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Institution

Instituto Politécnico Nacional

EducationMexico City, Mexico
About: Instituto Politécnico Nacional is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Population & Control theory. The organization has 43351 authors who have published 63315 publications receiving 938532 citations. The organization is also known as: Instituto Politécnico Nacional & Instituto Politecnico Nacional.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors used gravimetric and electrochemical techniques to investigate the effect of decylamides of α-amino acids derivatives on steel surface protection in hydrochloric acid.

299 citations

Journal ArticleDOI
TL;DR: In this article, the authors address two exploratory research questions: how active in network development and technology transfer are university spin-offs during their early years to overcome initial disadvantages? And is there any relationship between early networks development and knowledge creation and knowledge transfer in university spinoffs?

298 citations

Journal ArticleDOI
TL;DR: In this article, an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle is described, which is based on the measurement of the time of flight of a ultrasonic pulse which is reflected by the ground.
Abstract: This paper describes an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle. The sensor is based on the measurement of the time of flight of an ultrasonic pulse, which is reflected by the ground. A constrained optimization technique is employed to obtain reflected pulses that are easily detectable by means of a threshold comparator. Such a technique, which takes the frequency response of the ultrasonic transducers into account, allows a sub-wavelength detection to be obtained. Experimental tests, performed with a 40 kHz piezoelectric-transducer based sensor, showed a standard uncertainty of 1 mm at rest or at low speeds; the sensor still works at speeds of up to 30 m/s, although at higher uncertainty. The sensor is composed of only low cost components, thus being apt for first car equipment in many cases, and is able to self-adapt to different conditions in order to give the best results.

298 citations

Journal ArticleDOI
TL;DR: Recent developments in the area of third‐generation ionic liquids that are being used as APIs, with a particular focus on efforts to overcome current hurdles encountered by APIs, are summarized.
Abstract: Ionic liquids (ILs) are ionic compounds that possess a melting temperature below 100 °C. Their physical and chemical properties are attractive for various applications. Several organic materials that are now classified as ionic liquids were described as far back as the mid-19th century. The search for new and different ILs has led to the progressive development and application of three generations of ILs: 1) The focus of the first generation was mainly on their unique intrinsic physical and chemical properties, such as density, viscosity, conductivity, solubility, and high thermal and chemical stability. 2) The second generation of ILs offered the potential to tune some of these physical and chemical properties, allowing the formation of "task-specific ionic liquids" which can have application as lubricants, energetic materials (in the case of selective separation and extraction processes), and as more environmentally friendly (greener) reaction solvents, among others. 3) The third and most recent generation of ILs involve active pharmaceutical ingredients (API), which are being used to produce ILs with biological activity. Herein we summarize recent developments in the area of third-generation ionic liquids that are being used as APIs, with a particular focus on efforts to overcome current hurdles encountered by APIs. We also offer some innovative solutions in new medical treatment and delivery options.

297 citations

Journal ArticleDOI
TL;DR: The proposed similarity measure soft similarity is a generalize of the well-known cosine similarity measure in VSM by introducing what it is called “soft cosine measure” and various formulas for exact or approximate calculation of the softcosine measure are proposed.
Abstract: We show how to consider similarity between features for calculation of similarity of objects in the Vector Space Model (VSM) for machine learning algorithms and other classes of methods that involve similarity between objects. Unlike LSA, we assume that similarity between features is known (say, from a synonym dictionary) and does not need to be learned from the data.We call the proposed similarity measure soft similarity. Similarity between features is common, for example, in natural language processing: words, n-grams, or syntactic n-grams can be somewhat different (which makes them different features) but still have much in common: for example, words “play” and “game” are different but related. When there is no similarity between features then our soft similarity measure is equal to the standard similarity. For this, we generalize the well-known cosine similarity measure in VSM by introducing what we call “soft cosine measure”. We propose various formulas for exact or approximate calculation of the soft cosine measure. For example, in one of them we consider for VSM a new feature space consisting of pairs of the original features weighted by their similarity. Again, for features that bear no similarity to each other, our formulas reduce to the standard cosine measure. Our experiments show that our soft cosine measure provides better performance in our case study: entrance exams question answering task at CLEF. In these experiments, we use syntactic n-grams as features and Levenshtein distance as the similarity between n-grams, measured either in characters or in elements of n-grams.

297 citations


Authors

Showing all 43548 results

NameH-indexPapersCitations
Giacomo Bruno1581687124368
Giuseppe Mancia1451369139692
Giorgio Maggi135132390270
Salvatore Nuzzo133153391600
Giuseppe Iaselli133151491558
Marcello Abbrescia132140084486
Louis Antonelli132108983916
Donato Creanza132145289206
Alexis Pompili131143786312
Gabriella Pugliese131130988714
Giovanna Selvaggi131115983274
Heriberto Castilla-Valdez130165993912
Ricardo Lopez-Fernandez129121381575
Cesare Calabria128109576784
Paolo Vitulo128112079498
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202362
2022367
20214,942
20205,245
20194,787
20184,485