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Journal articles on the topic 'Storage and processing'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Pokorný, J., Š. Schmidt, and H. T T Nguyen. "Functionality changes of natural antioxidants during food processing and storage." Czech Journal of Food Sciences 22, SI - Chem. Reactions in Foods V (January 1, 2004): S80—S83. http://dx.doi.org/10.17221/10617-cjfs.

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Food raw materials and products contain inhibitors of oxidation reactions, both in the lipidic phase and the aqueous phase. The most important inhibitors are phenolic antioxidants. During food processing and storage, concentrations of antioxidants in the two phases reach an equilibrium. Phenolics react with lipidic free radicals, being converted into antioxidant free radicals, quinones, polymers and copolymers. Some degradation products possess an antioxidant activity, too. The relative antioxidant activity decreases with decreasing concentration of oxygen in the system and with increasing temperature. Antioxidants are more rapidly decomposed in surface layers. Health aspects of antioxidant degradation products are often neglected as the safety of antioxidant degradation products is mostly unknown.

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2

Manolopoulou, H., and C. Mallidis. "STORAGE AND PROCESSING OF APRICOTS." Acta Horticulturae, no. 488 (May 1999): 567–76. http://dx.doi.org/10.17660/actahortic.1999.488.93.

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3

Bogdanov, A. V., and Lwin Thurein Kyaw. "Storage database in cloud processing." Computer Research and Modeling 7, no. 3 (June 2015): 493–98. http://dx.doi.org/10.20537/2076-7633-2015-7-3-493-498.

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4

Egg, Richard P., Charlie G. Coble, Cady R. Engler, and Donald H. Lewis. "Feedstock storage, handling and processing." Biomass and Bioenergy 5, no. 1 (January 1993): 71–94. http://dx.doi.org/10.1016/0961-9534(93)90009-s.

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5

Song, Nae Young, Hyuck Han, and Heon Young Yeom. "Optimization of Graph Processing based on In-Storage Processing." KIISE Transactions on Computing Practices 23, no. 8 (August 31, 2017): 473–80. http://dx.doi.org/10.5626/ktcp.2017.23.8.473.

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6

Aoyagi, Seiichi. "Information processing apparatus, information processing method, and storage medium." Journal of the Acoustical Society of America 120, no. 1 (2006): 26. http://dx.doi.org/10.1121/1.2227697.

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7

Vaught, Jimmie B. "Blood Collection, Shipment, Processing, and Storage." Cancer Epidemiology Biomarkers & Prevention 15, no. 9 (September 2006): 1582–84. http://dx.doi.org/10.1158/1055-9965.epi-06-0630.

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8

Pratt, Polly, Frank Tallis, and Michael Eysenck. "Information-processing, storage characteristics and worry." Behaviour Research and Therapy 35, no. 11 (November 1997): 1015–23. http://dx.doi.org/10.1016/s0005-7967(97)00057-0.

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9

Schneider, Ann. "Blood Components: Collection, Processing, and Storage." Veterinary Clinics of North America: Small Animal Practice 25, no. 6 (November 1995): 1245–61. http://dx.doi.org/10.1016/s0195-5616(95)50152-4.

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10

Cooper, A., W. Kahari, and R. Such. "Image processing for electronic document storage." IEE Proceedings E Computers and Digital Techniques 135, no. 4 (1988): 196. http://dx.doi.org/10.1049/ip-e.1988.0026.

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11

Gold, Paul E. "Glucose modulation of memory storage processing." Behavioral and Neural Biology 45, no. 3 (May 1986): 342–49. http://dx.doi.org/10.1016/s0163-1047(86)80022-x.

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12

Ali, M., N. Q. Soomro, H. Ali, A. Awan, and M. Kirmani. "Distributed File Sharing and Retrieval Model for Cloud Virtual Environment." Engineering, Technology & Applied Science Research 9, no. 2 (April 10, 2019): 4062–65. http://dx.doi.org/10.48084/etasr.2662.

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Cloud-based storage services are multiplying and are being adapted mostly for data storage. At the same time, many potential problems pertaining to data storage and security are being addressed. This research provides the architecture for splitting user data and the solution in retrieving different data chunks, stored on different cloud storages. By doing this, not only the load on a single server is reduced but security and storage are efficiently used. Data would be stored and retrieved in slices, hence the chances of data forgery are diminishing. File processing will be faster as split parts would be fetched from different clouds, enabling parallel processing.

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13

Sawant, RB, SK Jathar, SB Rajadhyaksha, and PT Kadam. "Red cell hemolysis during processing and storage." Asian Journal of Transfusion Science 1, no. 2 (2007): 47. http://dx.doi.org/10.4103/0973-6247.33446.

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14

Duff, Simon C., and Robert H. Logie. "Processing and Storage in Working Memory Span." Quarterly Journal of Experimental Psychology Section A 54, no. 1 (February 2001): 31–48. http://dx.doi.org/10.1080/02724980042000011.

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15

Yuvienco, Janette Custodio. "Aspects of multilingual storage, processing and retrieval." International Journal of Multilingualism 11, no. 2 (May 7, 2013): 263–66. http://dx.doi.org/10.1080/14790718.2013.786592.

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16

Conway, T., R. Conway, and S. Tosi. "Signal processing for multitrack digital data storage." IEEE Transactions on Magnetics 41, no. 4 (April 2005): 1333–39. http://dx.doi.org/10.1109/tmag.2005.845394.

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17

Wylot, Marcin, Manfred Hauswirth, Philippe Cudré-Mauroux, and Sherif Sakr. "RDF Data Storage and Query Processing Schemes." ACM Computing Surveys 51, no. 4 (September 6, 2018): 1–36. http://dx.doi.org/10.1145/3177850.

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18

Lee, Jun, and Kees A. Immink. "Advanced signal processing technique for storage systems." IEEE Transactions on Consumer Electronics 56, no. 4 (November 2010): 2373–79. http://dx.doi.org/10.1109/tce.2010.5681114.

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19

Smith, J. P. "Insect Management for Food Storage and Processing." Canadian Institute of Food Science and Technology Journal 19, no. 4 (October 1986): 142. http://dx.doi.org/10.1016/s0315-5463(86)71614-3.

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20

Coultrap, Steven J., and K. Ulrich Bayer. "CaMKII regulation in information processing and storage." Trends in Neurosciences 35, no. 10 (October 2012): 607–18. http://dx.doi.org/10.1016/j.tins.2012.05.003.

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21

Thomas, Gary, and Richard Isaacs. "Processing, storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 10, no. 1 (January 2009): 65–69. http://dx.doi.org/10.1016/j.mpaic.2008.11.014.

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22

Dingley, John. "Processing, storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 12, no. 9 (September 2011): 426–29. http://dx.doi.org/10.1016/j.mpaic.2011.06.010.

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23

Thimbleby, Harold. "Processing storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 15, no. 7 (July 2014): 340–43. http://dx.doi.org/10.1016/j.mpaic.2014.04.021.

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24

Walton, Thomas E. F., and Lucinda A. Hardy. "Processing, storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 18, no. 7 (July 2017): 368–72. http://dx.doi.org/10.1016/j.mpaic.2017.04.010.

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25

Patey, Susannah J., and Ming Wilson. "Processing, storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 21, no. 5 (May 2020): 261–66. http://dx.doi.org/10.1016/j.mpaic.2020.03.001.

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26

Araiza-Esquivel, M., S. Guel-Sandoval, and J. Ojeda-Castañeda. "Multichannel image storage with image processing capabilities." Optics Communications 230, no. 1-3 (January 2004): 131–35. http://dx.doi.org/10.1016/j.optcom.2003.11.051.

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27

Bell, C. H. "Insect management for food storage and processing." Postharvest Biology and Technology 3, no. 1 (July 1993): 87. http://dx.doi.org/10.1016/0925-5214(93)90030-7.

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28

KHURDIYA, DALPAT SINGH, ISLAM, and OM PRAKASH VERMA. "PROCESSING AND STORAGE OF CARBONATED GUAVA BEVERAGE." Journal of Food Processing and Preservation 20, no. 1 (March 1996): 79–86. http://dx.doi.org/10.1111/j.1745-4549.1996.tb00342.x.

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29

Keiser, John R. "Seeds Handbook: Biology, Production, Processing, and Storage." Crop Science 38, no. 3 (May 1998): 880. http://dx.doi.org/10.2135/cropsci1998.0011183x003800030046x.

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30

Curnow, John. "Processing, storage and display of physiological measurements." Anaesthesia & Intensive Care Medicine 6, no. 12 (December 2005): 400–401. http://dx.doi.org/10.1383/anes.2005.6.12.400.

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31

Lozano, M., and J. Cid. "Recent advances in platelet processing and storage." ISBT Science Series 11, S1 (January 2016): 34–38. http://dx.doi.org/10.1111/voxs.12202.

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32

Wenkai, Li, Chi-Wai Hui, Ben Hua, and Zhongxuan Tong. "Scheduling Crude Oil Unloading, Storage, and Processing." Industrial & Engineering Chemistry Research 41, no. 26 (December 2002): 6723–34. http://dx.doi.org/10.1021/ie020130b.

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33

Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov, and Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY." Bulletin of the Angarsk State Technical University 1, no. 16 (December 27, 2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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34

Kryukov, Alexander, and Minh-Duc Nguyen. "A Distributed Storage for Astroparticle Physics." EPJ Web of Conferences 207 (2019): 08003. http://dx.doi.org/10.1051/epjconf/201920708003.

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In this paper we present the architecture of a distributed data storage for astroparticle physics. The main advantage of the proposed architecture is the possibility to extract data on both file and event level for further processing and analysis. The storage also provides users with a special service allowing to aggregate data from different storages into a single sample. This feature permits to apply multi-messenger methods for more sophisticated investigation of the data. Users can use both Webinterface and Application Programming Interface (API) for accessing the storage.

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35

Zhang, Ruili, Yanming Yang, and Wenxiu Wang. "Research on document digitization processing technology." MATEC Web of Conferences 309 (2020): 02014. http://dx.doi.org/10.1051/matecconf/202030902014.

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The digitalization of document information is the development direction of the digitalization of document information management, which involves various technologies such as digitalization technology, picture and text editing, storage format, etc. Through the PDF document loading display, change the page replacement storage, the technical page jump to achieve the PDF document programming control.

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36

Zhou, You. "Regional Financial Data Processing Based on Distributed Decoding Technology." Security and Communication Networks 2022 (September 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/1043140.

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Aiming at the problems of low storage efficiency, small storage space, and poor stability of traditional financial data storage systems, a dynamic and secure financial data storage system based on distributed decoding is designed. The system uses a cloud security storage service system to store a large number of financial data obtained from the sensing layer and achieve the reasonable scheduling of financial dynamic cloud data through the constraint software function. In addition, a distributed security computing scheme based on hierarchical coding is proposed, and the influence of different subtask sizes and different computing layers on the scheme is analyzed. The results show that the distributed security computing scheme based on hierarchical coding can significantly reduce the completion time of the overall computing task and enhance the stability of dynamic financial data storage. This system has a good storage performance of financial big data, and its efficiency is good, which provides strong support for the financial cause of our country.

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37

Zhang, Yanting, Zhe Zhu, Wei Ning, and Amir M. Fathollahi-Fard. "An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System." Sustainability 14, no. 17 (August 30, 2022): 10822. http://dx.doi.org/10.3390/su141710822.

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This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

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38

Bani-Ismail, Basel, and Ghassan Kanaan. "Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection." International Journal of Information Retrieval Research 2, no. 2 (April 2012): 52–67. http://dx.doi.org/10.4018/ijirr.2012040105.

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In the authors’ study they evaluate and compare the storage efficiency of different sparse matrix storage structures as index structure for Arabic text collection and their corresponding sparse matrix-vector multiplication algorithms to perform query processing in any Information Retrieval (IR) system. The study covers six sparse matrix storage structures including the Coordinate Storage (COO), Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), Block Coordinate (BCO), Block Sparse Row (BSR), and Block Sparse Column (BSC). Evaluation depends on the storage space requirements for each storage structure and the efficiency of the query processing algorithm. The experimental results demonstrate that CSR is more efficient in terms of storage space requirements and query processing time than the other sparse matrix storage structures. The results also show that CSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other point entry storage structures (COO, CSC). The results demonstrate that BSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other block entry storage structures (BCO, BSC).

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39

Antonis, Theofilidis. "Investigating memory storage difficulties in Multiple Sclerosis." Neuroscience and Neurological Surgery 9, no. 5 (November 24, 2021): 01–07. http://dx.doi.org/10.31579/2578-8868/185.

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Μemory by itself as a function, loses its capabilities with a normal deterioration. However, there are acquired conditions that negatively affect the functions of memory, resulting in dysfunction of its stages. Thus possible damage to the structures of the hemisphere that controls these processes disrupts the comprehension, organization and categorization of the material to be memorized. Patients with damage to these systems will have difficulty remembering because they have not adequately coded the material. Patients with Multiple Sclerosis report short-term memory difficulties in the sense that they have difficulty remembering details of recent conversations and events. Aim: To investigate the memory storage difficulties in Multiple Sclerosis. Materials and Methods: An international literature review was performed on Memory Disorders in Multiple Sclerosis. Conclusion: In patients with Multiple Sclerosis learning deficits are greatly aided by processing speed and working memory. It has been observed that slow mental processing makes it difficult for many patients with Multiple Sclerosis to capture an entire verbal message, especially if it is large, complex, delivered quickly and with external stimuli, such as a noisy environment

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40

Chernyshev, Denys, Svitlana Tsiutsiura, Tamara Lyashchenko, Yuliia Luzina, and Vitalii Borodynia. "THE USE OF DATA WAREHOUSES ON THE EXAMPLE OF THE APPLICATION PROCESSING SYSTEM IN GOVERNMENT AGENCIES." Management of Development of Complex Systems, no. 44 (November 30, 2020): 166–74. http://dx.doi.org/10.32347/2412-9933.2020.44.166-174.

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This article highlights the issue of the importance of information, the need to ensure its proper storage and use. Nowadays, the question of the importance of studying the functioning of data storage facilities in one of the most common design models – Data Flow Diagram, and data storage (DS or DW) in the whole. A study of abstract devices of this information security model was carried out: types, features and types of data storage, which are used in information systems (relational, multidimensional, and hybrid repositories), from the point of view of using models for presenting data; appearance of warehouses and building rules; Letter identifiers of storages “D”, “C”, “M” and “T” with the help of which the type of storages is determined; features of the numerical part of the identifiers for decompositions of the first and second tier processes; mechanisms that support data retention for their intermediate processing in information systems. Transition of properties and characteristics from physical to logical representation, rationalization of data warehouses by considering the features of the logical model. In the course of the work, the peculiarities of the construction of the DFD and the reflection of the interrelationships in all the component diagrams, defined in the general rules that are valid for the two, were considered. The highlighted issues concern the elements of the diagrams, which can be freely exploited within the borders of Ukraine, as well as the elemental content of the different types of diagrams in the diagrams. The unique functionality of domestic DFD diagrams allows to use a sufficient, albeit limited, elemental structure for construction. Depending on the actual possibilities, the diagrams of the Ukrainian manufacturer are somewhat simplified and imperfect according to modern technologies, but these factors do not diminish the importance and necessity of adequate data protection at the highest level. Therefore, in this work, the main most informative moments of the use of this or that type of storage, the demand for DS, in the field, are highlighted, possible advantages and disadvantages of using physical data storage facilities and features of virtual operation.

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41

Giannakourou, Maria, and Petros Taoukis. "Changes during Food Freezing and Frozen Storage." Foods 10, no. 11 (October 21, 2021): 2525. http://dx.doi.org/10.3390/foods10112525.

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42

Narasimhamurthy, S. B., P. C. Gurumohan, S. Sreenivasamurthy, and J. Y. Hui. "Quanta data storage: an information processing and transportation architecture for storage area networks." IEEE Journal on Selected Areas in Communications 23, no. 10 (October 2005): 2032–40. http://dx.doi.org/10.1109/jsac.2005.854128.

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43

Smith, Richard B. "Bulk Storage of Mechanically Harvested Strawberries for Processing." HortScience 21, no. 3 (June 1986): 478–80. http://dx.doi.org/10.21273/hortsci.21.3.478.

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Abstract Mechanically harvested fruit of the strawberry (Fragaria × ananassa Duch.) cv. Veeglow destined for processing can be stored at 1°C in bulk bins for 4 to 6 days if room-cooled, and for 6 to 8 days if forced-air cooled promptly after harvest, without appreciable loss due to rot development or of quality of processed product. Yields of puree from fresh fruit mechanically harvested on day 8 of the storage trial were lower than for fruit that had been forced-air cooled and stored at 0° for 8 days. Sulfur dioxide fumigation immediately after cooling reduced losses due to rot and lowered mold counts, particularly when the fruit was room cooled.

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44

Shevchuk, Elena, and Yury Shevchuk. "Modern trends in sensor data storage and processing." Program Systems: Theory and Applications 6, no. 4 (2015): 157–76. http://dx.doi.org/10.25209/2079-3316-2015-6-4-157-176.

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45

Rahman, Muhammad Zia ur, Saba Waseem, Sidra Riaz, Zainab Riaz, Aneeq Asif, Ayesha Saddiqa, and Ali Asghar. "Image Processing and Data Storage for Fire Alarm." Journal of Engineering Research and Sciences 1, no. 4 (April 2022): 87–92. http://dx.doi.org/10.55708/js0104012.

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46

Xianquan, S., J. Shi, Y. Kakuda, and J. Yueming. "Stability of Lycopene During Food Processing and Storage." Journal of Medicinal Food 8, no. 4 (December 2005): 413–22. http://dx.doi.org/10.1089/jmf.2005.8.413.

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47

SHIAU, C. Y., and T. CHAI. "Smoked Dogfish Processing and Its Refrigerated Storage Stability." Journal of Food Science 50, no. 5 (September 1985): 1348–50. http://dx.doi.org/10.1111/j.1365-2621.1985.tb10474.x.

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48

Kaplan, Roman, Leonid Yavits, and Ran Ginosar. "PRINS: Processing-in-Storage Acceleration of Machine Learning." IEEE Transactions on Nanotechnology 17, no. 5 (September 2018): 889–96. http://dx.doi.org/10.1109/tnano.2018.2799872.

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49

Oliveira, Gabriel Henrique Horta de, Ana Paula Lelis Rodrigues de Oliveira, Fernando Mendes Botelho, Pedro Casanova Treto, and Silvia de Carvalho Campos Botelho. "Coffee Quality: Cultivars, Blends, Processing, and Storage Impact." Journal of Food Quality 2018 (June 19, 2018): 1. http://dx.doi.org/10.1155/2018/9805635.

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50

Correia, Antonio. "PHANTOMS: Nanotechnology network for information processing and storage*." Nanotechnology 12, no. 2 (May 25, 2001): 89–90. http://dx.doi.org/10.1088/0957-4484/12/2/302.

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