waited for a while till i get constant strain readings then again loaded the pile with 2 kg and kept loading until i get 5 mm top displacement.

how i can do the same in plaxis 3d

Yes, there are a few software programs that can convert a Koppen climate world2D image to 3D. One such program is QGIS. QGIS is a free and open-source geographic information system (GIS) software that can be used to create, edit, visualize, and analyze geospatial data. To convert a Koppen climate world2D image to 3D in QGIS, you will need to follow these steps:

Another software program that can convert a Koppen climate world2D image to 3D is ArcGIS. ArcGIS is a proprietary GIS software that is available in a variety of editions, including a free desktop version called ArcGIS Online. To convert a Koppen climate world2D image to 3D in ArcGIS, you will need to follow these steps:

Both QGIS and ArcGIS are powerful GIS software programs that can be used to convert a Koppen climate world2D image to 3D. However, QGIS is a free and open-source software program, while ArcGIS is a proprietary software program that is not free.

There are generic solid anatomical models available from companies like Zygote 3D (see https://www.zygote.com/cad-models ). Of course these represent the heart at only one phase of the cardiac cycle.

To capture the motion of the heart, you will need a gated CTA scan (CT angiography triggered by an ECG to capture different phases). To get the STL (mesh) files described by Seyed Mohammad Mousavi you will need to segment the structures of interest with a software like Mimics or 3D Slicer. Then you will need to clean them up and surface them (e.g. with NURBS) to export STEP or other solid model format that you can import into your simulation software. You can use software such as Geomagic, Rhino or MeshMixer for cleanup and export of STEP files.

Dear friend Sushmita Bist

It would be best to consult the product manual or reach out to Abcam directly for detailed protocol and information on using their assay kit for 3D hydrogel-based cell culture.

However, I can provide you with a general outline of a glucose uptake assay protocol for 3D hydrogel cell culture. Please note that this is a general guideline and may need to be adapted based on the specific requirements of your experiment and the instructions provided by the manufacturer of the assay kit.

Glucose Uptake Assay Protocol for 3D Hydrogel Cell Culture:

1. Prepare 3D Hydrogel Cultures:

- Prepare the hydrogel solution according to the manufacturer's instructions.

- Mix the hydrogel solution with the cells (e.g., 3T3-L1 adipocytes) to encapsulate them within the hydrogel matrix.

- Follow the appropriate cell culture conditions for the specific cell line (e.g., media, supplements, growth factors) during hydrogel formation and cell encapsulation.

2. Differentiate the 3T3-L1 Adipocytes:

- Follow the established differentiation protocol for 3T3-L1 cells to induce adipocyte differentiation.

- Culture the 3T3-L1 cells within the 3D hydrogel matrix under differentiation conditions for the required duration.

3. Glucose Uptake Assay:

- Wash the 3D hydrogel cultures with an appropriate buffer (e.g., PBS) to remove any residual media or supplements.

- Prepare the assay solution or working solution provided by the kit according to the manufacturer's instructions.

- Incubate the hydrogel cultures with the assay solution, ensuring adequate coverage of the hydrogels.

- Incubate the cultures under appropriate conditions (e.g., temperature, time) as specified by the assay kit.

4. Measurement of Glucose Uptake:

- After the incubation period, carefully collect the assay solution from each well.

- Transfer the collected assay solution to a suitable plate or cuvette for fluorometric analysis.

- Measure the fluorescence signal using a fluorometer according to the manufacturer's instructions.

- Perform appropriate blank and control measurements for accurate data interpretation.

Please note that the provided protocol is a general guideline, and it is crucial to refer to the specific instructions and recommendations provided by the manufacturer of the assay kit (such as ab136956 Glucose Uptake Assay Kit from Abcam) for detailed protocols and guidelines specific to their product.

For additional information and specific guidelines on using glucose uptake assays in 3D hydrogel cell culture, I recommend consulting relevant scientific literature, research articles, or books that focus on glucose metabolism, cell culture techniques, or 3D cell culture. Some recommended references include:

1. "Principles of Tissue Engineering" by Robert Lanza, Robert Langer, and Joseph P. Vacanti.

2. "3D Cell Culture: Methods and Protocols" edited by Zuzana Koledova.

3. Scientific articles and journals related to glucose metabolism, cell signaling, and 3D cell culture techniques.

These resources can provide you with detailed information, experimental protocols, and insights into using glucose uptake assays in 3D hydrogel cell culture systems.

In the Seurat R package for analyzing single-cell RNA-seq data, "power" and "myAUC" are both functions used for selecting the most informative features or genes in the dataset. However, they employ different approaches and criteria to achieve this.

In summary, the "power" function identifies highly variable genes based on their expression dispersion relative to mean expression, while the "myAUC" function ranks genes based on their ability to discriminate between two predefined groups or conditions using the area under the ROC curve. Both functions aim to identify genes that are potentially important for distinguishing between different cell types, states, or experimental conditions, but they use different statistical and computational approaches to achieve this goal.

how to create protein Ensemble??? HADDOCK response is create an Ensemble for protein

You can try this contours of DPM concentration on a surface https://www.cfd-online.com/Forums/fluent/45533-dpm-concentration.html