RAR Parser |An RSS, ATOM, and RDF Parser


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The RAR Parser is a tool that lets you read RSS, ATOM, and RDF news feeds, without subscribing. It is available at http://minerazzi.com/tools/rar/feeds-parser.php

A practical example: By submitting the MIT Health Sciences and Technology news feeds url http://feeds.nytimes.com/nyt/rss/Health, the following news relevant to COVID-19 were obtained, among others. Results might change as time evolves.

MIT scientist helps build Covid-19 resource to address shortage of face mask http://news.mit.edu/2020/mit-scientist-jill-crittenden-helps-build-covid-19-resource-addressing-face-mask-shortage-0403

MIT initiates mass manufacture of disposable face shields for Covid-19 response http://news.mit.edu/2020/face-shield-ppe-manufacture-covid-19-0331

An experimental peptide could block Covid-19 http://news.mit.edu/2020/peptide-drug-block-covid-19-cells-0327

3 Questions: The risks of using 3D printing to make personal protective equipment http://news.mit.edu/2020/3q-risks-using-3d-printing-make-personal-protective-equipment-0326

Latent Simplex Position Model



This is an amazing research: Latent Simplex Position Model: High Dimensional Multi-view Clusteringwith Uncertainty Quantification, by Prof. Leo Duan from Department of Statistics University of Florida, Gainesville, FL.


Coronavirus (COVID-19) Miner



Just launched: The Coronavirus COVID-19 Miner http://www.minerazzi.com/coronavirus/

Resources will be added to the index as the coronavirus pandemic evolves. Find databases, research articles, and resources relevant to the coronavirus (COVID-19).

Build your own curated collection by extracting links from search results as well as scripts, contacts, and other type of data from same results.

You can also use this miner news channel to find news, stories, alerts, updates, and more from the CDC and other trusted sources.

The Ideal Gas Law Oracle


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Most online calculators reduce the user experience to returning results in response to some input data. As a tools developer, I know this quite well.

I’ve been asking to myself, “Why not use a different approach and build calculators that behave as oracles?” By an oracle I mean a black box that converts the input data into a user’s question (the query) and the output (the response) into the answer to the question.

To truly behave as an oracle, said tool should also take care of most of the tasks a user is expected to do. The tool should also “react” to mistakes made by a user.

With that in mind, here is my first attempt at turning an online calculator into an oracle-like tool: The Ideal Gas Law Oracle (http://www.minerazzi.com/tools/ideal-gas-law/oracle.php).

This one even takes care of significant figures and unit conversions. Chemistry teachers and students might find it useful. For instance, teachers can use the tool to add content to lecture notes, quizzes, and tests. Students can use it to double-check exercise results from homeworks and textbooks.

Weighted Averages of Correlation Coefficients


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There is a great discussion on weighted averages of correlation coefficients at https://www.researchgate.net/post/average_of_Pearson_correlation_coefficient_values

My most recent comments there are given below.

“The main reason for not averaging correlation coefficients in the arithmetic sense follows.”

“Correlations coefficients cannot be averaged in the arithmetic sense as they are not additive in the arithmetic sense. This is due to the fact that a correlation coefficient is a cosine, and cosines are not additive. This can be understood by mean-centering a paired data set and computing the cosine similarity between the vectors representing the variables involved.”

“If a paired data set violates the bivariative normality assumption (often overlooked, as Seifert correctly asserted), that worsens the picture. However, even if it doesn’t violates bivariative normality the computed average is a mathematically invalid exercise. If a meta analysis study is based on these averages the results can be easily challenged on these grounds.”

“Sample-size weighting is a good start, as Seifert asserted. We can certainly do better. We may compute self-weighted averages from one, more than one, or all of the constituent terms of a correlation coefficient, to account for different types of variability information present in the paired data, which otherwise might be ignored by simply sample-size weighting or applying Fisher Transformations. Which self-weighting scheme to use depends on the source of variability information to be considered (https://www.tandfonline.com/doi/abs/10.1080/03610926.2011.654037).”

On the Internet of Senses and Cyborg Organoids


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In a previous post on the Internet of Senses, https://irthoughts.wordpress.com/2020/01/01/internet-of-senses-miner/, the bell was rung. It is a matter of time for the human-electronic interfaces and hypersenses revolution.

Cyborg organoids are real and are here. Below is one bit of the blue print:

“Cyborg Organoids: Implantation of Nanoelectronics via Organogenesis for Tissue-Wide Electrophysiology”.

Links relevant to this reseach follow:




“Cyborg” Human Organ Grown in a Dish

Watch the beating of a cyborg heart here:


Internet of Senses Miner


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Are you ready for the Internet of Senses? Many reports set the year of 2030 as the definitive one for it, though kind of blueprints are already buzzing around. If not ready or to find relevant resources on the topic, we have just launched the Internet of Senses Miner (http://www.minerazzi.com/internet-of-senses). It is a small corpus that, as time goes by, we hope to improve.

Hypersenses are here to stay, along with new technologies, away from keyboard searching, social networks, and more into mind retrieval and cyborgsocials. That looks like the next marketing frontier. New technologies, academic research opportunities, and grants are a corner ahead. Don’t be left behind.

2018 – https://irthoughts.wordpress.com/2018/10/06/on-mind-retrieval-brainnet/
2017 – https://irthoughts.wordpress.com/2017/02/01/on-mind-retrieval-brain-computer-interfaces/
2016 – https://irthoughts.wordpress.com/2016/08/05/artificial-neurons-ibm-and-mind-retrieval/
2016 – https://irthoughts.wordpress.com/2016/04/28/a-step-closer-to-mind-retrieval/
2015 – https://irthoughts.wordpress.com/2015/06/17/say-hello-to-mind-retrieval/
2010 – https://ithinksearch.wordpress.com/2010/08/10/ideas-entrevista-a-edel-garcia/

On the Myth of d Orbitals Hybridization

With regard to the myth, taught to entire generations of chemistry students and perpetuated online in chemistry portals and by outdated tutorials, textbooks, and in classroom lectures, that elements beyond the second period can expand their octet by utilizing available d orbitals, consider this:

Since the 90’s, quantum chemists have shown this idea to be experimentally incorrect as it is energetically unfeasible to use d-orbitals for extra bonds (Kalemos & Mavridis, 2011; Durrant, 2015; Cowley, 2015; Northumbria, 2015). Indeed, the possibility of extensive d-orbital participation has been discredited more than a quarter century ago (Reed & Schleyer, 1990; Magnusson, 1990). As back then Cooper, Cunnigham, Gerratt, Karadakov, & Raimondi stated in a JACS article published by the ACS (Cooper et. al, 1994):

“Indeed, models based on d2sp3, dsp2, and dsp3 hybrid orbitals are still in widespread use among professional chemists and are described in many of the most widely used textbooks. It is tempting to speculate as to why such models continue to survive when there is so much theoretical evidence which does not support them.”

See references below. Additional references, links, and topic discussion are given at http://www.minerazzi.com/tools/bond-order/calculator.php


Cooper, D. L., Cunnigham, T. P., Gerratt, J., Karadakov, P. B., & Raimondi, M. (1994). Chemical Bonding to Hypercoordinate Second-Row Atoms: d Orbital Participation versus Democracy. J. Am. Chem. Soc. Vol. 116, No. 10, pp 4414-4426. doi: 10.1021/ja00089a033.

Kalemos A. & Mavridis, A. (2011). Myths and Reality of Hypervalent Molecules. The Electronic Structure of FClOx, x = 1-3, Cl3PO, Cl3PCH2, Cl3CClO, and C(ClO)4. J. Phys. Chem., 115, (11), pp 2378-2384.

Magnusson, E. (1990). Hypercoordinate molecules of second-row elements: d functions or d orbitals?. J. Am. Chem. Soc., Vol. 112, No. 22, pp. 7940-7951.

Reed, A. E. & Schleyer, P. V. R. (1990). Chemical bonding in hypervalent molecules. The dominance of ionic bonding and negative hyperconjugation over d-orbital participation. J. Am. Chem. Soc., 112, pp. 1434-1445.

Semantic Similarity of Healthcare Data


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In “Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching“, published in the International Journal of Medical Informatics 132:104002 DOI: 10.1016/j.ijmedinf.2019.104002, Kiourtis et al. (2019), address the following objective, and quote:

“Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields.”

The authors propose an elegant mechanism “that promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure.”

These are great news! Very cool and practical research that can solve so many problems in the healthcare informatics field.

Many thanks for citing our cosine similarity tutorial as reference 52.

My only reserve with the paper is that early in the article they suggest adding and averaging similarities, which is a mathematically invalid exercise. Distances are arithmetically additive, but similarities (of the same or different kind or source) are not. We can make similarities additive and average them, but not in the arithmetic sense. Other than that, they work is a noble effort.