Questions related to Decomposition
I am getting error when trying to react gypsum with biomass (ultimate) in a Gibbs reactor.
RGIBBS DID NOT CONVERGE. MAXIMUM ITERATIONS EXCEEDED
Hello, I have synthesized a powdery material that is highly sensitive to air and decomposes when exposed to air for a short period of time (a few seconds). Is there any way to prevent or delay sample decomposition for FTIR analysis? If I can't use this method to analyze my sample, what other method do you suggest?
I have been reading “Handbook of reference methods for plant analysis” (Kalra (ed.), 1998) and other methodological books and papers to learn how to properly dry and analyse samples of vegetables and other sources of organic matter (manure, or compost).
On the drying procedure, it is cleat to me that:
i) Above 60ºC you stop the enzimatic activity and microbial decomposition and the sample is dry enough to be powdered and analysed (Carbon and nitrogen in a CNHS elemental analyzer, and P and K by digestions and mass spectophotometer), but it is not completely dried.
ii) From 80ºC the sample must reach a water content of 2-5%, and thermal decomposition starts, but it can be only problematic in powered samples.
iii) At 105ºC you have lost some elements by volatilization (N and S, and probably a little part of C), therefore it’s not recomended to dry at this temperature for elemental analysis. The sample is completely dried. There’s some thermal decomposition but nobody takes it into account.
In most works, if they want to know the nutrient contents and the dry matter content, they dry a subsample at 60º (and analyse its content in NPK) and other at 105º (and calculate the dry matter content).
My question is: how do you assign the nutrient content obtained with the sample at 60º to the dry matter, if you don’t know the water content of the sample dried at 60º? Should you take another subsample of the 60ºC subsample and dry it at 105º? Other researchers confirmed me that they dry all the samples at 105ºC, even for elemental analysis. How much nitrogen and carbon can we lose at 105ºC? I guess it depends on the element form (if N is in NH3, NO3-... ), but on average for plant tissue, manure, or compost, does anyone know how much it could be?
On the other hand, biomass is matter mainly composed of carbon and hidrogen, with small amounts of other elements. To measure the organic matter content of a sample, it is incinerated at 550º or more, and the difference in mass from the dried sample to the incinerated matter is assumed to be the organic matter.
The question is, why do atoms of P and other elements bonded to carbon structure are not considered as organic matter? Probably it is not easily measurable, but from the definition of organic matter, I understand that these elements are part of it. In addition, some elements like nitrogen or sulphur that don’t stay on the ashes are also considered organic matter (because they contribute to the mass loss when incinerated). I guess that these elements don’t represent too much, but I am surprised that nobody accounts them.
Thank you in advance.
I have an ECG signal sampled at 500 Hz and, from that, I would like to compute the RR interval and then Welch's PSD. All this aims to understand the sympathetic activation of the person.
I wanted to enhance the R peaks using the 'sym4' wavelet. However, I have some difficulties in understanding how to choose the proper level of decomposition.
Can someone help me with this topic? Is there a "standard" way to assess the proper level of decomposition?
Thank you in advance for your time!
Hello, I am a graduate student from Lanzhou University in China, and I am now very interested in the biochemical cycle in the context of global climate change, especially the decomposition of litter, but because I have just been exposed to this field, I am not very familiar with this field. My current idea is to link the decomposition process of litter under warming and rainfall changes with the aboveground plant community and the underground microbial decomposer community, but I don't know which scientific problem to start from, I hope you will provide me with some research ideas if you have time, thank you very much!
Lower Matrix to represent the approximate final stages of payment and upper representing the early payments
Actually, I wish to understand the process and coding to define new wavelet transform. So that I can understand and modify some wavelet transform to get better results. There is inbuilt wavelet transform in MATLAB and we just have to choose wavelets. I wish to define new wavelet transform.
The processes that soil microbes convert organic matter to carbon dioxide can be termed "decomposition" or "mineralization". The difference between these two words is still ambiguous to me.
We have known the distance matrix (n*n samples) and feature abudance (n samples*s features).
How can we extract featrue importance through matrix decomposition or something else?
Thank you for your help!
I am implementing Distributionally robust optimization using bender decomposition in GAMS. Can anyone provide me with any helping material or source code for the implementation of expansion planning problems?
I have a question regarding the H-α, H-β and H-γ behavior in a CH4/N2 plasma.
Long story short: I am igniting a 5 sccm: 200 sccm CH4/N2 plasma (40W CCP plasma) at 5 mbar. Then I observe the plasma via a spectrometer. I did that for various temperatures inside my chamber.
Now I observe some typical Peaks. Most are related to N2 of course. And also the Balmer series.
What puzzles me is the transition from a increasing trend for the H-α and H-β lines but a decreasing trend for the H-γ line. Normaly I'd say these line can correspond to a) the density of hydrogen or b) to the mean energy of my electrons in the plasma. No hydrogen = no balmer series or no energy = no balmer series.
Clearly there is hydrogen. Most likely even more free hydrogen with icnreasing temperature. So why would the H-γ behave like it does? The energy difference between the three lines is not very big, so we should see a similar trend, right?
tl;dr: Do the three lines (H-α, H-β and H-γ) of the Balmer series have to follow the same intensity trend? Or can they show different behaviour? If so, why?
Thank you for your support :)
Is there any method that can assist me in estimating the decomposition rate of residues in the field?
For example, if we applied paddy residue at a rate of 6 t/ha, how can we estimate how much residue (t/ha) decomposed during the crop's growing cycle?
I have just finished running a plant decomposition experiment measuring the decomposition of pine needles across climate and lithological types. We have mass loss and plant chemistry data (c, n, labile carbon, cellulose, hemicellulose and lignin). I would like to fit a three pooled decomposition model in r but am struggling to figure out how. I would usually do this in sigmaplot but my licence has expired and I cannot get another key.
Any help is greatly appreciated
i need some helping material regarding hilbert transform and empirical mode decomposition. i want to apply on my vibration signal to evaluate the frequency at different phases of the signal. i am using EMD in matlab. if there is some kind of code regarding this kindly share. i want to ask some question related to EMD and hilbert transform
i will be thankful
I need to decompose a signal on Matlab but I ended up having IMFs that with FFT instead of having one peak it shows several. Does anyone know how to decompose a signal with this method?
I'd be appreciated someone who could help me out
I have Co/Al2O3 catalysts and carbon is deposited on the surface by decomposition of light hydrocarbon on the surface of catalysts, therefore the catalysts are containing cobalt carbides and some deposited carbon on the surface. By increasing the decomposition temperature, the content of carbon on the surface of the catalyst increased and the G and D bands were observed, while for the other samples (with lower gas decomposition temperature) there is not any G and D bands, while the 2D band with lower intensity and broader was observed in these samples. According to our XRD analysis, for the samples prepared at lower decomposition temeprature (No.1, 2, 3, 4) without G and D band and with only weak 2D band at 2670 cm-1, we have some cobalt carbides species, while for those with G, D and 2D bands (No. 5, 6) there was not any peak belong to cobalt carbides in XRD spectrum. I would like to know your opinion about the band at 2670 cm-1 in these Raman spectrums.
Thanks in advance.
Explain how to calculate (with formula) carbon-to-nitrogen ratio, its importance and how it influences the rate of OM decomposition ?
Let us discuss about the advantages, disadvantages, and use of powerful decomposition techniques like Bender's decomposition for large-scale optimization. I invite my esteemed colleagues and researchers to share important literature, ways of implementation, and potential application areas of decomposition algorithms, in this forum.
The overall cost of "my algorithm" is dominated by finding orthogonal basis, which costs (M p.^2) where p is less than M , for the input matrix. My concern is: is there nay alternative method (or low-cost QR decomposition) to find the orthogonal basis with lower cost, please?
Thank you so much for your consideration in advance
The error rate of tensor (CP) decomposition returned by python module tensorly is 0.4 or 0.9, is it satisfying? What is your advice to decrease error in tensor (CP) decomposition or matrix decomposition?
Our time series dataset have sparse multivariate variables, and need to be classified by unsupervised clustering. Since the sparse nature of dataset, we plan to use matrix/tensor decomposition. However, the date of each sample can not be binned or matched. Could you kindly suggest a decomposition method can be used in non-matching time series data?
Following an incident I investigated where a body was discovered in the same room as a small fire. The cause of death was undetermined, due to advanced decomposition. There was no evidence of the body being damaged by fire. It is believed the death occurred approximately 10 months before discovery. The body was found in the reclined position with the upper half of the body in advanced stages of decomposition. However, the lower half still had areas of intact tissue. So I am interested in the likelihood of analysing the blood in the remaining tissue to determine if carboxyhemoglobin was present to evaluate if death was caused as a result of the inhalation of fire fumes.
We know that LU decomposition is an important method for stochastic simulation of 2D RF. Assume the covariance matrix of regionalized RVs is C, it can be decomposed as C=LU according to the LU algorithm, then a RF can be generated by X=L'*y, where y is a vector consisting of independent standard normal random numbers. I want to know whether can I use LU decomposition for simulation if n observations exist as the conditioning data? If so, how can it be demonstrated?
Wavelets are a set of mathematical functions used to decompose a signal continuously into its frequency components, the resolution of each component being equal to its scale.
A wavelet transform is the decomposition of a function based on wavelet functions. wavelets are transmitted and scaled samples of a function with finite length and highly damping oscillation. Compared to the Fourier transform, it can be said that the wavelet has a very good localization property.
For example, the Fourier transform of a sharp peak has a large number of coefficients, because the basic functions, the Fourier transform, are sine and cosine functions whose amplitude is constant over the whole interval.
Wavelet functions, on the other hand, are functions in which most of their energy is concentrated in a small interval and diminishes rapidly. Therefore, with proper selection of mother wavelets, better compression is performed compared to Fourier transform.
The mother wavelet is horizontally and vertically deformed through the effect of the signal.
The wavelet coefficients, which are equivalent to the Fourier decomposition amplitudes, are thus a measure of the correlation between the signal shape and the contour of the deformed mother wavelet that follows the signal.
As wavelets are constructed over a scale change operator, we can better understand their effectiveness using a fractal analysis, and more generally using any multi-scale approach.
They should become an incontrovertible tool across the whole of scientific geography, with its interest in multi-scalar and multi-scale phenomena.
More specifically, the signal − whether it is a chronicle series or an image − is decomposed into one general part and some details representing some irregularities.
The general part is then itself decomposed into a new general part and more details, and so on. This is an iteration model similar to that of the construction of a fractal.
This connection explains the effectiveness of wavelets in the study of fractals.
What other characteristics of wavelets can be used in fractal analysis?
Can someone please let me know what does "ensemble average for a given window of pixels" mean when calculating a coherency or covariance matrix? I have taken this sentence from a paper "Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation".
According to my understanding I have to consider a window of size m x n and take the pixels of a radar image in that window and then perform ensemble averaging i.e. mean of those pixels. Please let me know whether my understanding is correct or not.
As of now I am performing a 2D convolution with a square window and varying their size to analyse the performance.
Thanks in advance.
I am interested in this topic because I want an organism that consumes food waste at a high rate and the data for insects and worms seem more homogeneous. I have to admit that terrestrial isopods have me completely surprised. Some species reproduce at much higher rates than other invertebrates. However, the data in the literature speak of very low daily intakes of less than one mg per g of live weight. On the other hand, as they live at overcrowding levels of tens of thousands per square meter they could compensate this low feeding rate. Even so, it is not clear to me. The species that I keep in the laboratory as a companion to the terrariums is Porcellio laevis. I don't know if I have minimized the effect of terrestrial isopods as detritivores. I know that isopods crush the food considerably and help the bacterial processes of decomposition. I do not care whether they degrade directly or indirectly. I want to know their exact contribution and I repeat, the results are very disparate and confusing. For example in these papers:
Effects of Terrestrial Isopods on the Decomposition of Woodland Leaf Litter Author(s): M. Hassall, J. G. Turner and M. R. W. Rands Source: Oecologia , 1987, Vol. 72, No. 4 (1987), pp. 597-604.
Abd El-Wakeil, Khaleid. (2015). Effects of terrestrial isopods (Crustacea: Oniscidea) on leaf litter decomposition processes. The Journal of Basic & Applied Zoology. 69. 10.1016/j.jobaz.2015.05.002.
These studies report composting rates of more than 70% of the biomass ingested (~millipedes), which is logical if we look at their diet. But the most surprising thing is that they also talk about feeding conversion ratios (FCR) between 1.5 to 2, which would place them at the same level as the tenebrionidae. I would like to set up a discussion on isopods can be used on an industrial scale to firstly degrade waste and secondly to compost. The thousands of isopods I have in my lab inside terrariums don't seem to be effective enough to attract attention. Judging by their numbers the breeding conditions are appropriate. In short: I don't know what to think of isopods. I guess I'll have to do a lot of tests before I decide.
Daniel Patón. Numerical Ecology. Ecology Unit
Department of Plant Biology, Ecology and Earth Sciences
Faculty of Sciences. University of Extremadura
Avda. Elvas s/n 06071 Badajoz (Spain)
I intend to use Adomian decomposition combined with Laplace transform method, I am having issue to interprete the conditions as attached herewith.
I am trying to activate HA with PEI at high temperatures but PEI will decompose at 180°C, it there any kind of reticulation method or ... to increase the thermal stability of PEI?
The AIBN in our lab is purchased from Sigma Aldrich and dissolved in acetone at 12 wt %. Does this still need to be recrystallized before use in a free radical polymerization or are the effects of decomposition lessened by its presence in the solvent?
Combination reactions can be exothermic or endothermic, but are more often exothermic. Decompositions reactions are therefore mostly endothermic. What is the pattern for combination reactions that are exothermic, and endothermic?
In the case of decomposition based MaOPs, for example NSGA3, some predefined Ref.Points are associated to the population members in order to decompose a Many Objective problems to the series of multi/single objective problem!!
so how it works??
I am working on a project which is about clustering the time series signals. I wanted to apply some smoothing or compression signal to remove unimportant shruggs and spikes from the signals. I learnt about the Wavelet Decomposition which compresses or stretches the signal can anyone help me about the implementation of this python ?
Vitamin C (Ascorbic acid) when oxidized will create brown decomposition. But it is used as an antioxidant excipient in pharmaceuticals or an anti-brown substances in fruit & vegetable. Does its brown decomposition affect the appearance of the drug solution or create its own brown in fruit & vegetable. Could anyone having experience give me opinion about these applications of Ascorbic acid ? How can we prevent brown colors created from Ascorbic acid.
I want to blend HDPE(BL3) with PA6 to improve its barrier properties with extrusion molding process. this product need to be white and shiny but when i blend these, yellowing occurs !
what is the problem?
I am examining the decomposition of the forecast error, but in my chart you can see that the largest share is the variables that, according to Granger, are not even the cause of the variable. So what is the relationship between the two studies (Granger and FEVD)?
Based on a resent question; what is the difference between a saprophyte and a saprotroph?
In Greek saprophyte would be saprós (“putrid; decayed; rotten”) and phyte ("plant") thus meaning a plant that will live of dead or decaying organic matter. Saprotroph on the other hand, would be saprós (“putrid; decayed; rotten”) and trophē (“food; nourishment”) and thus include all organism with this lifestyle.
It seems as if the term saprophyte would be incorrect as plants are in actuality not saprophytic. The same problem comes in with saprophytic vs saprotrophic. My concern is this, why were we taught that "saprophytic" or saprotrophic fungi are saprophytes as this would seem to be incorrect. Recent publications and textbooks still refer to saprophytes. Has the term saprophyte been abolished or regardless of the difference we now consider saprophyte and saprotroph as synonyms?
I'm currently working on acoustic denoising algorithm in leakespecially different type of wavelet shrinkage and other sparse component decomposition. I'm working on a dataset of real-life mesurements and I'm looking for indicators that describe a good denoising such as MSE, SNR... But those rely on the knowledge of a perfectly denoised signal or at least some knowledge about the signal you are looking for which I don't have since I only got access to the noisy signal.
Do you have some suggestions of performance indicators that could evaluate the quality of the denoising without relying on the knowledge of a denoised signal.
I am looking for a way to analyze the interaction of a gas with the adsorption site of a solid.
SAPT seems very attractive for non-covalent interactions but when the adsorption site contains a transition metal, covalent phenomena may occur.
So is there any reliable method that can account for covalent bonding?
when I analyse the solution of dynamical system(in time series) by wavelet decomposition, how can I distinguish the differences in figures d1,d2,...,d5?
The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques.
A Matlab code for power-line interference removal is available in: https://github.com/brunobro/power-line-interference-removal-in-ECG.
I request the evaluation of fellow researchers.
Wavelet is a function that is localized in time and frequency, generally with a zero mean.
It is also a tool for decomposing a signal by location and frequency.
Wavelet decomposition is original in two respects relative to Fourier decomposition. On one hand, sine and cosine functions, which are periodic, are replaced by a mother function, which is quite regular.
The mother wavelet is horizontally and vertically deformed through the effect of the signal. The wavelet coefficients, which are equivalent to the Fourier decomposition amplitudes, are thus a measure of the correlation between the signal shape and the contour of the deformed mother wavelet that follows the signal.
the signal − whether it is a chronicle series or an image is decomposed into one general part and some details representing some irregularities. The general part is then itself decomposed into a new general part and more details, and so on. This is an iteration model similar to that of the construction of a fractal. This connection explains the effectiveness of wavelets in the study of fractals.
Any probable technique or catalytic agent to degrade the plastic material without polluting environment? Kindly share your opinion or any article related to this.
I have modelled the optimization problem to minimize the distribution system losses and modelled it as a mixed-integer second-order cone programming(MISOCP) problem. I used several well-known solvers such as GAMS-MOSEK/GUROBI/CPLEX to solve it, but they cannot solve it in 24 hours or higher time. I want to apply benders decomposition or some other approach to solve the problem within 2 hours.
Please suggest some references to learn benders decomposition for MISOCP type problem or some other approach to solve formulated MISOCP problem in less time.
I want to compare accuracy among these four methods in terms of reconstruction. These four methods are mentioned below:
1. EMD (empirical mode decomposition)
2. DMD (dynamic mode decomposition)
3. QN (quasi newton method)
4. GAN (generative adversarial network)
It would be highly appreciated if anyone provides detailed explanation along with some publications.
M project requires me to decompose a time series using empirical mode decomposition and its variations and group the resulting components into high frequency, Low frequencies and trend.. The next step is use a machine learning algorithm to forecast each category and use another machine learning algorithm to to get the final forecast.
Any resources in PYTHON OR R will be greatly appreaciated
How can we compute the correlation coefficient if the original signals(s) are significantly positively correlated with the approximation coefficient series (a1, a2, a3, a4, a5) in the wavelet decomposition and reconstruction of a specific signal?
In designing an experiment to investigate regional influence of these elements (temperature, humidity) on decompositional stages and insects colonization of pig carcasses, how would a control be setup for the elements given its uncharacteristic nature to be carried out in a laboratory?
Hi all I have now finished drafting my article on the importance of soil to forensic science, if anyone has anything that is relevant to soil and decomposition that can be included within my final type up please let me know and I will include a citation of your help. thanks
My team has been collecting leaf litter annually over the past 10 years for quantification of the leaf primary productivity of our study site. We put aside dried samples of the litter every year, stored them in a cool and dry place (in closed plastic tube), but we are just about to analyze their elemental composition.
I would like to know whether there is a possibility that the elemental content modified over time due to decomposition of the litter. I would say "mostly no" because the biological activity must have been very low in these dry samples, but I cannot find papers documenting this.
Could anyone help on this aspect? Thanks in advance.
i am looking for the package or command, which performs times series decomposition in STATA. So far I did not find anything. Example can be found here: https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b at figure 5.
Look forward to valuable comments)
When a surface wave is superimposed over turbulent current, a periodic velocity fluctuation is observed and the conventional Reynolds decomposition of the velocity field is not satisfactory and phase-averaging of the velocity signal needs to perform why?
Why phase averaging is necessary for such type of flow?
I am working with the hourly electricity spot price data, due to the large dimensionality I convert the discrete data into functional data. The model I use the functional autoregressive model of order more than one, as per my knowledge there no R package available to deal with such model, so I apply an alternative method, using the functional principal components (FPC's) as dimensional reduction, utilizing the associated principal components for the forecasting through multivariate time series model. Then I convert these forecast scores into functional curves through Karhunen-Loeve decomposition into a functional form, in such a way I obtained a forecast of each day as a single curve. Know, to check the accuracy of the model I want to calculate percentage mean square error or mean absolute error. know my problem start from here, So I want to reverse back each curve into 24 discrete points, is there is any package in R which is helpful in dealing with such a problem.
using Cholesky decomposition in the UKF induces the possibility, that the UKF fails, if the covariance matrix P is not positiv definite.
Is this a irrevocable fact? Or is there any method to completely bypass that problem?
I know there are some computationally more stable algorithms, like the Square Root UKF, but they can even fail.
Can I say, that problem of failing the Cholesky decomposition occurs only for bad estimates during my filtering, when even an EKF would fail/diverge?
I want to understand if the UKF is not only advantagous to the EKF in terms of accuarcy, but also in terms of stability/robustness.
Can you always say that the upper bound obtained in Benders decomposition is better than the upper bound in the Lagrangian relaxation method (minimization) and the lower bound vice versa?
I ask for some help with the energy decomposition calculation by mean of gammess (or another) software. I am studying a complex and I need to know the magnitude of the electrostatic component.
Obviously, in the manuscript, we will recognize this collaboration.
Looking forward to any collaboration,
can anyone please help me, how to implement the CORDIC algorithm for eigenvalue decomposition using the Jacobi method?
in a traction compression uni-axial test on cylindrical samples, I use a set of 3 extensometers spaced at 120° around the sample to obtain the average deformation of the cylinder. this allows me to determine the stress and strain tensor in the sample. now, if I decompose the cylinder in tree equal sector cylinders (decomposition along the axe of loading) is it possible to obtain the stress and strain tensor of each sector cylinder from the stress and strain tensor of the hole cylinder?
I am working on single trial motor imagery data i.e. EEG data. I am following a paper which has a dataset from BCI challenge IV (dataset 2b). After removing noise and extracting all the data from files into matrices, I am doing frequency band separation using wavelet decomposition(discrete wavelet transform) for frequencies 8Hz to 30Hz. after using dwt2 (2d wavelet transformation) I get four values LL, LH, HL, HH (L=LOW, H=HIGH). I want to confirm are these 4 combinations alpha band, beta band ,gamma band and delta bands? I cant find this anywhere.
Any technique could detect the degree of litter decomposed by microbes? Or any indexes could do this job? I want to split the contribution of microbes during decomposition, so any suggestions are appreciated.
To maintain the proper crop plan most of the times there is not enough time to make the field ready for the next crop.
In this particular context , can bio-decompser be a solution? What about time management? How long it takes to decompose the materials? What about the economics?