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Статті в журналах з теми "Systems of making"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

Borden, Andrew. "Human Intuition and Decision-making Systems." Information & Security: An International Journal 1, no. 2 (1998): 67–72. http://dx.doi.org/10.11610/isij.0117.

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2

Mateosian, R. "Making Systems." IEEE Micro 17, no. 5 (September 1997): 74–76. http://dx.doi.org/10.1109/mm.1997.621217.

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3

Kolodchak, Оlha. "Problems of Designing a Control Systemfor Decision Making Systems of Cyberphysical Systems." Advances in Cyber-Physical Systems 3, no. 2 (November 10, 2018): 84–91. http://dx.doi.org/10.23939/acps2018.02.084.

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4

Pan, Jie. "Making complex systems computable." Nature Computational Science 2, no. 9 (September 26, 2022): 550–52. http://dx.doi.org/10.1038/s43588-022-00316-y.

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5

Voronin, Albert, and Alina Savchenko. "EXPERT DECISION-MAKING SYSTEMS." Journal of Automation and Information sciences 6 (November 1, 2021): 114–21. http://dx.doi.org/10.34229/1028-0979-2021-6-11.

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Анотація:

An approach to making complex technical and economic decisions is proposed in cases where there is insufficient (or no) information about the experimental statistical data necessary for the construction of regression models of criterion functions. The situation is aggravated when a decision is made according to several conflicting quality criteria. In conditions of an acute shortage of experimental data, it is proposed to obtain the necessary information («quasi-experimental» data) from experts - specialists with sufficient experience in making decisions for the class in question. A qualified specialist (expert) with sufficient experience in making decisions for this class can perform a thought experiment. He must imagine what, in his opinion, the levels of criterion functions will be at various reference points of factors. The method is based on an individual opinion (postulate), expressed by a specialist-expert about the estimated value, based on his professional experience. To assess a certain quantitative characteristic, the postulates of not one, but several persons competent in this issue are used. It is assumed that the «true» value of the unknown quantitative characteristic is within the range of expert assessments and the «generalized» collective opinion is more reliable. To solve the problem under consideration, a multicriteria optimization approach is taken using a nonlinear trade-off scheme. This study makes it possible to identify the main trends in the development of multi-criteria decision-making systems in the absence (or lack) of experimental data. A model example is given.

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6

Roth, Anders, and Tomas Kåberger. "Making transport systems sustainable." Journal of Cleaner Production 10, no. 4 (August 2002): 361–71. http://dx.doi.org/10.1016/s0959-6526(01)00052-x.

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7

Liu, Qiao, Keiichi Nakata, and Kazuo Furuta. "Making control systems visible." Cognition, Technology & Work 6, no. 2 (May 1, 2004): 87–106. http://dx.doi.org/10.1007/s10111-003-0148-5.

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8

Boulos, Paul F. "Making Wastewater Systems Smarter." Opflow 39, no. 6 (June 2013): 22–24. http://dx.doi.org/10.5991/opf.2013.39.0033.

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9

MĂRGINEANU, Cătălin, Anișor NEDELCU, and Dorin LIXĂNDROIU. "EVOLUTIONS AND TRENDS IN INFORMATION SYSTEMS ASSISTING MANAGERIAL DECISION-MAKING. STUDY ON ERP SYSTEMS." Review of the Air Force Academy 18, no. 1 (July 30, 2020): 23–33. http://dx.doi.org/10.19062/1842-9238.2020.18.1.3.

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10

Zubovа, Lyudmila V., Eduard Viktorovich Korovin, Alexey Sergeevich Smirnov, Vladimir N. Kuzmin, and Andrey Valerievich Kurakov. "Development of Problem-Oriented Management and Decision-Making System and Optimization of Economic and Social Systems." Webology 18, SI05 (October 30, 2021): 436–51. http://dx.doi.org/10.14704/web/v18si05/web18239.

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Анотація:

The main goal of this study is to develop the theory of risk tolerance in task-oriented processes (using the example of enterprises engaged in research, development, and engineering), which is preceded by an analysis of scientific and methodological provisions for substantiating management decisions when developing promising space rocket technology under economic risks. The complexity of developing the theory of risk tolerance using the example of enterprises involved in the development of military and dual-use equipment lies in the multistructured evaluating system itself, justified by its multicomponent structure and large scale of topological complexity and logic of functioning in various modes and under different conditions, which leads to the need to divide it into a set of subsystems as moving substances in the process of task-oriented processes that have informational, methodological, and algorithmic commonality. That is accompanied by the decentralization of information processing in a structural parametric uncertainty. In this regard, in order to parameterize the uncertainty processes, the authors present a risk tolerance level assessment process diagram in task-oriented processes when developing military and dual-use equipment. Using the algorithm for determining the marginal cost of risk, marginal risk tolerance, and marginal risk level of an economic entity according to the method of L.V. Zubova, the work presents an approach of potentially dangerous risks (PDR) categorization of the financial and economic sphere and suggests ways to minimize risk, taking into account, if possible, risk rejection, determining the "cost of no action" in the face of uncertainty.

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11

Jagdishchandra, Mehta Sunilkumar. "Medical ethics and decision making in health care management Systems." Indian Journal of Applied Research 1, no. 12 (October 1, 2011): 147–48. http://dx.doi.org/10.15373/2249555x/sep2012/51.

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12

SONG, WonWook, and YoungKyo SUH. "Making Gatekeepers in Supplier Systems." Annals of Business Administrative Science 15, no. 2 (April 15, 2016): 59–73. http://dx.doi.org/10.7880/abas.0150805a.

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13

Lederer, Albert L., and Raghu Nath. "Making Strategic Information Systems Happen." Academy of Management Perspectives 4, no. 3 (March 1990): 76–83. http://dx.doi.org/10.5465/ame.1990.4274688.

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14

Milbourn, Andrew. "Making sense of information systems." Perspectives: Policy and Practice in Higher Education 1, no. 3 (September 1997): 89–96. http://dx.doi.org/10.1080/1360318970010305.

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15

Rossel, Pierre. "Making anticipatory systems more robust." Foresight 12, no. 3 (June 2010): 72–85. http://dx.doi.org/10.1108/14636681011049893.

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16

Sanfey, Alan G., and Luke J. Chang. "Multiple Systems in Decision Making." Annals of the New York Academy of Sciences 1128, no. 1 (April 2008): 53–62. http://dx.doi.org/10.1196/annals.1399.007.

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17

Frank, Michael J., Michael X. Cohen, and Alan G. Sanfey. "Multiple Systems in Decision Making." Current Directions in Psychological Science 18, no. 2 (April 2009): 73–77. http://dx.doi.org/10.1111/j.1467-8721.2009.01612.x.

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18

Passi, Samir, and Phoebe Sengers. "Making data science systems work." Big Data & Society 7, no. 2 (July 2020): 205395172093960. http://dx.doi.org/10.1177/2053951720939605.

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Анотація:

How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work.

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19

Schreiner, K. "IRIS: making smart systems smarter." IEEE Intelligent Systems 13, no. 6 (November 1998): 4–6. http://dx.doi.org/10.1109/5254.735995.

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20

Mathijs, Erik, and Erwin Wauters. "Making Farming Systems Truly Resilient." EuroChoices 19, no. 2 (August 2020): 72–76. http://dx.doi.org/10.1111/1746-692x.12287.

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21

Zimmermann, Chris. "Making distributed multimedia systems secure." ACM SIGOPS Operating Systems Review 28, no. 1 (January 1994): 88–100. http://dx.doi.org/10.1145/164853.164879.

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22

Basri, Gibor. "Solar systems in the making." Nature 346, no. 6284 (August 1990): 515. http://dx.doi.org/10.1038/346515a0.

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23

Gwatkin, Davidson R., Abbas Bhuiya, and Cesar G. Victora. "Making health systems more equitable." Lancet 364, no. 9441 (October 2004): 1273–80. http://dx.doi.org/10.1016/s0140-6736(04)17145-6.

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24

Stajic, J. "Making hybrid quantum information systems." Science 349, no. 6246 (July 23, 2015): 392–93. http://dx.doi.org/10.1126/science.349.6246.392-f.

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25

Brown, A. "Expert systems for decision-making." International Journal of Information Management 10, no. 4 (December 1990): 321–22. http://dx.doi.org/10.1016/0268-4012(90)90039-u.

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26

Sarma, V. V. S. "Decision making in complex systems." Systems Practice 7, no. 4 (August 1994): 399–407. http://dx.doi.org/10.1007/bf02169361.

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27

Brown, Carl E. "Making Small Water Systems Strong." Journal of Contemporary Water Research & Education 128, no. 1 (May 1, 2009): 27–30. http://dx.doi.org/10.1111/j.1936-704x.2004.mp128001005.x.

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28

Allen, Timothy F. H. "Making livable sustainable systems unremarkable." Systems Research and Behavioral Science 27, no. 5 (August 24, 2010): 469–79. http://dx.doi.org/10.1002/sres.1059.

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29

Hamilton, Gail Thornburg. "Expert systems for decision-making." Information Processing & Management 24, no. 6 (January 1988): 715–16. http://dx.doi.org/10.1016/0306-4573(88)90011-8.

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30

Shane, Scott A. "Making new franchise systems work." Strategic Management Journal 19, no. 7 (July 1998): 697–707. http://dx.doi.org/10.1002/(sici)1097-0266(199807)19:7<697::aid-smj972>3.0.co;2-o.

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31

Schott, Peter, Torben Schaft, Stefan Thomas, and Freimut Bodendorf. "Making IoT Run." International Journal of Hyperconnectivity and the Internet of Things 1, no. 2 (July 2017): 26–44. http://dx.doi.org/10.4018/ijhiot.2017070103.

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Анотація:

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

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32

Henshaw, J. L. "Systems Thinking for Systems Making: Joining Systems of Thought and Action." Systemic Practice and Action Research 32, no. 1 (April 10, 2018): 63–91. http://dx.doi.org/10.1007/s11213-018-9450-2.

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33

Ncube, Cornelius, Patricia Oberndorf, and Anatol W. Kark. "Opportunistic Software Systems Development: Making Systems from What's Available." IEEE Software 25, no. 6 (November 2008): 38–41. http://dx.doi.org/10.1109/ms.2008.153.

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34

YOSHIDA, Yuji, and Masami YASUDA. "Decision-Making in Dynamic Fuzzy Systems." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1053–62. http://dx.doi.org/10.3156/jfuzzy.10.6_1053.

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35

Dubina, Igor N., Nikolay M. Oskorbin, and Dmitriy S. Khvalynskiy. "Decision-Making Coordination in Hierarchical Systems." World of Economics and Management 19, no. 2 (2019): 5–18. http://dx.doi.org/10.25205/2542-0429-2019-19-2-5-18.

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Анотація:

The work is aimed at analyzing coordination mechanisms of corporate solutions and at studying a number of mechanisms, decision-making processes and information links with the use of hierarchical algorithms of block program ming. The work presents a routine task for high dimensional optimization adequate for the design of hierarchical systems of production companies and corporations, an objective function and a restriction system with composition-block structure. The special methods of block programming, where interaction between the center and the performers corresponds to the centralized and decentralized mechanisms of decision-making coordination in economic systems have been developed for this type of optimization problems. For the analysis of decision-making coordination in big systems, the unique classification of the mechanisms of decision-making coordination in hierarchical systems based on the level of information awareness of the center and the ways to increase this level has been applied. In the systems of the first type, the center is fully informed on the parameters of the objective function of the elements of the system (subsystems) and the set of admissible values of decision-making coordination or seeks to achieve such level of awareness. In this case the decision-making process is carried out in 2 stages. On the first stage, the center finds the optimum coordinating variables; on the second stage, the elements of the bottom level (blocks) sel ect ‘technological’ variables which implementation provides an optimal goal for the center. To achieve greater effectiveness and strict follow-up of the decisions made, the center can use such mechanisms of control as strong penalties. In the systems of another type, the center implements the principles of institutional and indicative management, i.e. it creates an institutional environment (structures, links, restrictions, etc.) for effective functioning of the elements (performers) and delegates it powers on decision-making to achieve the target parameters (indicators) optimal fr om the point of view of the center. It is shown that the convergence of the sequences of the specifying solutions of performers to an optimal decision-making for a system in general seems to be a necessary condition of institutional coordination methods.

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36

Jankovic, Ivana. "Collective decision making in complex systems." Theoria, Beograd 60, no. 2 (2017): 101–19. http://dx.doi.org/10.2298/theo1702101j.

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Анотація:

This paper aims to offer arguments in support of epistemic value of collective decision-making, as well as to identify factors that contribute to its success. Given that this form of decision-making is present in various fields of human action, we will present the parallels between decision-making in the economic organizations and in democratic theory. Noting the similarities in the development of decision-making theory in those two areas and the general shift from individual to collective cognitive competencies, we will argue that the specific insights of organizational theory came should be applied to democratic decision-making if we are to improve it. More specifically, we will argue that the Page?s cognitive model of collective decision-making, that came from modern organizational theory, based on the logic of diversity, can well be justified and applied to the democratic, especially deliberative institutions for solving political problems.

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37

Dorofeev, N. V., and E. S. Pankina. "Decision Making in Geotechnical Monitoring Systems." IOP Conference Series: Earth and Environmental Science 988, no. 3 (February 1, 2022): 032049. http://dx.doi.org/10.1088/1755-1315/988/3/032049.

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Анотація:

Abstract The article considers an algorithm for estimating the moments of correction of the list of controlled and measured parameters, selecting control points and correcting an individual model of a geotechnical object. As a criterion for correcting the list of parameters, it is proposed to use the moments when the values of the controlled parameters approach the critical zone. The choice of parameters for measurement is carried out on the basis of optimization of the ratio of the cost of monitoring to possible damage. The definition of key control zones is carried out according to the criterion of sensitivity of monitoring zones to changes in external parameters. The practical verification was carried out on the data of geotechnical monitoring of a three-storey building in which destructive processes were recorded.

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38

Markandya, Anil. "Making Agricultural Systems More Environmentally Sustainable." QA Rivista dell'Associazione Rossi-Doria, no. 1 (March 2009): 25–30. http://dx.doi.org/10.3280/qu2009-001002.

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39

Underwood, Jim, and Edin Tabak. "Making Information Systems Material through Blackboxing." International Journal of Actor-Network Theory and Technological Innovation 3, no. 1 (January 2011): 16–26. http://dx.doi.org/10.4018/jantti.2011010102.

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Анотація:

In this paper, a case study of the evolution of an organisational intranet is used to compare the concepts of “materiality” with actor-network theory’s black-boxing. The authors argue that information systems need to become material through “due process”. Through this paper, questions arise as to what types of material allies are useful in this process, and whether these allies can co-evolve (or “co-materialise”) with the system. In this case there seemed to be existing technical actors, but the authors question whether this is always the case.

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40

Dagher, Marie-Claire, and Odile Filhol-Cochet. "Making Hybrids of Two-Hybrid Systems." BioTechniques 22, no. 5 (May 1997): 916–22. http://dx.doi.org/10.2144/97225st05.

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41

BAYNE, C. GRESHAM. "Support Systems for Medical Decision Making." Nursing Management (Springhouse) 28, no. 7 (July 1997): 51. http://dx.doi.org/10.1097/00006247-199707010-00014.

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42

O'Sullivan, Bryan. "Making sense of revision-control systems." Communications of the ACM 52, no. 9 (September 2009): 56–62. http://dx.doi.org/10.1145/1562164.1562183.

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43

Coury, Bruce G., and Michele Terranova. "Collaborative Decision Making in Dynamic Systems." Proceedings of the Human Factors Society Annual Meeting 35, no. 13 (September 1991): 944–48. http://dx.doi.org/10.1177/154193129103501311.

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44

Najafabadi, Amir T. Payandeh, Fatemeh Atatalab, and Maryam Omidi Najafabadi. "Credibility premium for rate-making systems." Communications in Statistics - Theory and Methods 46, no. 1 (September 30, 2016): 415–26. http://dx.doi.org/10.1080/03610926.2014.995823.

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45

I.Yu. MAKUSHEV. "Making Intelligent Aircraft Onboard Control Systems." Military Thought 29, no. 001 (March 31, 2020): 159–68. http://dx.doi.org/10.21557/mth.59216006.

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46

Pejanovic, Milos, and Svetomir Minic. "Automated systems for decision making support." Vojnotehnicki glasnik 48, no. 3 (2000): 295–304. http://dx.doi.org/10.5937/vojtehg0003295p.

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47

Fetter, Marilyn S. "Interoperability—Making Information Systems Work Together." Issues in Mental Health Nursing 30, no. 7 (January 2009): 470–72. http://dx.doi.org/10.1080/01612840902722138.

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48

Grady, Tim Porter-O. "Structure Decision-Making & Authority Systems." Nursing Management (Springhouse) 23, no. 2 (February 1992): 14. http://dx.doi.org/10.1097/00006247-199202000-00005.

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49

O'Sullivan, Bryan. "Making Sense of Revision-control Systems." Queue 7, no. 7 (August 2009): 30–40. http://dx.doi.org/10.1145/1594204.1595636.

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50

Schaefer, Robert. "The limits of systems-making organizations." ACM SIGSOFT Software Engineering Notes 35, no. 6 (November 27, 2010): 1–20. http://dx.doi.org/10.1145/1874391.1874406.

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