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Journal articles on the topic 'Technical optimization'

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Author: Grafiati
Published: 11 March 2023

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1

Chaudhuri, Surajit. "Technical perspectiveRelational query optimization." Communications of the ACM 52, no. 10 (October 2009): 86. http://dx.doi.org/10.1145/1562764.1562786.

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2

Kochergin, Victor, Daniil Plotnikov, Andrey Ilinykh, and Sergey Glushkov. "Optimization of Technical Monitoring Processes." Transportation Research Procedia 54 (2021): 166–72. http://dx.doi.org/10.1016/j.trpro.2021.02.061.

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3

Pohl, M. "Optimization of Biocatalysts for Technical Processes." Engineering in Life Sciences 1, no. 1 (July 2001): 17–20. http://dx.doi.org/10.1002/1618-2863(200107)1:1<17::aid-elsc17>3.0.co;2-q.

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4

Mamoulis, Nikos. "Technical Perspective." ACM SIGMOD Record 51, no. 1 (May 31, 2022): 50. http://dx.doi.org/10.1145/3542700.3542712.

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The optimal assignment problem is a classic combinatorial optimization problem. Given a set of n agents A, a set T of m tasks, and an n×m cost matrix C, the objective is to find the matching between A and T, which minimizes or maximizes an aggregate cost of the assigned agent-task pairs. In its standard definition, n = m and we are looking for the 1-to-1 matching with the minimum total cost. From a graph theory perspective, this is a weighted bipartite graph matching problem. A classic algorithm for solving the assignment problem is the Hungarian algorithm (a.k.a. Kuhn-Munkres algorithm) [3], which bears a O(n3) computational cost (assuming that n = m); this is the best run-time of any strongly polynomial algorithm for this problem. There are many variants of the assignment problem, which differ in the optimization objective (i.e., minimize/maximize an aggregate cost, achieve a stable matching, maximize the number of agents matched which their top preferences, etc.) and in whether there are constraints on the number of matches for each agent or task.
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5

Shapovalov, V. V., P. V. Kharlamov, S. L. Gorin, V. B. Mischinenko, R. A. Kornienko, and V. E. Zinovev. "Optimization of dynamically loaded nonlinear technical systems." IOP Conference Series: Materials Science and Engineering 1029 (January 19, 2021): 012043. http://dx.doi.org/10.1088/1757-899x/1029/1/012043.

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6

Malakov, Ivo, and Velizar Zaharinov. "Optimization of Size Ranges of Technical Products." Applied Mechanics and Materials 859 (December 2016): 194–203. http://dx.doi.org/10.4028/www.scientific.net/amm.859.194.

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The paper presents a formulation of the problem for choosing an optimal size range of technical products and a mathematical model for solving of the said problem. Particular attention is paid to the ways for satisfying the types of demand for the size ranges’ elements. Two ways are proposed, and they are formalized as constraints in a mathematical model. Methods are proposed for solving the problem in accordance with the chosen way of satisfying the demand, and an algorithm is developed. The methods and the algorithm are approbated by solving a particular problem.
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7

Gordeev, A. V., and N. Yu Loginov. "TECHNICAL PARAMETERS OPTIMIZATION WHILE SOLVING ENGINEERING PROBLEMS." Vektor nauki Tol'yattinskogo gosudarstvennogo universiteta, no. 4 (2015): 25–30. http://dx.doi.org/10.18323/2073-5073-2015-4-25-30.

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8

GASPARINI, M., M. MANTICA, P. GALIMBERTI, U. LAMARCHESINA, M. MANGLAVACCHI, F. FALETRA, and E. GRONDA. "Optimization of cardiac resynchronization therapy: technical aspects." European Heart Journal Supplements 4 (April 2002): D82—D87. http://dx.doi.org/10.1016/s1520-765x(02)90164-1.

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9

Gasparini, M., M. Mantica, P. Galimberti, U. La Marchesina, M. Manglavacchi, F. Faletra, and E. Gronda. "Optimization of cardiac resynchronization therapy: technical aspects." European Heart Journal Supplements 4, suppl_D (April 1, 2002): D82—D87. http://dx.doi.org/10.1093/ehjsupp/4.suppl_d.d82.

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10

Cox, Landon P. "Technical perspective: Measuring optimization potential with Coz." Communications of the ACM 61, no. 6 (May 23, 2018): 90. http://dx.doi.org/10.1145/3205913.

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11

GASPARINI, M., M. MANTICA, P. GALIMBERTI, U. LAMARCHESINA, M. MANGLAVACCHI, F. FALETRA, and E. GRONDA. "Optimization of cardiac resynchronization therapy: technical aspects." European Heart Journal 4 (April 2002): D82—D87. http://dx.doi.org/10.1016/s0195-668x(02)80032-x.

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12

Bocioaca, Laurentiu. "Technical and Tactical Optimization Factors in Judo." Procedia - Social and Behavioral Sciences 117 (March 2014): 389–94. http://dx.doi.org/10.1016/j.sbspro.2014.02.233.

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13

Afridi, Zohaib Ur Rehman, and Naseha Wafa Qammar. "Technical Challenges and Optimization of Biogas Plants." ChemBioEng Reviews 7, no. 4 (July 8, 2020): 119–29. http://dx.doi.org/10.1002/cben.202000005.

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14

Shapovalov, Vladimir V., Marina Burakova, Tat'yana Sayamova, Vasiliy Mischinenko, and Roman Kornienko. "Optimization of Dynamically Loaded Nonlinear Technical Systems." Transportation Research Procedia 68 (2023): 811–18. http://dx.doi.org/10.1016/j.trpro.2023.02.114.

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15

Knobloch, Roman, and Jaroslav Mlýnek. "DIFFERENTIAL EVOLUTION ALGORITHM IN MODELS OF TECHNICAL OPTIMIZATION." Anwendungen und Konzepte der Wirtschaftsinformatik, no. 14 (December 9, 2021): 6. http://dx.doi.org/10.26034/lu.akwi.2021.3321.

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At present, evolutionary optimization algorithms are increasingly used in the development of new technological processes. Evolutionary algorithms often allow the optimization procedure to be performed even in cases where classical optimization algorithms fail (e.g. gradient methods) and where an acceptable solution is sufficient to solve the optimization task. The article focuses on possibilities of using a differential evolution algorithm in the optimization process. This algorithm is often referred to in the literature as a global optimization procedure. However, we show by means of a practical example that the convergence of the classic differential algorithm to the global extreme is not generally assured and is largely dependent on the specific cost function. To remove this weakness, we designed a modified version of the differential evolution algorithm. The improved version, named the modified differential evolution algorithm, is described in the article. It is possible to prove asymptotic convergence to the global minimum of the cost function for the modified version of the algorithm.
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16

ABLAEV, A. R., E. V. KHROMOV, R. R. ABLAEV, and A. P. POLYAKOV. "DIGITAL DESIGN OPTIMIZATION OF A COMPLEX TECHNOLOGICAL SYSTEM." Fundamental and Applied Problems of Engineering and Technology 3 (2021): 85–90. http://dx.doi.org/10.33979/2073-7408-2021-347-3-85-90.

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The article investigates the issue of optimization of a complex technical system at the stage of its design using a heuristic–phenomenological approach. The analysis of the principles of complex optimization of complex technical systems is carried out. A four–level structure for the synthesis of methodological, informational and software support for complex optimization of complex technical systems is proposed, which will allow controlling the programmable parameters of complex technical systems at each stage of their design.
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17

Kumar Mandal, Manoj, Arun Prasad Burnwal, and Avinash Kumar. "OPTIMIZATION TECHNIQUE FOR MANPOWER TRAINING IN TECHNICAL COLLEGE." International Journal of Students' Research in Technology & Management 8, no. 2 (June 16, 2020): 21–24. http://dx.doi.org/10.18510/ijsrtm.2020.824.

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Purpose: In the modern era, the number of technical colleges increases rapidly due to the huge growth of the students. But the number of teachers is less compare to the number of students. So, there is a need for optimization of manpower in the institutes. Methodology: In this paper, mathematical optimization is used with the help of linear programming by using permanent, contractual, and adjunct faculties with the help of three objectives for the engineering department, general department, and non-teaching staffs. Main Finding: Finally, the outcome of the paper is different optimal values for three stated objectives. Implication: The proposed method is the mathematical formulation of the optimization in a technical institute. The novelty of Study: This paper deals with a mathematical model of manpower problem. A sample problem has solved to illustrate the procedure.
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18

Zeng, Qing Liang, Xi Wei Wang, and Ping Qiang Zhao. "Research on Energy-Saving Technology Based on Optimization of Mechatronic System for Coal Mine." Advanced Materials Research 338 (September 2011): 436–39. http://dx.doi.org/10.4028/www.scientific.net/amr.338.436.

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In this paper, in view of the energy consumption problems of coal enterprises ,the overall technical framework of high efficiency and energy-saving for coal mines is proposed, and staged structure optimization model and network matching optimization model of mechatronic systems for coal mine are established ,which provide a theoretical basis for optimizations of mechatronic systems and promote the development of energy-saving technologies of coal mines.
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19

Zhang, Zhenguo, Huixing Feng, Weihua Zhao, Maochao Li, Xiaobing Xu, and Jiuqiang Li. "The Technical Scheme Optimization of Nuclear Vessel Hydrostatic Test." IOP Conference Series: Earth and Environmental Science 769, no. 4 (May 1, 2021): 042051. http://dx.doi.org/10.1088/1755-1315/769/4/042051.

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Abstract Nuclear vessel hydrostatic test is an important means of sealing and pressure resistance, because the nuclear vessel hydrostatic test involves important system and key equipment, how to optimize the nuclear vessel hydraulic test is a worthy topic. This paper discusses how to optimize nuclear vessel hydrostatic test technology, mainly from three aspects to optimize, such as the optimization of working ticket logic, the optimization of vessel hydrostatic test implementation, and the optimization of hydrostatic test equipment. Optimization of hydrostatic test scheme provides significance reference for plant maintenance.
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20

Désir, Antoine, Vineet Goyal, and Jiawei Zhang. "Technical Note—Capacitated Assortment Optimization: Hardness and Approximation." Operations Research 70, no. 2 (March 2022): 893–904. http://dx.doi.org/10.1287/opre.2021.2142.

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Assortment optimization is an important problem arising in various applications. In many practical settings, the assortment is subject to a capacity constraint. In “Capacitated Assortment Optimization: Hardness and Approximation,” Désir, Goyal, and Zhang study the capacitated assortment optimization problem. The authors first show that adding a general capacity constraint makes the problem NP-hard even for the simple multinomial logit model. They also show that under the mixture of multinomial logit model, even the unconstrained problem is hard to approximate within any reasonable factor when the number of mixtures is not constant. In view of these hardness results, the authors present near-optimal algorithms for a large class of parametric choice models including the mixture of multinomial logit, Markov chain, nested logit, and d-level nested logit choice models. In fact, their approach extends to a large class of objective functions that depend only on a small number of linear functions.
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21

Dolinskii, A. A., B. Kh Draganov, and V. A. Dubrovin. "Optimization of Technical Systems by Exergo-Economic Methods." Heat Transfer Research 36, no. 3 (2005): 229–35. http://dx.doi.org/10.1615/heattransres.v36.i3.60.

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22

Liu Qiang, 刘强, 邬融 Wu Rong, 张晓波 Zhang Xiaobo, 李永平 Li Yongping, and 田杨超 Tian Yangchao. "Technical Optimization of Multi-Level Diffractive Optical Elements." Chinese Journal of Lasers 35, no. 8 (2008): 1165–68. http://dx.doi.org/10.3788/cjl20083508.1165.

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23

Bächler, Richard, Horst Bunke, and Lutz-Peter Nolte. "Restricted Surface Matching—Numerical Optimization and Technical Evaluation." Computer Aided Surgery 6, no. 3 (January 2001): 143–52. http://dx.doi.org/10.3109/10929080109146000.

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24

Burov, Genady. "Optimization Algorithms of Objective Control of Technical Objects." Boundary Field Problems and Computer Simulation 53 (February 5, 2015): 36. http://dx.doi.org/10.7250/bfpcs.2014.006.

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25

FENG, Xiaojun. "Technical Parameter Optimization of Rapid Tooling Filling Process." Journal of Mechanical Engineering 45, no. 11 (2009): 163. http://dx.doi.org/10.3901/jme.2009.11.163.

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26

Klostermann, T., and E. O. K. Zhan. "Control Based Optimization of Collaborative Technical Customer Service." International Journal of Computers and Applications 29, no. 1 (January 2007): 96–102. http://dx.doi.org/10.1080/1206212x.2007.11441837.

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27

Feldman, Jacob, Alice Paul, and Huseyin Topaloglu. "Technical Note—Assortment Optimization with Small Consideration Sets." Operations Research 67, no. 5 (September 2019): 1283–99. http://dx.doi.org/10.1287/opre.2018.1803.

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28

Shepelev, S., V. Shepelev, and Z. Almetova. "Optimization of Technical Equipment for Crop Sowing Processes." Procedia Engineering 150 (2016): 1258–62. http://dx.doi.org/10.1016/j.proeng.2016.07.142.

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29

Pintér, János D., and Frank J. Kampas. "Benchmarking nonlinear optimization software in technical computing environments." TOP 21, no. 1 (August 17, 2011): 133–62. http://dx.doi.org/10.1007/s11750-011-0209-5.

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30

Wang, Fei, Philip L. H. Yu, and David W. Cheung. "Combining technical trading rules using particle swarm optimization." Expert Systems with Applications 41, no. 6 (May 2014): 3016–26. http://dx.doi.org/10.1016/j.eswa.2013.10.032.

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31

Martikka, Heikki Ilmari, and Ilkka Pöllänen. "Multi-objective optimization by technical laws and heuristics." Memetic Computing 1, no. 3 (September 26, 2009): 229–38. http://dx.doi.org/10.1007/s12293-009-0020-0.

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32

Wong, Jinn-Tsai, Sui-Ling Li, and David Gillingwater. "An Optimization Model for Assessing Flight Technical Delay." Transportation Planning and Technology 25, no. 2 (January 2002): 121–53. http://dx.doi.org/10.1080/03081060290033212.

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33

Beste, Y. A., W. Arlt, T. Laiblin, K. Lenz, H. Boysen, G. Wozny, and M. Lisso. "Design and Optimization of the Technical Chromatographic Process." Chemie Ingenieur Technik 73, no. 6 (June 2001): 721. http://dx.doi.org/10.1002/1522-2640(200106)73:6<721::aid-cite7211111>3.0.co;2-7.

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34

B�chler, Richard, Horst Bunke, and Lutz-Peter Nolte. "Restricted surface matching?Numerical optimization and technical evaluation." Computer Aided Surgery 6, no. 3 (2001): 143–52. http://dx.doi.org/10.1002/igs.1017.

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35

Howe, Bill. "Technical Perspective." ACM SIGMOD Record 51, no. 1 (May 31, 2022): 59. http://dx.doi.org/10.1145/3542700.3542714.

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There is a tension between an imperative style for control flow that has been shown to be easier to use, especially for novices, and a functional style for control flow that better exposes optimization opportunities, thereby making the optimizers more capable. The authors of "Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance" propose Mitos, a program rewriting framework that achieves the best of both worlds by borrowing program analysis concepts from compilers and lifting them to the distributed dataflow regime. Dataflow systems require significant data movement during processing, which can be highly redundant and wasteful in the context of iteration: naive execution plans can reprocess the same massive dataset on each iteration, and iteration i+1 must wait until iteration i is finished. The authors design a mechanism for labeling each intermediate result with its execution path, allowing the system to simultaneously manage complex branching situations while also implementing efficient processing via loop pipelining, all by reasoning about and comparing execution paths.
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36

Chou, Qiang, Daochuan Ge, and Ruoxing Zhang. "PSO Based Optimization of Testing and Maintenance Cost in NPPs." Science and Technology of Nuclear Installations 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/286826.

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Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP) to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT), maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs) have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.
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37

Zeng, Juying. "Nonparametric Optimization of Preference in Technical Efficiency in China." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 3 (May 20, 2015): 430–38. http://dx.doi.org/10.20965/jaciii.2015.p0430.

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Applying nonparametric path-converged approach, the research innovatively provides the measurement of preference in technical efficiency by the ratio of labor elasticity to capital elasticity and further attempts to realize the optimization of preference in technical efficiency by a strategy of 30% abolishment of initial Drug Addition and a strategy with combination of smoothed governmental fiscal expenditure, which sheds fresh light on promoting hospitals’ efficiency in China from perspective of management engineering. With sample data of provincial public hospitals in Zhejiang Province during period of 200901-201306, the research obtains following conclusions. First, benchmark preference in technical efficiency shows production has shifted from physical capital preference to labor skilled preference in technical efficiency. Second, the changing trend of preference in technical efficiency validates initial Drug Addition and governmental fiscal expenditure pushes and restrains the labor skilled preference in technical efficiency respectively. Third, the strategy of 30% abolishment of Drug Addition will strengthen labor skilled preference in technical efficiency with less promotion intensity of initial Drug Addition. The strategy with combination of governmental fiscal expenditure restrains labor skilled preference in technical efficiency. The facts validate great urgency of raising payments for doctors and nurses so as to promoting efficiency effectively.
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38

Grenville, N. Delia, and Brian M. Kleiner. "Level of Joint Optimization." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 13 (October 1998): 959–63. http://dx.doi.org/10.1177/154193129804201303.

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The macroergonomic construct of joint optimization was operationally defined as time allotment to the personnel and technological subsystems in a laboratory quality inspection experiment. Individual and group performance as well as perceived level of joint optimization were measured for each treatment condition. The experiment was organized as a one-factor between subjects design with six levels: 30%, 40%, 50%, 60%, 70% and 80% allotted to the technological subsystem. While there were no significant differences in individual performance due to the amount of time allotted to tasks in the technical or social subsystems, differences did occur at the group performance level. Overall, the results of the study are encouraging and suggest there may be a cause and effect relationship between time allotment and level of joint optimization and performance in the technical subsystem.
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39

Reichert, Gabriel, and Christoph Schmidl. "SWOT Analysis of Non-Technical and Technical Measures towards “(Nearly) Zero-Emission Stove Technologies”." Energies 16, no. 3 (January 30, 2023): 1388. http://dx.doi.org/10.3390/en16031388.

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Firewood stoves are widespread and popular for renewable heat supply in Europe. Several new technological measures have been developed recently that aim at improving the appliance performance in terms of emissions and efficiency. In order to support the trend towards “(nearly) zero-emissions technologies”, the objective of this study was to provide a profound overview of the most relevant technical primary and secondary measures for emission reduction and to analyze their functionality, the relevant framework conditions for their application and their costs. Since user behavior is essential for emission and efficiency performance, the state of knowledge about user behavior is summarized and the latest measures for its optimization are evaluated as non-technical primary measures. Primary and secondary measures were analyzed separately, but also potentially promising combinations of primary and secondary optimization were evaluated using SWOT analysis. The results showed that complementary application of primary and secondary measures will be necessary in order to achieve “(nearly) zero-emission technologies”. The paper is useful for manufacturers and provides them with guidance and recommendations for future developments. They can specifically select appropriate measures for their products and applications not only based on technical aspects, but also with a strong focus on user behavior and user comfort.
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40

Mărguță, Daniel, Ramona-Iuliana Popa, Eugen Herghelegiu, and Constantin Cărăușu. "TECHNICAL OPTIMIZATION OF WATER JET CUTTING OF BIODEGRADABLE MATERIALS." International Journal of Manufacturing Economics and Management 2, no. 1 (June 20, 2022): 23–34. http://dx.doi.org/10.54684/ijmem.2022.2.1.23.

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The main purpose of the technical optimization is to determine the optimal values of the processing parameters in order to increase the processing performance or decrease the processing time. Abrasive material water jet cutting is a processing process whose applicability is increasing in the conditions of the appearance of high-performance equipment. The technical optimization of this machining process aims at determining the distance between the machined material and the cutting head, determining the optimum length of the focusing tube, establishing the optimum machining pressure and determining the optimum amount of abrasive material so as to ensure maximum penetration depth of water jet with abrasive or minimizing surface roughness. During the research, the part subjected to abrasive water jet cutting was obtained by injection from Arboblend V2 Nature. The experiments were carried out according to a complete factorial plan 23, where the parameters on two levels were: water jet pressure, cutting speed and abrasive material flow. The optimization criterion followed was to minimize the standard roughness Ra. The experimental results showed that the parameter flow rate of abrasive material has the greatest influence on the roughness, the highest values of roughness are obtained when using a larger amount of abrasive (300g / min). The lowest value of the roughness of the cut surfaces is obtained for the following process parameters: low water pressure - 100MPa, high cutting speed - 150 mm / min and high flow of abrasive material - 300g / min.
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41

Alekseenko, M., V. Litvyak, and N. Novikova. "Optimization of technical conditions for obtaining acid-hydrolylated starch." Scientific Works of National University of Food Technologies 25, no. 4 (August 2019): 188–99. http://dx.doi.org/10.24263/2225-2924-2019-25-4-21.

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42

Jasemi, M., A. M. Kimiagari, and M. Jasemi. "On Development of Technical Analysis Based Portfolio Optimization Models." Asian Journal of Industrial Engineering 3, no. 1 (December 15, 2010): 1–12. http://dx.doi.org/10.3923/ajie.2011.1.12.

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43

Li, Ting, TongHua Zou, and HongXuan Li. "Research on technical Optimization of solid regenerative electric Boiler." Journal of Physics: Conference Series 2247, no. 1 (April 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2247/1/012015.

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Abstract This paper briefly introduces the principle and device of solid heat storage in electric boiler, analyzes the advantages and necessity of solid heat storage boiler technology. The development status and application examples of heat storage technology for solid thermoelectric boiler are introduced, and the efficiency of solid heat storage device is verified through experiments.
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44

Matusov, Leonid B. "The vector optimization and modeling of a technical systems." MATEC Web of Conferences 224 (2018): 04020. http://dx.doi.org/10.1051/matecconf/201822404020.

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The construction a feasible solution set with a given accuracy is a main problem in multicriteria optimization and modeling. In order to construct the feasible solution set, a method called the Parameter Space Investigation has been created and successfully integrated into various fields of industry, science, and technology. Multicriteria modeling (identification) is a new direction that is of great value in applications. In the most common usage, the term “identification” means construction of the mathematical model of a system and determination of the parameters (design variables) of the model. The construction a feasible solution set with a given accuracy is a common way for solving multicriteria optimization and modeling problems. The issues of the estimation of the Parameter Space Investigation method convergence rate, the approximation of the feasible solution set are described. Besides these, the multicriteria identification problems of mechanical systems are discussed too.
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45

SNIEDOVTCH, MOSHE. "TECHNICAL NOTE On Fractional Programming Problems in Engineering Optimization." Engineering Optimization 12, no. 3 (October 1987): 247–50. http://dx.doi.org/10.1080/03052158708941098.

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46

Chen, Ning, Chen-Guang Xing, Xi-Xian Xie, and Qing-Yang Xu. "Optimization of technical conditions of producing ribavirin byBacillus subtilis." Annals of Microbiology 59, no. 3 (September 2009): 525–30. http://dx.doi.org/10.1007/bf03175141.

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47

Peng, Zhong-dong, Xu-guang Gao, Guo-rong Hu, Xian-yan Tan, Ke Du, Xin-rong Deng, and Ye-xiang Liu. "Technical optimization of LiFePO4 preparation by water quenching treatment." Journal of Central South University of Technology 14, no. 5 (October 2007): 656–59. http://dx.doi.org/10.1007/s11771-007-0126-9.

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48

Nikolić, D., R. R. Nikolić, B. Krstić, V. Lazić, I. Ž. Nikolić, I. Krstić, and V. Krstić. "Optimization of Technical Diagnostics Procedures for Hydroelectric Power Plants." Procedia Engineering 40 (2012): 322–27. http://dx.doi.org/10.1016/j.proeng.2012.07.102.

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49

Bodas-Sagi, Diego José, Pablo Fernández-Blanco, José Ignacio Hidalgo, and Francisco José Soltero-Domingo. "A parallel evolutionary algorithm for technical market indicators optimization." Natural Computing 12, no. 2 (September 28, 2012): 195–207. http://dx.doi.org/10.1007/s11047-012-9347-4.

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Jie, Lim Pui, Rohaizan Ramlan, Rohayanti Hassan, Rashidah Omar, and Chan Shau Wei. "Website quality of Malaysian Technical University." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1624. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1624-1628.

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Abstract:
<span>University website is the platform for university to interact with people. It is also an interphase for people to infer and getting known about the university. In addition, quality of the university website is vital to let people get positive response to the university. Therefore, university website should be evaluated for better performance. This study evaluate website of Malaysian Technical University (MTUN) on year of 2016 based on the criteria selected using online web diagnostics tools. There are nine criteria selected to measure the website; load time, response time, page rank, traffic, design optimization, page size, number of items, markup validation and broken link. The result shows website of UMP performed better in design optimization, page rank and markup validation. Meanwhile, UTHM performed in page rank, load time and page size. UTEM excellences in response time, number of items and broken link. Lastly, UniMAP performed in traffic criteria only. However, the (MTUN) University website is almost not meets with the criteria that selected with the quality standard that have been suggested. </span>
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