Estimation - Science topic

Thank you Sumit Naskar for the information.

Reasons?

Here are some recommended ones: 1. "Sampling Techniques" by William G. Cochran This classic book covers a wide range of sampling methods with practical examples. It’s comprehensive and delves into both theory and application, making it valuable for students and professionals. 2. "Survey Sampling" by Leslie Kish'' This is another foundational text, known for its detailed treatment of survey sampling design and estimation methods. Kish's book is especially useful for those interested in practical survey applications. 3. "Model Assisted Survey Sampling" by Carl-Erik Särndal, Bengt Swensson, and Jan Wretman This book introduces model-assisted methods for survey sampling, which blend traditional design-based methods with model-based techniques. It's ideal for more advanced readers interested in complex survey designs. 4. "Sampling of Populations: Methods and Applications" by Paul S. Levy and Stanley Lemeshow This text is widely used in academia and provides thorough explanations of different sampling methods with a focus on real-world applications. It also includes case studies and practical exercises, making it helpful for hands-on learners. 5. "Introduction to Survey Sampling" by Graham Kalton This introductory book offers a concise and accessible overview of survey sampling methods. It’s well-suited for beginners who need a straightforward introduction to key concepts. 6. "Designing Surveys: A Guide to Decisions and Procedures" by Johnny Blair, Ronald F. Czaja, and Edward A. Blair This book focuses on the practical aspects of designing and conducting surveys, with particular emphasis on decision-making and procedural choices in the survey process.

Para estimar la reducción de emisiones de gases de efecto invernadero (GEI) al reemplazar fertilizantes químicos con biocompost en un sistema típico de cultivo de trigo y arroz, se deben considerar varios factores, incluyendo las emisiones asociadas con la producción y uso de fertilizantes químicos, así como las emisiones generadas por el uso de biocompost.

1. Emisiones de Fertilizantes Químicos

Los fertilizantes químicos, especialmente los nitrogenados, son responsables de emisiones significativas de óxido nitroso (N₂O), un potente gas de efecto invernadero. Según la literatura, se estima que por cada kilogramo de nitrógeno aplicado, se pueden emitir entre 1.0 y 1.5 kg de N₂O.

Ejemplo de Cálculo:

2. Emisiones de Biocompost

El uso de biocompost generalmente resulta en menores emisiones de N₂O en comparación con los fertilizantes químicos. Esto se debe a que el biocompost mejora la salud del suelo y su capacidad de retención de nutrientes, lo que puede reducir la necesidad de aplicaciones adicionales de nitrógeno.

Estimación de Emisiones:

3. Cálculo de la Reducción de Emisiones

La reducción de emisiones al reemplazar fertilizantes químicos con biocompost sería:

Reduccioˊn de Emisiones=Emisiones con fertilizantes quıˊmicos−Emisiones con biocompost Reduccioˊn de Emisiones=125kg N₂O/ha−75kg N₂O/ha=50kg N₂O/ha

4. Conversión a CO₂ Equivalente

Para tener una idea más clara del impacto, se puede convertir las emisiones de N₂O a CO₂ equivalente. El potencial de calentamiento global (PCG) del N₂O es aproximadamente 298 veces mayor que el del CO₂.

CO₂ Equivalente=50kg N₂O/ha×298kg CO₂e/kg N₂O=14,900kg CO₂e/ha

I think you could let ChatGPT or other AI tools make some sample data.

Hi did you ever find the answer? If so, care to share? Thanks!

Yes, I found that need at least 3 additional constraints.

Calculating the diameter of plasmids typically involves determining the length of the plasmid DNA molecule. Plasmids are circular, double-stranded DNA molecules, and their size is commonly expressed in terms of base pairs (bp). Each base pair corresponds to approximately 0.34 nanometers (nm) of linear distance along the DNA molecule's length.

Here's how you can calculate the diameter of a plasmid:

Here's a step-by-step example: Let's say you have a plasmid with 3,000 base pairs.

So, the estimated diameter of the plasmid is approximately 325.05 nanometers.

It's important to note that this calculation provides an approximation of the plasmid's diameter based on its linear length. In reality, the plasmid molecule is not a perfect circle, and its shape and size can be influenced by factors such as supercoiling and protein binding. Additionally, experimental techniques like electron microscopy can provide more accurate measurements of plasmid size and shape.

l Take a look at this protocol list; it could assist in understanding and solving the problem.

you can deploy logistic regression algorithm for this task

I'd be glad to assist you with understanding the losses in switched reluctance motors (SRMs) and the research article you mentioned. However, I'm unable to access the full content of the article due to restrictions. To provide the most accurate and relevant information, I'll need more context about the specific losses you're interested in and any key findings or methodologies discussed in the article.

Here are some general points to consider regarding SRM losses:

Arc Gs pro or Envi or Matlab.

Dear Gill Ammara Please do recommend my answer as I am sure this link will aid to a helpful article

Vasquez and Beggs empirical correlations have proven the most reliable for me in my comparison with PVT measurements from black and volatile oils.

Here is a link to get you started...

Thank you for your question. I have searched the web for information about the Diagonally Weighted Least Squares (DWLS) and Unweighted Least Squares (ULS) estimators, and I have found some relevant sources that may help you with your decision.

One of the factors that you should consider when choosing between DWLS and ULS is the sample size. According to Forero et al. (2009)1, DWLS tends to perform better than ULS when the sample size is small (less than 200), but ULS tends to perform better than DWLS when the sample size is large (more than 1000). Since your sample size is 658, it falls in the intermediate range, where both methods may provide similar results.

Another factor that you should consider is the degree of non-normality of your data. According to Finney and DiStefano (2006), DWLS is more robust to non-normality than ULS, especially when the data are highly skewed or kurtotic. However, ULS may be more efficient than DWLS when the data are moderately non-normal or close to normal. Since your data have slight negative skewness and kurtosis, it may not be a serious violation of the ULS assumptions.

A third factor that you should consider is the model fit and parameter estimates. According to Forero et al. (2009)1, both methods provide accurate and similar results overall, but ULS tends to provide more accurate and less variable parameter estimates, as well as more precise standard errors and better coverage rates. However, DWLS has higher convergence rates than ULS, which means that it is less likely to encounter numerical problems or estimation errors.

Based on these factors, it seems that both DWLS and ULS are reasonable choices for your data and model, but ULS may have some advantages over DWLS in terms of efficiency and accuracy. However, you should also check the sensitivity of your results to different estimation methods, and compare them with other criteria such as theoretical plausibility, parsimony, and interpretability.

I hope this answer helps you with your analysis. If you need more information, you can refer to the sources that I have cited below.

1: Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation by Carlos G. Forero, Alberto Maydeu-Olivares & David Gallardo-Pujol in British Journal of Mathematical and Statistical Psychology (2009)

: Non-normal and categorical data in structural equation modeling by Sara J. Finney & Christine DiStefano in Structural equation modeling: A second course (2006)

Good luck

If you don't see anything odd (like almost all accurate for an item, or near infinity log-odds ratios for bivariate), look up Heywood cases. But in general, don't start with a complex model without looking at the data. Or make friends with your data as I say here: . Anything that you find in your IRT models you should be able to show why that arises from the log-odds ratio table.

A complete code in R is the best option right now. I would appreciate that. Learning Python is on my agenda too but I'm yet to allocate time for it. Thanks for the video.

Dynamic panel logit/probit models are used to estimate the probability of an event occurring over time. These models are used when the dependent variable is binary and the independent variables are continuous or categorical.

There is a user-friendly program called FRET-Calc, which offers a straightforward interface and is available for free. This program allows users to easily obtain a range of FRET parameters.

Implementing geospatial technologies in precision agriculture for crop yield optimization and soil health can bring significant economic benefits. These include increased crop yields, resource efficiency, improved soil health, reduced losses, data-driven decision-making, and improved access to markets and certifications.

While estimating the precise impact of widespread soil carbon stabilization is complex, studies have shown that it has the potential to offset a significant portion of global carbon dioxide emissions. By sequestering carbon in the soil, it can contribute to achieving climate change mitigation targets and help limit global temperature rise.

Dear Sepideh Jahandideh

Certainly! When working with variables that are not normally distributed in the context of generalized estimating equations (GEE), it's important to select appropriate families and link functions that suit the distributional characteristics of your data. Here are some guidelines and resources that can help you understand and choose suitable options:

1. General Guidelines: Depending on the nature of your data, you can consider different families and link functions. Here are some common combinations:

- For continuous data with a symmetric distribution, the Gaussian family with the identity link function is often appropriate.

- For binary or dichotomous data, the binomial family with the logit or probit link function is commonly used.

- For count data, the Poisson or negative binomial family with the log link function is frequently employed.

- For ordinal data, the cumulative logit or proportional odds model can be applied.

2. Statistical Literature: Several statistical textbooks and research papers provide detailed information on the selection of appropriate families and link functions for GEE models. Some recommended sources include:

- "Generalized Estimating Equations" by Hardin and Hilbe.

- "Generalized Linear Models for Categorical and Continuous Limited Dependent Variables" by Long and Freese.

- "Generalized Linear Models" by McCullagh and Nelder.

- "Analysis of Longitudinal Data" by Diggle, Heagerty, Liang, and Zeger.

3. Software Documentation: If you are using statistical software packages like R, SAS, or Stata to fit GEE models, their documentation often provides guidance on the available families and link functions. For example, the documentation for the "geepack" package in R or the "PROC GENMOD" procedure in SAS can be helpful in understanding the options and making appropriate choices.

4. Consultation and Collaboration: If you have access to a statistician or a research collaborator with expertise in GEE modeling or generalized linear models (GLMs), it would be beneficial to consult with them. They can provide personalized guidance based on the specific characteristics of your data and research objectives.

Remember that the choice of the family and link functions in GEE depends on the distributional assumptions of your data and the specific research question you are addressing. It's important to evaluate the goodness of fit and interpret the estimated coefficients accordingly.

The family and link functions are:

When selecting appropriate family and link functions for Generalized Estimating Equations (GEE), it is essential to consider the distributional characteristics of your data. Here are some guidelines to help you make suitable choices:

1. Gaussian Family with Identity Link:

- Appropriate for continuous data with a symmetric distribution, assuming the response variable follows a normal distribution.

- It is often used when the response variable is measured on an interval or ratio scale and shows a relatively symmetric distribution.

2. Binomial Family with Logit or Probit Link:

- Suitable for binary or dichotomous data where the response variable takes only two possible outcomes.

- The logit or probit link functions are commonly used to model the relationship between the linear predictor and the probability of success.

- The logit function is the default choice and is often preferred due to its simplicity and interpretability.

3. Poisson Family with Log Link:

- Appropriate for count data where the response variable represents the number of occurrences within a given interval.

- The log link function is typically used to ensure the estimated rates remain positive.

- If there is evidence of overdispersion (variance exceeds the mean), the negative binomial family with log link can be considered as an alternative.

4. Gamma Family with Inverse Link:

- Suitable for continuous data with a positively skewed distribution, such as durations or response times.

- The inverse link function is often used to model the reciprocal of the response variable.

- The gamma distribution assumes that the response variable follows a gamma distribution with a specific shape and rate parameter.

5. Other Families and Link Functions:

- There are additional families and link functions available for specific data types and distributions. For example, the ordinal family with a logit or probit link can handle ordered categorical responses.

- Different software packages may offer additional options, such as the quasi-likelihood family for accommodating data with non-standard distributions.

It is important to note that the choice of family and link functions should align with the characteristics and assumptions of your data. Consider the scale, distributional properties, and nature of the response variable when making your selection.

Additionally, consulting statistical textbooks, research papers, and software documentation specific to your chosen statistical package can provide further guidance and examples in selecting appropriate families and link functions for GEE models.

I would like to restart this question.

I have noted a few papers on cutoff or quasi-cutoff sampling other than the many I have written, but in general, I do not think those others have had much application. Further, it may be common to ignore the part of the finite population which is not covered, and to only consider the coverage, but I do not see that as satisfactory, so I would like to concentrate on those doing inference. I found one such paper by Guadarrama, Molina, and Tillé which I will mention later below.

Following is a tutorial i wrote on quasi-cutoff (multiple item survey) sampling with ratio modeling for inference, which can be highly useful for repeated official establishment surveys:

"Application of Efficient Sampling with Prediction for Skewed Data," JSM 2022: 

https://www.researchgate.net/publication/362370770_Application_of_Efficient_Sampling_with_Prediction_for_Skewed_Data 

This is what I did for the US Energy Information Administration (EIA) where I led application of this methodology to various establishment surveys which still produce perhaps tens of thousands of aggregate inferences or more each year from monthly and/or weekly quasi-cutoff sample surveys. This also helped in data editing where data collected in the wrong units or provided to the EIA from the wrong files often showed early in the data processing. Various members of the energy data user community have eagerly consumed this information and analyzed it for many years. (You might find the addenda nonfiction short stories to be amusing.)

There is a section in the above paper on an article by Guadarrama, Molina, and Tillé(2020) in Survey Methodology, "Small area estimation methods under cut-off sampling," which might be of interest, where they found that regression modeling appears to perform better than calibration, looking at small domains, for cutoff sampling. Their article, which I recommend in general, is referenced and linked in my paper.

There are researchers looking into inference from nonprobability sampling cases which are not so well-behaved as what I did for the EIA, where multiple covariates may be needed for pseudo-weights, or for modeling, or both. (See Valliant, R.(2019)*.) But when many covariates are needed for modeling, I think the chances of a good result are greatly diminished. (For multiple regression, from an article I wrote, one might not see heteroscedasticity that should theoretically appear, which I attribute to the difficulty in forming a good predicted-y 'formula'. For psuedo-inclusion probabilities, if many covariates are needed, I suspect it may be hard to do this well either, but perhaps that may be more hopeful. However, in Brewer, K.R.W.(2013)**, he noted an early case where failure using what appears to be an early version of that helped convince people that probability sampling was a must.)

At any rate, there is research on inference from nonprobability sampling which would generally be far less accurate than what I led development for at the EIA.

So, the US Energy Information Administration makes a great deal of use of quasi-cutoff sampling with prediction, and I believe other agencies could make good use of this too, but in all my many years of experience and study/exploration, I have not seen much evidence of such applications elsewhere. If you do, please respond to this discussion.

Thank you - Jim Knaub

..........

*Valliant, R.(2019), "Comparing Alternatives for Estimation from Nonprobability Samples," Journal of Survey Statistics and Methodology, Volume 8, Issue 2, April 2020, Pages 231–263, preprint at 

https://www.researchgate.net/publication/337950671_Comparing_Alternatives_for_Estimation_from_Nonprobability_Samples 

**Brewer, K.R.W.(2013), "Three controversies in the history of survey sampling," Survey Methodology, Dec 2013 -  Ken Brewer - Waksberg Award article: 

Bruce Weaver , he also states that SPSS has a procedure for SUR with correlated errors. Given the specifics that Samuel Oluwaseun Adeyemo 's post includes either he is working with a system beyond what (https://www.ibm.com/support/pages/seemingly-unrelated-regression) is refers to that we don't know about, or there is some other explanation for how he came up with this information. Shame in his response above he did not answer where he got this information. Since he didn't answer on the other thread, the using a new version explanation seems unlikely.

Dear friend Yaseen Muhammad

Rietveld analysis is a powerful method for determining the crystal structure and phase composition of materials from X-ray diffraction (XRD) data. However, it cannot directly provide the mass fractions of individual phases.

To estimate the mass fractions, additional information is needed, such as the densities of the individual phases and the total mass of the sample. The mass fraction of a phase is equal to the volume fraction of the phase multiplied by its density. The volume fraction of a phase can be calculated from the Rietveld analysis by dividing the integrated intensity of the diffraction peaks from that phase by the total integrated intensity of all phases in the sample.

Once the volume fractions of the individual phases are determined, the mass fractions can be estimated using the following formula:

Mass fraction = Volume fraction x Density

The densities of the individual phases can be obtained from the literature or by experimental measurements such as Archimedes' principle or pycnometry.

It is important to note that the estimated mass fractions obtained from XRD data may not be very accurate due to various factors such as preferred orientation, peak overlap, and other instrumental and experimental factors.

References:

1. Cullity, B. D., & Stock, S. R. (2001). Elements of X-ray diffraction (3rd ed.). Prentice-Hall.

2. Young, R. A. (1995). The Rietveld method. Oxford University Press.

Theory & examples:

Just a note: if your goal is to report the findings to regulatory agencies (FDA, EMA), it's commonly advised to do that, please find the EMA guideline linked below. But not because of importance of the baseline differences (it's an RCT, so even if present, differences are due to chance and must be ignored), but to "filter out" these potential fake differences from the treatment and improve inference. The adjustment applies for both analysing post-values and change from baseline*.

----

* Just a warning about modelling the change-from-baseline in RCTs. It poses a serious problem if there was actual "virtual" (regardless of the randomization) difference at baseline and also the between-arm difference in post-values, of similar magnitude. In this case the slopes connecting pre-post in both arms may be close-to-parallel suggesting "no difference in differences", EVEN if the post-values DO indicate statistically signifiant (and hopefully also clinical) difference. This is called the Lord's paradox and it's a sound reason to use change scores in observational studies and post-values in RCTs. Change scores = parallelism of slopes approach actually "looks" at the baseline differences (which makes a lot of sense in non-RCTs and not in RCTs), post-values approach ignores the baseline difference and looks only at the follow-up post-treatment findings (makes a lot of sense in RCT and not non-RCT). It's up to you which approach is the best for you.

There are many estimation techniques that can be used to estimate system poles. Here are a few popular ones:

To apply an algorithm to estimate system poles, you can start with a simple transfer function and apply the algorithm to estimate the poles. You can then compare the estimated poles with the known poles of the transfer function to evaluate the accuracy of the algorithm. It may also be useful to test the algorithm on more complex systems to see how well it performs.

[1] Bazzi, Ahmad, Dirk TM Slock, and Lisa Meilhac. "Efficient maximum likelihood joint estimation of angles and times of arrival of multiple paths." 2015 IEEE Globecom Workshops (GC Wkshps). IEEE, 2015.

[2] Bazzi, Ahmad, Dirk TM Slock, and Lisa Meilhac. "On a mutual coupling agnostic maximum likelihood angle of arrival estimator by alternating projection." 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016.

[3] Bazzi, Ahmad, Dirk TM Slock, and Lisa Meilhac. "On Maximum Likelihood Angle of Arrival Estimation Using Orthogonal Projections." 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018.

Dear Ioana Grozea ,

Look the link, maybe useful.

Regards,

Shafagat

Hello, and thank you so much for responding to such a ill described question!

I will definitely go through the articles your referred to me, some of which

I already possess. I might have skipped the answers in them.

Thanks again!

Hi,

I suggest you see the links on the topic.

https://gargicollege.in/wp-content/uploads/2020/03/Dummy-Variables-Econometrics.pdf

Best regards

see this

Hi,

I suggest you see the links on the topic.

Best regards

Dear Uzzal

Have a Good Day!

Will you please share with us the reference in which they talked about the 0.2g weight of leaves because whatever the paper you have mentioned as a reference does not have that information?

Regards

Harsh

Please note that the incidence is usually (but not necessarily) calculated for acute communicable diseases, and the prevalence usually calculated for the chronic non-communicable disease. You have asked about the best time to calculate the incidence: of course it should be at the end of the period. eg incidence of COVID-19 during the year 2021, it is the number of cases of COVID-19 developed during 2021 (numerator) among people who were free from COVID-19 at Jan 1, 2021 (denominator). If you calculate the point prevalence of COVID-19 now, it would be very low, but if you calculate the period prevalence (eg during the previous year) it would be high.

One more time you don't need to linearize anything it's the regression coefficients that matter. Please read the Kutner book it's a free download from the z-library. David Booth

If you have used the instruments before and can make assumptions about their performance (even things as far fetched as being unbiased and random), then you can do things like have lots of samples and if you believe things like the random and unbiased then as Christian Geiser says you can assume as n increases the estimates improve in accuracy. You need to provide more information about what you can assume in order for people to properly address your question.

Not more than 40oC

For the long T panel, a possible option is the panel data techniques that account for possible cross-section dependence. See for instance: https://www.sciencedirect.com/science/article/abs/pii/S0304407620301020.

Hope this helps. Thank you.

Could elaborate a little bit on what you mean by effect size in this context? Do you mean the estimate of the slope factor mean in a particular class? The mean should be part of the parameter estimate output. From that, you could compute a standardized effect size measure by hand (using the estimate of the slope factor mean and variance).

Christian Geiser Yes, they do give the same std.all solution. I forgot that the "estimate" collumn in the variance standardized method is supposed to be the same as the std.all collumn that is shared in both methods since variance is standardized and i got mixed up with that. Thank you for the fast reply!

Please refer to the following paper that presents a comprehensive review of the progress in remote sensing as well as field methods for soil moisture studies.

Regards

Dear Dr Bipul Das Chowdhury,

I think the protocol found in this article (attached) may actually help you in your research.

Best wishes,

Sabri

My suggestion would be to first understand your code before asking questions. In my opinion, you did not ask questions, you gave two observations. Related to your observation 2. what do you mean by analog? In computer/Matlab everything is digital. You can model analog-to-digital conversion in Matlab but signals will still be digital. Try to understand code and then pose questions.

Luise Novikov Apologies, my first answer was actually incorrect - I have removed it to avoid misleading anyone.

If you pass both arguments separately, the function will return the results for partial tests i.e. multiplicity-adjusted p-values for each hypothesis under the assumption that the coefficients being tested are simultaneously zero. What you are looking for is a global test for the second option. The first option is incorrect because the sum of the coefficients can be zero without either coefficient being zero. Thus, hypothesis tests assuming only that the sum is zero will have an inappropriate additional degree of freedom and the p-values will be incorrect as a result.

What you are looking for is a global test under the assumption that both coefficients are zero, which you can obtain using car::linearHypothesis. For your data:

mod <- ordinal::clm(Sat~Type*Cont + Freq, data = MASS::housing, link = "probit")

car::linearHypothesis(mod, c("TypeApartment = 0", "TypeApartment:ContHigh = 0"))

Dear Mst Shamim Ara Supty ,

Look the link, maybe useful.

Regards,

Shafagat

check the pvalue

Robert Shour

Have a look at this:

Thanks!

Yes, you can do that. Please see the document at this link;

Can we talk ? I have to talk regarding my paper. I have few queries regarding it. (@Mohamed-Mourad Lafifi)

Willow is a tree that you may not be able to hit the center all the time with an increment borer (as mentioned by Dr. Mohl). Since Salix if cut sprouts back from the roots (coppice), sometimes an increment bore may have two or more stem centers that grew together with time. As I remember, if especially worried about health of tree, return the increment core to the tree, or apply the spray that horticulturalist sand tree surgeons use on cut surfaces. The annual rings can be counted visually, or get a hand lens if the rings are close. Typically, increment cores should not damage or kill tree. If you don’t have any sealant, candle wax would probably due to cover hole, and discourage insects, excess moisture or disease entry.

Benjamin Hume

The eigenvectors with the highest eigenvalues correlate to the dimensions with the highest correlation in the dataset by calculating the eigenvalues and eigenvectors of the covariance matrix. This is the most important component

Philipp Schulz I agree with David Eugene Booth . Since your items are ordinal (ordered categorical), you would typically want to use classical latent class analysis (LCA), not latent profile analysis (LPA). LPA is designed for continuous (metrical, interval) indicators, whereas LCA is for categorical (binary, ordinal) indicators. The problems that you encountered with the BLRT may be related to the large number of items (30 items is a lot for both LCA and LPA). This does not necessary mean that using fewer items is better (in principle, it is good to have many indicators, as long as they are good class indicators), but it may explain the problems in successfully conducting the bootstrap. The BIC is a simple and good alternative in my experience. And yes, as the number of classes / profiles goes up, you should definitely increase the number of starts for both the target (estimated) class model and the bootstrap. You should also check that the best loglikelihood value for each model can be replicated at least a few times so as to minimize the risk of local likelihood maxima (potentially invalid solutions and/or invalid bootstrap results).

With regard to ML vs. WLSMV, you may be confusing latent class/latent profile analysis with confirmatory factor analysis (CFA) and structural equation modeling (SEM). Both LPA and LCA typically make use of maximum likelihood (ML) estimation, not WLSMV. WLSMV is used for ordinal (ordered categorical) variables used as indicators of continuous latent variables ("factors") in CFA and SEM.

Dear Yerra Kanakaraju thank you for posting this interesting technical question on RG. In order to give you a qualified answer, it would be helpful to know a few more details about the conditions / environment in which you want to determine the ethanol content (e.g. in blood, alcoholic beverages, biodiesel etc.). In general, NMR spectroscopy is a suitable method for determining ethanol in mixtures. For some potentially useful information please have a look at the following article which might help you in your analysis:

This method does not require fancy and expensive equipment. The good thing about this paper is that it is freely accessible as public full text on RG. Thus you can download it as pdf file.

Good luck with your work and best wishes!

Very important and useful research.

Dear Mr Mohamed-Mourad Lafifi ,

Thanks a million for the links.

It would be uncommon to prescribe a lower relative load to the weaker muscle. Why don't you do 1-RM testing on each arm and then prescribe the same training load (% 1RM) for each arm based on it's own 1-RM... the weaker arm will ultimately have a lower absolute load, but same relative load...

The National Strength and Conditioning Association uses the following RepMax table:

1 repetition max = 100% of 1RM

2 repetition max = 95% of 1RM

4 repetition max = 90% of 1RM

6 repetition max = 85% of 1RM

8 repetition max = 80% of 1RM

This would be a good starting point.... but remember that these are repetition max estimates and therefore training should be slightly below that for repeated sets. Also, it looks like you are just doing as many repetitions as possible during each 30 sec work bout, if I am reading it correctly. You may want to go on the lower end of the relative load, but 50-70% seems like a reasonable range. It depends on how many repetitions you want them to complete.... there is no prescribed load for your proposed protocol. Try it out on a few people and see where their repetition ranges fall and let that guide your decision!

Good luck!

You can check this: https://www.nrv.gov.au/introduction for more information

It is in French

:Logistic distribution or sigmoid distribution is one of the essential portion of statistics . Here the specific question is m/c learning application - https://machinelearningmastery.com/logistic-regression-for-machine-learning/

Hi Louie! You can find what you need on Kenneth Train's website, in my opinion, he is the main reference on this topic. I leave you this link to his book, I recommend especially chapter six.

All the best in your work!

Dear Madhav Kumar ,

You have three main parameters to maximize in order to maximize the power conversion efficiency PCE of the solar cell.

It is so that the PCE= Isc. Voc . FF/ Incident solar radiation AM1.5

So, as you see the three factors are the short circuit current Isc,

the open circuit voltage Voc and the fill factor.

On has to adjust the physical and the technological parameters of the solar cell to achieve the maximum of the three quantities.

One has to make the absorber thickness at least equal to the absorption depth of the highest radiation wavelength so d>= 1/alph(almbdamax)

where alpha is the absorption coefficient,

The thickness d must also made at the same tome less than the diffusion length of the minority carriers L; so, d<=L

One has to minimize the reverse situation current Is of the dark current to achieve the highest Voc such that:

Voc= nVt ln Isc/Is

Is is minimized by reducing the injection across the junction and the minority carrier life time through out the device.

In order to increase the fill factor one has to reduce the shunt resistance Rs and increase the shunt resistance Rsh.

In this way one gets the highest efficiency.

For more information about building the solar cell to achieve the highest efficiency please refer to the book chapter:

Best wishes

Dear Isam Mohammed Abdulhameed many thanks for asking this very interesting technical question. In addition to the relevant literature references suggested by Mohamed-Mourad Lafifi please also have a look at the following potentially useful link:

This book chater is freely available as public full text on the internet (please see the attached pdf file).

Please also see the following irrigation depth calculator:

Good luck with your work and best wishes!

I agree with David, use mean

Thanks, Gerhard Martens it was indeed insightful

I have one more case where relatively good data (up to k=14) gives the following fitting (see attached).

it still show a huge error bar? can it be solved somehow?

Thanking you,

You may try the software KaKs calculator.

It depends on the number of mining areas. If they are few there is no need for sampling but if there are many then you can use fishers method for sampling.https://www.geopoll.com/blog/sample-size-research/

dear Amnah Alalmaie ,

AWH calculates the free energy along an order parameter of the system. Free energy barriers are overcome by adaptively tuning a bias potential along the order parameter such that the biased distribution along the parameter converges toward a chosen target distribution. The fundamental equation governing the tuning is: log(target) = bias - free energy, where the bias and free energy are initially unknown. Typically the target distribution is simply chosen uniform, such that the bias completely flattens the free energy landscape.

Regards,

Shafagat

Your very welcome Sajad Saraygord Afshari

Kind Regards

Dear Mr. Magnani,

I agree with Janez Podobnik. I assume you are using the Joint Unscented Kalman Filter for this purpose. In this case, the parameter is considered as a state and all the initial conditions, noise and measurement covariances will affect the estimation of the parameter. In 2019, I have proposed a different approach for this purpose and I am attaching the article link below:

The main advantage of this approach is you can determine weights for the measurements (a Jacobian approach) for parameter estimation. If one of the measurements significantly affects the parameter estimation, then, you can increase the weight of that measurement. The only drawback of this approach is that the estimated parameter must be linear in terms of measurements. The system itself can be a nonlinear system, but parameter(s) must be linear in terms of measurements. For frequency estimation, we have modified this approach with my colleagues (link below). In this case, the parameter is no longer linear in terms of measurements. However, we haven't tested this approach for other nonlinear systems in which the parameter(s) is nonlinear in terms of measurements.

You may try this approach and it can be useful for your aim.

Best regards,

Altan

Dear Nandi

Although there are various methods for determination of NOx, the simplicity, rapidity, and cheapness of the Griess assay have made this method more popular than others, in which deproteinization is a necessary step in measurement of it concentration in the serum, mostly because of the turbidity resulting from protein precipitation in an acidic environment. I considering acetonitrile an adeguate method to obtain a good extraction sample in which acetonitrile is mixed in ratio 1:1 with serum, vortexed for 1 min and centrifuged at 10000 × g for 10 min at 4 °C and supernatant used for NOx determination.