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Relevant bibliographies by topics / Multi-environment / Journal articles

Journal articles on the topic 'Multi-environment'

To see the other types of publications on this topic, follow the link: Multi-environment.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Shirvani, Mojtaba, Mostafa Abdollahi, Ali Akbar Dusti, and Iman Baghbani. "Wind parks fluctuations at multi machine environment." International Journal of Academic Research 6, no. 3 (May 30, 2014): 202–7. http://dx.doi.org/10.7813/2075-4124.2014/6-3/a.28.

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2

Kainz, O., D. Cymbalák, and F. Jakab. "Anthropometric Proportions Estimation Using recCAL in Multi-camera Environment." International Journal of Modeling and Optimization 5, no. 4 (2015): 300–303. http://dx.doi.org/10.7763/ijmo.2015.v5.478.

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3

Globerson, Shlomo, and Gad Vitner. "Measuring productivity in multi-stage, multi-product environment." International Journal of Productivity and Quality Management 26, no. 3 (2019): 290. http://dx.doi.org/10.1504/ijpqm.2019.098365.

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4

Globerson, Shlomo, and Gad Vitner. "Measuring productivity in multi-stage, multi-product environment." International Journal of Productivity and Quality Management 26, no. 3 (2019): 290. http://dx.doi.org/10.1504/ijpqm.2019.10019762.

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5

Osipov, Oleg Ivanovich. "CACHE IN MULTI-CORE ENVIRONMENT." Economy. Business. Computer science, no. 3 (January 1, 2016): 115–24. http://dx.doi.org/10.19075/2500-2074-2016-3-115-124.

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6

Borisoglebskaya, L. N., A. A. Fedotov, O. V. Pilipenko, S. M. Sergeev, and O. Ja Kravets. "Multi-disciplinary educational information environment." Journal of Physics: Conference Series 1691 (November 2020): 012065. http://dx.doi.org/10.1088/1742-6596/1691/1/012065.

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7

Ipser, Edward A., David S. Wile, and Dean Jacobs. "A multi-formalism specification environment." ACM SIGSOFT Software Engineering Notes 15, no. 6 (December 1990): 94–106. http://dx.doi.org/10.1145/99278.99287.

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8

Oklobdzija, Vojin. "Clocking in multi-GHz environment." Facta universitatis - series: Electronics and Energetics 15, no. 1 (2002): 13–31. http://dx.doi.org/10.2298/fuee0201013o.

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An overview of clocking and design of clocked storage elements is presented. Systematic design of ip-op is explained as well as "time borrowing" and absorption of clock uncertainties. We show how should different clocked storage elements be compared against each other. The issues related to power consumption and low-power design are presented.

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9

Montesinos-López, Osval A., Abelardo Montesinos-López, José Crossa, Fernando H. Toledo, Oscar Pérez-Hernández, Kent M. Eskridge, and Jessica Rutkoski. "A Genomic Bayesian Multi-trait and Multi-environment Model." G3: Genes|Genomes|Genetics 6, no. 9 (June 24, 2016): 2725–44. http://dx.doi.org/10.1534/g3.116.032359.

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10

Lukić, Ivica, Mirko Köhler, and Erik Kiralj. "Appointment scheduling system in multi doctor/multi services environment." International journal of electrical and computer engineering systems 12, no. 3 (August 27, 2021): 171–76. http://dx.doi.org/10.32985/ijeces.12.3.6.

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Appointment scheduling systems are used by health care providers to manage access to their services. In this paper an algorithm and a web application for automatic appointment scheduling is presented. Both are implemented using the concept of booking appointments for patients for a specific service offered by each doctor. The purpose of the application is to make signing up for a specific service easier for patients and to improve health tourism in Croatia by maximizing doctor’s efficiency and minimize patient waiting time. Medical providers are added to the system, they add the services which they provide, and each service offered has its own duration time. Users register, search for services matching their parameters, and schedule an appointment for the requested service. Available appointments are generated using the presented algorithm, which is the main part of this paper. The algorithm searches the database and returns possible appointments. If patient has more than one appointment, possible appointments time can be before the existing appointment, between two appointments, or at the end of the last appointment. Thus, web application enables the patient to reserve desirable appointment time.

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11

Kachani, Soulaymane, and Kyrylo Shmatov. "Competitive Pricing in a Multi-Product Multi-Attribute Environment." Production and Operations Management 20, no. 5 (November 15, 2010): 668–80. http://dx.doi.org/10.1111/j.1937-5956.2010.01195.x.

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12

Rana, Akanksha, and Srinivas Arukonda. "Multi Tiered Security and Privacy- Enhancing Multi-cloud Environment." International Journal of Computer Trends and Technology 23, no. 1 (May 25, 2015): 7–11. http://dx.doi.org/10.14445/22312803/ijctt-v23p102.

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13

Alitappeh, Reza Javanmard, Kossar Jeddisaravi, and Frederico G. Guimarães. "Multi-objective multi-robot deployment in a dynamic environment." Soft Computing 21, no. 21 (June 4, 2016): 6481–97. http://dx.doi.org/10.1007/s00500-016-2207-x.

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14

Montesinos-López, Osval A., Abelardo Montesinos-López, Francisco Javier Luna-Vázquez, Fernando H. Toledo, Paulino Pérez-Rodríguez, Morten Lillemo, and José Crossa. "An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction." G3: Genes|Genomes|Genetics 9, no. 5 (February 28, 2019): 1355–69. http://dx.doi.org/10.1534/g3.119.400126.

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15

V, Amrutha Mohanan K., Hema E. K, and Monisree S. Audline Beena S. P. "Raspberry Pibot Manipulated with Skid-Steered Mechanism for Multi-Environment Surveillance." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1483–91. http://dx.doi.org/10.31142/ijtsrd11240.

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16

Oliveira, SRM. "Multi-Model for Planning High Complexity Environment using Hybrid Intelligent Architecture." International Journal of Advances in Management and Economics 01, no. 04 (July 2, 2012): 60–73. http://dx.doi.org/10.31270/ijame/01/04/2012/09.

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17

Gokila, L., V. Poongodi, and Dr K. Thangadurai. "Multi Scheduling Reactive Resource Sharing for Dynamic Dataflow in Cloud Environment." Bonfring International Journal of Data Mining 6, no. 4 (December 31, 2016): 46–52. http://dx.doi.org/10.9756/bijdm.8307.

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18

Hussin, Masnida, Azizol Abdullah, and Rohaya Latip. "Agent-Based Pricing Determination for Cloud Services in Multi-Tenant Environment." International Journal of Computer and Communication Engineering 3, no. 6 (2014): 454–59. http://dx.doi.org/10.7763/ijcce.2014.v3.368.

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19

Parand, Fereshteh-Azadi, Nasrollah Moghaddam Charkari, and Sara Afrasyabia. "Response Fusion in Multi-Agent Environment." American Journal of Applied Sciences 6, no. 1 (January 1, 2009): 57–63. http://dx.doi.org/10.3844/ajas.2009.57.63.

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20

Parand. "Response Fusion in Multi-Agent Environment." American Journal of Applied Sciences 6, no. 1 (January 1, 2009): 57–63. http://dx.doi.org/10.3844/ajassp.2009.57.63.

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21

Kotikov, Ju G. "Quantomobile as a multi-environment vehicle." Вестник гражданских инженеров 17, no. 4 (2020): 182–88. http://dx.doi.org/10.23968/1999-5571-2020-17-4-182-188.

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The advance of science and technology by the beginning of the XXI century has enabled the creation of quantum engines (QuEn) using the energy of physical vacuum. This is going to lead to the re-equipment of the transport industry: the classic automobile will be transformed into the quantomobile with a quantum engine installed on it. We consider the possibility of creating a quantum - powered vehicle - a QuEn vehicle that will be able to function in all kinds of physical environment (in the air, on land and water) a multi-environment vehicle (MEV), as well as forming a model of its power balance in these kinds of environment. There are specified 10 variants of the quantomobile motion, from an air quantum helicopter (quantocraft) to a quantum submarine (quantomarine). Taking into account the previously published author's concepts of land and air versions of the quantomobile, attention is paid to its motion in the mode of the quantum submarine, which shows the possibility of significantly simplifying the design schemes of existing submarines when implementing quantum thrust on them.

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22

Marchewka, Adam, Zbigniew Lutowski, Beata Marciniak, Mścisław Śrutek, and Sławomir Bujnowski. "Control Algorithms in Multi-Agent Environment." Image Processing & Communications 17, no. 3 (December 1, 2012): 47–62. http://dx.doi.org/10.2478/v10248-012-0021-3.

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Abstract In this paper the general information regarding controlling multi-agent systems has been given. The simulation results of using algorithms, proposed by authors, used for controlling groups of robots servicing the virtual library has been presented. These simulations concerned estimation of following parameters: number of iterations needed to complete a task, average customer service time and average customer awaiting service time in function of number of robots, size of library and number of exchange points.

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23

Sebestyen, Zoltan, and Tamas Toth. "Ranking Projects in Multi-Criteria Environment." Organization, Technology & Management in Construction: An International Journal 7, no. 2 (November 2015): 1295–301. http://dx.doi.org/10.5592/otmcj.2015.2.4.

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24

Sklar, Elizabeth. "NetLogo, a Multi-agent Simulation Environment." Artificial Life 13, no. 3 (July 2007): 303–11. http://dx.doi.org/10.1162/artl.2007.13.3.303.

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25

Glinert, Ephraim P., R. Lindsay Todd, and G. Bowden Wise. "The adaptive multi-interface multimodal environment." ACM Computing Surveys 28, no. 4es (December 1996): 134. http://dx.doi.org/10.1145/242224.242394.

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26

Jałowiecki, Konrad, and Jerzy Dajka. "Multi-partite entanglement in Davies environment." European Physical Journal Special Topics 227, no. 15-16 (February 21, 2019): 2037–41. http://dx.doi.org/10.1140/epjst/e2019-800042-9.

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27

Oliveira, Eugénio, R. Camacho, and C. Ramos. "A multi-agent environment in robotics." Robotica 9, no. 4 (December 1991): 431–40. http://dx.doi.org/10.1017/s0263574700000618.

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SUMMARYThe use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks.Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while the overall aim is devoted to the community of cooperative Agents. These Agents, which our testbed concerns, include Vision, Planner, World Model and the Robot itself.

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28

Carmona, Josep, and Jetty Kleijn. "Compatibility in a multi-component environment." Theoretical Computer Science 484 (May 2013): 1–15. http://dx.doi.org/10.1016/j.tcs.2013.03.006.

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29

van der Pas, R. J., and J. M. van Kats. "Parallelism in a multi-user environment." Parallel Computing 17, no. 2-3 (June 1991): 285–96. http://dx.doi.org/10.1016/s0167-8191(05)80114-0.

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30

Biggins, J. D., H. Cohn, and O. Nerman. "Multi-type branching in varying environment." Stochastic Processes and their Applications 83, no. 2 (October 1999): 357–400. http://dx.doi.org/10.1016/s0304-4149(99)00049-6.

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31

MA, Liang, Jihong YAN, Jie ZHAO, and Zhifeng CHEN. "Virtual Environment-Based Multi-Operator Multi-Robot Cooperative Teleoperation System." ROBOT 33, no. 2 (August 3, 2011): 169–73. http://dx.doi.org/10.3724/sp.j.1218.2011.00169.

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32

Ma, Cong, Zhizhong Li, Dahua Lin, and Jiangshe Zhang. "Parallel Multi-Environment Shaping Algorithm for Complex Multi-step Task." Neurocomputing 402 (August 2020): 323–35. http://dx.doi.org/10.1016/j.neucom.2020.04.070.

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33

Huby, Guro, and M. R. Salkind. "Social work in a multi-racial and multi-cultural environment." Journal of Social Work Practice 4, no. 2 (May 1990): 90–97. http://dx.doi.org/10.1080/02650539008413431.

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34

Kundu, Pradip, Samarjit Kar, and Manoranjan Maiti. "Multi-objective multi-item solid transportation problem in fuzzy environment." Applied Mathematical Modelling 37, no. 4 (February 2013): 2028–38. http://dx.doi.org/10.1016/j.apm.2012.04.026.

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35

HILLION, H., and J. M. PROTH. "Finite capacity flow control in a multi-stage/multi-product environment." International Journal of Production Research 32, no. 5 (May 1994): 1119–36. http://dx.doi.org/10.1080/00207549408956991.

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36

Mathioulakis, E., and V. Belessiotis. "Integration of solar still in a multi-source, multi-use environment." Solar Energy 75, no. 5 (November 2003): 403–11. http://dx.doi.org/10.1016/j.solener.2003.07.006.

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37

Cui, Song, Huaihai Chen, Xudong He, and Wei Zheng. "Control algorithm update for multi-input multi-output random environment test." Mechanical Systems and Signal Processing 111 (October 2018): 643–62. http://dx.doi.org/10.1016/j.ymssp.2017.11.032.

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38

Korelc, J. "Multi-language and Multi-environment Generation of Nonlinear Finite Element Codes." Engineering with Computers 18, no. 4 (November 27, 2002): 312–27. http://dx.doi.org/10.1007/s003660200028.

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39

Keshmiri, Soheil, and Shahram Payandeh. "Regression Analysis of Multi-Rendezvous Recharging Route in Multi-Robot Environment." International Journal of Social Robotics 4, no. 1 (September 8, 2011): 15–27. http://dx.doi.org/10.1007/s12369-011-0102-2.

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40

Xu, Zeshui. "Multi-person multi-attribute decision making models under intuitionistic fuzzy environment." Fuzzy Optimization and Decision Making 6, no. 3 (August 29, 2007): 221–36. http://dx.doi.org/10.1007/s10700-007-9009-7.

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41

Vainshtain, David, and Oren Salzman. "Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 2021): 239–41. http://dx.doi.org/10.1609/socs.v12i1.18596.

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Planning collision-free paths for multiple agents operating in close proximity has a myriad of applications ranging from smart warehouses to route planning for airport taxiways. This problem, known as the Multi-Agent Path-Finding (MAPF) problem, is highly relevant to real-world applications in automation and robotics, and has attracted significant research in recent years. While in many applications, the robots are tasked with transporting objects and thus have the means to move obstacles, common formulations of the problem prohibit agents from moving obstacles en-route to a task. This often causes agents to take long detours to avoid obstacles instead of simply moving them to clear a path. In this work we present multi-agent terraforming, a novel extension of the MAPF problem that can exploit the fact that the system contains movable obstacles. We build upon leading MAPF solvers and propose an efficient method to solve the multi-agent terraforming problem in a manner that is both complete and optimal. We evaluate our method on scenarios inspired by smart warehouses (such as those of Amazon) and demonstrate that, compared to the classical MAPF formulation, the extra flexibility provided by terraforming facilitates a notable improvement of solution quality.

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42

Ahmad, Qazi Shoeb, Mohammad Faisal Khan, and Naeem Ahmad. "Multi-Criteria Group Decision-Making Models in a Multi-Choice Environment." Axioms 11, no. 11 (November 21, 2022): 659. http://dx.doi.org/10.3390/axioms11110659.

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The best–worst method (BWM) has recently demonstrated its applicability in addressing various decision-making problems in a practical setting. The traditional BWM method is based on deterministic information gathered from experts as pairwise comparisons of several criteria. The advantage of BWM is that it uses fewer calculations and analyses while maintaining good, acceptable consistency ratio values. A multi-choice best–worst method (MCBWM), which considers several options for pairwise comparison of preferences between the criteria, has recently been developed. The experts are given the option to select values from several comparison scales. The MCBWM technique has been shown to be better. Presenting the options for which an optimal solution has been found simplifies the calculation and establishes the ideal weight values. This study proposes two different mathematical programming models for solving multi-criteria decision-making problems having multiple decision-makers. The two methods are proposed considering the multi-choice uncertainty assumption in pairwise criteria comparisons. Additionally, it considers the best–worst method as the base model. The multi-choice uncertainty is applied to determine the best choice out of multiple choices. It gives a real-life scenario to the decision-making problems. Although there are many other forms of uncertainty, such as fuzzy, intuitionistic fuzzy, neutrosophic, probabilistic, etc., it focuses on choices instead of ambiguity in terms of the probabilistic or fuzzy nature of parameters. The parameter considered as multi-choice is the pairwise comparison. These parameters are handled by applying the Lagrange interpolating polynomial method. The proposed models are novel in terms of their mathematical structure and group decision-making approach. The models are formulated and further validated by solving numerical examples. It provides a framework for solving mcdm problems where the weightage to the decision-makers is also incorporated. The CR values for all the models of example 1 and 2, and the case study has been found acceptable.

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43

Covre, André Monzoli, Flavia Alves da Silva, Gleison Oliosi, Caio Cezar Guedes Correa, Alexandre Pio Viana, and Fabio Luiz Partelli. "Multi-Environment and Multi-Year Bayesian Analysis Approach in Coffee canephora." Plants 11, no. 23 (November 28, 2022): 3274. http://dx.doi.org/10.3390/plants11233274.

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This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.

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44

Zeynalov, Eldar, Joerg Friedrich, Manfred Wagner, and Gundula Hidde. "Effect of Br-Grafted Multi-Walled Carbon Nanotubes on the Model Oxidative Environment." Chemistry & Chemical Technology 9, no. 1 (March 15, 2015): 51–54. http://dx.doi.org/10.23939/chcht09.01.051.

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45

Oakey, Helena, Brian Cullis, Robin Thompson, Jordi Comadran, Claire Halpin, and Robbie Waugh. "Genomic Selection in Multi-environment Crop Trials." G3: Genes|Genomes|Genetics 6, no. 5 (March 14, 2016): 1313–26. http://dx.doi.org/10.1534/g3.116.027524.

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46

Sen, P., D. Sarddar ., S. K. Sinha, and R. Pandit. "Web Service Scheduling in Multi-Cloud Environment." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 30–38. http://dx.doi.org/10.26438/ijcse/v7i1.3038.

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47

Yan, Jiaqi, Xiuxian Li, Yilin Mo, and Changyun Wen. "Resilient multi-dimensional consensus in adversarial environment." Automatica 145 (November 2022): 110530. http://dx.doi.org/10.1016/j.automatica.2022.110530.

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48

Baines, Robert, Sree Kalyan Patiballa, Joran Booth, Luis Ramirez, Thomas Sipple, Andonny Garcia, Frank Fish, and Rebecca Kramer-Bottiglio. "Multi-environment robotic transitions through adaptive morphogenesis." Nature 610, no. 7931 (October 12, 2022): 283–89. http://dx.doi.org/10.1038/s41586-022-05188-w.

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49

Krunić, Momčilo V., Nenad B. Četić, Miodrag M. Đukić, Ivan Đ. Považan, and Miroslav V. Popović. "Integrated development environment for multi-core systems." Tehnika 69, no. 5 (2014): 818–25. http://dx.doi.org/10.5937/tehnika1405818k.

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50

H, Jayanth, Gurudath A S, and Umadevi V. "Intelligent Task Allocation in Multi Core Environment." International Journal of Computer Applications 115, no. 13 (April 22, 2015): 34–39. http://dx.doi.org/10.5120/20215-2484.

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