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Journal articles on the topic 'Text compression'

To see the other types of publications on this topic, follow the link: Text compression.

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Divakaran, Sajilal, Biji C. L., Anjali C, and Achuthsankar S. Nair. "MALAYALAM TEXT COMPRESSION." International Journal of Information Systems and Engineering 1, no. 1 (April 30, 2013): 1–11. http://dx.doi.org/10.24924/ijise/2013.04/v1.iss1/1.11.

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2

Davison, Andrew. "Vague text compression." ACM SIGACT News 24, no. 1 (January 15, 1993): 68–74. http://dx.doi.org/10.1145/152992.153009.

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3

Bol'shakov, I. A., and A. V. Smirnov. "Text compression methods." Journal of Soviet Mathematics 56, no. 1 (August 1991): 2249–62. http://dx.doi.org/10.1007/bf01099202.

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4

Murugesan, G., and Rosario Gilmary. "Compression of text files using genomic code compression algorithm." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.31.13399.

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Text files utilize substantial amount of memory or disk space. Transmission of these files across a network depends upon a considerable amount of bandwidth. Compression procedures are explicitly advantageous in telecommunications and information technology because it facilitate devices to disseminate or reserve the equivalent amount of data in fewer bits. Text compression techniques section, the English passage by observing the patters and provide alternative symbols for larger patters of text. To diminish the depository of copious information and data storage expenditure, compression algorithms were used. Compression of significant and massive cluster of information can head to the improvement in retrieval time. Novel lossless compression algorithms have been introduced for better compression ratio. In this work, the various existing compression mechanisms that are particular for compressing the text files and Deoxyribonucleic acid (DNA) sequence files are analyzed. The performance is correlated in terms of compression ratio, time taken to compress/decompress the sequence and file size. In this proposed work, the input file is converted to DNA format and then DNA compression procedure is applied.

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5

P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (March 2, 2023): 36–42. http://dx.doi.org/10.46610/joits.2022.v09i01.005.

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The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usage has not only raised the utility but also the storage of text, audio and video files. These multimedia files demand more storage space as compared to traditional files. This has given rise to the requirement for an efficient compression algorithm. There is a considerable improvement in the computing performance of the machines due to the advent of the multi-core processor. However, this multi-core architecture is not used by compression algorithms. This paper shows the implementation of lossless compression algorithms namely the Lempel-Ziv-Markov Algorithm, BZip2 and ZLIB algorithms using the concept of multithreading. The results obtained prove that the ZLIB algorithm proves to be more efficient in terms of the time taken to compress and decompress the text. The comparison is done for both compressions without multithreading and compression with multi-threading.

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6

Stecuła, Beniamin, Kinga Stecuła, and Adrian Kapczyński. "Compression of Text in Selected Languages—Efficiency, Volume, and Time Comparison." Sensors 22, no. 17 (August 25, 2022): 6393. http://dx.doi.org/10.3390/s22176393.

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The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, authors conducted an analysis on the comparison of compression time and the volume of the text before and after compression. The results of the study confirmed the hypothesis, based on which the planned language, Esperanto, gives better text compression results than the natural languages represented by Polish and English. The confirmation by scientific methods that Esperanto is more optimal for text compression is the scientific added value of the paper.

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7

Nguyen, Vu H., Hien T. Nguyen, Hieu N. Duong, and Vaclav Snasel. "n-Gram-Based Text Compression." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9483646.

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We propose an efficient method for compressing Vietnamese text usingn-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it inton-grams and then encodes them based onn-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Eachn-gram is encoded by two to four bytes accordingly based on its correspondingn-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to buildn-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.

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8

Omer. "Arabic Short Text Compression." Journal of Computer Science 6, no. 1 (January 1, 2010): 24–28. http://dx.doi.org/10.3844/jcssp.2010.24.28.

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9

KAUFMAN, YAIR, and SHMUEL T. KLEIN. "SEMI-LOSSLESS TEXT COMPRESSION." International Journal of Foundations of Computer Science 16, no. 06 (December 2005): 1167–78. http://dx.doi.org/10.1142/s012905410500373x.

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A new notion, that of semi-lossless text compression, is introduced, and its applicability in various settings is investigated. First results suggest that it might be hard to exploit the additional redundancy of English texts, but the new methods could be useful in applications where the correct spelling is not important, such as in short emails, and the new notion raises some interesting research problems in several different areas of Computer Science.

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10

man, Suher, and Andysah Putera Utama Siahaan. "Huffman Text Compression Technique." International Journal of Computer Science and Engineering 3, no. 8 (August 25, 2016): 103–8. http://dx.doi.org/10.14445/23488387/ijcse-v3i8p124.

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11

Schmidhuber, J., and S. Heil. "Sequential neural text compression." IEEE Transactions on Neural Networks 7, no. 1 (1996): 142–46. http://dx.doi.org/10.1109/72.478398.

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12

Bell, Timothy, Ian H. Witten, and John G. Cleary. "Modeling for text compression." ACM Computing Surveys 21, no. 4 (December 1989): 557–91. http://dx.doi.org/10.1145/76894.76896.

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13

Brisaboa, Nieves, Antonio Fariña, Gonzalo Navarro, and José Paramá. "Dynamic lightweight text compression." ACM Transactions on Information Systems 28, no. 3 (June 2010): 1–32. http://dx.doi.org/10.1145/1777432.1777433.

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14

Moffat, Alistair. "Word-based text compression." Software: Practice and Experience 19, no. 2 (February 1989): 185–98. http://dx.doi.org/10.1002/spe.4380190207.

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15

Sessa, Maria I., and Andrea Pierro. "A text compression algorithm." Information Systems 15, no. 3 (January 1990): 391. http://dx.doi.org/10.1016/0306-4379(90)90009-e.

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16

Suneetha, D., D. Rathna Kishore, and P. Narendra Babu. "A Compression Algorithm for DNA Palindrome Compression Technique." ITM Web of Conferences 32 (2020): 03006. http://dx.doi.org/10.1051/itmconf/20203203006.

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Data Compression in Cryptography is one of the interesting research topic. The compression process reduces the amount of transferring data as well as storage space which in turn effects the usage of bandwidth. Further, when a plain text is converted to cipher text, the length of the cipher text becomes large. This adds up to tremendous information storing. It is extremely important to address the storage capacity issue along with the security issues of exponentially developing information. This problem can be resolved by compressing the ciphertext based on a some compression algorithm. In this proposed work used the compression technique called palindrome compression technique. The compression ratio of the proposed method is better than the standard method for both colored and gray scaled images. An experimental result for the proposed methods is better than existing methods for different types of image.

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17

Subada, Mhd Ali. "Comparisonal Analysis Of Even-Rodeh Algorithm Code And Fibonacci Code Algorithm For Text File Compression." Journal Basic Science and Technology 11, no. 1 (February 28, 2022): 1–7. http://dx.doi.org/10.35335/jbst.v11i1.1765.

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The growing requirement towards bigger storage space is the main cause of new compression techniques being developed. By doing compression, big chunks of data will be lowered in size to save storage space. In this research we will be using the Even-Rodeh Code and Fibonacci Code Algorithm, the performance will be benchmarked with bitrate, compression ratio, space saving, compression and decompression time in text files. Compression is done by reading strings on text files, and then the Even-Rodeh Code and Fibonacci Code Algorithm makes a string code and performs compression. The compression results are *.erc and *.fib files containing character information and string bit which can be decompressed. The decompression result is the original text file which can be saved using the extensions *.doc, *.docx, *.pdf. In this system test the sample used are strings that contains one type of character (Homogen String) and strings that contain several types of character (heterogeneous string) saved in text files *.doc,*.docx, *.pdf. In homogeneous string compressions we can observe that the Fibonacci Code is more efficient in compression ratio, averaging at 0.25 and faster decompression time at 0.011 millisecond average compared to the Even-Rodeh Code Algorithm. In heterogeneous string compression the Fibonacci Code performs better than the Even-Rodeh Code on an average compression ratio of 0.65 and a shorter average decompression time of 0.293 milliseconds.

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18

S. J, Rexline, Robert L, and Trujilla Lobo.F. "Dictionary Based Text Filter for Lossless Text Compression." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 143–49. http://dx.doi.org/10.14445/22312803/ijctt-v49p122.

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19

Amir Sultan, Duha. "Better Adaptive Text Compression Scheme." JOURNAL OF EDUCATION AND SCIENCE 27, no. 2 (March 1, 2018): 48–57. http://dx.doi.org/10.33899/edusj.2018.147575.

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20

Sailunaz, Kashfia, Mohammed Rokibul Alam Kotwal, and Mohammad Nurul Huda. "Data Compression Considering Text Files." International Journal of Computer Applications 90, no. 11 (March 26, 2014): 27–32. http://dx.doi.org/10.5120/15765-4456.

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21

S. Bhadade, Umesh, and A. I. Trivedi. "Lossless Text Compression using Dictionaries." International Journal of Computer Applications 13, no. 8 (January 12, 2011): 27–34. http://dx.doi.org/10.5120/1799-1767.

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22

Sangwan, Nigam. "Text Encryption with Huffman Compression." International Journal of Computer Applications 54, no. 6 (September 25, 2012): 29–32. http://dx.doi.org/10.5120/8572-2307.

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23

Bell, T. "Better OPM/L Text Compression." IEEE Transactions on Communications 34, no. 12 (December 1986): 1176–82. http://dx.doi.org/10.1109/tcom.1986.1096485.

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24

Brisaboa, Nieves R., Antonio Fariña, Gonzalo Navarro, and José R. Paramá. "Lightweight natural language text compression." Information Retrieval 10, no. 1 (September 9, 2006): 1–33. http://dx.doi.org/10.1007/s10791-006-9001-9.

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25

Gillman, Peter. "Data handling and text compression." Journal of Information Science 18, no. 2 (April 1992): 105–10. http://dx.doi.org/10.1177/016555159201800204.

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26

Raita, Timo, and Jukka Teuhola. "Predictive encoding in text compression." Information Processing & Management 25, no. 2 (January 1989): 151–60. http://dx.doi.org/10.1016/0306-4573(89)90003-4.

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27

Al-Fedaghi, Sabah S., and Humoud B. Al-Sadoun. "Morphological compression of Arabic text." Information Processing & Management 26, no. 2 (January 1990): 303–16. http://dx.doi.org/10.1016/0306-4573(90)90033-x.

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28

Platoš, Jan, Václav Snášel, and Eyas El-Qawasmeh. "Compression of small text files." Advanced Engineering Informatics 22, no. 3 (July 2008): 410–17. http://dx.doi.org/10.1016/j.aei.2008.05.001.

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29

Vines, Phil, and Justin Zobel. "Compression techniques for Chinese text." Software: Practice and Experience 28, no. 12 (October 1998): 1299–314. http://dx.doi.org/10.1002/(sici)1097-024x(1998100)28:12<1299::aid-spe203>3.0.co;2-e.

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30

Nguyen, K. "Comment: Text compression as rule-based pattern recognition; Text compression using rule based encoder." Electronics Letters 31, no. 9 (1995): 701. http://dx.doi.org/10.1049/el:19950510.

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31

Elmegreen, Bruce, Binquan Luan, Rodrigo Neumann Barros Ferreira, and Breanndan O’Conchuir. "A method for near-perfect gas separation in two interconnected streams." Journal of Applied Physics 133, no. 1 (January 7, 2023): 015001. http://dx.doi.org/10.1063/5.0127983.

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Gas separation such as CO[Formula: see text] from N[Formula: see text] in flue gas is an important step in reducing greenhouse gas emission. We discuss a separation method that moves mixed gas between two tracks at different pressures with ever-increasing purity at each stage along the tracks. Designs for adsorbents driven by volumetric pumps and membranes driven by a pressure drop are illustrated. For selectivity exceeding 10 in a three-stage system, the purity of the CO[Formula: see text] output is 97.8% with 99.75% recovered. For a six-stage example, the purity of CO[Formula: see text] is 99.996% and 99.9996% is recovered. The cost of compression is estimated from the number of times the two components have to be pressurized as they move between the tracks. For large selectivity, the effective number of times for CO[Formula: see text] is 2 for the adsorbent case and 3 for the membranes in the three-stage design; these numbers are 5 and 6 in the six-stage design. As a result, the compressional energy requirement per input mole is [Formula: see text] in the isothermal case for compression factor [Formula: see text], effective number of re-compressions [Formula: see text], and input CO[Formula: see text] fraction [Formula: see text]. This compressional energy exceeds the minimum energy from entropy by the factor [Formula: see text] if [Formula: see text] for efficient membrane use.

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32

Yoshii, Yuichi, and Naoyuki Ochiai. "PHYSIOLOGICAL RESPONSE OF PERIPHERAL NERVE TO REPETITIVE COMPRESSION: A RABBIT EXPERIMENTAL MODEL." Journal of Musculoskeletal Research 24, no. 02 (May 6, 2021): 2150008. http://dx.doi.org/10.1142/s0218957721500081.

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Objective: The objective of this study is to clarify the differences in physiological responses following the different modality of repetitive compressions versus continuous compression (CC) of the peripheral nerve. Methods: Thirty-two rabbits were used. Under anesthesia, four conditions of compression; CC, low-frequency release compression (LFR, one second release time every 30 s), high-frequency release compression (HFR, one second release time every 10 s), and high-frequency compression (HFC, one second compression time every 10 s) were each applied with 80[Formula: see text]mmHg of pressure for 90[Formula: see text]min to the sciatic nerve. Compound nerve action potential (CNAP) and nerve blood flow were evaluated from the beginning of compression to 90[Formula: see text]min after compression release. Endoneurial microvascular permeability was evaluated histologically. Results: During compression, the nerve blood flow and the amplitude of CNAP decreased for all four groups. There were significant delays in the recovery of the amplitude and nerve blood flow in the LFR and HFR groups compared to CC group ([Formula: see text]). Endoneurial edema was observed in the repetitive compression groups. Conclusions: Even if the overall compression time was short, the accumulation of repetitive compression-release cycles causes to delay the recovery of nerve physiological function with endoneurial edema.

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33

Ganjiani, M., R. Naghdabadi, and M. Asghari. "Analysis of Concrete Pressure Vessels in the Framework of Continuum Damage Mechanics." International Journal of Damage Mechanics 21, no. 6 (October 14, 2011): 843–70. http://dx.doi.org/10.1177/1056789511419692.

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In this article, a constitutive model in the framework of continuum damage mechanics is proposed to simulate the elastic behavior of concrete in tension and compression states. We assume two parts for Gibbs potential energy function: elastic and damage parts. In order to obtain the elastic-damage constitutive relation with the internal variables, two damage thermodynamic release rates in tension and compression derived from the elastic part of Gibbs potential energy are introduced. Also, two anisotropic damage tensors (tension and compression) are defined which characterize the tensile and compressive behaviors of concrete. Furthermore, two different linear hardening rules for tension and compression states are adopted for characterizing the damage evolution. The spectral decomposition technique is used to resolve the stress tensor into tensile and compressive components. The accuracy and performance of the proposed model are validated by comparing the predicted results with different experimental data, such as monotonic uniaxial tension and compression tests, and monotonic biaxial compression test. As an application, an analytic closed-form solution for a concrete thick-walled cylinder is obtained. It is shown that two damages: tensile damage [Formula: see text] and shear damage [Formula: see text] propagate in the cylinder. These two damages introduce anisotropy in the elastic behavior of the concrete structure. The influence of these two damages is investigated on the stress field in the cylinder. It is found that effect of shear damage [Formula: see text] on radial and tangential stresses as well as the effect of tensile damage [Formula: see text] on radial stress are negligible, while the effect of tensile damage [Formula: see text] on the tangential stress in a concrete thick-walled cylinder is significant.

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34

Wijaya, Bayu Angga, Sarwando Siboro, Mahendra Brutu, and Yelita Kristiani Lase. "Application of Huffman Algorithm and Unary Codes for Text File Compression." SinkrOn 7, no. 3 (July 30, 2022): 1000–1007. http://dx.doi.org/10.33395/sinkron.v7i3.11567.

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Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huffman algorithms and unary code can solve this problem. By compressing the selected data also encrypts it as an extra security. The Huffman algorithm is a lossless compression algorithm or a technique that does not change the original data, by converting the unit of data content into bits. So this algorithm is widely used in the compression process. The Unary Codes algorithm is also a lossless compression technique that is generally used by combining several modification techniques. In this unary codes algorithm, each symbol in the string will be searched for its frequency. Then sorted from the last order (descending). The use of these two text data compression techniques results in a file size that is smaller than the original but can be returned to the original data

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35

Abu Hilal, Tariq, Hasan Abu Hilal, and Ala Abu Hilal. "Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 11–15. http://dx.doi.org/10.5383/juspn.15.01.002.

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Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. For Arabic Text Compression, a sequential encoding technique was suggested that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques. In this research, a multistage compression process was implemented on Turkish and Arabic languages, by using the new encoding technique, in addition to the 7-Zip application, which has shown a significant file size reduction.

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36

Cankaya, Ebru Celikel, and Hina Vinayak. "A Novel Text Processing for Better Compression and Security in Cloud." International Journal of Computer Theory and Engineering 8, no. 1 (February 2016): 1–6. http://dx.doi.org/10.7763/ijcte.2016.v8.1011.

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37

Bhadade, Umesh S., and A. I. Trivedi. "Text Compression Methods Based on Dictionaries." International Journal of Computer Applications 8, no. 7 (October 10, 2010): 30–37. http://dx.doi.org/10.5120/1220-1767.

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38

S, Senthil, and Robert L. "TEXT COMPRESSION ALGORITHMS - A COMPARATIVE STUDY." ICTACT Journal on Communication Technology 02, no. 04 (December 1, 2011): 444–51. http://dx.doi.org/10.21917/ijct.2011.0062.

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39

Robert, L., and R. Nadarajan. "Fault-Tolerant Text Data Compression Algorithms." International Journal of Information Technology and Web Engineering 4, no. 2 (April 2009): 1–19. http://dx.doi.org/10.4018/jitwe.2009040101.

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40

Begum. "A NOVEL MULTIDICTIONARY BASED TEXT COMPRESSION." Journal of Computer Science 8, no. 12 (December 1, 2012): 1940–45. http://dx.doi.org/10.3844/jcssp.2012.1940.1945.

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41

Moffat, A., J. Zobel, and N. Sharman. "Text compression for dynamic document databases." IEEE Transactions on Knowledge and Data Engineering 9, no. 2 (1997): 302–13. http://dx.doi.org/10.1109/69.591454.

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42

Crochemore, Maxime, and Thierry Lecroq. "Pattern-matching and text-compression algorithms." ACM Computing Surveys 28, no. 1 (March 1996): 39–41. http://dx.doi.org/10.1145/234313.234331.

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43

Moffat, Alistair, Timothy C. Bell, and Ian H. Witten. "Lossless Compression for Text and Images." International Journal of High Speed Electronics and Systems 08, no. 01 (March 1997): 179–231. http://dx.doi.org/10.1142/s0129156497000068.

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Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.

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44

Abel, J., and W. Teahan. "Universal Text Preprocessing for Data Compression." IEEE Transactions on Computers 54, no. 5 (May 2005): 497–507. http://dx.doi.org/10.1109/tc.2005.85.

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45

Hilal, Tariq Abu, and Hasan Abu Hilal. "Turkish Text Compression via Characters Encoding." Procedia Computer Science 175 (2020): 286–91. http://dx.doi.org/10.1016/j.procs.2020.07.042.

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46

Satir, Esra, and Hakan Isik. "A compression-based text steganography method." Journal of Systems and Software 85, no. 10 (October 2012): 2385–94. http://dx.doi.org/10.1016/j.jss.2012.05.027.

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47

Long, P. M., A. I. Natsev, and J. S. Vitter. "Text compression via alphabet re-representation." Neural Networks 12, no. 4-5 (June 1999): 755–65. http://dx.doi.org/10.1016/s0893-6080(99)00022-2.

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48

CONLEY, EHUD S., and SHMUEL T. KLEIN. "USING ALIGNMENT FOR MULTILINGUAL TEXT COMPRESSION." International Journal of Foundations of Computer Science 19, no. 01 (February 2008): 89–101. http://dx.doi.org/10.1142/s0129054108005553.

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Multilingual text compression exploits the existence of the same text in several languages to compress the second and subsequent copies by reference to the first. We explore the details of this framework and present experimental results for parallel English and French texts.

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49

Tsay, Mu-King, Chia-Hsu Kuo, and Rong-Hauh Ju. "Effective Modelling Of chinese Text Compression." International Journal of Modelling and Simulation 14, no. 4 (January 1994): 155–59. http://dx.doi.org/10.1080/02286203.1994.11760233.

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50

Khan, H. U., and H. A. Fatmi. "Text compression using rule based encoder." Electronics Letters 30, no. 3 (February 3, 1994): 199–200. http://dx.doi.org/10.1049/el:19940146.

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