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Two essays on entropy.

  • by Rudolf Carnap (Author) , Abner Shimony (Editor)
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This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1977.

Two Essays on Entropy

Published under licence by IOP Publishing Ltd Physics Bulletin , Volume 30 , Number 1 Citation D A Bell 1979 Phys. Bull. 30 27 DOI 10.1088/0031-9112/30/1/023

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Rudolf Carnap (ed by A Shimony) 1978 London : University of California Press xxii + 115 pp price £9

As if entropy did not cause us enough trouble already, between the exponents of statistical mechanics, thermodynamics, physics and information theory, we have here the posthumous publication of some of the not so recent ideas of a mathematical philosopher who agreed with none of the previous work on entropy. The information theorist will look askance at the fact that these two 'essays' (they are by no means easy reading) were written between 1952 and 1954; and the definition of 'semantic information', which would not be accepted by linguists or psychologists, comes from a paper by Bar Hillel and Carnap which was presented to the 1952 symposium on applications of communication theory.

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  • Published: 05 June 2024

Superfluids

Surprising entropy transport

  • Marion Delehaye   ORCID: orcid.org/0000-0002-3809-2524 1  

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A superfluid is a macroscopic system with zero viscosity through which entropy is reversibly transported by waves. An unexpected transport phenomenon has now been observed between two superfluids, where irreversible entropy transport is enhanced by superfluidity.

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Entropy Based Defect Detection for Patterned Fabrics

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two essays on entropy

  • N. Ramya 8 &
  • T. Jayalakshmi 9  

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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This research work focuses on identifying defects in patterned textiles, which are essential for improving fabric quality and reducing financial losses. The proposed hybrid denoising algorithm, ksvd dwt, effectively removes various types of noise from fabric photographs. To enhance fault detection in patterned fabrics, a feature extraction and selection strategy are employed, using robust feature descriptors like SIFT and SURF. Particle Swarm Optimization (PSO) is applied for feature selection, which, combined with the entropy-based feature selection method, significantly improves the accuracy of defect identification in patterned fabrics. The study emphasizes the importance of two-step processes involving denoising and feature extraction to distinguish between defective and defect-free fabric images.

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Alsenan SA, Al-Turaiki IM, Hafez AM (2020) Auto-KPCA: a two-step hybrid feature extraction technique for QSAR modeling. https://doi.org/10.1109/Access.2020.3047375

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Department of Computer Science, LRG Govt Arts College for Women, Tirupur, India

Department of Computer Science, Puratchi Thalaivi Amma Govt Arts and Science College, Palladam, India

T. Jayalakshmi

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Correspondence to N. Ramya .

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Graphic Era Hill University, Dehradun, Uttarakhand, India

Vrince Vimal

Department of Computer Engineering and Informatics, University of Patras, Patra, Greece

Isidoros Perikos

University of South Bohemia, České Budějovice, Czech Republic

Amrit Mukherjee

Department of Computer Science, Universita' degli Studi di Milano, Crema, Italy

Vincenzo Piuri

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Ramya, N., Jayalakshmi, T. (2024). Entropy Based Defect Detection for Patterned Fabrics. In: Vimal, V., Perikos, I., Mukherjee, A., Piuri, V. (eds) Multi-Strategy Learning Environment. ICMSLE 2024. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-1488-9_35

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Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

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When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

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