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# Fitness - Science topic

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Some vertebrates are entirely self-sufficient and never see their parents, whereas other vertebrates (including humans) have extended periods of obligatory parental care; which is a behavior and evolutionary strategy adopted by some animals, making a parental investment -which is any parental expenditure that benefits one offspring at a cost to parents' ability to invest in other components of fitness- into the evolutionary fitness of their offspring. why is that?
The animal's life expectancy, physical environment, body size, and social structure all play a role in the level of parental care.
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Hi, I am wondering if there is a correlation between colony size on a YPD plate and fitness. I am working with S. cerevisiae.
Also, does the fitness calculated with the size of the colony also correlate with the fitness calculated from the growth on liquid media?
#yeast #saccharomycescerevisiae #fitness
If your data are gaussian, you may try Pearson correlation. cor.test() in R. Run a Shapiro-wilk test first to test for normal distribution and then test if the covariance is linear.
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I need to fit a transient absorption kinetics, starting from -500 fs to 4000 fs. Around time-zero, it has a Gaussian function due to instrument response (region-1) and from about 100 fs (to 4000 fs) it shows the actual exponential kinetics (region-2). I need to fit both the region simultaneously in Origin (i.e. deconvolution around time-zero and exponential growth(/decay) afterwards). Please suggest how do I perform the fit in Origin? Thanks.
Tushar Debnath Deconvolution of a composite peak into its individual peaks plays an important role in the interpretation of many types of graphs including XRD, XPS, FTIR, and PL etc. In this video, I have discussed how to deconvolute simple combined peaks, composite peaks and how to correct missing data in a given peak with the help of deconvolution in OriginLab. In the case you want to further ask about it, please do comment on the specific video, I'll respond to it shortly. I have provided the practice files (OriginLab) here. Thanks
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I'm trying to fit RRM model manually using any of the three software.
Hi,
I think you need to check Apollo user manual that you can find its link below:
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Hello,
Many protein shake powder mixes contain digestive enzymes such as protease, lipase, and cellulase. However, the products do not detail from which organism the proteins are produced or which protein homolog is used. I would imagine that the enzymes are produced in yeast or fungi. I am am curious regarding which organisms are used. Also, is the native form of the protein harvested from these organisms, or are they made to produce a recombinant human version? In the case of cellulase, there is no human homolog, so it must be from another organism. Regardless, should not the host species be listed on the product? Additionally, if the protein is not naturally occurring in humans, is it ever a good idea to consume it in high quantities?
Thank you!
Digestive enzymes are extracted from animals,plants and microorganisms. Pancreatic enzymes for digestion are extracted in the Middle East from Camel, Cattle and Cheeps.Also yeasts are an important sources in our country as a source of huge enzymes.Lactic acid bacteria are used as lactase source for treatment of lactose-intolerant people.
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The Novel Coronavirus Pandemic is currently and permanently changing the location of where Resistance Training (RT) is going to be performed. The who, what where, how, and desired effect of RT is never going to be the same.
So many are home bound developing new habits of exercising and cohabitating.
I am wondering how my colleagues here on ResearchGate envision ways that Resistance Training is going to adapt to the new world order of exercise, fitness, health and human movement?
In my view, due to the COVID-19, we are realizing the full potential of the web-based physical Activity and exercising at home. It appears that the pandemic has demonstrated that the internet can expand the scope of educational and professional care and include people who possibly were not comfortable in a traditional physical activity setting. People will cherish more PA, including RT, even in non traditional setting.
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Is there any danger of sweat, aerosols or bioaerosols in the case of indoor exercise and/or high-intensity exercise in relation to SARS-CoV-2?
Is sweat a danger?
Are bio aerosols a problem indoors with higher-intensity exercise? Would you have to account for ventilation and air flows?
Is there more danger with heavier breathing patterns or more powerfull ones?
Can you somehow try to make sure if a person has an infection when they do not have symptoms?
Please check out Dr Bromage's blog posting of environmental risks. Although he did not mention indoor gyms in particular, the virus is easier to spread indoors with poor circulation and poor airflow. The more infected individuals in a single area, and the longer the duration one's exposure...the higher the viral load one is exposed to and the more likely one would be infected.
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hi
how can i draw a forest plot graf in STATA with favorus option like aerobic vs. control
i type this command,
metan n1 mean1 sd1 n2 mean2 sd2, favours(aerobic # control)
but i faced with this problem by STATA:
option favours() not allowed
can anyone help me?
All the commands of meta-analysis in either stata or R can easily be accessed online and you can use the metan function of stata or meta package of R software for effectiveness studies.
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Any ideas/suggestions related to re-opening of fitness centra and personal training and safety measures and/or risk factors related to covid-19?
Risk of spread with breathing, with higher-intensity training?
What kind of safety measures would you suggest?
First of all, you need to consider providing high standards hygiene. As Ashraf mentioned earlier, ensure you have good ventilation system while maintaining regular and ongoing sterilization. Second, since the environmental contamination is likely to accure, you need to limit the number of people per each session while maintaining the social distance. This measure is crucial at this time, and recall your plan according to the situation by following the WHO updates and recommendations. Ensure people are wearing PPE ( masks, gloves , goggles, etc). Regarding the training machines, ensure they are cleaned and sterilized efficiently after each use.
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I have a XRD data with a number of overlapping peaks. Hence I am not able to go below GOF 2 and Rwp 10 .
What Goodness of Fit and Rwp could be considered to be the best for Rietveld Refinement in XPert High Score Plus for a material with overlapping peaks?
Forget about "best" in terms of both Rwp, GOF, or DW statistics. OK, Rwp is said to be "best" if below 10; GOF - if between 1 and 2. But we need to be careful here: adding additional (or any) parameters to the refinement may easily lead to a drop of Rwp --> false positive result / overparametrization. When I do Rietveld (I use the TOPAS software), then I use as low number of parameters/corrections as possible. My approach is tested via the Reynolds Cup competition and it works well, at least with non-clay minerals. I owe a lot of my knowledge in this field to the Rietveld Mailing List, and David Bish. That said, sample displacement and LP factor are a must, so are other, KNOWN, instrumental or related parameters. Regarding the Crystallite Size and Strain (and also most of the REFINED parameters): the value calculated must be > error. If the value calculated is below the related error, then the particular parameter is out of being physically meaningful to the refinement, and in the next run it must be unmarked.
Indeed, I've seen good (reasonable) models with Rwp >10 and even GOF >2. These are "just numbers", they DO say something, but the graphical representation of the result, and its fit to the original data, are also important.
What helps me is the famous Robert A. Wilson and his "forget about 100%" and "forget about IS".... A thing is SUPPOSED TO BE / SIMILAR TO / LIKE.... (:
Cheers,
Luke Kruszewski
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XPS
Peak Fitting
CASAXPS
Residual
My residual value is so big because I have the right side of the data peak uncovered. Is it possible to adjust the right side of the component peak to cover the open side of the data peak as shown in the attached picture? I am a beginner at XPS Peak fittting.
Look here:
and here:
Hope It helps :)
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This is the result of my goodness of fit test in AMOS:
P = 0.046
CMIN/DF = 1,473
RMSEA = 0,069
CFI = 0,969
GFI = 0,921
TLI = 0,953
Most of the fit indices show that the model is fit except for P-value. Do I have to revise my model or is this good enough?
Muhammad Anwar Salam, as you mentioned about GOF and P-Value (.000) may consider good model fit. Is there any article to support this statement. In my case, my CFI, GFI, TLI, RMSEA, DF meet the threshold but p is .ooo but my SV is asking me to meet .05 for P then processed. If any ref you have please share.
Thanks
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have a curve in a tripartite plot and have to do least square fitting of 7 straight line segments through the curve I have and the constraint is that the third line segment should have a slope of -45 degrees (anticlockwise ) and the fifth line segment should have slope of +45 degrees and the fourth line segment should be a horizontal line. From the piecewise fitted line segment curve I have to get the optimum values of intersection points and the optimum perpendicular distance of 3rd line segment from the -45 degree axis and optimum perpendicular distance of fifth line segment from +45 degree axis. I don't have an objective function to be minimized or optimized but have some 1000+ x and y values of the original curve and know the equation to find third and fourth parameter on the diagonal axes from the x and y values . Is it possible to find these optimum points and values using fminunc function in matlab or any other function?
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As stated above, before attempting to improve the model, RMSEA and SRMR suggest the model may fit, but CFI value is low.
According to what I know, it shows that the model is not improved a lot from the null model. In other words, it may implies that some of the coefficients are small, although they are different from 0.
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What is fitness definition in your opinion ?
Fitness is the ability to do daily tasks without fatigue
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Every athlete requires an adequate amount of protein. It’s not only good to increase lean muscle mass , it will also optimize anabolic hormone levels, increase metabolism relative to other nutrients and improve cardiovascular risk profiles.
So which are better protein supplements or protein in foods ?
that is depend on the constituents of food protein and its contents of essential and non essential amino acids and their percent
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We are usually told to take a 15 minutes nap after lunch and a walk after dinner. How long after taking meals one should take a nap or walk? Should we walk briskly or leisurely after meals? Should it be a short walk or a long one? Walk before a meal helps burn more calories or walk after meals? How long should we walk before and after a meal?
Dear Bisma, maybe the following papers will help you on the subject:
Hijikata Y, Yamada S. Walking just after a meal seems to be more effective for weight loss than waiting for one hour to walk after a meal. Int J Gen Med 2011;4:447-50. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3119587/pdf/ijgm-4-447.pdf
Reynolds AN, Mann JI, Williams S, Venn BJ. Advice to walk after meals is more effective for lowering postprandial glycaemia in type 2 diabetes mellitus than advice that does not specify timing: a randomised crossover study. Diabetologia 2016;59:2572–2578. https://link.springer.com/content/pdf/10.1007%2Fs00125-016-4085-2.pdf
Pahra D, Sharma N, Ghai S, Hajela A, Bhansali S, Bhansali A. Impact of post-meal and one-time daily exercise in patient with type 2 diabetes mellitus: a randomized crossover study. Diabetol Metab Syndr 2017;9:64. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580296/pdf/13098_2017_Article_263.pdf
Have a good walking day, Martin
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Your ability to lose, gain or maintain your weight is dependent on genetic, environmental, and behavioral factors. But how much of a role does genetics play in weight loss versus eating a healthy diet? Is there any truth to genetics playing a substantial role in your ability to lose weight to improve health and overall body composition? Or the exercise and diet is the driving factor?
I have diabetes because my mother was also diabetic. However; I am fine only with low carb diet and no medecine at all. It is not easy ; especially in the beguining. However diet not only solved my diabetes problem but also made my immunity system stronger. I feel much healtier now than when I had diabetes fifteen years ago.
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can someone explain about of effect of this both training model on muscle activation and muscle damage? moreover what effect they have on the muscle hypertrophy?
eccentric -> mostly in force there is contribution of passive elements, and less of active elements (cross-bridges). Because of lengthening the "damage", which is more in reality sensing of receptors leads to, generallly, addition of sarcomeres in series. So lengthwise the muscle cell will grow, and in parallel some addition can be done because there is some activity of myosin-actin.
In case of drop set, there is more fatigue, and you keep training with lower intensity or load, but because of higher fatigue, you keep on getting more higher or highest threshold groups. Also there is both eccentric and concentric work done, so more addition of parallel sarcomeres (at least in theory).
With the eccentric training the question remains how soon or how many highest threshold groups are involved as the passive elements can contribute a lot towards the force production. It might be that less high(er/est) threshold groups are activated. Eccentric training can be done with overload, as in very heavy weights only eccentric, with less voluntary control. Or with for instance a bit more load which can still be controlled.
Eccentric has more muscle damage related to intensity, because of higher loads, while drop sets have less high loads and more fatigue, so there would be in the first, hypothetical more mechanical damage, and in the second more metabolic damage. However, the question is open if muscle damage contributes to HT in the first place.
So drop set with controlled eccentric movement in the exercise might be better for overall hypertrophy as you train both eccentric and concentric.
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Seeking co-authors/collaborators to participate with me in database studies involving NSQIP, NIS, SEER, NAMC, etc. This is a good opportunity for post-docs and research fellows who can work remotely. Must have a basic understanding of biostats, surgical/medical outcomes, and the afore-mentioned databases. Send me a message and let's see if you are good fit.
Hi Inwould like to. I am orthopedic Surgeon and would like to be part of surgical outcome studies. How can i be part of it
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I am currently working with 2 non-linear equations; One is with 3 coefficients; The other is with 4 coefficients; I have used EXCEL SOLVER to minimise the sum of squared normalised residuals. I am not sure to have 3 coefficients or 4 coefficients in terms of goodness of fit.
Best regards
Ravi
Measuring the goodness of fit is a big topic to discuss, however, we can summarize it in calculating two or more statistical measures for the results of each model (regardless of its formula or number of coefficients).
The top recommended statistical measures are the coefficient of determination (R squared), the root mean squared error (RMSE), chi squared, and the mean absolute error (MAE).
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I am runing a SME model. It has four main constructured which are again represented through sub-constructed.
Variable counts (Group number 1)
Number of variables in your model:126
Number of observed variables:50
Number of unobserved variables:76
Number of exogenous variables:64
Number of endogenous variables:62
Computation of degrees of freedom (Default model)
Number of distinct sample moments:1275
Number of distinct parameters to be estimated:113
Degrees of freedom (1275 - 113):1162
Chi-square = 6643.039
Degrees of freedom = 1162
Probability level = .000
is the chi-square statistic in expected size or it is in abnormal size?
because, so far i have not see such large size value of chi-square.
please responde on the size of chi-square statistic and also whether the model fit is acceptable in this case or not.
thank you prof. Viswanath C Narayanan
what type of modification should be done to SEM to reduce the value of chi-square
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In medical and biological research we are often interested in making predictions or in building a model to predict a continuous outcome based on a continuous predictor type variable or possible biomarker. After constructing the scatter plot and visually looking at the paired data points we may conclude that the relationship is linear and that this seems plausible and then go about using least squares regression to derive a fitted line or equation of the form, Y = a + bX. Before we can rely on this strategy as a research tool, how do we assess whether the relationship in indeed linear? Other than residual plots, is there a useful statistical test or assessment to verify this?
Agree with Ette Etuk
regards
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What CAN or SHOULD science do in relation to body building, natural bodybuilding, men's physique and the misuse/abuse of the term "natural" in this?
Should we give recommendations?
Should we tell the general public how doping tests have no value?
Should we reach out more to misusers of the term "natural"
Can we see or show if someone is "faking".
Any thoughts or idea's on this?
Also in relation to commercial companies, young people and doping use.
Strategies of "natural" bodybuilding are addressed in the work of Doug Brignole on "The Physics of Fitness" and resultant training plans.
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how i can calculate the fitness function of the chromosome in cloudlets scheduling to vms in cloudsim ???
The Computation is entirely dependent on your problem definition.
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What is the vacuum fitting size of
1- Edwards Pirani gauge head (PR 10-C) (D024-23-000)
2- Edwards Penning gauge head (CP 25-S) (D145-33-000)
Thanks Saim
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Dear friends,
Can any one advise me on which ordination methods is most fit for analyzing diversity with the environment. please share few useful paper or links available with you.
Sileesh Mullasseri
It truly depends on the specific question that you are considering. There are many different options depending on the context of your question. Investigating each ordination option, their assumptions, and data requirements is critical and the "fun" part of working with community data. To be brief, if you are interested in how biodiversity is affected by environmental metrics that you have data for, I would suggest Direct Gradient Analysis (DGA). There are methods of DGA that don't require an ordination, but if you do have to do an ordination you should look into Redundancy Analysis (RDA) or Canonical Correspondance Analysis (CCA). CCA's are generally better for ecological datasets.
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I understand the theory for PSO but i cannot seem to identify the fitness function for PSO to optimize FLC.
you may try to design your own FLC and optimize the MFs like this paper posted on https://goo.gl/HrFxnY
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I have developed a model in my study. Now, I'm going to test the model to see if it fits the data. I wonder if I should develop the questionnaire to measure the current status or if the items of the questionnaire should measure the ideal conditions?
Hi Mahdi Namdaripejman,
I suggest you review the future applications of this questionnaire that you want to develop. If this questionnaire is built based on your model, it can only be used by other researchers who find a condition similar to yours.
Best regards
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The real challenge when using WinBUGS is to formulate the BUGS model in such a way that the Gibbs sampler actually converges to the posterior distributions of the model parameters. Negative binomial regression can be particularly challenging in this regard.
nice question.
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I’m conducting a measurement invariance analysis on a 5-item dichotomous scale using Mplus 7. The estimator that I am using is WLSMV. The first model (configural invariance) which is the least restraint model resulted in a chi-square of 53.83, CFI = 0.971, TLI = 0.941, RMSEA = 0.055. The second model (metric invariance) which is supposed to be more constrained than the first model resulted in a chi-square of 56.13, CFI = 0.973, TLI = 0.963, and RMSEA = 0.043. Based on the CFI, TLI and RMSEA, the data seems to fit the more constrained model than the relaxed model (ideally it should be the other way around). I have double checked my analysis and there isn’t anything wrong there.
My question is what is the possible explanation for this?
What you found is not rare and it is not the result of you doing anything wrong. The reason is that in WLSMV you estimate (adjust) the X2. Your X2 is not simply the distance between your model and your data. Because you have 2 models, your adjustments to the X2 are estimated. Your unexpected results are because in one of the models the adjustment is better estimated than in the other.
In the Mplus webpage they describe how to do nested test with mean and variance adjustments (such as those used in WLSMV). Follow the steps describe there (it requires saving derivative matrices, etc. fun!) and see if you really get improper results for the model comparison (which is what you're really concerned with). Good luck!
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My outcome variable is a cost of specific disease, I want to design a linear regression model with some independent variable such as education, age, sex, residential palace etc. I need to know which model would be fitted for the regression analysis?? If I want to design linear regression model, what is the procedures?
Hi Alexander. I think that by treatment, Nausad meant does he have to transform his DV (for example).
Hello Nausad. I infer that you are worried about violating a "normality" assumption. The only normality assumption for OLS linear regression is that the errors are independently and identically distributed as Normal, with a mean of 0 and some variance. And as the sample size increases, even the normality of the errors becomes less and less important, because the sampling distributions of the regression coefficients converge on the normal. A good reference for these assumptions is Introductory Econometrics, by Jeffrey M. Wooldridge. Here is a Google Books link:
In light of this, I would advise you to fit your model and then examine residual plots. If the residual plots look good, fear not and carry on. If the residual plots do not look good, you may need to transform your outcome variable. It would not surprise me if there are some standard practices regarding this in your field of study.
I gave a short conference talk on this general topic a while ago, by the way. You can view the slides here:
HTH.
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Hi,
does anyone have the access to the following? will appreciate if you can share.
Hutcheson, J. M. (1999), An examination of three levels of person environment fit. Doctoral dissertation, University of Houston, 1999
regards,
will you be able to share it.
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Im trying to associate cumulative drug release profile with a mathematical model. How is this achieved?
Shaikh HK, Kshirsagar RV, Patil SG. MATHEMATICAL MODELS FOR DRUG RELEASE CHARACTERIZATION: A REVIEW. WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES 2015;4(4):324-338. www.wjpps.com/download/article/1427863836.pdf
Best wishes from Germany,
Martin
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I am trying to know the algorithm of Matlab Polynomial Surface Fitting.
hi Dear Enayet ?
you should of use syntax. for example:
function=fun(x,y,z.typefunction,value function)
"data" for run funvalue
valuefunction= fun_namealgorithm("data")
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I have x and y values. The y values are calculated somewhere else and are reported as y ± error. I want to do a linear fitting of "x and y± error". How would the errors of y affect the error of the slope of the fitted line?
I used Prism to do 2 linear fittings ("x vs y" and "x vs y±error") and the both gave me the same std error for the slope. I feel like this doesn't make sense as errors in the Yi should increase the errors in the fitted slope.
then you should be also aware that if y has no error you no longer have a statistical model but a deterministic one, so your se(beta) is 0 .
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I am planning to do a Netnographic analysis mixed with individual in-depth interviews about women's motivation for posting fitness media on Instagram.
I would suggest a Content Analysis.
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If a pop. of 50 has high allelic diversity, low Ho, a moderately high F-index, does this indicate genetic drift, inbreeding and non-random mating? Would it be all three? Can't yo have non-random or selective mating without inbreeding? Could selective mating indicate high fitness? Thanks!
First question you have to ask:
- can this relate to the some issues with the markers (null alleles in STR for instance)?
- if not, this can be typically explained by non-random mating / population structure/ Walhund effect. The use of inbreeding is tricky here in the sense where individuals are expected to be more more inbred than under HW; however at the population level, this does not imply any specific genetic drift.
- Does it indicate genetic drift? Not at population level. Inbreeding? Not at population level. Non-random mating. Yes, clearly
- Can't yo have non-random or selective mating without inbreeding? It depends how do you define inbreeding. If it is in the sense of genetic variability reduction at population level, non-random or selective mating does not necessarily imply inbreeding.
- Could selective mating indicate high fitness? In general the explanation is not this one, but without knowing the context it is difficult to say.
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This is what we research at Aquatech Food Support Systems in Springfield, Missouri on a daily basis.
You can also use leaves from the trees that fall and dry out and old newspapers cut up into small pieces then mix with water untill they turn into an oatmeal looking substance. You then fill a small two foot piece of 4" PVC pipe and fill it with the substance untill it dries. Push it out and you get a log looking cyclider. They burn longer than logs, smell like wood, and clean for the enviroment.
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SEM using maximum likelihood is showing chi square and df equal to zero. Please guide how i could get/report fitness indices?
I will assume that the model is correctly specified, based on a conceptual model, and that the current model is not the result of relaxing constraints one by one in order to oportunistically improve fit indices. Then what you have is esentially a regression model, and just must evaluate your model in those terms. Are estimated relationships strong? Are signs consistent with theory? Are assumptions violated? A basic regression text can guide you.
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Hi there, Can someone please share any relevant papers of the influence of technology on avoidance and anxious attachment styles? My research examines the moderating effects of task technology fit on attachment styles and group processes at the workplace. Any suggestions will be useful. Thanks, Rahul
Hi Beatrice,
Thanks for responding. I have sent you an email. Please do check and respond.
Thanks,
Rahul
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I hope i can find explanatory answers for my 2 questions
1) When i plot data results obtained from UV-Vis experiments, I get confused which function i should to fit data points to the best fitted line and what does the fit infers. BTW, I always find that gaussian, boltzmann and asymptotic1 functions gives me the best fit.
2) Regarding Boltzmann function y = A2 + (A1-A2)/(1 + exp((x-x0)/dx)), does values for A1, A2, X0 and dx gives important meaning ? My plot represents fluorescence maximum as function of excitation wavelength.
According to the Beer-Lambert Law, absorbance is proportional to concentration, and so you would expect a straight line.
Considering that you are using a UV-Vis then following the Beer-Lambert Law, absorbance is proportional to concentration, and so you would expect a straight line and therefore a linear fit might be the best option. However, bear in mind that at higher concentrations this is not always the case and the law breaks down.
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The first 3.2 billion years of evolution were replication by mitosis, how does MET fit into the exact copy of DNA model of mitosis? And since the cell monitors the surroundings to determine if the oconditions warrent replication how does major change occur versus regular change? In other words how can a new external condition signal proceed with replication.
I look forward to your response.
I agree. Mitosis requires the imense complexity of cells as we know them. Beginning requires a non-living structure acquire properties of internal chemical processes which evlove(see Luisi ) as thermodynamic systems long before cellular mitosis. But my question is still what is the reltionship between the organism and the enviornment such that an MET can occur. prorocellsprotocells aIf the environment stays constant can a MET occur? If
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Some of the inferences that I could draw are
(My Sample is 500)
from Regression Weights: (Group number 1 - Default model)
There are only 4 linkaages with P value greater than 0.05
from the Model fit
For the default model
RMSEA=0.074 (acceptable is less than 0.10)
NFI=0.663
CFI=0.727
Acceptable for above 2 is...should be greater than 0.90
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Lets take a close look at three related terms (Deep Learning vs Machine Learning vs Pattern Recognition), and see how they relate to some of the hottest tech-themes in 2015 (namely Robotics and Artificial Intelligence). In our short journey through jargon, you should acquire a better understanding of how computer vision fits in, as well as gain an intuitive feel for how the machine learning zeitgeist has slowly evolved over time.
thank you of you mrs shafagat mahmudova?
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Hi All,
I'm fitting a logistic regression and I summed up the deviance and used a chi-square distribution to test over-dispersion with this code below:
2 Questions:
Q1: I'm getting a p-value < 0.05, so does this mean overdispersion id detected?
Q2: How does it impact my model coefficient? What should be my next step?
Thank you so much and have a wonderful day!
All the best,
Kathy
P.S. Here is the R code:
p_hat = predict(model_obj,newdata = model_obj$model, type = "response") D = -2 * sum(p_hat * log(p_hat/(1-p_hat))+log(1-p_hat)) chi_sq_n = nrow(model_obj$model)
chi_sq_p = length(strsplit(Reduce(paste, deparse(model_obj$formula)), "[~+ ]+")[[1]])-1 chi_sq_df = chi_sq_n - chi_sq_p chi_sq_pval = pchisq(D, df = chi_sq_df) Where model_obj is a logistic regression object. • asked a question related to Fitness Question 4 answers I would like to find out if Hasofer-Lind's Reliability Index is important for the estimation of reliability of structures. I've made some good predictions using a meta-heuristic algorithm and one question that I have is if the usage of this index helps an engineer in some way or is just an easiest way to calculate the reliability rather than using the integral of the probability function. Relevant answer Answer Dear Prof Caridis, I've send you a personal message about your answer. Please, take a look. I'm looking forward for your response. • asked a question related to Fitness Question 2 answers I gained nyquist plot(below figure) by EIS, and i want to fit this data with equivalent circuit. So, i wonder if i could fit my data to the this equivalent circuit(below figure). I know that nyquist plot should have two semicircle which has bigger 1st circle than 2nd circle by using this equivalent circuit. However, my data shows two semicircle which has bigger 2nd circle. So, i want to know can i apply this equivalent cirucuit to my nyquist plot data. Thanks in advance Sanghyun Bae Relevant answer Answer Hi Bao, You can use this equivalent circuit to fit your experimental results. But I prefer to use R0(R1C1)(R2C2). The diameter for the first semi-circle or second semi-circle is related to the time constant of your interface (tau=RC). I am not sure about your interface. can you explain a little bit your experiment. The equivalent circuit is only a model to describe your interface. • asked a question related to Fitness Question 9 answers I'm working with RSM and some doubts have arisen, I'd be very thankful if someone could help me. I'm working with the optimization of polyphenols extraction and analyzing the antioxidant activity (response, dependent variable) by three different methods (ABTS, FRAP and Total Phenolic Content). I would like to know : 1) What does it mean the quadratic effect being significative or not? Which is it influence in the response? 2) What does it mean if all the effects of the variables (linear, quadratic, mean/interc, interaction) are significative? Does it mean that my model is incorrect? 3) I obtained an ideal extraction condition, with a specific temperature and a specific concentration of the extractor solvent. In ANOVA analysis I obtained that the lack of fit isn't significative (p>0,05), so I understand that the model fits well and is predictive. When I insert the values of X and Y variables of the experimental design (DCCR), the predicted and the observed values are very close, the relative error is small. But when I insert the conditions of temperature and concentration of the extractor solvent (that is a condition that is in the interval studed, not a point of the complete design) in the correct terms of the mathematic model (X and Y values), I obtain a predicted response very different from that one observed in the laboratory analysis. The observed value is higher than the predicted. 4) I made an experimental design with alfa value = +/- 1,41 (Q values), I have 5 levels for each independet variable. If the Q values aren't significant in the effects analysis, is it correct to say that my planning is a first order experimental design? Should I repeat all the optimization analysis again but only with the levels +/- 1,00 for each variable? Thank you for the support!! Best Relevant answer Answer You say you have 5 levels by factor and a value of alpha of 1.41 (square root of 2). So, I suppose you realized a central composite design with 2 factors. Am I wight? In such a case, during the analysis of results, removing non statistically significant effects and minimizing the PRESS (maximizing the prediction R²) will give you a quite stable model. If your response vary very much (max/min is about 100 or 1000...) it could be interesting to use a transformation of your response (Box-Cox transformation) or model the log of your response or inverse (these 2 last cases are particular cases of Box-Cox transformation). With data, it would be more simple to answer. Hope this can help you. Pierre Chagnon • asked a question related to Fitness Question 4 answers Hello everybody I used a RCCD to optimize phenolic compounds extraction parameters of a plant. Before this, I performed a factorial design to choose an adequate zone. As a result, central point was used for optimization. Levels for the factors in RCCD were adjusted near to central point values. Finally, response surface was generated (image attached). The aim was to obtain the highest phenolic content but it was opposite. Lack of fit was significant which indicates that experimental values don't adjust well to the model. It is known that central points show repeatibility of treatments in an experimental design, but what happen if a RCCD is generated from a factorial design in which central points (0/0) showed an optimum zone to work besides of other treatments (+1/+1, +1/-1 or -1/-1)? I mean, I worked in an optimum zone through RCCD but response surface is opposite when it must reflect a maximum optimum. How do you explain this? Thank you in advance. Relevant answer Answer You used a factorial design with center points. With this DOE, you can estimate main effects and eventually interaction effects, depending of the resolution of your design. If the center points are out of the confidence interval for the prediction at the center of the experimental domain, it means that at least one factor has a quadratic effect, but you cannot know which ones. To answer this question, you must add experiments. This can be easy with a central composite design, because you keep all the runs from the factorial design. If you have a center point effect (curvature effect), you cannot interpolate your model with main effects and interaction effects (eventually) because you don't know what factors are responsible of the curvature. When you analyse the results of your DOE, did you remove non significant effects? Examining the contour plot with the experimental results does not show evidence effect of the factors, but it can be an "optical" effect. With the data, it would be more simple. Hope this can help you. Pierre Chagnon • asked a question related to Fitness Question 2 answers I have my own work so that I need to publish if you have a room to review and publish educational research in your project. If yes, please let me know all the requirements/ criteria, my work is large document so I can minimize in a journal fit size for publication. I am looking a free charge journal. Thank you in advance! Relevant answer Answer the information about hypotheses, methods and results are rather sparse to give advice. • asked a question related to Fitness Question 1 answer Hi, The speed with which robots are becoming part of our life is exorbitantly high. The robots may be physical that we see in manufacturing industry or home support or they could be virtual like chat bots, virtual workforce. It is critical that these robots are certified for fit for use to avoid issues in live. Are there any quality guidelines set up and validated before they are certified as "fit for use" and brought in to the market? Regards, Anshuman Relevant answer Answer This is a burning issue, which is also looked at as "who has responsibility for impact of actions from a robot or autonomous system?". You will find answers and criteria in the presentation of a recent workshop of the European Commission, in Brussels, in July 2017. Free access to presentations here https://ec.europa.eu/digital-single-market/en/news/workshop-liability-area-autonomous-systems-and-advanced-robots-and-internet-things-systems • asked a question related to Fitness Question 1 answer Is it possible to obtain an effect size (such as correlation coefficient or some measure of goodness of fit) from a locally weighted scatterplot smoothing plot? The reason for obtaining an effect size is to use the data in a meta analysis. Relevant answer Answer • asked a question related to Fitness Question 1 answer When you have a larger order matrix (16x16) in your manuscript. The matrix will not be able to fit properly in the script. Relevant answer Answer Dear Bashir, you can use it: \documentclass[12pt]{article} \usepackage{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \begin{document} Properly resize variable:$x\scriptscriptstyle{x}$Properly resize matrix:$\begin{bmatrix}a & b\\ c & d\end{bmatrix}\scriptscriptstyle{\begin{bmatrix}a & b\\ c & d\end{bmatrix}}$Improperly resize matrix:$\begin{bmatrix}a & b\\ c & d\end{bmatrix}\scriptsize{\begin{bmatrix}a & b\\ c & d\end{bmatrix}}\$ \end{document}
P.S. by changing the value of point you can control the size of matrix.
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Raman spectroscopy is a now used by a few scientists to do dapant mapping in doped Silicon through some fit parameters and fitting.
The doping density can be mapped expointing Fano resonances. E.g. in
Eqn. (8) describes the fit function.
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I want to generate the dihedral constants for a force field.
Doing a potential energy scan of by rotating around the 2-3 bond using an QM software we can get the variation of the dihedral energy as a function of the dihedral angle, something like what is shown in the figure. This profile can be fit using a suitable function, for example an expansion in cosines.
But when we have to implement this force field in a MD software like LAMMPS (which is what i intend to use), it requires that the dihedral constants be specified for all possible bonds associated with a dihedral. For example in the cartoon, the dihedral constants need to be specified for 1-2-3-4 and also for 1-2-3-5. My question is how do I split the constants obtained from fitting the ab-inito dihedral scan.
Though I have come across a few, I would like to be pointed to some exhaustive review papers on force field parametrization.
dear Salman,
The main issues are three:
- the QM scan should be relaxed, i.e. optimized at each angle, and so it should be the MM scan. You want to stay close to the minimum of the potential energy surface at each angle, and measure there your potential.
- the total MM torsional potential is not given only by the explicit dihedral parameters, but there's also an intramolecular mostly non-bonded contribution given by LJ and electrostatic interactions (but also bond stretching, ...), let's call it U_NB(phi). To measure it, you perform a MM scan with the explicit dihedral parameters all set to zero.
- as you point out, the QM scan gives the sum of the terms present in the force field for a given torsion. For instance the QM scan about the 2-3 bond will give you the potential of 1234+1235, accounting also that they could be phase shifted of an angle gamma. So at the end, what you want to get is
U_QM (phi) = U_1234 (phi) +U_1234 (phi+gamma)+U_NB(phi)
There are no unique solutions in the most general case; one can be of setting the two potentials to be equal, and then modify only one of the two to eventually match U_QM.
You might have a look at the attached tutorial - is it focus on NAMD but most of the concepts should apply for any MD code.
Regards
L
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Anybody can help me?
How to calculate and step by step create decision support system using genetic algorithm?
I stuck on criteria and find fitness function
I'm doing the same thing,
first you need to define your data as cromosom in GA, as in my research on Vehicle routing problem define base route and its distance,
second create your population example in mine create initial distribution route and code it,
third create fitness and its probability value, fitness will be total of distance divide by value of each vehicle route, accumulate all as total fit, as probability fitnes each route divide by total fitness
fourth, selection code using its route and probability
fifth, crossover process.
sixth, will be its mutation
last will be result from mutation as decision result.
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I wish to use a placebo drink for comparison with a protein feed in humans that is matched for texture/consistency as well as volume and flavour. Are there any non-caloric placebos that fit this description that have been previously used in human research studies? Any advice would be greatly appreciated.
Gelatin based formula may work in this case
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Hello,
currently I am fitting a VAR(3) model on macroeconomic variables. Unfortunately the Jarque Bera test rejects nomaly distributed residuals of the S&P index and on industrial production. Is there a way to run the model anyway, or do I have to drop variables from the model?
Thanks beforehanded,
Nils
All of the above comments are non sense. Non normality only generates 2 problems in standard VAR's: first, it implies that the standard errors of your impulse response functions need to be calculated via bootstrap, since asymptomatic approximations are not valid. Second, if you are interested in running a test in a sub-set of your coefficients - say, you want to test Granger causality of one variable other another, or you want to test that a particular coefficient is different from 0 - non normality implies that if your model is large relative to sample size you should bootstrap your standard errors you calculate the statistics of these tests. However, if your model is small relative to the sample - say, a VAR(2) with 3 variable run over 120 observations - then asymptotic approximations on these standard errors work well enough not to warrant using bootstrap.
So, to summarize, you only need to calculate differently the std. Errors from impulse responses and maybe bootstrap of your doing a test on some coefficients. However, you should never drop that variable from the VAR.
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The model consists 4 fixed effects and a random effect. I'm keep getting a convergence error
Error: nlminb problem, convergence error code = 1 message = false convergence (8)
How do I solve this?
You can test if nlme:lme converge with your data with some different optimers. If so, open option is to create wrapper function which calls   nlme:lme  with a different optimiser and pass other arguments through. Then you need to modify glmmPQL (change mcall[[1L]] <- quote(nlme::lme) to mcall[[1L]] <- quote(my.wrapper.function.name).
There might be an easier solution. A good place to ask is r-sig-mixed-models@r-project.org (just subscribe before asking).
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I want to publish my research in the area of Time series cluster and model fitting. Please suggest me journals for which I do not have to pay
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I wonder if primates also feel sexual humiliation when they are naked or in other context like human, does it lowers reproduction fitness?
I guess there are almost no studies, but curious.
I do not know but it is probably sexual abstinence ... in the absence of many experiences which in a small environment which is cage / small space /, causes such other behaviors .... Similar behavior masturbation is noticed in mentally handicapped people where not There are many inhibitions ... but please ask sexologists or psychiatrists because there are attempts to solve this problem
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Can anyone please recommend a company that can isolate single compounds from natural materials, with GMP certificate - fit for human consumption?
Igor Lukić
Hi!
Please define: what do you mean with "isolate"? Should the component be isolated or only identified? Which type of natural material do you want to have analyzed? Blood, ... ect?
Greetings
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Can we accept a model fit in which some of the indices (GFI as example) are near but less than 0.9? The other indices are excellent. Software AMOS.
Thanks Dear Mohammed.
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I am trying to analyse monthly trends over the past 10 years on year to year basis with the intention to determine the seasonality connectivity to predict. Meaning, it is about putting yearly layers of monthly trends to determine R square and to attach that closeness of fit onto the current part of the year to determine predictivity. Does anyone have an advice on how to perform this and on which software could this be easily performed ?
Dear Wajid, Thank you very much for your feedback. I will dive into it.
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In India bath rooms in trains are being fitted with biotoilets.I desire to know the working principle of the biotoilet,how the human waste is treated and disposed off.Has it been used for manurial purpose in horticulture etc.?
Biotoilets how they work:
1. Composting toilets use nature's decomposition process to reduce waste by 90% and convert it into nutrient rich compost 2. They do not require water hook ups either which is great for our alreadystressed water supply. In short, composting toilets are a way to allow waste to decompose safely and without odors. 3. Composting toilets use oxygen loving bacteria that is naturally present in human waste to do all the work. 4. Bugs, worms, and other critters have absolutely NO role in BioLet's composting process. 5. You just use a BioLet like you would a regular toilet, toilet tissue and all. The main difference is you just toss in compost mix after each fecal use instead of flushing. The air flow inside the toilet pulls all odors up the 'chimney' and out of your home. 6. Composting reduces waste volume by 90%; the majority of the material inside the toilet is mulch and not waste. You do not even have to see it with the way BioLet is designed. Yes, you do have to empty the lower compost tray periodically, depending on how many people are using the toilet, but it is only compost, soil. There is no waste mixed in the tray.
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I am working on ion channels and see an apparently biphasic dose-response in my experiments. How do I fit this? None of the functions I used so far works! I am using OriginPro 2016! Thank you for your help!
If your data are bi-exponential, then any curve fitting program that has a bi-exponential function will work. But accurate fitting is best if the two phases are equal in magnitude and perhaps an order of magnitude different in dose. The farther you are from that situation, the less accurate your fit.
If you data appear to be bi-linear, then you could try the old fashioned 'V-slope' method. Fit straight lines to the data starting from each end, and record the break point and the two slopes.
Good luck
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I am troubled in fitting the dielectric data i.e imaginary part of dielectric constant ,  electric modulus (M") and Z" w r t frequency. Can anyone help me out with detailed steps of fitting these parameters
Dear Alaka, I would be sending the worksheet for fitting the imaginary part and real part for dielectric constant calculations and other parameters . Hopefully, it will help you in fitting the calculated in origin software for graphs. My e-mail: nsareecha@hotmail.com
Regards
Nasira Sareecha
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What kind of data do you have available to base your quantitative model on? Ideally, could you have data of only radiated, only immuno-treated, and radiated and immuno-treated subjects? Then, you could as a first step fit a simple linear model with interaction term a la doi:10.1038/nmeth.1581 (methods section), to identify factors associated with synergy or even antagonism.
Hi Frederick. Yes, such data already exists (in mice) -  I just added a reference to the project (Dewan at al., Fractionated, but not single...) in which cases you mentioned are studied experimentally. What is interesting in this reference is that the overall result depends strongly on the radiotherapy fractionation. Together with Heiko we used that data to calibrate a tumor-immune system model - hopefully it will be published soon.
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I need lateral motion coefficients (a22, a24, a26, a44, b22, b44, b46 so on) of any kind of ship for simulation purposes.
They might be obtained by conformal mapping, close- fit, panel methods whatever.
Best regards.
Ferdi, you must use parametre estimation technique for example Extended Kalmam Filter. Your study is the inverse problem. Parameter estimation problem is related digital signal processing.
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I have a question.
I did MTT assay for cytotoxicity with a variety of drugs.
It was different pattern depending on cell lines in high concentration drug
If cell not die (~near 20% of viability remain) in high concentration drug, will i be able to use log(inhibitor) vs. normalized response fitting formulation?
This situation, i used log(inhibitor) vs. response variable slope (four parameters).
is it ok ?
First, it is a common situation that the survival curves will have non-monophasic course. In such a case it might happen that you will end up with >5% survival plateau in a certain range of the drug concentrations, in which the cells remain viable, but may be quiescent, senescent or slowly dying. You will find more information about non-monophasic curves and a software to deal with this problem in the publication below.
Normally, in addition to standard concentrations, I would try to use a really high concentration of the drug just to reach ~100% dead cells (often caused by unspecific toxicity of the drug). By reaching this point you will be ensured, that the background subtraction is ok. If for some reason you cannot reach that high concentrations, you can always set upper and lower asymptote for the fitting of dose-response curves, provided you use a software which allows such constrains (such as Origin, which I use a lot). But remember that in such case you will calculate IC50 values for the particular activity of the drug, which often will not be drug toxicity effect, but e.g. target-specific growth inhibitory effect (which might be even more compelling). By the way, Origin software has also a bi-doseResp model for fitting of bi-phasic curves.
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For example, if Sb3+ is doped to Fe2O3, and EXAF data is collected at Sb edge. then I want to fit Fe2O3 model to Sb3+ doped Fe2O3. Is this a right move?
Dear Mumtaz,
when starting from the Sb k-edge you see the neighbours of Sb atoms. From these measurements  you can estimate the Sb-O bond length(s?)  and the SB-Fe bond.
But I think you want to have a look at the Fe-O bonds  when you write that you will model the Fe2O3 (in the doped state). From the Sb as center atom it is difficult or even impossilble (to my opinion) to access the Fe-O bonds or a Fe2O3 model. But you may try it in any way.
But you may start  modelling  the Sb vicinity and then model  F2O3 around (but I would no know how to do that, sorry).
For Fe-O it is better to start from the Fe k-edge. Here you have direct access to the Fe-O bond(s) and the Fe-Fe bond. However any difference to the undoped case will be very small due to the low concentration of the dopand. So also here a model for the Fe2O3 will not be easily achievable.
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I'm currently writing my Masterthesis about attitudes of (kindergarten-)teachers towards transition of children with Special Needs ans role of OTs in this process.
Would this fit the topic of the Special issue? Let me know!
Thanx
Katrin Pechstädt
Hi Katrin,
Yes, by all means, please do submit a manuscript.  It is certainly suitable for this special issue.
Regards, Kim and Timo
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Dear all,
I am doing a moderation analysis in Lisrel and this is the output I got:
Degrees of Freedom = 0
Minimum Fit Function Chi-Square = 0.0 (P = 1.00)
Normal Theory Weighted Least Squares Chi-Square = 0.00 (P = 1.00)
The Model is Saturated, the Fit is Perfect !
How am I supposed to read this?
Regards.
Three different types of  models available in structural equation modeling framework. Overidentified, underidentified, and just identified models. If models is overidentified your number of parameters were higher than the number of parameters you used, just identified models have the same number of parameteres with your model, underidentified models have less parameteres than your estimated models.
Just identified models generally occur when you used regression analysis.
In order to clarify, Let I have five items and want to test a one factor model.
I have  p. (p+1)/ 2 free parameteres. ın other words   (5 x 6)/2 = 15 free parameters. If ı test one factor model I have to use (using this formula= b = (p * m) + [(m * (m + 1)] / 2) + p –m^2,    where p is the number of observed variables and m is the estimated factor))=  (5*1)+(1*2)/2)+ 5- 1= 5+ 1+4 = 10 parameters
thus ıf I test this model I will have 15-10 = 5 degrees of freedom. model overidentified.
ıf ı test for above example for two factor model I will have 17 degrees of freedom and model underidentified.
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