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Journal articles on the topic 'Programming applications'

Author: Grafiati

Published: 28 June 2021

Last updated: 10 February 2022

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1

Saleem, Nada, and Mohammad Hamdy. "Programming Mobile Applications." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 2 (2013): 189–202. http://dx.doi.org/10.33899/csmj.2013.163494.

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2

Burnham, W. D., A. R. Hall, and Ramachandran Bharath. "Prolog Programming and Applications." IEEE Expert 1, no. 3 (1986): 108. http://dx.doi.org/10.1109/mex.1986.4306988.

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3

Gupta, Omprakash K. "Applications of Quadratic Programming." Journal of Information and Optimization Sciences 16, no. 1 (1995): 177–94. http://dx.doi.org/10.1080/02522667.1995.10699213.

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4

Kalashnikov, Vyacheslav V., Stephan Dempe, Gerardo A. Pérez-Valdés, Nataliya I. Kalashnykova, and José-Fernando Camacho-Vallejo. "Bilevel Programming and Applications." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/310301.

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A great amount of new applied problems in the area of energy networks has recently arisen that can be efficiently solved only as mixed-integer bilevel programs. Among them are the natural gas cash-out problem, the deregulated electricity market equilibrium problem, biofuel problems, a problem of designing coupled energy carrier networks, and so forth, if we mention only part of such applications. Bilevel models to describe migration processes are also in the list of the most popular new themes of bilevel programming, as well as allocation, information protection, and cybersecurity problems. This survey provides a comprehensive review of some of the above-mentioned new areas including both theoretical and applied results.

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5

Vandenberghe, Lieven, and Stephen Boyd. "Applications of semidefinite programming." Applied Numerical Mathematics 29, no. 3 (1999): 283–99. http://dx.doi.org/10.1016/s0168-9274(98)00098-1.

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6

Wilson, W. G. "Prolog for applications programming." IBM Systems Journal 25, no. 2 (1986): 190–206. http://dx.doi.org/10.1147/sj.252.0190.

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7

Tzotzis, Anastasios, Cesar Garcia-Hernandez, Jose-Luis Huertas-Talon, Dimitrios Tzetzis, and Panagiotis Kyratsis. "Engineering applications using CAD based application programming interface." MATEC Web of Conferences 94 (2017): 01011. http://dx.doi.org/10.1051/matecconf/20179401011.

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8

Shen, Hua, Guo Shun Zhou, and Hui Qi Yan. "A Study of Parallelization and Performance Optimizations Based on OpenMP." Applied Mechanics and Materials 321-324 (June 2013): 2933–37. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2933.

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The primary consequence of the transition to multicore processors is that applications will increasingly need to be parallelized to improve application's throughput, responsiveness and latency. Multithreading is becoming increasingly important for modern programming. Unfortunately, parallel programming is no doubt much more tedious and error-prone than serial programming. Although modern compilers can manage threads well, but in practice, synchronization errors (such as: data race errors, deadlocks) required careful management and good optimization method. This paper presents a preliminary study of the usability of the Intel threading tools for multicore programming. This work compare performance of a single threaded application with multithreaded applications, use tools called Intel® VTune Performance Analyzer, Intel® Thread Checker and OpenMP to efficiently optimize multithreaded applications.

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9

Yerekesheva, M., and A. Beder. "DEVELOPMENT OF APPLICATIONS FOR LEARNING PROGRAMMING." BULLETIN Series of Physics & Mathematical Sciences 70, no. 2 (2020): 211–15. http://dx.doi.org/10.51889/2020-2.1728-7901.33.

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Most modern students are ready to use mobile technology in the learning process, and understand the need to consider new opportunities for the best use of the potential of mobile devices and technologies. The solution of this task requires the introduction of mobile learning strategies, forms and methods in the educational process of universities. Currently, the development of mobile applications is one of the most popular tasks in the field of information technology. Applications can now analyze information from multiple sources and help the user make decisions, control processes, and solve other important tasks. This, in turn, helps to optimize business processes, increase productivity and efficiency of decisions. The article describes the process of designing and building a mobile application for teaching programming, software, content, and functionality.

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10

Erdem, Esra, Michael Gelfond, and Nicola Leone. "Applications of Answer Set Programming." AI Magazine 37, no. 3 (2016): 53–68. http://dx.doi.org/10.1609/aimag.v37i3.2678.

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ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.

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11

McKay, A. C. "Linear Programming Applications on Microcomputers." Journal of the Operational Research Society 36, no. 7 (1985): 633. http://dx.doi.org/10.2307/2582484.

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12

Kim, Gi-Hwan, Jong-Cheol Park, Jae-Hong Kim, and Choong-Shik Park. "Programming Accounting Applications using BRMS." Journal of the Korean Institute of Information and Communication Engineering 14, no. 6 (2010): 1359–64. http://dx.doi.org/10.6109/jkiice.2010.14.6.1359.

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13

István, Fekete, Gregorics Tibor, Kovácsné Pusztai Kinga, and Veszprémi Anna. "Programming Theorems and Their Applications." Teaching Mathematics and Computer Science 17, no. 2 (2020): 213–41. http://dx.doi.org/10.5485/tmcs.2019.0466.

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14

Iyengar, Garud, and Wanmo Kang. "Inverse conic programming with applications." Operations Research Letters 33, no. 3 (2005): 319–30. http://dx.doi.org/10.1016/j.orl.2004.04.007.

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15

Dangerfield, Janet, and J. K. Strayer. "Linear Programming and Its Applications." Mathematical Gazette 74, no. 470 (1990): 402. http://dx.doi.org/10.2307/3618167.

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16

Wood, Kevin, and Vincent A. Sposito. "Linear Programming With Statistical Applications." Journal of the American Statistical Association 85, no. 409 (1990): 265. http://dx.doi.org/10.2307/2289574.

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17

Sette, S., and L. Boullart. "Genetic programming: principles and applications." Engineering Applications of Artificial Intelligence 14, no. 6 (2001): 727–36. http://dx.doi.org/10.1016/s0952-1976(02)00013-1.

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18

McKay, A. C. "Linear Programming Applications on Microcomputers." Journal of the Operational Research Society 36, no. 7 (1985): 633–35. http://dx.doi.org/10.1057/jors.1985.110.

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19

Brooks, Daniel G. "Linear Programming With Statistical Applications." Technometrics 32, no. 3 (1990): 355–56. http://dx.doi.org/10.1080/00401706.1990.10484707.

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20

Bratko, Ivan, and Stephen Muggleton. "Applications of inductive logic programming." Communications of the ACM 38, no. 11 (1995): 65–70. http://dx.doi.org/10.1145/219717.219771.

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21

Bales, Scott A., and Mark A. Shifman,. "Database Programming for Laboratory Applications." Laboratory Medicine 18, no. 4 (1987): 256–58. http://dx.doi.org/10.1093/labmed/18.4.256.

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22

Yelick, Katherine A. "Programming models for irregular applications." ACM SIGPLAN Notices 28, no. 1 (1993): 28–31. http://dx.doi.org/10.1145/156668.156680.

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23

Pukacki, Juliusz, Michał Kosiedowski, Rafał Mikołajczak, et al. "Programming Grid Applications with Gridge." Computational Methods in Science and Technology 12, no. 1 (2006): 47–68. http://dx.doi.org/10.12921/cmst.2006.12.01.47-68.

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24

Farnum, Rob. "Applications programming interface for Windows." StandardView 4, no. 2 (1996): 100–103. http://dx.doi.org/10.1145/234999.235003.

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25

Dübel, S. "Programming antibodies for novel applications." Chemie Ingenieur Technik 90, no. 9 (2018): 1248. http://dx.doi.org/10.1002/cite.201855255.

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26

Jajuga, Krzysztof, and Vincent A. Sposito. "Linear Programming with Statistical Applications." Statistician 39, no. 4 (1990): 467. http://dx.doi.org/10.2307/2349092.

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27

Shin, Wan S. "Linear Programming with Statistical Applications." Journal of Quality Technology 22, no. 1 (1990): 83–84. http://dx.doi.org/10.1080/00224065.1990.11979216.

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28

Bratko, Ivan, and Ross King. "Applications of inductive logic programming." ACM SIGART Bulletin 5, no. 1 (1994): 43–49. http://dx.doi.org/10.1145/181668.181678.

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29

Lloyd, J. W., and K. S. Ng. "Declarative programming for agent applications." Autonomous Agents and Multi-Agent Systems 23, no. 2 (2010): 224–72. http://dx.doi.org/10.1007/s10458-010-9138-1.

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30

Liu, Yuhan. "Uncertain random programming with applications." Fuzzy Optimization and Decision Making 12, no. 2 (2012): 153–69. http://dx.doi.org/10.1007/s10700-012-9149-2.

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31

Arsham, H. "Linear programming methods and applications." European Journal of Operational Research 48, no. 1 (1990): 165–66. http://dx.doi.org/10.1016/0377-2217(90)90076-n.

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32

Lev, Ben. "Applications of mathematical programming models." European Journal of Operational Research 160, no. 1 (2005): 1–2. http://dx.doi.org/10.1016/j.ejor.2003.06.016.

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33

Haridi, Seif, Peter Van Roy, Per Brand, and Christian Schulte. "Programming languages for distributed applications." New Generation Computing 16, no. 3 (1998): 223–61. http://dx.doi.org/10.1007/bf03037481.

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34

Kalva, Hari, Aleksandar Colic, Adriana Garcia, and Borko Furht. "Parallel programming for multimedia applications." Multimedia Tools and Applications 51, no. 2 (2010): 801–18. http://dx.doi.org/10.1007/s11042-010-0656-2.

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35

Rommelfanger, Heinrich. "Fuzzy linear programming and applications." European Journal of Operational Research 92, no. 3 (1996): 512–27. http://dx.doi.org/10.1016/0377-2217(95)00008-9.

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36

Wallace, Mark. "Practical applications of constraint programming." Constraints 1, no. 1-2 (1996): 139–68. http://dx.doi.org/10.1007/bf00143881.

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37

Selby, Peter, Rafael Abbeloos, Jan Erik Backlund, et al. "BrAPI—an application programming interface for plant breeding applications." Bioinformatics 35, no. 20 (2019): 4147–55. http://dx.doi.org/10.1093/bioinformatics/btz190.

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Abstract Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

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38

Abdelaziz, Fouad Ben. "Multiple objective programming and goal programming: New trends and applications." European Journal of Operational Research 177, no. 3 (2007): 1520–22. http://dx.doi.org/10.1016/j.ejor.2005.12.023.

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39

Luiz Schiavoni, Flávio, Luan Luiz Gonçalves, and José Mauro da Silva Sandy. "Mosaicode and the visual programming of web application for music and multimedia." Revista Música Hodie 18, no. 1 (2018): 132–46. http://dx.doi.org/10.5216/mh.v18i1.53577.

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   The development of audio application demands a high knowledge about this application domain, traditional program- ming logic and programming languages. It is possible to use a Visual Programming Language to ease the application development, including experimentations and creative exploration of the language. In this paper we present a Visual Programming Environment to create Web Audio applications, called Mosaicode. Different from other audio creation platforms that use a visual approach, our environment is a source code generator based on code snippets to create complete applications.   

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40

LaLonde, Steven M., and Frank C. DiIorio. "SAS Applications Programming, a Gentle Introduction." Technometrics 34, no. 3 (1992): 366. http://dx.doi.org/10.2307/1270061.

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41

Tofallis, C., and I. M. Stancu-Minasian. "Fractional Programming: Theory, Methods and Applications." Journal of the Operational Research Society 49, no. 8 (1998): 895. http://dx.doi.org/10.2307/3009971.

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42

Varatharajan, R., Muthu Senthil, and Perumal sankar. "Memetic Programming Approach for Floorplanning Applications." International Journal of Information Engineering and Electronic Business 4, no. 4 (2012): 39–45. http://dx.doi.org/10.5815/ijieeb.2012.04.06.

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43

Fateman, Richard J., and John Gray. "Mastering Mathematica: Programming Methods and Applications." Mathematics of Computation 64, no. 210 (1995): 903. http://dx.doi.org/10.2307/2153469.

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44

Ferraguti, Federica, Chiara Talignani Landi, Cristian Secchi, Cesare Fantuzzi, Marco Nolli, and Manuel Pesamosca. "Walk-through Programming for Industrial Applications." Procedia Manufacturing 11 (2017): 31–38. http://dx.doi.org/10.1016/j.promfg.2017.07.126.

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45

Lee, Hyunok. "Recent Applications of Nonparametric Programming Methods." Northeastern Journal of Agricultural and Resource Economics 21, no. 2 (1992): 113–20. http://dx.doi.org/10.1017/s0899367x00002622.

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Nonparametric techniques have recently come into vogue in agricultural economics: Applications abound in both consumer and producer models of the agricultural economy. Moreover, several distinct approaches to nonparametric analysis exist. There are nonparametric statistical techniques, semiparametric estimation techniques, nonparametric revealed-preference analysis of consumption data, and nonparametric analysis of production data. Both revealed-preference analysis and nonparametric analysis of production data rely on the basic fact, which provides the foundation for much of modern duality theory, that convex sets can be completely characterized by their supporting hyperplanes. This observation allows one to apply simple mathematical programming (in particular, linear programming) methods to analyze production and consumption data. My task today is to provide an overview of nonparametric programming approaches to production data. Thus, I will not address any of the other topics cited above. However, I would be remiss if I did not mention the close connection between these subject areas and what I intend to survey today. Moreover, one should also recognize that very closely related to the literature on nonparametric programming analysis of production data are the fields of estimation of efficiency frontier via statistical methods. (A useful survey here is Lovell and Schmidt).

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46

Liu, Yi-Hsin, and Jerald P. Dauer. "Bicriteria programming with several modern applications." Computers & Industrial Engineering 37, no. 3 (1999): 563–80. http://dx.doi.org/10.1016/s0360-8352(00)00021-8.

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47

Badia, Rosa M., Jesús Labarta, Raül Sirvent, Josep M. Pérez, José M. Cela, and Rogeli Grima. "Programming Grid Applications with GRID Superscalar." Journal of Grid Computing 1, no. 2 (2003): 151–70. http://dx.doi.org/10.1023/b:grid.0000024072.93701.f3.

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48

LeGuernic, P., T. Gautier, M. Le Borgne, and C. Le Maire. "Programming real-time applications with SIGNAL." Proceedings of the IEEE 79, no. 9 (1991): 1321–36. http://dx.doi.org/10.1109/5.97301.

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49

Tofallis, C. "Fractional Programming: Theory, Methods and Applications." Journal of the Operational Research Society 49, no. 8 (1998): 895. http://dx.doi.org/10.1057/palgrave.jors.2600597.

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50

Boyd, Stephen, César Crusius, and Anders Hansson. "Control applications of nonlinear convex programming." Journal of Process Control 8, no. 5-6 (1998): 313–24. http://dx.doi.org/10.1016/s0959-1524(98)00011-0.

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