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Andreas Schleicher, SDirector and Special Advisor on Education Policy to the OECD's Secretary-General.
This image is not from a statistically significant sample size--although it is from a real analysis I did (aka, the figures are not made up).
After reading any comments before yours, write 1 comment about this photograph from one of the two areas below:
Composition & Framing - this is to do with how the photographer has arranged the various elements within the photograph including their viewpoint, angle and distance. It also takes into account perspective, use of space and line.
Message & Meaning - does the image have message for the viewer? Do you see a meaning behind the work? What do you think the purpose of the image is and how does it make you feel?
There are often many possible reasons why engineering components fail in service, and the cause and mechanisms of failure can only be determined with the right combination of analytical equipment and experienced engineers.
TWI has both the state-of-the-art facilities to carry out complex failure investigations and the critical mass to interpret the results and offer the solutions to avoid further failures in the future.
For more information www.twi.co.uk/services/failure-investigation/
If you wish to use this image each use should be accompanied by the credit line and notice, "Courtesy of TWI Ltd".
Asbestos in non-friable form, such as asbestos-cement shingles, roofing, and board, is generally considered safe.
If you can, please spare the time to visit my current exhibition of drawings here on Flickr. Simply follow the link below. Thank you very much. Julian.
www.flickr.com/groups/globalworldawards/discuss/721576241...
IFPRI Research Fellow, Danielle Resnick introduces the speakers at the even, Donor Approaches to Political Economy Analysis.
IFPRI hosted a policy seminar titled “Donor Approaches to Political Economy Analysis” on February 5, 2015. For more information, please visit: www.ifpri.org/event/donor-approaches-political-economy-an...
©IFPRI/Xinyuan Shang
Statistics rarely give a simple view's Yes/No type answer to the question under analysis. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value).
In this graph the black line is probability distribution for the test statistic, the critical region is the set of values to the right of the observed data point (observed value of the test statistic) and the p-value is represented by the green area.
The standard approach is to test a null hypothesis against an alternative hypothesis. A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator doesn't belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false.
Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably.
While in principle the acceptable level of statistical significance may be subject to debate, the p-value is the smallest significance level that allows the test to reject the null hypothesis. This is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the p-value, the lower the probability of committing type I error.
Some problems are usually associated with this framework (See criticism of hypothesis testing):
A difference that is highly statistically significant can still be of no practical significance, but it is possible to properly formulate tests to account for this. One response involves going beyond reporting only the significance level to include the p-value when reporting whether a hypothesis is rejected or accepted. The p-value, however, does not indicate the size or importance of the observed effect and can also seem to exaggerate the importance of minor differences in large studies. A better and increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as those of hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.
Fallacy of the transposed conditional, aka prosecutor's fallacy: criticisms arise because the hypothesis testing approach forces one hypothesis (the null hypothesis) to be favored, since what is being evaluated is probability of the observed result given the null hypothesis and not probability of the null hypothesis given the observed result. An alternative to this approach is offered by Bayesian inference, although it requires establishing a prior probability.
Rejecting the null hypothesis does not automatically prove the alternative hypothesis.
As everything in inferential statistics it relies on sample size, and therefore under fat tails p-values may be seriously mis-computed.
Working from a null hypothesis, two basic forms of error are recognized:
Type I errors where the null hypothesis is falsely rejected giving a "false positive".
Type II errors where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative".
Standard deviation refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean, while Standard error refers to an estimate of difference between sample mean and population mean.
A statistical error is the amount by which an observation differs from its expected value, a residual is the amount an observation differs from the value the estimator of the expected value assumes on a given sample (also called prediction).
Mean squared error is used for obtaining efficient estimators, a widely used class of estimators. Root mean square error is simply the square root of mean squared error.
Misuse of statistics can produce subtle, but serious errors in description and interpretation—subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors. For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics.
Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data—which measures the extent to which a trend could be caused by random variation in the sample—may or may not agree with an intuitive sense of its significance. The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy.
There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.[26] A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics[26] outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).[27]
Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.[28] Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias.[29] Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs.[28] Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented.[29] To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole.[30] According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism.
A least squares fit: in red the points to be fitted, in blue the fitted line.
Many statistical methods seek to minimize the residual sum of squares, and these are called "methods of least squares" in contrast to Least absolute deviations. The latter gives equal weight to small and big errors, while the former gives more weight to large errors. Residual sum of squares is also differentiable, which provides a handy property for doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares. Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise. Both linear regression and non-linear regression are addressed in polynomial least squares, which also describes the variance in a prediction of the dependent variable (y axis) as a function of the independent variable (x axis) and the deviations (errors, noise, disturbances) from the estimated (fitted) curve.
Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal." Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal".
Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types (like income), while frequency and percentage are more useful in terms of describing categorical data (like race).
When a census is not feasible, a chosen subset of the population called a sample is studied. Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are also due to uncertainty. To still draw meaningful conclusions about the entire population, inferential statistics is needed. It uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis). Inference can extend to forecasting, prediction and estimation of unobserved values either in or associated with the population being studied; it can include extrapolation and interpolation of time series or spatial data, and can also include data mining.When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting through statistical models. To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. A major problem lies in determining the extent that the sample chosen is actually representative. Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures. There are also methods of experimental design for experiments that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population. Sampling theory is part of the mathematical discipline of probability theory. Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method. The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction—inductively inferring from samples to the parameters of a larger or total population.
The basic steps of a statistical experiment are:
Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability. Consideration of the selection of experimental subjects and the ethics of research is necessary. Statisticians recommend that experiments compare (at least) one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.
Design of experiments, using blocking to reduce the influence of confounding variables, and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage, the experimenters and statisticians write the experimental protocol that will guide the performance of the experiment and which specifies the primary analysis of the experimental data.
Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol.
Further examining the data set in secondary analyses, to suggest new hypotheses for future study.
Documenting and presenting the results of the study.
Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity. It turned out that productivity indeed improved (under the experimental conditions). However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness. The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself. Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.
Observational study
An example of an observational study is one that explores the association between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a cohort study, and then look for the number of cases of lung cancer in each group.[15] A case-control study is another type of observational study in which people with and without the outcome of interest (e.g. lung cancer) are invited to participate and their exposure histories are collected.Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.
Other categorizations have been proposed. For example, Mosteller and Tukey (1977)[distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990)[described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman (1998), van den Berg (1991). The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer".Consider independent identically distributed (IID) random variables with a given probability distribution: standard statistical inference and estimation theory defines a random sample as the random vector given by the column vector of these IID variables. The population being examined is described by a probability distribution that may have unknown parameters.
A statistic is a random variable that is a function of the random sample, but not a function of unknown parameters. The probability distribution of the statistic, though, may have unknown parameters.
Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. Commonly used estimators include sample mean, unbiased sample variance and sample covariance.
A random variable that is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's t-value.
Between two estimators of a given parameter, the one with lower mean squared error is said to be more efficient. Furthermore, an estimator is said to be unbiased if its expected value is equal to the true value of the unknown parameter being estimated, and asymptotically unbiased if its expected value converges at the limit to the true value of such parameter.
Other desirable properties for estimators include: UMVUE estimators that have the lowest variance for all possible values of the parameter to be estimated (this is usually an easier property to verify than efficiency) and consistent estimators which converges in probability to the true value of such parameter.
This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed: the method of moments, the maximum likelihood method, the least squares method and the more recent method of estimating equations.
before looking at others with judgement you must first judge yourself.
Big thanks to MONDO RODRIGUEZ for helping me out with this shot !
enjoy :]
I'm guessing about the authors of these three pieces of graffiti written on a ventilation shaft of the MTR at Kowloon Tong station: there were three teenage, female, native English speakers, who shared the same felt tip pen while waiting for a bus. One or more was smoking a cigarette (because it's hidden from street view). The thoughts are interesting/sensitive, but they didn't finish one of them because their bus had arrived. I note the same mistake of a dotted "i" while the rest of the letters are capitalized. However, the mistake was probably made by different authors suggesting they are in the same class at school, and were taught the same mistake/were not corrected. The writing is delicate, suggesting the authors were female, but the "quiet hope" sentence was written with a heavier hand, suggesting either confidence or possibly a male author. There is some inconsistency about the dots at the end of each sentence, with most placing three, one four and one five.
KUANTAN, Malaysia (June 6, 2023) - Malaysian and United States Armed Forces service members complete a mission analysis during the staff exercise of Exercise Bersama Warrior 23 at the Joint Warfighting Center on Malaysian Armed Forces Headquarters Base, Kuantan, Padang, Malaysia, June 5, 2023. Bersama Warrior is an annual bilateral joint exercise sponsored by U.S. Indo-Pacific Command and hosted by the Malaysian Armed Forces. This year marks the ninth iteration of the exercise, enhancing the U.S. and Malaysian defense readiness and improving interoperability. (U.S. Marine Corps photo by Gunnery Sgt. Alexandria Blanche) 230606-M-QS647-1061
** Interested in following U.S. Indo-Pacific Command? Engage and connect with us at www.facebook.com/indopacom | twitter.com/INDOPACOM | www.instagram.com/indopacom | www.flickr.com/photos/us-pacific-command; | www.youtube.com/user/USPacificCommand | www.pacom.mil/ **
Analysis of iron in water from Pacific surface region by the Flow Injection analysis in the Chemistry Lab.
A finely-constructed small glass Schrotter reaction vessel [1890s] in front of the laboratory procedure for analysis of carbonic acid in limestone, from Outlines of Quantitative Analysis by A.Humboldt Sexton 1903 .
A textbook for laboratory use and for students undertaking degrees, diplomas or advanced certificates in Science or Chemistry.
Published by Charles Griffin& Co., London. Brown cloth boards, 178 pages 20cm x 13.5cm.
Carbonic acid is a weak acid formed when carbon dioxide gas is dissolved in water with formula H2CO3.
race analysis: 100% Chinese !!
gender: Female
intelligence: 5.6
risk: 6.2
ambition: 6.2
gay factor: 1.3
honor: 5.8
politeness: 3.4
income: 6.0
sociability: 5.0
promiscuity: 5.9
archetype: Beta Boss
Personality Profile:
You are a power driven risk-taker. Your desire to obtain power quickly propels you to take large risks, both financially and socially. You are so effective and confident when it comes to making short term decisions at crucial moments that it almost becomes contagious. Your high confidence level leads you to focus on the rewards of your decisions, rather than over-calculating. Your directness may make others think you are rude. You know what you want and you want it now.
Some people consider you a political mastermind as you are able to take advantage of peoples strengths and weaknesses so effectively, that it enables you to undertake and achieve monumental tasks. Others tend to characterize you as confrontational, but you take this as a compliment, and in turn you characterize these people as push-overs. You would rather stand up for yourself and those you care about and face confrontation, than avoid confrontation and give in. You act promptly when it comes to making big decisions and thus others look to you to carry the burden of responsibility.
Your view of other types
You consider Academic Types to be a valuable resource of advice. You constantly interact with them to make better decisions and you respect the opinion of certain Academics. Artist types do not concern you for you look down on them, you believe they waste too much time. You like Charmers but you dont always trust them or bring them too close. You can relate to the Gambler types' risk taking personality but for the most part you do not trust them. You may have some things in common with Blue collar types, but for the most part you only interact with Blue collar and White collar types during the course of business. You feel no pity for Drifters. You believe that they are lazy.
Other types view of you
Academic Types view you as potential business clients and as great resources to expand their contacts. Therefore they treat you with respect. Artist types see you as egotistical and selfish and they may only interact with you during their employment. Blue collar and White collar types sometimes disagree with you, but they respect you for the most part. They may even be envious of your lifestyle. Charmer Types tend to like you and try to get close to you because you possess the intimidating factor and confrontational attitude that they lack. Gambler types regard your type as their main competition to be on top. Drifter Types tend to feel that you types are a bad tempered bully.
Alpha Boss:
- Most ambitious
- Makes the smartest decisions
- Occupations examples: Military general, Dictator, Organized crime boss, CEO of large company, Large Business owner (management/people oriented business operation)
Beta Boss
- More ambitious than Theta but less than Alpha
- Makes smarter decisions than Theta but less than Alpha
- Occupation examples: High/medium ranking military officer, Small/medium business owner (management/people oriented business operation), Gangster
Theta Boss
- Fears and distractions may affect your ambition
- Makes less wise decisions than Alpha and Beta
- Occupation examples: Manager, Low level criminal, entertainer
Analysis of iron in water from Pacific surface region by the Flow Injection analysis in the Chemistry Lab.
Diana F+ (Mr. Pink), 120 Kodak Portra 800. 22 Feb 2012.
Fox Commentators talking about the Pacers and Bobcats.
a fat roll caused the light leaks.
Analysis of iron in water from Pacific surface region by the Flow Injection analysis in the Chemistry Lab.
35 research participants and 1.5 hour interview for each leads to a lot of stuff to analyze. We've really gone crazy with the stickies since this photo was taken last week. I'll post a version of the current space as soon as I stitch together the photos I just took.
Materials are the building blocks of all functional structures. TWI's expertise lies in understanding the complex interactions that take place when basic materials are transformed into functional shapes and joined together in different working environments.
Choosing materials that are cost-effective and also offer predictable in-service performance is essential to ensure reliable operation and avoid the potentially catastrophic consequences of in-service failure.
TWI has over 60 years' experience in material characterisation, testing, performance evaluation and suitability studies. It can provide assistance at any stage of a project - from feasibility studies, to design, implementation, and proof of performance.
In addition, TWI can advise on choice of materials and fabrication routes and validate new developments. In the unfortunate situation of a failure, TWI's experts can carry out a failure investigation and propose the necessary recommendations to avoid failures in the future.
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This is a mental health practitioner's perspective on hoarding that confirms much of what I've observed with my clients. Some common threads: perfectionism plays a large part in hoarders getting behind with what they do with their stuff thus prompting them to become overwhelmed and then giving up on any sort of housecleaning. Hoarders have an appreciation for things beyond their obvious uses and often are very creative, but seem to lack the organizing gene.
Some interesting insights on the hoarding brain, described as having too many branches on the tree. Meaning that there are multiple paths the mind is prompted to navigate when processing which also leads to overwhelm.
I attended a day long workshop with one of the writers and the take away message from that day was that hoarders have a perception problem. They do not see that their stuff is making their home a dumpster and that they are not actually rescuing anything by picking up one more thing from the "Free" pile on the street. My experience with one client is that she has an inability to know what is important because everything is important.
Some poignant insight about causes of hoarding include absence of warmth, acceptance and support in childhood leading the child to form attachments to things and endow those things with human emotions. Thus they can't throw an item away because there would be no one to look after it.
Little is discussed about treatment in the book. The authors have written other books to help practitioners in the field. This is largely an anecdotal book and the stories of hoarders do offer a lot of insight since you get the whole history.
I have a couple of bones to pick with the book. The authors mention the National Study Group of Chronic Disorganization which was started by one of my organizing colleagues. The authors believe the term "chronic disorganization" is a euphemism for hoarders. I think that's unfair; a case of a hammer seeing everything as a nail. Mental health practitioners are guilty of seeing every thing in pathological terms. Everyone who has clutter is not automatically a hoarder. Hoarders have an attachment to their stuff that goes beyond the utility of the item. Plenty of people with clutter would be happy to let it go once they found the things they really needed.
The authors also don't think that the members of the NSGCD go far enough to actually help hoarders. I think we're changing that especially since she told us how. Cognitive therapy is the prescribed mental health treatment for hoarders. This just means that someone sits with the hoarder and asks questions that would lead them through to a rational conclusion about why they are keeping an item.
In addition I like to keep the hoarder working on sorting their stuff for so long, they are sick of it and they vow never to let it get this bad again. Then I coach them on setting limits on the incoming stuff. I also let them talk about the stuff or if they prefer, they can ask me to talk about something—anything if they want to take their minds off it. I feel this raises their self-esteem to be able to relate to me on an intellectual topic rather than wallow in the shame of their mess. Higher self-esteem often results in a higher level of energy for the task at hand. This is the Amanda theory that will either make or break me in this work.
Cognitive therapy is all about getting the client to look at their feelings while sorting through stuff. Then helping them to get past their sticking points. The theory goes that when the emotion is worked through the therapist can help diminish it, but I had a therapist tell me yesterday that this isn't necessarily so. People just like to go on about their feelings about everything. Some therapists create a crisis around the stuff on purpose to light a fire under them. This is too messy for me. Therapists like emotional messes. I like to bypass all the guilt, blame, grief, etc., etc., in favor of just getting the job done. I have had clients tell me they appreciate this quiet efficiency. Others like the drama and I am there for that too, but I don't go looking for it.
A new affordable field-based phenotyping platform combining cutting edge aeronautics technology and image analysis was developed through collaboration between researchers from the University of Barcelona, Spain; Crop Breeding Institute, Zimbabwe; Instituto Nacional de Innovación Agraria, Peru; AirElectronics; and Sustainable Agricultural Institute of the High Research Council, Spain; the project was funded by MAIZE CRP.
The new platform uses ‘Sky Walker,’ an unmanned aerial vehicle with thermal and spectral cameras mounted under each wing. Its flight path and image capturing are controlled via a laptop using Google Earth images.
Photo credit: J.L. Araus, University of Barcelona/CIMMYT.