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Dear all,
I have installed GPU GROMACS 2023 on two PCs, and one is working without errors. However, the other one gives a segmentation fault (core dumped) error during the gmx run. I launched Valgrind to check memory using the following command:
$ valgrind --leak-check=full --show-leak-kinds=all gmx mdrun -deffnm nvt -v
The results are provided in the attached text file.
Please give your valuable advice on how to solve this problem.
Sincerely,
Alisher
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ok. Thank you Sir
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Dear all,
I need the collaboration of native speakers of English around the Maule, Ñuble, Bio-Bio, Metropolitan or O'Higgins regions (Chile) as participants in my PhD. thesis. The participants will be asked to listen to and type recorded words uttered by Chilean EFL trainee teachers. The data collection could also be carried out online, but it would require the installation of computer software on the participants' PCs and copying some files.
Thanks in advance.
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Thank you, Baiben. Unfortunately, only native Speakers of English or Spanish can be considered for participation. I really appreciate your willingness. Thanks again.
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What techniques are required for identification.
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UV is not selective and a very poor choice for phospholipids (*just look at their structure to understand why). ELSD, CAD and MS are preferred, but also require far more practical experience (many years) and knowledge to use properly (to acquire valid data) too.
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How weights (WQI) derived from relative loads and eigenvalues ​​associated with water parameters in principal components (PCs) are calculated? I need help
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At first, calculate the PC of all parameters and sum up the F1 values of selected parameters (normally the highest F1 valued parameters select for WQI operation).
After that, divide each parameter's F1 values by the calculated sum value.
These results are the weight values for the WQI calculation.
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To diagonalize a covariance matrix 500+x500+ is extremely difficult, almost impossible. To maximize a mean squared projection of the vectors on some direction a=(a1,a2,.....a500+) You are to solve a systems of 500+ nonlinear equations. What is the rest? Heuristic methods like genetic algorithm, ant colony, simulated annealing etc ?
I wonder when people easily say: "We've considered first 2/3/4.. PCs!"
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Stam, and what about so called singular decomposition of nonsquared matrix X=(xij), i=1..30, j=1..500+ composed of 500+-dim vectors?
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can someone provide an answer if it is possible to use IPython for parallel computing on Windows PCs, and if so how should connections be made?
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It is a known fact from the literature that phytochelatins (PCs) are Cys-rich peptides responsible for cadmium detoxification. Is it indeed the real (or only one) function? What about plants/fungi/worms living in a cadmium-free environment? Why they need PCs? How does induction work in a cadmium-free environment? The fact is that organisms have it even if there is not cadmium around. Any smart suggestions? The answer is probably not trivial.
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Possibly be the secondary source, at demands, for anti-oxidant/radical scavenger, mechano-strength, and metal electron-partaking!
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I would like to ask for some advice for building a personal computer for general home use and bioinformatics (mainly molecular dynamics, like CUDA calculations in GROMACS, AMBER, ect) Budget is somewhere around 1500$.
1) Is there any preference between Intel or AMD for bioinformatics? For example, core i5-9600kf vs ryzen 5 3600. Like probable optimization issues of software for the processor architecture? I have heard that AMD is better for linux, but i am not sure about it
2) I already have Nvidea RTX 2060 super and GTX 970 from my old PCs. Can i use them both for CUDA calculations?
3) Is there anything special that needs to  be considered in motherboard choice for CUDA calculations/bioinformatics?
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Hi!
I'm reading yours opinions, and I have a question:
Is better Intel than AMD? obviusly about CPU, because I think to buy Ryzen5 3600XT or I7 10400F
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I am using PCA to extract major meaningful data from many different parameters that are embedded in my remote sensing satellite imagery. I know that PCA is a dimension reduction model and the final components are perpendicular to each other and the very first PCAs are the most important ones and can explain the major part of my data's variance.
Since I am using R studio to extract my PCs, I first calculated the normalized matric and then proceed with the eigenvalues and eigenvectors. my first PC has around 80 percent of the total variance and a higher association with all parameters.
The only question here is that how can we physically explain the PC1 or PC2? what are their numbers tell us? imagine we have a PC1 range from -10 to +25, so how can I define the exact meanings behind these numbers?
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Take the famous eigenface example, suppose each person's image has 1000*1000 pixels, we reshape it into a tall (1million, 1) vector. Suppose we have 5000 people's image, so our dataset is in the shape of (1 mil, 5000).
After centering the dataset, we take full SVD:
x = U * S * V.T
this can been viewed as linear combination of column vectors of U from V.T and scaling by S.
What PCA does is to take first r (n_components) columns vectors of U and truncate the others, and use the linear combination of this r column vectors to approximate the original dataset.
Suppose we do a 2 components PCA, U will be in shape of (1 mil, 2). Pick one data vector R, which is in shape of (1 mil, 1):
P (PC Scores) = U.T * R
(2 by 1 vector)
PC Scores represent the linear combination of of truncated U to approximate R. Once you have truncated U matrix stored, each data point can be represented just by TWO PC scores instead of 1 MILLION numbers, since you can easily reconstruct the approximated data by U * P.
So basically PC scores mean nothing, if the principal components have no realistic meaning. One of the most popular usage of PCA is to do cluster analysis of the same dataset, since PC scores can be easily visualized in lower dimension.
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Hello everybody
I am eager to know what is the procedure in software like Catia, Cloud Compare, Geomagic or etc, that remove noise from Point Clouds(PCs); especially when the PCs does not contain RGB or intensity information and it just contains 3-D coordinates of points. (e.g.PCs are obtained from laser scanner. )
Thanks in advance
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Hi Elnaz,
From your listed software, I'm most familiar with CloudCompare. I like it because it's quite "open" as opposed to (some) commercial PC editing software, not to mention quite powerful as well. As far as I undertand it, CC's noise filter uses an SOR (Statistical Outlier Removal") based algorithm. Essentially it computes a local plane using a neighbourhood of points, and then tests the Euclidean distance of each neighbouring point against this local plane. The point gets deleted if this distance is farther than the set threshold value.
If I remember correctly CC also has an implementation of the "pure" SOR algorithm in which the point's Euclidean distance is directly taken into account instead of the distance towards the local plane.
Hope this helps, and in any case I encourage you to join the CC forum (https://www.cloudcompare.org/forum/) and also the CC wiki (https://www.cloudcompare.org/doc/wiki). They're very useful resources and many questions are answered readily by the community.
best,
arnadi
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Hello All,
I have a very large dataset. After computing the PCA I have selected its first and second Principal Components (PC).
In the attached file the scatter plot of these two PCs is illustrated.
I want to use these two PCs to classify two tasks.
Do you think is there any way or any transformation to change the feature space for separating these two PCs?
Thanks,
Hamed
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Your Data seems to be nonlinear. You can Apply nonlinear PCA (KPCA) for that.
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In support of crime prevention the human monitoring systems are widely deployed in companies, schools and elsewhere. There are number of effective methods to predict the migration route of a person in a crowd by using high-order particle filter and online-learning. There are method focused on group structure to improve tracking accuracy in a situation when the detection ranges of cameras overlap.
Papers:
M. Shiozuka, T. Yotsumoto, K. Takahashi, M. Nishiyama, T. Kawamura, and K. Sugahara, “Countermeasure to Human Recognition Error for Agent-based Human Tracking System,” 12th International Conference on Mobile Ubiquitos Computing, Systems, and Technologies (UBICOMM2018), pp. 65- 70.
M. Shiozuka, T. Yotsumoto, K. Takahashi, T. Kawamura, and K. Sugahara, “Implementation Example with Ultra-Small PCs for Human Tracking System Based on Mobile Agent Technologies,” 11th International Conference on Mobile Ubiquitos Computing, Systems, and Technologies (UBICOMM2017), pp. 73-78.
Y.J. Cho, S.A. Kim, J.H. Park, K. Lee, and K.J. Yoon, “Joint Person Re-identification and Camera Network Topology Inference in Multiple Camera,” arXiv:1710.00983, 2017.
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Interesting topic.
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Smart phones, tablets, smart PCs, smart watches and many other smart devices lead to new life style. Mobile applications is the new smart human industry. Facebook, twitter, Instagram, Uber, Careem, and many other applications did not involve hundreds or thousands of employees. Just few smart developers and some softwares and laptops to create such applications that value billions of $s.
Uber and Careem exploits the new generation of mobile phones and high connectivity to bring an efficient and costless transportation solution. In the coming years, the current classical public transportation will be completely reshaped. No more classical taxis, trains, etc. Everything will be replaced by smart and on demand cars and buses using the smart phones and location based services like GPS.
Do you think emerging and new research topics should be oriented towards these new features and technologies.?
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Very good suggestions from all colleagues , I think yes a lot of research and scientific papers have become interested in technology to employed with different sectors. The concept of artificial intelligence has become the focus of attention of researchers and conferences, in addition to the presence of high global competition and this technology is an important factor in the lives of individuals, it was noticed recently that smart applications in the field of transport has helped to bridge distances and facilitate mobility.
some of paper covering aspects of this topic:
and if you interested in modern technology you can check my papers on my profile
Best regards,
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I have a scaled PCA biplot of the first two PCs of a data set with 43 observations and 5 variables.
The biplot indicates certain relationships between variables, based on the angles between the vectors. Some variables are positively correlated, others are negatively or not correlated at all. Why then, if I calculate and plot a Pearson's correlation between two columns in my data set, would the relationship be different from what's indicated on the biplot? (i.e. Pearson indicates a strong and significant positive correlation, but biplot shows vectors with an angle less than 90 degrees?)
Note that all my variable are highly positively correlated.
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hi,
in PCA, longer the line and closest to positive is the Principal component, but Pearson's Correlation indicates the significance between the parameters. they are different so you can proceed with both PCA and PC
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I am using PCA to reduce the number of dimensions in a kinematic assessment tool (there are currently >100 metrics over 3 tasks).
My intention was to run a PCA to reduce the number of dimensions (on one dataset) into theoretically-meaningful constructs (i.e. items related to "Speed", "Accuracy" etc.).
Once these new PCs had been established, I was planning on then testing this new structure on different data using CFA. Is this appropriate? Or is there an alternative method to achieve this same outcome?
Thanks in advance
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You can use this method as David Morse said. But your results may be different from CFA. I think you should use principle axis factoring in EFA for more accurate results. Because of PCA doesnt divide variance as unique/specific/error (as David Morse said) your results may not be acurrate.
Hope helps,
Faruk
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Hi, Does any one know how to stabilize phytochelatin during analysis. I know it should be kept in -20 and it's only stable for maximum 4 h in room temperature but I'm looking for something to keep PCs stable for longer hours.
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Dissolve with perticular solvent while analysis?
Regards
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I used MVD for docking, it produces different results everytime,even whn I ran it on same ligand and receptor, and the differences are more obvious when I operated it on different PCs. Why is it so? Please anyone suggest me how to obtain a reproducible result form MVD. Thank you
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All search algorithms in molecular docking, including MDV's genetic search algorithm, use stochastic (random) processes, so the results will always be non-deterministic. This is because the number of possible protein-ligand poses for even the simplest cases are so large, it would take a very, very long time to assess exhaustively.
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Hello,
I've used the procedure proposed by Marrapu et al. (2015) --> To synthesize di-C:8, di-C:12 and di-C:18 glycero-phosphocholines, I've followed the procedure 6a-K in the reference above (with 5 eq. acyl cholrides for 1 eq. GPC for all). I've obtained my di-C:18 successfully with a yield near 100%. In contrast, I got less than 5% of di-C:8 and di-C:12 glycero-phosphocholines!
Is there any researchers who faced the same problem for short chain PCs using this protocol? What could be the problem?
Thank you,
Nabil Adrar
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Check the reaction conditions, such as anhydrous and anaerobic. Use NMR,chromatography and other methods to check whether the impurities and whether they are the substances you need.
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Is there a difference in average age among computer buyers based on their preference for Mac or PCs?
Guys, I need help! I believe the DV is Age which is being measured and the IV are the choice of computers? HELP
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What are the dependent and Independent variable (s)??
Merely based on your research question i.e. " Is there a difference in average age among computer buyers based on their preference for Mac or PCs?" - sounds like you can use analysis of variance e.g. dependent t-test rather than correlational analysis using dependent & independent variables.
If your research conceptual framework / research model is based on correlational study with multiple independent variables (IV) & a dependent variable (DV) - may be DV can be Mac / PC preference, IV should be more e.g. social economic status, previous computer brand experience, friends pressure, apps to be run etc. beside age as the IV. Because there can be other IVs carry more weight in influencing a buyer decision to purchase Mac over PC etc.
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It is hard in classes, academic as well as in customer organizations, to protect the learning experience from disruptions and distractions. This problem has grown with omnipresence of connected devices such as smartphones, tablets, and laptop PCs.
Often, one is not in a position to force students to leave these items in their bags and closed. What are great recommendations to protect the learning (and teaching!) experience?
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Simply make them crazy to their research work rather than smartphone!
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Hello!
I am trying to run DAPC analysis in my genome-wide dataset incluiding 188736 genotypes for 188 individuals from 18 different geographic populations. I already know there is some genetic structure in the dataset, at least 2 or 3 groups could be defined. However, when running "find.clusters()" function in order to define the most plausible number of groups that could explain my dataset I obtain strange plots of "Cumulative variance explained by PCA" and "Value of BIC vs number of clusters". The cumulative variance should be higher in the first PCs and decrease as we look at the next ones, showing a curve in the graph up to the 100%. Furthermore, the BIC graph should show an elbow at some point with the smallest level of BIC and do not to represent such a perfect line.
Do you have any idea about why obtaining these results and what do they actually mean? Could it be because the amount of genotypes and samples is such high that the function cannot work with them?
Thank you in advance.
André
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I am not sure if there are recommendation for max dataset size. Obviously you are looking for a Cumulative-Variance plateau, however the first figure suggests that there isn't one for any number of retained PCA's. Something you could try is to randomly reduce your dataset by half (or even more) and see whether these figures change. That would be my next step.
Good luck, and please let me know whether that worked, interesting to know!
Matthijs
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Given current state of pollution in the seas and oceans with plastic and other waist products and ever increasing volume of electronic-chemical devices, such as cell phones, tables, PCs, laptops, home electric appliances and plus, it is critical to develop proper methodologies and strategies to eliminate, ideally recycle all the waist all over the world. What smart technology practical solutions are or will be available to recycle and eliminate a global pollution?
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Thank you all. This is a journey of exploration and new beginning for further research, innovation and development of new technological solutions that will solve this problem. Naturally, people, all of us individually are responsible for the way we deal with waist and treat the environment where we live and do. In smaller scale there are many excellent solutions already. However, there is much more to be done to make sure that future generations live on our beautiful blue planet Earth in four seasons with all their natural wonders and live supporting resources.
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Hi I want to find out if there is statistical significant separation between the enzyme group and non-enzyme groups for the CD type I proteins using Principle component Analysis. Using parallel analysis, I found out that 10 PCs should be retained. Using two sample Hotelling's t-squared test and F-statistics, I found out that the enzyme and non-enzyme group in my score plot is separated significantly( I used this paper as my reference for analysis: http://www.sciencedirect.com/science/article/pii/S0169743911001754). My question is: Does Hotelling's T-squared Test for two samples (PC1 and PC2 score values) will give the same F-statistics as using Hotelling's T-squared for 10 PCs ? I am assuming "No". But, can I say that by using first two principle components will give the similar results as using 10 components. I have problem on how to perform Hotelling's T-squared test for more than 3 variables in Excel or SPSS.
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@ Angeles Camacho . Based on the paper that I used, http://www.sciencedirect.com/science/article/pii/S0169743911001754, I am trying to used Hotelling's T-squared test ( it measured if there is significant difference between the multivariate means of Enzyme and non-enzyme groups using 10 PCs.) I can calculate the Hotelling's T-square for two PCs. But, I am having difficulty calculating T-squared with 10 PCs.
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I am looking for the scoring instructions for the PCS and MCS of the SF-36. Thank you.
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Hi Jennifer. Scoring for PCS and MCS is country-weight specific. Here (attachment) is an example on how I do calculate these scores for NZ population. I think you should find Canada weights and then you could use this spreedsheet by simple replace the weights from NZ with the weights from Canada. Basicall,y the value that people  gives to the different dimensions of HRQoL differ from country to country and that is why weights are country-specific. Hope this helps. Best, Borja
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My advisor has a Mac and uses Text Wrangler (which converts to fasta) and Se-Al (for mass editing) neither of which works on PCs. I've been having difficulties getting other programs to work. Any suggestions? Thanks for the help!
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Try ClustalW.
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Principal component analysis (PCA) to obtain the principal components (PCs) that best fits the shape of the face in order to relate the SNP data with that PCs by using the partial least squares regression (PLSR) method as it is explained in Claes et al. 2014? 
Don't understand very well the methodology there explained. 
Thanks in advance.
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PCA isn't method for regression but for data description. You can try PCA to find relation between SNP data and face shape data if you create one data matrix with both sets. Investigation loadings could tell something about relations.
Different approach to solve your task is to use some regression method for example PCR (principal component regression) or PLSR.
In my opinion there is no sense to calculate PLSR based on PCA data.
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Hi Professors, I am conducting a research about adoption of mobile technologies in Palestinian schools from teachers' perspectives. I focused on tablet PCs as a specific type of mobile technologies because this innovation (tablet PCs) is going on in Palestinian schools. I know there are a lot of models and theories about identifying factors that influence adoption process. Some of these are UTAUT and Roger's theory (Diffusion of Innovation). Which one is the best. My data collection instrument is semi-structured interview and weekly lesson plan. 
Thank you 
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You might also find useful  "Kolb’s Experiential Learning Cycle". If you want have a look at the following link:
Konak, A., Clark, T. K., & Nasereddin, M. (2014). Using Kolb’s Experiential Learning Cycle to improve student learning in virtual computer laboratories. Computers and Education, 72, 11–22. http://doi.org/10.1016/j.compedu.2013.10.013
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I am having huge number of independent variables which are highly correlated and I want to run clogit. I tried all combinations of independent variables. It is a very laborious process. So I tried PCA and run the clogit with PCs scores, is it correct to use PCA before running clogit? 
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In short the answer is yes.  You have to remember that the purpose of a PCA is to reduce the dimentionality of the data by coming up with linear combinations of the variables that maximize the variance explained.  So I've had a set of 13 variables be reduced down to 2 PCs that explain 90% of the data.  Each of the PC axes is orthogonal to all the others so treating them as independent is fine.  The key is in interpreting what the PCs mean.  You have to look at the loadings on each of the PCs to do that.  I'm attaching a paper where we performed a PCA on measurements of crab embryos.  The first PC explained 80% of the data and was easily interpreted as the maturity of the embryos (the lower the score the closer they were to hatching), we then ran an ANOVA on the PC to look at differences among our treatments and interpreted and significant terms as affect the maturity of the embryos.
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Hello, I am new to handle pc3 cell. I have thawed two times this cell, both times after thawing, the cell grows but as I subculture it the cell undergoes apoptosis. I am using RPMI 1640 as subculture media. Please suggest how I overcome this?
Thanks
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can we thaw pc3 cells using rpmi media or ham f12 instead of f12k?
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What is the methodology to decide required number of PCs? Can I extract PCs from a single image?
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You can not extract PCs from single image. PCs represent the variance from image to image, so for single image the variance is zero.
The required number of PCs can be estimated from the energy contained in the remaining PCs or equivalently from eigenvalues corresponding to specific PCs.
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I want to develop a microcontroller based system which can communicate with PC/Laptop through USB port. (As simple as possible eg. toggle leds connected to any port of microcontroller when any key is pressed from keyboard).
I worked on atmel's 8051 and atmega microcontrollers. So these families will be preferable to me.
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It´s possible to use any other Arduino- like plattaform. Suggestions: MBED, based on NXP microcontroller, Freedom Based on Freescale microcontroller.
If you want to make it yourself from scratch, I'll suggest 18FX550 PIC microcontroller using C language. 
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What is the best PC program for calculation of the stability constants of the binary systems from Raman spectroscopy data?
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I used Dalton program for the calculation of polarizabilities and Raman intensities..It´s good..
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I'm looking to undertake research in this area, any guidance is appreciated
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Hi 
Now a days many available devices have this option of portabililty/wifi /linked/pc based/laptop based features. However, they vary in their cost and add on features .
SOMNOscreen™ plus - SOMNOmedics
The Alice PDx portable diagnostic:Phi;ips
ResMed’s ApneaLink™ Plus: for portable 
Embletta X 100
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I developed a FORTRAN based model for the heating and evaporation of multi-component (automotive fuel) droplets. When replacing the large number (100s) of components with smaller representative number of components, the CPU time will decrease significantly. For a given PC, what other factors apart from the format of the written code, routines, subroutines, syntax ..etc., can increase the CPU efficiency of the model?
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@Peter,
Yes, you are correct, he did say for a given PC.
In that case, I would try a different compiler.  Perhaps the Intel compiler at high optimization, and make sure he has not turned on run-time checks.  Padding of common blocks to avoid mis-alignments, and enabling inter-procedural analysis  can sometimes also help, especially if the entire source is compiled withing one file, as this may allow inlining of routines.
I would also make sure to link in vendor high performance libraries, like Intel's MKL.