Questions related to Scaling
Here I described an issue with the scaling factor for Landsat 8 Level 2 products. I've used the scaling factor from here but when I compute the NDVI the result has many pixels out of the validity range.
I am working on an Abaqus simulation using Dynamic/Explicit solver and user subroutine VUMAT to implement the damage model that I am developing now.
Target time increment is set to be 5E-05 via mass scaling. Yet, at some point during the simulation, the time increment became smaller than 5E-05 (ex. 5.436E-06).
How can this happen?
Can the stable time increment change DURING the simulation?
I wonder whether this is technically possible in Abaqus/Explicit software.
Please share your thoughts and experiences.
Thank you in advance.
Hi I want to use mass scaling in seismic analysis in abaqus.
can we use mass scaling in seismic analysis?
Hi, I am looking to performing a regression analysis with the CATREG function in SPSS. Some of the guides I have found online use terminology I am unfamiliar with so I was wondering if anyone had advice on some of the practical steps involved. All help is hugely appreciated!
Task description: Expert and Amateur drummers performed a series of drumming tasks at five different speeds (80, 160, 240, 320, 400 hits per minute). Motion Capture was used to record arm/hand/stick movements, and forearm muscular activity recorded with EMG. A questionnaire was used to collect information about participants’ practice history.
Aim: The aim is to investigate if performance characteristics (e.g., variability in timing) can be predicted by physiological variables (EMG, movement patterns) and practice history variables.
Analysis: I have 8 predictor variables, and 1 outcome. To my knowledge, CATREG has less emphasis on assumptions (normality, etc.) and is a supported alternative to dummy-coding categorical variables. I believe that makes it a viable choice for our aim.
However, I am still uncertain on some points. Would anyone be able to advise on the following? -
1. Is CATREG only appropriate if your outcome/dependent variable is categorical? In my case the DV is continuous.
2. In regards to scaling/discretization, am I correct that numerical predictors do not require specific scaling, only nominal /ordinal ones? If this is correct, are the following SPSS scaling options for each variable ok?
- For the variable 'expertise' (2 levels: Scaling is Ordinal, Descretization is Grouping Normal 2)?
- For variable 'Tempo' (5 levels, Scaling is nominal, Descretization is Grouping Uniform 5)
Is it possible to determine the correction factor (Km) to estimate the (AED) for Lepidopteran species? Haven't found any literature that discusses the MRSD for Lepidopteran species. Would very much appreciate it if someone has any insight into it. Need to calculate the drug dose for Bombyx Mori.
Hi all, I am using Abaqus to simulate cylindrical turning. No matter how I set the mass scaling factor (16,100,256, etc.), the final kinetic energy is always greater than 5% of the internal energy. Finally, I cancel the mass scaling, and the results show that the kinetic energy is also greater than 5% of the internal energy. How do I set up the model to meet the 5% condition? Thanks!
Compiled allometric data might help to detect scaling patterns.
Or similarities in the scaling relationships might suggest connections otherwise too subtle to find.
Does such a list exist?
Does such a list exist for biological phenomena?
If such lists do not exist should they?
i have been working on ASTM standard model in ANSYS workbench. its been crashing due to error message says, "your product license has numerical problem size limits, you have exceeded these problem size limits and the solver cannot proceed". is there any way like scaling, merging nodes to reduce no of nodes for further analysis.
E.g. I have a given interest of a balance between quality, cost and time. I have in the data set 6 features: 3 features for cost, 2 for time and only 1 for quality.
I would like to:
- quality features * 2
- cost features *2/3
- times *1
When should I do that: before the transformation/scaling? after the transformation but before scaling or even after trasnfromation/scaling? and why then?
-Data set is very small: <50 data points
1. Can anyone help me confirm if their is a generally accepted range for scaling the Kessler Psychological (K6) scores into categories of Psychological distress i.e 0-4 Low, 5-9 Mild, 9 -24 High.
Using SPSS how can I compute individual respondents scores for into these categories.
After reading a lot through research papers and available literature, I am confused about whether we can perform arithmetic mean on a 5-point Likert type scale which is ordinal in nature. There exist research papers that have calculated the mean of the responses collected through the Likert scale. On the other hand, many statements are made that it is inappropriate to measure arithmetic mean for ordinal data.
Can someone please give a solution to this question?
I am analysing LC-MS data. I have combined all NEG and POS mode peak intensities in a single file retaining all metabolites with higher peak intensities (appearing in either modes). Now, when I go for analysis in MetaboAnalyst, which ones should I select among:
1. Sampe normalization
2. Data transformation
3. Data Scaling
Thank you for guidance!
In the understanding of Pedagogy issues with Mathematical assumptions in Simulation, research outcomes of optimising modelling outcomes via scaling techniques could correlate to what Cryptographical analysis could help with remodelling. How could the Scaling approach help in such a scenario, consider some of the ethical standards of deploying Artificial Intelligence?
(For me: the most.)
Arguments in favor of this proposition include:
In animals, 3/4 scaling applies over many orders of magnitude.
Historically, the applicable mathematical approach depends on dimension: Galileo’s 1638 work on weight-bearing animal bones; Sarrus and Rameaux 1838 on the scaled rate of mammalian breathing; Max Rubner’s and Max Kleiber’s measurements, and the geometric approach in 1997 by West Brown and Enquist.
If the underlying physical relationship prevails at all scales (sizes), then it applies to quanta, to metabolism, to cosmology, and to dark energy.
I have a concern regarding the application of correlation cofficient/matrix.
I have a dataset which comprises of X variables that are captured at different scales/measure. My Y variable a total score, and is a summation of X variables.
Can i run a correlation between Y and X1/X2.. Xn.?
Slightly confused here since correlation helps with the linear relationship identification, however since outcome is a summation, hence will a corr b/w Y and X1 make sense? Moreover i am scaling my data before running the correlation, since the scales are different for the X variables.
Would appreciate a clarity over this.
I am simulating friction stir welding with Abaqus dynamic explicit and CEL technique. I have taken tool as rigid body and uniform mesh size of 2mm. Stable time increment is coming out to be 1.7 *e-7 and total time for simulation is 120s. I have tried mass scaling but due to ridid body of too, it doesn't apply. So, you might get that my computation time is quite high. Please guide me through the problem or where i am making mistake.
Do such measurements make sense? Do they exist?
Comparing redshift and luminosity distances, if that is a sensible question, may bear on the 4/3 scaling hypothesis as it relates to dark energy.
I have done regression analysis consisting one dependent and 7 continuous independent variables. I found vary small coefficient value (like .00000000000001 etc) while using the original independent variables. When I divide some of the large digit independent variables by 10000, then it is giving quite better value. I want to know whether this type of scaling is accepted in regression analysis?
I am trying to determine the most appropriate way to define a beam source of a given intensity. Normally one define their beam intensity via an FM card scaling to power but I am planning to use my flux spectra (obtained by an E card) for depletion analysis in ORIGEN and I'm not sure the FM will affect the spectra results.
I am simulating a reinforced concrete beam with CDP model under four points bending test. However I keep encountering negative internal energy issue and wonder what might be the problem causing this.
I am using dynamic explicit step with default mass scaling and time period of 1. Also, displacement load of 20mm and mesh size of 15mm.
Thanks for you help in advance.
I am trying to downscale GCM-(CanCM4) data to bhima basin catchment(finer scale) for projecting future scenarios. Further I have used following variables ta,ua,zg,va(all these @ 925,850,700,200 pressure levels) and pr,psl(total 6 variables). I am attaching image which I got from working on GCM, now considering mid point of these GCM grid points only 2 station lie on periphery(+ mark) for down scaling. Can I downscale these GCM points to the 0.5deg grid points? If yes, how to consider the weights?
I've been reading forever statements (primarily, but not exclusively) in the biomedical literature like "a 1 year old rhesus monkey is the equivalent of a 4 year old child" but have not seen references for this. I think that this scaling may have to do with brain development, but again, no reference. Does anyone know the source (or *a* source) for statements like this?
Possibly: 4/3 scaling is a fundamental universal principle. Nothing underlies it. Why? It accounts for expanding cosmological space. Since 4/3 scaling brings 3 dimensional space, and hence everything else, into existence, it must be fundamental.
Can that be right? What favors and disfavors this notion?
Hello. My name is Malte. In my master thesis I study assemblages of Meso and Makrofauna associated with arboreal soil in tree-cavities, following an approach of island biogeography. In my study design the two predictors "size of tree cavity (amount of arboreal soil)" and "isolation (distance between tree cavity and terrestrial ground)" are included. Taking samples of twelve trees - sampling 1 tree cavity per each tree, 2 surveys with one year inbetween were conducted, including the following steps: Removal of arboreal soil of tree-cavities, Extraction of Fauna, Sterilization and restoration of arboreal soil into tree-cavities. I determined taxonomic groups to order level and counted the individuals. Having my dataset complete, the first thing I want to look at, analyzing my data, is the occurence probability of the insular groups (those that are bound to the arboreal soil in tree-cavities, e.g. Crassiclitellata) as a function of the predictors described above. I want to perform a zero-inflated regression model as it can consider the two distinct stochastic processes - 1. Colonization and 2. Abundace > 0 (Kéry and Royle 2015).
I am currently working on designing an EC reactor and scaling it up, for actual industrial use.
After doing research for around one month and testing I have found the current density required for actual industrial plant. But the issue arises while selecting a power supply for it. I can calculate the power required for small lab scale projects, but I couldn't do it for scaling up factor.
It would be really appreciated if anyone would like to assist me on this
What is your opinion about the criterion recommended in seismic codes for determining scaling period, which are used to scale ground motion records?
As you know, the mentioned criterion is the period of the structure’s dominant mode, which has the largest modal participating mass ratio (usually the first vibration mode). Hence, the period of the mode with the second largest modal participating mass ratio is not considered in the scaling process. Consequently, although this criterion usually results in the largest value of scaling period, it is not logical ones.
This is especially important when Tuned Mass damper (TMD) or Base-Isolation system is utilized, which cause the modal properties of the structures to change.
I used a new criterion based on the weighted mean value of the periods for the structures equipped with TMD.
Have you used any criteria other than the criterion mentioned in the seismic codes?
Please I need guidelines on the possible ways of scaling hydraulic fracturing field data to be used in a modelling software. Your response will be helpful. Thank you
I am training a deep learning model toward a image classification task. The VGG-16 model is trained individually on two different training sets with varying degrees of data imbalance. Set-1 had only 25% positive samples (class-1 (positive, label:1): 100; Class-0 (negative, label:0): 400). Set-2 had only 80% positive samples (class-1 (positive, label:1): 320; Class-0 (negative, label:0): 400). Set-1 is highly imbalanced compared to Set-2. The individual trained models were evaluated on a common test with equal numbers of positive and negative (n=100 each) samples. Since the dataset is imbalanced in both Set-1 and Set-2, the output probabilities would not be calibrated, so, I applied temperature scaling to rescale the probabilities. Figure (a) and (b) shows the results before and after calibration for the model trained on Set-1 and (c) and (d) for the model trained on Set-2. I observe that the expected calibration error and maximum calibration error reduce after temperature scaling and the probabilities are scaled to closely follow the y=x diagonal. However, it is not clear how to interpret the pre-calibration and post-calibration curves in terms of data imbalance. How the fraction of positives in each confidence bin, pre- and post-calibration, can be explained in terms of data imbalance?
Which would be inverse to the way metabolism scales according to Kleiber’s Law, that is, inverse to the way metabolism scales as mass to a 3/4 exponent?
Which would be inverse to the way metabolism scales according to Kleiber’s Law, that is, inverse to the way metabolism scales as mass to a 3/4 exponent?
So that the rate of metabolism scaled, times longevity scaling is invariant?
Has this hypothesis been proposed? Are there are articles on the scaling of mammalian longevity?
I recently have a confusion about single cell ATAC-seq integration analysis between samples. I have read many discussions about that issue. So, I summarized them into two solutions as follows:
SOLUTION 1. (data QC ignored here) find the union feature set from different samples -> generate count matrix for each sample -> merge them into one large count matrix -> normalization/Scaling/cell clustering/ cluster annotations……
SOLUTION 2. generate the count matrix for each sample -> normalization/Scaling/cell clustering/ cluster annotations for each sample -> find common features among all samples -> generate count matrix against the selected common features for each sample -> merging data using pipelines, e.g. Signac/Harmony, to perform cell clustering, cluster annotation and other following analysis (which usually with give a new assay for common features).
Either one selected, I will have cell clusters now. So the next plan for me is retrieving differential features for each cell type/cluster, which will be the key to the further investigation of biological functions.
Q1. I know that batch effect indeed exists between samples, but for SOLUTION 1, will normalization and scaling for a single large count matrix work for differential enrichment analysis between samples?
Q2. If SOLUTION 1 is not reasonable, SOLUTION 2 will give rise to a new assay only contain the selected common features, based on which the batch effect should be well corrected and the cell might be better clustered. However, how to perform the differential analysis for non-common features in each clusters? (That's to say, will the batch effect correction in the newly integrated assay by SOLUTION 2 will work for total differential feature detection in raw assays at the sample level?)
Thanks and best regards!
Does anyone know a method for predicting (with more or less uncertainty) the contribution of organisms (community, species, functionnal group...) to a function/process?
I'm not a mathematical modeler and I don't pretend to create something robust. Rather, I am in an exploratory process to enable a better understanding among stakeholders.
For the moment I have found a few publications on plants that can help me (below) but I wonder if there are any others publications, on others organisms, ecosystems?
-Garnier, E., Cortez, J., Billès, G., Navas, M. L., Roumet, C., Debussche, M., ... & Toussaint, J. P. (2004). Plant functional markers capture ecosystem properties during secondary succession. Ecology, 85(9), 2630-2637.
-Suding, K. N., Lavorel, S., Chapin Iii, F. S., Cornelissen, J. H., DIAz, S., Garnier, E., ... & Navas, M. L. (2008). Scaling environmental change through the community‐level: A trait‐based response‐and‐effect framework for plants. Global Change Biology, 14(5), 1125-1140.
-Zwart, J. A., Solomon, C. T., & Jones, S. E. (2015). Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology, 96(8), 2257-2264.
I want to perform a nonlinear time history analysis of a steel frame. I am using 3% rayleigh damping for my model. For nonlinear time history analysis, I want to scale the time histories. What should be the value for damping to develop the response spectrum for scaling my time histories.
Generally, 5% is used but as I am using 3% damping in my model. Should I prepare a response spectrum for 3% damping and then do the scaling or should I use the 5% damped response spectrum?
Please help me with this problem.
Hi every one
I am working on a model with stretching and bending in Abaqus. In my first step I apply the stretching to my model and in the second step I apply the bending. The magnitude of kinetic energy is small in 1st step but it increases significantly in bending step and exceeds the internal energy. Is there anyway to reduce the magnitude of kinetic energy? Also I should say that I have not used the mass scaling!
We just published an article about systems change, and this could be of your interest:
https://nextbillion.net/systems-change-scaling-innovations-development-challenges/ (Links to an external site.)
Feedback and comments are very welcome! 😊
I have all the items on 5-point likert scale (1-strongly disagree-5-strongly agree), but one construct is having items on 5 -point likert scale (0- not applicable-4- severe). Can I run all the items together on EFA. Will different range of point scaling cause any problem in factor loadings or the EFA result?
In my knowledge: Feature scaling is to standardize and normalize data. Feature selection is to optimize for best features.
In python, feature scaling is enough to get good accuracy %.
But in MATLAB, Normalization concept in feature scaling is required with optimization to get good accuracy %.
Any other extra benefits?
Can you give examples?
A comment in a related discussion prompted this question. In the discussion, https://www.researchgate.net/post/Is_it_worthwhile_reading_old_and_really_old_science_articles,
Vadim S. Gorshkov added a reply October 9, 2018: "My personal record is 1872".
Reading this reply of Dr. Gorshkov today, reminded me that I have relied on articles older than 1872 several times.
For example, about Galileo’s Two New Sciences, I wrote, Why scaling and not dimension, Galileo?
A comparatively more `recent’ article by Sarrus and Rameaux contains clues to providing an explanation of Kleiber’s Law. Under the Project Name Metabolic Scaling are several articles that rely on principles based on dimension, as used by Sarrus and Rameaux. They were, I suspect, the first to use dimension in connection with a biological process.
An important article about mean path lengths published in 1860 by Clausius is, I think, connected to dark energy. I have relied on Clausius’s 1860 article several times, and I believe it remains relevant and important for cosmology, as mentioned in for instance: https://www.researchgate.net/publication/320707338_Clausius's_1860_article_on_mean_path_lengths
What are your examples?
If in a multivariate model we have several continuous variables and some categorical ones, we have to change the categoricals to dummy variables containing either 0 or 1.
Now to put all the variables together to calibrate a regression or classification model, we need to scale the variables.
Scaling a continuous variable is a meaningful process. But doing the same with columns containing 0 or 1 does not seem to be ideal. The dummies will not have their "fair share" of influencing the calibrated model.
Is there a solution to this?
The subframe parameters are mentioned along with scaling factors in the ICD. How to use them while extracting navigation parameters from binary data?
Best-worst scaling(object case) is a method to collect data instead of using rating scales.In this, different choice sets are prepared each with more than 3 attributes out of which the most important and least important attributes are to be selected. This is similar to discrete choice experiment where data is collected using choice sets but in this method, we select the best choice and also the worst choice, and only attributes will be used to prepare choice sets and levels won't be there. I am searching for a software to prepare these choice sets and found sawtooth, Q, JMP might serve the purpose but I am not sure which is freely available software and which would be more suitable. Please help me with your suggestions if anyone knows about this.
After downloading the data (JPL/CSR/GFZ) from ftp://podaac-ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/ to calculate TWS (Terrestrial Water storage) do we need to do any additional post-processing without multiplying with the scaling factor? Especially before using the TWS to calculate groundwater level or drought severity index"?
I am doing Dynamic, Explicit simulation in Abaqus. The total time of my simulation is about 50s but it is taking about 2 weeks for the simulation to run. Going through some literature I realized that I can use mass or time scaling to speed up my simulation. But now I am not sure which one to use and what scaling factors to choose.
Thanks for your help in advance.
I did PGA scaling i got the results . Now i am interested to do spectrum scaling for that and i will comparing PGA scaling and Spectrum scaling for the respective structure.
Hi! I have gone through some documents about how to apply the mass scaling factor into Abaqus. The "limit" I have found out is that the kinetic energy should not exceed 5% of the internal energy.
My case is not simply like that, the internal energy is increasing over time and the kinetic energy is a fixed value from the start to the end. So the ratio of kinetic energy and internal energy is dropping over time and after a certain timing, the ratio will below 5%.
In a case like this, how to know if the mass scaling factor is good? There will always be a period of time that the ratio is larger than 5%.
I just want to know if you guys have any suggestions about this.
Thanks for the help!
A. Bejan, A. Almerbati and S. Lorente have concluded that `the economies of scale phenomenon is a fundamental feature of all flow (moving) systems, animate, inanimate, and human made’ (https://doi.org/10.1063/1.4974962).
The universe’s space everywhere flows — expands — outwards from its beginning. Economies of scale appear to arise in flowing systems. Is cosmogenesis an economy of scale phenomenon for the entire universe?
Are the physics of cosmogenesis and economies of scale the same?
I don’t think that the textbook scaling equations can be used for fair comparisons for recent nodes.
I am currently writing my master thesis where I am analysizing my data using path analysis and SEM in R (lavaan package). Now that I like to write up my results, I am struggling with the R output. Since normality assumption is violated I decided to use the Yuan bentler correction.
Consequently, R is giving me an output containing a column with Robust values (that I thought were the corrected ones) as well as as extra robust fit indices.
Did I make a mistake? Otherwise, I would appreciate if you can give me a hint on which values to use (or better said, what the difference is between them)!
Thanking you in advance and best regards,
To work on enormus large size dataset analysis
How do we design an optimal system to process big size data by choosing scaling approach?
I have a single storey reinforced system which is strenghtened with Steel Plate Shear Wall. due to finite elements having very small dimensions (because of studs) stable time incerement is very small (around 1 x e-7). Therefore, I need to speed up the analysis and use mass scaling factor.
the question is, what kind of mass scaling factor should I choose? 10, 100, 1000... or much more? at the same time, I need to get the least kinetic energy as possible as I can as a need of cyclic loading process.
I am working on an experimental SWRO plant with Ultrafiltration module. The Chemical Enhanced Backwash cycle for the UF membrane constitutes a biocidal flushing (NaOCl) followed by low pH HCl (2-3) and finally high pH NaOH (11-12)wash. The backwash solution is generated by injecting the said chemicals directly into UF permeate line from UF permeate storage tank. It is observed that that the NaOCl and NaOH dosing ports are getting frequently clogged due to precipitation of calcium carbonate (confirmed by chemical analysis of the precipitated scale).
Any suggestions on how to prevent this without reducing the pH (since the high pH is required for efficient cleaning)?
Can some alternate dosing arrangement be made to prevent this? Or using RO permeate instead of UF permeate solve this issue? Any other suggestions based on experience are also welcome.
On my project a set of questionnaire is developed. Three constructs of the questionnaire are using in different scaling methods. For variable 1, we are using dichotomous (true/false) scale where variable 2 is formulated using 5-likert scale ranging from 1 to 5, and for variable 3 using 5-likert scale ranging 0-4. Now, pilot study is conducted and sample data on the hand. My question is, chronbach alpha is enough to validate all the three variables and fulfill the assumption of psychometric analysis. If not, what should we have to do?
I'm just wondering if that is possible to simulate microchannel flow behavior in a larger channel with dimensionless parameter scaling of the operating condition
I would like to explore all performance metrics to evaluate an auto-scaling system.
The desired auto-scaling system scales out/in a web application, which is hosted in the cloud environment.
I know some metrics such as (1) Cost, (2) Response time, (3) Delay time, (4) Resource utilization, (5) Total Provisioned VMs, (6) Oscillation mitigation (or scaling overload), (7) Time to Adaptation, (8) VM minute, (9) Contradictory scaling decisions, (10) Contradictory scaling actions, (11)The rate of SLA Violation, (12) Stability, . . .
anymore? If yes, you can be a part of our team.
Huge and vague inquiry from a non-professional active in mental health social work: I have read that there is not much evidence for the value of "I-messages." This leads me to ask about a lot of things I use:
-mindfulness in trauma reactions-
-challenging questions to people [in therapy, although I am not a therapist]
-Broken-record technique to avoid arguments
-Application of motivational interviewing to misinformation, e.g. "I don't want to have therapy because only crazy people need therapy."
-very simple screening questions
We have an optically accessible atmospheric combustor. We want to perform the instability analysis of the combustor like flashback, blowout and instabilities occurring near lean blowout. We are looking for some scaling parameters such that whatever we conclude from the atmospheric combustor applies well to an industrial combustor operating at higher pressures.
Instances of the 4th dimension include:
Time in Minkowski’s space-time (Raum und Zeit).
As flow or motion in various 4/3 laws.
In a space-time distance, time squared is preceded by a sign opposite to that of the other lengths squared. Time is different.
Flow, motion and time trace a moving point along a line. The 3 spatial dimensions are static.
In the 4/3 law pertaining to energy the same energy in 4 dimensions has 4/3 as much energy in the corresponding 3 dimensional space. How can energy occupy a 4th dimension that models a moving point? Perhaps the model in the 4/3 law is wrong or incomplete? If it is incomplete, how is it incomplete? Is some aspect of time missing? In this portion of the comment on the question, accounting for the 4th dimensional status of motion affects understanding of the 4/3 laws.
Or is the 4th dimension nothing more than a mathematical construct?
Can someone tell me use cases that uses multiple combination of custom metrics to scale or combination of custom and system metrics?
when it comes to scaling in Kubernetes, we rely on metrics. By default, we use CPU and Memory metrics. Considering use cases where we need custom metrics (such as 'request count per second'), Kubernetes allows us to plug in custom metrics. For an application, there might be interrelated metrics that help in scaling. For example, combining "CPU" and "memory" metrics, we can use "CPU^2/memory" as the combined metric to scale. Can someone direct me towards real-world scenarios similar to this, but relating *custom metrics, where multiple *custom metrics are correlated in order to perform scaling? will this be a better approach?
Dear Research Scholars , I request you to provide the information for above question and what is the Advantage of Logarithmically Scaling of Image .
Dear fellow researchers and peers,
My name is Patrick, who is just starting to pursue my PhD in Marketing. My research topic requires me to conduct Best-Worst scaling questionnaires to elicit consumers' ranking of product attributes.
What I'm struggling with is my inability to use the Balanced Incomplete Block Design to create Choice Sets for my study. I have a list of 45 - 50 attributes that I want to test; thus, it would be impossible to follow the normal instruction (each attribute appears at least 3-5 times for each survey). Hope that I could get some help or guidance from this community.
I look forward to hearing from you guys.
I have this dimensionless governing equations that I want to solve in Ansys Fluent. But then, in Ansys Fluent, the geometry only dimensional. So my question is, does dimensionless equations means that we can apply it to any geometry i.e. scaling?
I have tried to apply multidimensional scaling methods for the construct developed but the data collection becoming so difficult through factor analysis. Please suggest me the alternatives
1. What are the other multi dimensional scaling methods can be adapted?
2. Can any additional procedure with Likert and Thurstone scaling methods can be used for multi-dimensional scale development
I hope everyone doing well.
While i was studying Phenomenology and Turbulence Scaling , total energy in the system for the largest eddies was approximated to U^2/2. Where U is the Rotational velocity of largest eddies.
I ddint find how or on what basis Spectral energy was approximated to U^2/2 ?
let me know if anybody has an idea about it.
appreciated in-advance for your perspective on this.
Usually, whist analyzing sc-RNA-seq data, using SEURAT, a standard log normalize step is performed on the data prior to scaling the mean values of the data. In this step, the normalize method suggests to use a scale variable across cells of 10^4. I would have assumed, without any scale factor, each transcription value (t_ij) where i= gene and j= cell one simply does log10( t_ij + 1). Now, multiplying t_ij by some scale would not change much as taking log will simply add a constant say 4 for the suggested 10^4. But, if it means, that the method actually sums counts for each cell (S_j) then divides by it and multiplies by the scale i.e. (t_ij/N_j)*10^4, then it means after this step there is no more difference in sum of the columns i.e. across cells transcript counts are all 10,000. But when I plot hist(sumcols(expressionobject@data) I find a distribution. So it is not clear to me what is the method doing ? Any ideas ?
Scaling deposits are common in flowlines subject to changes in pressure or temperature.Scale formation results in reduced diameter or blocked pipes.
A very delicate problem for managers, especially in southern Algeria using the waters of the albian , despite the cooling of the water.
What do you think is the best pratical solution for scaling in pipelines ?