Interpretation - Science topic

In the realm of machine learning, the terms "interpretability" and "explainability" are often used interchangeably, but they do carry subtle differences in their connotations and implications.

**Interpretability** generally refers to the ability to understand and make sense of the internal workings or mechanisms of a machine learning model. It's about grasping how the model arrives at its predictions or decisions, often in a human-understandable manner. Interpretable models tend to have simple and transparent structures, such as decision trees or linear regression models, which allow stakeholders to follow the model's reasoning and trust its outputs.

**Explainability** on the other hand, extends beyond just understanding the model's internal workings to providing explicit explanations for its outputs or predictions. Explainable models not only produce results but also offer accompanying justifications or rationales for those results, aimed at clarifying why a certain prediction was made. This could involve highlighting important features, demonstrating decision paths, or providing contextual information that sheds light on the model's reasoning process.

In essence, interpretability is about comprehending the model itself, while explainability is about articulating the model's outputs in a way that is meaningful and useful to human stakeholders. While a model may be interpretable by virtue of its simplicity or transparency, it may not necessarily be explainable if it fails to provide clear justifications for its decisions. Conversely, a complex model may not be easily interpretable, but it can still strive to be explainable by offering insightful explanations for its predictions.

Both interpretability and explainability are crucial aspects of deploying machine learning models in real-world applications, especially in domains where trust, accountability, and regulatory compliance are paramount. By fostering understanding and trust in AI systems, interpretability and explainability pave the way for more responsible and ethical AI adoption, ultimately benefiting both developers and end-users alike.

I would examine correlation matrices for your various scales. If the correlations are all uniformly high (say .8 and above), then that would indeed be a problem.

Yes.... How may I help you

For Fatigue Analysis I would rather recommend to use Fe-Safe (an specialized Application for Fracture and Fatigue which comes in bundle of Simulia Abaqus software itself).

Proteins don't exist. "Proteins" is just an idea in consciousness.

Conflicting results in Generalized Linear Mixed Models (GLMM) analysis could stem from variations in data, model specification, or underlying phenomena, highlighting the complexity of the studied system and the need for further investigation or model refinement.

I would recommend joining the Q-Method discussion group of 800+ students and practitioners worldwide. Send the command SUB Q-METHOD YOURNAME (in upper- and/or lower-case) to [email protected].

Hedgs G is pretty similar to Cohen's d; in fact, it is kind of like a correction for Cohen's d (see page 4: https://pubs.asha.org/doi/pdf/10.1044/cicsd_33_S_42#:~:text=This%20means%20that%20if%20repeated,a%20high%20of%201.42%20SD.)

Therefore, I'm pretty sure that a negative value interpretation is the same as with Cohen's d: i.e., negative usually means the control group had a higher mean than the treatment group. I think a lot of studies tend to use the absolute value of Cohen's d and then you look at the averages to see which is bigger, which might be what is confusing you.

If a study only reports a median, I don't know if you can calculate an effect size that requires an average from it. In some cases, it is quite impossilbe if the variance should be pooled (for many dependent samples cases). You might try emailing the authors and seeing if they're willing to either share the data or provide the average for you?

I appreciate the resources shared by R.Eugene Veniaminovich Lutsenko.

However, these references seem to focus on a different aspect of healthcare modeling. I'm still interested in gathering insights specifically about the suitability of Rough Set Theory and Neutrosophic Logic for handling discrete data in machine learning healthcare models.

Please feel free to contribute to this discussion if you have expertise in this area. Thank you

These figures show PCA scores projected onto their corresponding pixels.

Using AI technologies for data analysis and study interpretation highlights a number of ethical concerns. There is the issue of biases in the data,records since AI algorithms can give biases in the data on which they are taught, resulting in unjust conclusions.

Transparency is essential for data analysis, understanding how AI makes its decisions enables accountability.

Privacy risks occur as a result of the massive volumes of data that AI demands, demanding strict data protection procedures.

The social implications of Artificial intelligence adoption impose some concerns, like as its effects on employment and inequality, which must be examined.

Responsible AI usage for predicting unanticipated outcomes and prioritizing ethical principles to guarantee research helps society without harming individuals or aggravating current imbalances.

Balancing in innovation with ethical integrity is critical to the ethical use of AI technologies in research and data analysis.

There are other pros and cons also but while using AI in research we should look for ethical concerns always. It's always better to work, and produce less, rather than work false or to follow the malpractices, and falsification of data in the research.

Thanks.

What is the rationale for wanting unit-level increases/decreases for interpretation? If you are married to the log-log model, I would assume that is because you have multiple extreme right-skewed variables, which necessitates an interpretation that there are multiplicative changes over additive changes given the compression of the distribution. To me, it doesn't necessarily make sense to then back-transform this sort of distribution into a unit-level interpretation since the increases/decreases are not going to be as meaningful or intuitive.

I'm sure it is possible to achieve this to some degree with smearing or alternative transformations, but there is still a loss of information in the process that, to me, isn't necessary. Exponentiating your outcome will inevitably lead to some bias. You may be interested in this Stata thread on the topic:

Bruce Weaver A useful app but it appears that its focus (currently) is exclusively on power. I believe that a simulation that is run "from scratch" provides greater flexibility and yields a lot more information than just on power. For example, you can simulate the effects of non-normal and missing data and evaluate the performance of fit statistics, as well as parameter and standard error bias in addition to estimating power. Power estimations can be misleading when your SEM parameters or standard errors are biased due to, for example, an insufficient sample size.

It is an absolute DISGRACE for the world scientific community to let publishers (often full of incompetent individuals - editors, reviewers) enjoy the right to to FORCE us to submit a paper to ONLY ONE publisher at a time. It must be exactly the opposite: a scientist must have the right to submit a paper to as many publishers as s/he wishes! And the publishers MUST FIGHT for a paper/book to publish it - when it is a good one. This is so simple and so obvious and so straightforward that I am shocked why this DISGRACE continues. Suppose I make bread, or wine, or computers, or whatever. What? Shall I be obliged to propose them to one buyer only at a time? Hey, scientific community, start THINKING at last, will you?

Chinedu Onyeke, you can interpret TEM and SEM images of carbon materials by examining pores and their sizes. In SEM, analyze surface features such as particle size and shape, while TEM allows for direct observation of internal structures. Consider the following pore size ranges: Micropores (< 2 nm) and Mesopores (2 - 50 nm).

I hope you find these helpful.Warm regards

XCMS online remains the standard tool.

Thank you for your reply and advice, David!

I've had a look at Inductive Thematic Analysis. I'm thinking this may work well, under an IP framework 🤔

The previous answer is describing a mean or average-not a median.

The median and mean of a distribution are not the same. While for symmetrIc distributions they will be close in value, for asymmetic distributions they will be different.

The origin corresponds to the point where the X and Y coordinate axes are zero. The average structure is just that: The structure that results from averaging the XYZ Cartesian positions of your protein. The variance that explains the PCA analysis is based on how close or far the Cartesian positions of the protein are from the average structure.

As you will be aware, plants are a very rich source of phytochemicals and this will be reflected in your aqueous extract. The IR spectra of your extract will be complex, containing the overlapping spectra of every compound. You will not be able to identify any individual compound with any degree of certainty.

If you want to identify the components of your extract then you need to resort to a Metabolomics type experiment. You need to add in several chromatographic steps and then resort to GC-MS (headspace for volatiles), after appropriate derivatisation, and to LC-MS. The spectra can be searched against various libraries. Even then you will be left with many unknowns.

The online Cambridge Dictionary gives the following definitions of intimacy: (i) a situation in which you have a close friendship or sexual relationship with someone: Intimacy between teachers and students is not recommended.

(ii) things that are said or done only by people who have a close relationship with each other: It was obvious from their witty intimacies that they had been good friends for many years.

(iii) the state of having a close, personal relationship or romantic relationship with someone: He was always polite, but he shunned intimacy.

At least two definitions include sexual and romantic relationships. I don't think men insist on interpreting intimacy as involving sex, but it is the socio-culturally acquired acceptation of intimacy deeply-rooted in the mind of Anglo-Americans that includes sex as part of its meaning. If you like, the problem is linguistic or more precisely semantic tightly linked to the meaning attributed to intimacy. In cognitive linguistic terms, the root problem relates to social cognition.

Hello Wenxia Zhou. Did you by any chance GLM > Repeated Measures in SPSS to estimate your model? If so, I suggest that you mean-center the covariate (the T1 score) and then estimate your model again using the mean-centered covariate. See this letter to the editor for more info:

HTH.

If you are adding controls prior to the extraction step these are known as surrogate standards. They help you assess the efficiency of your extraction procedure. You would need to set up a calibration for each of them just like you calibrate for your analytes (PAH, in this case). If you are adding the controls after all of your sample preparation steps but before your injection into the GC-MS then these are internal standards. You use them to correct for variability in the GC-MS responses.

Typically you calibrate your compounds corrected for the response of the internal standard (IS), and all samples, standards, etc get the same concentration of internal standard, so (Area compound/Area IS) plotted against (concentration compound/concentration IS) will become your calibration. The slope of this plot is typically referred to as the relative response factor (RRF); if you assume a linear response (I never do) you can simplify the equation to get to the standard RRF equation of Analyte conc = (analyte response * IS conc.) / (IS response *RRF).

I don't really want to write a discussion of the new results, because it would feel as if I was doing your homework. But here is an outline based on what I posted for the previous analysis. You can carry out the needed calculations (of percentages) and fill in the relevant statistics.

You also asked:

I'm not entirely sure what you mean. Are you thinking of a quantitative comparison, or a qualitative comparison? Maybe someone else will understand what you mean.

Money!

It would be useful if you provided a picture and explained how you labelled the nucleus and protein of interest and what sort of microscope you used to take the image etc. Qualitative analysis of protein localisation is fairly easy but quantitative analysis depends so much on how your sample is prepared for imaging and how you take your images. A lot depends on the question you want to answer as well. For example you can get numbers from software such as ImageJ (FIJI) (which is free) but you need to know what to measure.

According to my knowledge it depends on the type of literary work what you translate.for eg a poem doesn't require word to word translation coz it will lose its charm where as scientific essays, political survey, formal correspondence require word to word translation orelse the matter will not be conveyed exactly.

hi dear sorry for delay my answer you can depend semiotic theory and there is a lot of method you can collect concepts and make personal model if you can give me time to find suitable theory

The relationship described here is not a relationship between the states of a quantum system. In fact, if it were, the implication should hold: given the vectors x, y in a Hilbert space on the complex field, if <x-y/ A ( x-y)> >or= 0 then for every pair a,b of complex numbers it should result < ax-by/ A (ax-by)> >or=0. This is because the states of a quantum system are defined up to arbitrary complex constants. In fact, the following counterexample applies: x= (1,0); y= (0,1); a=1, b=5; A = diag (2,-1). The relation introduced above is therefore only a reflective and symmetric (non-transitive) binary relation between the vectors of a Hilbert space (real or complex).

Dear Park Jun Jae ,

for calculating the transmittance of a sample you fortunately do not have to know the absolute values of the intensity I impinging onto the detector or the entrance slit of your monochromator.

The count-scale (CS) presented at your spectrum is proportional to the intensity (I) at a particular wavelength for the light measured by the spectrometer:

CS ~ I.

For gathering the transmittance your have to acquire two spectra,

a) the primary spectrum CS₀ without any sample and

b) the transmitted spectrum CS_tr with the sample being passed by the light, and afterwards you have to

c) divide them:

transmittance(lambda) = CS_tr(lambda)/CS₀(lambda) = I_tr(lambda)/ I₀(lambda):

The Biolog Eco Plate technique is a powerful tool used for assessing the metabolic diversity and activity of microbial communities in various environmental samples. Data collection involves inoculating the Eco Plates with a sample, incubating them, and then measuring the color changes in each well to determine substrate utilization patterns. Analysis of the data typically involves calculating indices such as Shannon diversity or Simpson's evenness to quantify the overall metabolic diversity present in a sample. Additionally, principal component analysis (PCA) can be used to visualize similarities and differences between samples based on their substrate utilization profiles. Interpreting the results requires an understanding of microbial ecology principles, as changes in substrate utilization patterns may indicate shifts in community composition or ecosystem function. Overall, the Biolog Eco Plate technique provides valuable insights into the functional potential of microbial communities and allows for comprehensive ecological assessments.

I don't think the way you have framed the hypotheses is correct. Your null hypothesis is literally in it's name: it is a hypothesis of null effects. So it should be:

H0: There is no difference between A and B with respect to recovery percentages.

H1: There is a difference.

Also Andreas is unfortunately incorrect and this is a common misconception about p values. Even Fisher, the guy who developed the original workings of null hypothesis testing, noted that we can neither accept the null or alternative hypothesis, but we can tentatively reject the null. These are not equivalent statements so it is important to be careful about the wording. For a Bayesian view this would be a bit different, but since you highlight p values this would be important to understand.

As always, the usual caveats go with a low p-value. It would be very important to include things like effect sizes, confidence intervals, etc. to quantify what the actual effect is since p-values say nothing about the actual magnitude of the effect, only that the probability of a null effect being very low.

Dear Kristy Gourab Sinha ,

after a short search I found that card (JCPDS # 039-1894) available at one of the answers provided by Marchella Bini in:

https://www.researchgate.net/post/

I_performed_xrd_analysis_of_my_chitosan_sample_can_someone_help_me_interpret_its_diffractogram/1

The file is attached there; so I will not attach it here again...

Good luck and

best regards

G.M.

You're very welcome - good luck!

Can you elaborate WHAT exaclty is confusing? It seems pretty straight forward that with increasing values for W, the effect of X becomes more negative. (And I would recommend using 16, 50, and 84 percentiles instead of SD, it makes it more wasy, prevents predictions for values which are not in the scale anymore (which might happen, if the predictor is skewed) and in case of normality of the predictor [which is not a necessary condition in any way!!] both approaches will lead to the same conclusion)

Maybe as a help to undestand what is going on, you should rearrange the regression formula. Without intercept it is basically:

b1*X + b2*W + b3*X*W

To see how the interaction affects the X variable:

(b1 + b3*W)*X + b2*W.

Now you see that the effect of X is conditional on the b3 weight AND the value of W. If you now take the values of your variables and the equation, you will get exactly the results

(-.262 + (-.028*-6.361)*X =

(-.262 + (0.178))*X = -0.083*X (rounding error)

(-.262 + (-.028*0)*X =

(-.262 + 0)*X = -.262*X

(-.262 + (-.028*6.361)*X

(-.262 + (-0.178))*X = -0.44*X

Hope this helps

Amin Kordi yoosefinejad, at their core, models are grounded in the estimation of model parameters and the accompanying fit statistics which can help to determine the appropriateness of the estimates for the model parameters. In the case of simple Rasch analysis, these elements are being estimated: person ability and item difficulty. The beauty of the Rasch approach is that both forms of these estimates are on the same scale.

The output of the TAM package should allow you to review all of the person ability statistics as well as their accompanying model fit statistics. In addition, there should be an output that displays the item difficulty statistics as well as their accompanying model fit statistics. After you review those two sets of results, you should be able to make decisions about your instrument.

Hope you won't let ChatGPT do optimization. That System can't even solve simple geometry Tasks correctly 😡

To enhance the interpretability and explainability of AI systems, thereby fostering trust and accountability, several strategies and approaches can be adopted. These strategies not only aim to make the decision-making processes of AI systems more transparent but also ensure they are understandable to a broad audience, including those without technical expertise. Here are key methods to achieve this goal:

1. Model Simplification

2. Explainability Techniques

3. Visualization Tools

4. Prototyping and Simulation

5. Regulatory and Ethical Frameworks

6. Collaboration and Stakeholder Involvement

7. Documentation and Transparency

8. Continuous Monitoring and Feedback

Implementing these strategies requires a multidisciplinary approach, combining expertise in AI and machine learning, domain knowledge, ethics, and design. By making AI systems more interpretable and explainable, we can ensure they are used responsibly, ethically, and effectively, thereby increasing trust among users and stakeholders.

Incorporating Explainable AI (XAI) into critical domain machine learning models, such as healthcare, finance, and autonomous vehicles, significantly enhances trust and transparency, which are crucial for acceptance and ethical deployment. XAI aims to make the decisions of AI systems transparent and understandable to humans, addressing the "black box" nature of many advanced AI models, where the decision-making process is often opaque.

Enhancing Trust with XAI

Challenges in Adoption

Incorporating XAI into critical domain machine learning models is essential for building trust, ensuring accountability, and facilitating wider adoption of AI technologies. However, overcoming the challenges of explainability requires ongoing research, interdisciplinary collaboration, and the development of standards and best practices that prioritize ethical considerations and stakeholder needs.

To interpret labor efficiency results from Strategic Factory Analysis (SFA), you can apply key factors in measuring supply chain performance. In the documents provided, we can consider the following aspects:

Product design: Labor efficiency can be measured by considering factors such as product quality, availability, and production cost calculations.

Production scheduling: Labor efficiency analysis can include measuring compliance with production schedules, production speed, and flexibility in meeting product demand.

Factory management: Labor efficiency can be evaluated through factors such as manpower utilization rate, time utilization rate, and efficiency in the use of production facilities.

Distribution planning: Labor efficiency in distribution can be measured through factors such as delivery speed, order accuracy, and flexibility in meeting customer needs. .

During an SFA analysis, you can compare results with industry standards or your own business goals to evaluate labor efficiency. You can then identify recommendations to improve efficiency, such as optimizing production processes, training people, or increasing flexibility in the supply chain.

Bruce Weaver I'm sorry, I forgot to include the other indirect effects (which were negative, significant). Yes, the sum of these effects + direct effect is the total effect.

Total Effect: Negative (-0.42)

Indirect Effect 1: Positive (0.03)

Indirect Effect 2: negative (-0.22)

Indirect Effect 3: positive (0.06) - Not significant

Direct Effect: Negative (-0.29)

Now, can you tell me how I could interpret this positive indirect effect in the face of the total and direct positive effects?

The interpretations depends on all aspects either positive nor negative.simply advantages and disadvantages .

Alaya Kouki With respect to „From Nothing you get the theory of Everything.„ well not from nothing (exactly in the human sense of this word) …

… but from simple first order multiplicative base entities like shown within the framework of iSpace theory able to derive value and geometry of constants of nature, that is (e.g only) GoldenRatio iSpaceAmpere being the quantum of Ampere, 1/6961 iSpaceSecond being the quantum of time, and so on) all multiplied up by any arbitrary positive integer to the values we see once lossless (keeping initial integer geometric exactness!) converted back to iSpace-SI based MKS/A-SI lab compatible measurement values (to compare to experimental results of all kind, given no other theoretical corrections have been applied like QCD/QED when involved in such calculation).

But otherwise - indeed - from nothing (but a little bit pre-school multiplicative math).

The most common X-ray diffractometer uses a flat stationary sample holder, which is referred to as Bragg-Brentano geometry.

For such a stationary diffractometer, the intensity of a Bragg reflection is proportional to the relative number of crystallites in the sample that happen to be in an orientation with the hkl plane parallel to the sample surface. In this geometry, only those crystals will diffract the incoming xrays that happen to be oriented with the respective (hkl) plane parallel to the surface.

If all crystals are randomly oriented, this probability is independent of the indices hkl. If on the other hand more you have preferred orientation, a group of lattice planes will be more likely to be parallel to the surface compare to other reflections. The relative intensity of Bragg reflections of this group of lattice planes will be increased compared to that of other hkl groups.

In your case, if the diffraction pattern shows only 200, 400, 600 that means that the direct space direction [100] is very much preferentially normal to the sample surface. Other directions like [111], which is normal to the hhh lattice planes are much less likely to occur normal to the flat sample surface. Accordingly their intensity is diminished. As you state that you observe h00 reflections only, your sample has a very extreme preferred orientation.

It does not tell you anything about stacking of a 2D material. The preferred orientation tells you something about the orientation distribution of the crystallites, not their internal structure. If your 2D- material consists of layers which are parallel to the h00 planes of the bulk material, the material is most likely stacked with such layers parallel to each other.

First of all, it is important to mention that individuals are responsible of caring about their descendants based on the values inculcated to them in family and community they belong to. So if the non-whites prefer to have several children detrimental to socio-economic comfort, the whites privilege socio-economic comfort. As a matter of proof, it is not common to see non-white with the deliberate choice of not having a child but among the white. So it is normal that the white decline.

Hello. Green and red is simply a spectrum between Inactive (green) and active (red). The degree of saturation (how dark or light the colors are) in the prediction box indicates the level of confidence in the prediction, which is supported by the probability score on the right. The figure you've displayed shows that in terms of hepatotoxicity, Protox II predicts that goniothalamin is inactive but it is worth noting that the confidence level is not so high, being only at 0.56. This means that despite the predicted value, there still may be a relatively high chance that goniothalamin will exert hepatotoxic activity, since the prediction is lacking certainty or is not completely guaranteed.

actually, the experiments were run that confirmed the following thesis:

when one sees 'duck-rabbit' as a duck, then one's gaze is differently located to the situation when one sees 'duck-rabbit' as a rabbit.

Hence, it is not true that we supply different visual interpretations of the same set of visual data; the very sets are different from the start.

I think that Wittgenstein should be blamed for the confusion.

Yes, work-from-home falls under the work-from-anywhere concept. When you work anywhere, it could be at home, a coffee shop, the library, etc. It means from anywhere.

Hi,

Incorporating a Bayesian framework and variable selection into Wasserstein Regression could be a valuable contribution to the field. These methods could enhance the robustness and clarity of the models, offering a meaningful advancement in statistical analysis, particularly for complex data.

Just share my thoughts.

Hi,

I am not sure if this is going to be helpful, but in my opinion, if you can determine the important parameters in each Nyquist of EIS tests like Rs, Rct, Cdl, mass transfer resistance and ,etc. then you can graph each parameter vs time. in addition, you can use bod and phase diagram that represent Z vs frequency which is a different representation of time.

Different fields of application rely on algorithms in different ways. In healthcare contexts, it is challenging to depend solely on algorithmic processes without supplementary contextual understanding and skill. But the black box method is useful when the consequences are small, like when we utilize algorithms to play chess. On the other hand, if you're going for a black box approach, it's a good idea to remove irrelevant or redundant data and determine which variables/features in the data are used by the algorithm making the conclusion. You should constantly be aware of the inputs you give algorithms, as models tend to gather noise and redundant information from data and are vulnerable to data perturbation which will make more trouble to get reproducible results.

Hello Aman Govind Soni

A specific pattern and blank line termination must be per the code requirement. I am attaching two links, one which refers to the Gaussian official documentation of the Gen keyword, and the other one is the blog by Dr. Joaquin Barroso, where he explains the Gen and related keywords and provides examples that you may find helpful.

Gaussian: https://gaussian.com/gen/

Blog: https://joaquinbarroso.com/2011/11/02/gen-keyword-gaussian/

Since you are an undergrad, go through the blog thoroughly. You will find more topics of your interest.

Here is a link for the Gaussian YouTube channel: https://www.youtube.com/@GaussianInc

Link for another channel: https://www.youtube.com/@dbinfotech/playlists

I hope this helps. Happy Computing.

Regards,

Dilawar

I believe that when interpreting research metrics, it is critical to consider the context, such as the field of study, research objectives, and career stage. Because no single metric can fully capture the quality and impact of research, it is best to use a combination of indicators and qualitative assessments to create a comprehensive evaluation. Furthermore, metrics should not be the sole criterion for determining the value of research because they have limitations and biases that must be considered.

poverty creates dis.ease, which is the root effect of being disenfranchised (say can_sir, and then follow the $).

yes, in fact, poverty is eradicated one soul at at time, and it comes through the epiphany of life, realizing e_fil isn't a mis.spelling, is it the chance realization we are all in this together ~ @Sages #Bullets & Snack Crackers

Olena Yarys If you are looking for patterns and relationships among those variables (species and biotopes), additional approaches like Canonical Correspondence Analysis (CCA) or regression models may be appropriate. Then you could validate and perform the sensibility analysis of your results.

People's behavior in history is based on different motives, ideologies and personal views. Although motivational factors may influence decision making, individuals and groups often act within the context of their own authority and time.

Dear Himanshu Tiwari,

Please see the paper: for image enhancement techniques.

The merits and demerits of various image enhancement techniques are discussed in detail in the above article.

Bruce Weaver - thank you so much for this, that is perfect. Once I reversed the coding for my DV it made perfect sense. Thank you again - I've been confused by this for days!

A great power I suggest needs to be able to run a regional war and maintain its infrastructure. Russia cannot do that. What Russia does, and Putin in particular, is talk a good fight rather than fight one. Russian propaganda, its effects now diminishing because understood better than at this war's beginning, spearheaded by Putin has created a mirage of efficiency, still referenced by peripheral observers rather than more sophisticated ones, and gets eulogised over in a 'wait until the Russians start fighting', which, of course, they did two years ago. Putin's repeated claim that the war is over and Ukraine finished flies against reality (although I have heard it referenced by a number of self ordained experts like Mearsheimer, an academic grifter whom many quoted and quote). Putin may be actually deliberately misinformed by those around him who wisely want to keep the old lunatic happy, that is until all Russian windows are properly fixed.

Imagine if USA had begun to fall apart as regularly Russia does when faced with resistance. Is this the reason Russia mainly attacks civilians? All these factors present the reality of the Russian state and its inability to build up efficient infrastructure due mainly to the cronyism and nepotism within the elite.

Hi Artem, as a Ghanaian and African, I will contextualize your question within the Ghanaian and African contract law context and make statements to support the answer I provide.

Ghana's Contract Act is based on English common law principles of offer, acceptance, consideration, capacity, and intention to create legal relations. Smart contracts challenge these foundations by encoding agreements directly into blockchain networks using rigid, self-executing code. Ghana's focus on objective determination of mutual assent and valid consideration struggles to adapt to this automated paradigm. Similarly, Nigeria, Kenya, and Rwanda rely on identical English contract law doctrines around consent, invalidating factors, and enforceability. None of their prevailing acts conceptualized blockchain's ability to transmit assets and trigger irrevocable transfers through decentralized execution. Regional legislation also emphasizes written documents and wet ink signatures, which smart contracts bypass through cryptographic validation. While Ghana's Contract Act values flexibility and good faith in contractual dealings, smart contracts' utter reliability on strictly defined parameters clashes with this. Ultimately, the rigid and transnational qualities of smart contracts conflict with common law African nations upholding English principles valuing documented intent, subjective mutuality, and jurisdictional authority. Adapting legacy frameworks remains a key challenge.

You can look into Online applets that present the evolution of the polarization vector associated with single Qubit quantum states on or inside of the Bloch sphere, under the effect of Unitary transformation or decoherence transformations.

On the Quantum Computing platform QISKIT, this can also be possibly visualized.

The phylogenetic tree helps place a species in its family tree, revealing similarities and differences with other species. If you need guidance on creating a tree from the ITS sequence, feel free to email me, and I'll share some helpful video links.

This seems like the last place to use unverified AI conclusions. Here we need conventional, provable fact and derivation, not a cauldron of probabilistic beliefs and correlations.

After three days, no-one has answered because it looks like an assignment or homework question.

Do some reading and also work through some exercises in which you have not done any feature engineering and where you have completed some feature engineering on the same data. After that, do please ask more specific questions.

Hello, I found a video tutorial on ImageJ for western blot analysis. Hope it helps https://www.youtube.com/watch?v=u-u3G7JhIAo

Hey there Santosh Arade! 👋 When it comes to analyzing nanoferrite samples using HRTEM (High-Resolution Transmission Electron Microscopy), understanding the ins and outs of SAED (Selected Area Electron Diffraction) patterns and d spacing is crucial. 🔍 SAED is a powerful technique that allows us to study the crystal structure of materials at the nanoscale by diffracting a beam of electrons off the sample. 💡 The resulting pattern contains information about the crystallographic planes in the material, and by analyzing these patterns, we can determine the d spacing (the distance between crystal planes) and infer information about the crystal lattice. 🤝 To analyze SAED patterns in nanoferrite samples, we typically follow these steps: 1. Spot Analysis: 🔍 Identify and analyze the spots on the SAED pattern. Each spot corresponds to a set of crystallographic planes, so we need to determine which planes these are. 2. Indexing: 🔍 Determine the Miller indices of the planes associated with each spot. This helps us identify the specific crystallographic orientation of the planes. 3. Determination of d Spacing: 🔍 Measure the distance between the spots and use this information to calculate the d spacing for each set of planes. 4. Crystal Structure Analysis: 🔍 Once we have the d spacing values, we can compare them with known crystal structures of nanoferrites. This step helps us identify the crystal phase and gain insights into the material's properties. As an engineer, I can tell you Santosh Arade that interpreting HRTEM data requires a solid understanding of crystallography and materials science. 🔬 It's important to have a good grasp of the underlying principles to accurately analyze the data and draw meaningful conclusions. So, if you Santosh Arade have any specific data or patterns you'd like to analyze, feel free to share them with me! 🤝 I'd be happy to help you Santosh Arade interpret them and gain a deeper understanding of your nanoferrite sample. 🔍

AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) MODEL 1

Autoregressive distributed lag (ARDL) model was established by Pesaran, Shin, and Smith (2001). Autoregressive distributed lag model is used where the dependent variable is a function of its own past lagged values as well as current and past values of other explanatory variables.