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Journal articles on the topic 'Weighting factors'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Vosgerau, Gottfried, and Matthis Synofzik. "Weighting models and weighting factors." Consciousness and Cognition 21, no. 1 (2012): 55–58. http://dx.doi.org/10.1016/j.concog.2011.09.016.

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2

Allisy, A., W. A. Jennings, A. M. Kellerer, and J. W. Müller. "Appendix B: Tissue Weighting Factors." Reports of the International Commission on Radiation Units and Measurements os-26, no. 2 (1993): 13. http://dx.doi.org/10.1093/jicru_os26.2.13.

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3

Allisy, A., W. A. Jennings, A. M. Kellerer, and J. W. Müller. "Appendix B: Tissue Weighting Factors." Journal of the International Commission on Radiation Units and Measurements os26, no. 2 (1993): 13. http://dx.doi.org/10.1093/jicru/os26.2.13.

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4

Zhao, Fuzhang. "Weighting Factors for Single-Step Trapezoidal Method." Journal of Heat Transfer 128, no. 4 (2005): 409–12. http://dx.doi.org/10.1115/1.2175155.

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The uses of a weighting factor along with a time step in a single-step trapezoidal method to solve a first-order parabolic system have been systematically studied. The weighting factors are used in two main types: constants and variables. The most commonly used constant weighting factors can be defined by the ratio of the Fibonacci sequence. Among them, the optimal weighting factor is 0.618, resulting in a balance between the overall accuracy and efficiency. With the finite element formulation, the space and time dimensions can be discretized separately. For the time discretization only, there

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5

Karamaşa, Çağlar, Ezgi Demir, Salih Memiş, and Selçuk Korucuk. "WeIghtIng the factors affectıng logıstıcs outsourcıng." Decision Making: Applications in Management and Engineering 4, no. 1 (2021): 19–33. http://dx.doi.org/10.31181/dmame2104019k.

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Today, growing and changing competitive conditions, products, and services, free movement of labor, and businesses with the information they develop strategies that create value to obtain a competitive advantage. Now, final buyers have the convenience of purchasing the products they demand with the features and conditions they want and at the price they accept. In such an environment, businesses use their supply chain and logistics activities more effectively and efficiently than their competitors. Today, achieving a strategic superiority in a global market where the content and quality of the

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6

Paule, R. C., and J. Mandel. "Consensus values, regressions, and weighting factors." Journal of Research of the National Bureau of Standards 94, no. 3 (1989): 197. http://dx.doi.org/10.6028/jres.094.020.

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7

Azizi, Majid, Seyed Alireza Dehghan, Mohsen Ziaie, and Nematollah Mohebi. "Identifying the customer satisfaction factors in furniture market." Economics, Management and Sustainability 2, no. 1 (2017): 6–18. https://doi.org/10.14254/jems.2017.2-1.1.

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<em>Purpose</em> – the purpose of this research is to identify the influential factors on customer satisfaction in the Iranian furniture market in order to get acquainted with the fundamental items for planning future sales programs with the purposes of extolling competitive advantages. <em>Design/methodology/approach</em> – A commixture of 6 items and 31 factors were educed from interviewing with 20 experts in furniture designing and manufacturing industry. The collected data from customer need indexes in previous research were also used. <em>Findings</em> – results showed that such factors a

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8

Azizi, Majid, Seyed Alireza Dehghan, Mohsen Ziaie, and Nematollah Mohebi. "Identifying the customer satisfaction factors in furniture market." Economics, Management and Sustainability 2, no. 1 (2017): 6–18. https://doi.org/10.5281/zenodo.998714.

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<em>Purpose</em> – the purpose of this research is to identify the influential factors on customer satisfaction in the Iranian furniture market in order to get acquainted with the fundamental items for planning future sales programs with the purposes of extolling competitive advantages. <em>Design/methodology/approach</em> – A commixture of 6 items and 31 factors were educed from interviewing with 20 experts in furniture designing and manufacturing industry. The collected data from customer need indexes in previous research were also used. <em>Findings</em> – results showed that such factors a

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9

Pelliccioni, M. "Radiation Weighting Factors and High Energy Radiation." Radiation Protection Dosimetry 80, no. 4 (1998): 371–78. http://dx.doi.org/10.1093/oxfordjournals.rpd.a032556.

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10

Yi, C. S., S. A. Choi, M. P. Shim, H. S. Kim, and B. S. Kim. "Water Allocation by Weighting Factors Considering Multiple Criteria." Water Supply 5, no. 3-4 (2005): 105–14. http://dx.doi.org/10.2166/ws.2005.0089.

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Typically, water has been allocated by some priority criteria like water rights in each country . However, water allocation by priority might have the problem in which the area of lowest priority can suffer from water shortage all the time. So, this study has tried to develop water allocation rules by weighting factor techniques. The weighting factors are estimated by sectoral and regional factors. The Analytic Hierarchy Process (AHP) is applied to estimate the sectoral factors and the regional factors can be divided by the regional-characteristic and regional-scale ends. The water allocation

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11

Andrae, Anders. "Weighting of circularity dimensions." Engineering and Applied Science Letters 5, no. 1 (2022): 1–9. https://doi.org/10.30538/psrp-easl2022.0085.

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Methods to determine the environmental consequences of circular strategies may be a prerequisite for the circular economy. However, the weighting factors of the criteria groups in the international L.1023 circularity scoring standard need to be determined beforehand. No comprehensive analysis of the connection between carbon footprint based life cycle assessment (LCA) results – of the product to be evaluated and redesigned – and these weighting factors has been published. Here a method, based on lifetime reduction and Analytical Hierarchy Process (AHP), for establishing weighting factors in th

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12

Cheng, Fan-Tien, and Ming-Shan Shih. "Multiple-goal priority considerations of redundant manipulators." Robotica 15, no. 6 (1997): 675–91. http://dx.doi.org/10.1017/s0263574797000805.

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In this paper, a systematic algorithm, the Parallel Scheme, is proposed to solve the multiple-goal problem of redundant manipulators. It can adaptively adjust all the weighting factors of the corresponding goals according to their individual needs. Therefore, conflicts due to redundancy competition are resolved. To upgrade the redundancy-release capability of the original Parallel Scheme, the Improved Parallel Scheme is also developed by using the exponential function to adjust the weightings. The Improved Parallel Scheme is shown to improve both the redundancy-release capability and the weigh

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13

Rimassa, Jorge Arroba, Rafael Muñoz Guillena, and Fernando Llopis. "The Weighting Factors to Improve Predictability on Twitter." Technology and Investment 09, no. 01 (2018): 68–79. http://dx.doi.org/10.4236/ti.2018.91005.

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14

손유진 and Sanghoon Bae. "Calculation of Weighting Factors for Sustainable Transportation Index." Journal of Transport Research 18, no. 2 (2011): 47–60. http://dx.doi.org/10.34143/jtr.2011.18.2.47.

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15

Leslie, William D. "Stochastic Variability in Effective Dose Tissue Weighting Factors." Health Physics 67, no. 1 (1994): 70–75. http://dx.doi.org/10.1097/00004032-199407000-00009.

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16

Xiang, W.-N., and D. L. Whitley. "Weighting land suitability factors by the PLUS method." Environment and Planning B: Planning and Design 21, no. 3 (1994): 273–304. http://dx.doi.org/10.1068/b210273.

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17

Rojas, Christian A., Jose Rodriguez, Felipe Villarroel, José R. Espinoza, César A. Silva, and Mauricio Trincado. "Predictive Torque and Flux Control Without Weighting Factors." IEEE Transactions on Industrial Electronics 60, no. 2 (2013): 681–90. http://dx.doi.org/10.1109/tie.2012.2206344.

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18

Heneghan, S. P., and M. Vangsness. "Instrument noise and weighting factors in data analysis." Experiments in Fluids 9, no. 5 (1990): 290–94. http://dx.doi.org/10.1007/bf00233130.

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19

Yang, Yifeng David, Changsoon Park, and Daisuke Kihara. "Threading without optimizing weighting factors for scoring function." Proteins: Structure, Function, and Bioinformatics 73, no. 3 (2008): 581–96. http://dx.doi.org/10.1002/prot.22082.

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20

Miao, Yanping, Zeyang Liu, Xuhe Zheng, Xiaosheng Chen, Xianhui Wang, and Yifan Zeng. "An Improved Water Yield Evaluation Method Based on Partition Variable-Weight Theory." Water 17, no. 4 (2025): 486. https://doi.org/10.3390/w17040486.

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Existing aquifer water richness evaluation methods typically employ fixed indicator weights, failing to account for variations within individual controlling factors or interactions among multiple factors. This study introduces an enhanced water richness index method based on zoned variable weighting theory. Through unified normalization of water inrush controlling factors for each main mining coal seam, construction of variable weighting status vectors, division of unified variable weighting intervals, and determination of vulnerability index zoning thresholds, the method dynamically assigns w

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21

KALUTARA, Pushpitha, Guomin ZHANG, Sujeeva SETUNGE, and Ron WAKEFIELD. "PRIORITISING SUSTAINABILITY FACTORS FOR AUSTRALIAN COMMUNITY BUILDINGS’ MANAGEMENT USING ANALYTICAL HIERARCHY PROCESS (AHP)." International Journal of Strategic Property Management 24, no. 1 (2018): 37–50. http://dx.doi.org/10.3846/ijspm.2018.318.

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The essence of Australian community buildings’ sustainable management drives through a previously established decision-making structure with four sustainability aspects and accompanying 18 criteria. Informed decisions are supported with a decision-making model that generates sustainability impacts of building components based on this decision-making structure. Building components’ individual impacts can be assigned using a numbering scale incorporated with linguistic terms. However, similar importance given to each aspect or criterion is arguable when the combined effect is considered. Hence,

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22

Gezi Fajri, Syafriandi Syafriandi, Nonong Amalita, and Zamahsary Martha. "Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java." UNP Journal of Statistics and Data Science 1, no. 3 (2023): 203–10. http://dx.doi.org/10.24036/ujsds/vol1-iss3/67.

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Poverty is crucial problem that negative impact on all sectors, including economic, social, and cultural development in East Java Province. Poverty can also increase unemployment, crime, trigger social disasters and hinder progress East Java province. One efforts overcome problem of poverty in East Java province is detect factors that influence. This effort can be done through statistical modeling to determine factors that influence poverty in East Java province. statistical model that can identify factors that influence poverty and explain relationship between region and surrounding area is s

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23

Higley, K. A., D. C. Kocher, A. G. Real, and D. B. Chambers. "Relative biological effectiveness and radiation weighting factors in the context of animals and plants." Annals of the ICRP 41, no. 3-4 (2012): 233–45. http://dx.doi.org/10.1016/j.icrp.2012.06.014.

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Radiation weighting factors have long been employed to modify absorbed dose as part of the process of evaluating radiological impact to humans. Their use represents an acknowledgement of the fundamental difference in energy deposition patterns of charged and uncharged particles, and how this can translate into varying degrees of biological impact. Weighting factors used in human radiation protection are derived from a variety of endpoints taken from in-vitro experiments that include human and animal cell lines, as well as in-vivo experiments with animals. Nonetheless, the application of radiat

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24

Prasetyani, Marita, R. Rizal Isnanto, and Isnaini Rosyida. "Determining The Location Detection on Several Sectors to Support The Business Center: Literature Review." E3S Web of Conferences 448 (2023): 02050. http://dx.doi.org/10.1051/e3sconf/202344802050.

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Determining a business location is an important aspect in making strategic decisions in various business sectors. In this review, we analyze the location determination of some sectors and the method used, with emphasis on the weighting used in the research reviewed. We collect and review relevant journal articles for different sectors, including business, construction, energy, environment, health, hotel, industry, logistics, military, and transportation. Further, we analyze the methods used in decision-making and provide a better understanding of preferences and relevant criteria and focusing

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25

Lenzuni, Paolo. "On the Low Frequency Noise Assessment Method." Journal of Low Frequency Noise, Vibration and Active Control 22, no. 2 (2003): 53–61. http://dx.doi.org/10.1260/026309203322770301.

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A new assessment method for low frequency noise annoyance is proposed. A new frequency weighting is introduced, and its robustness tested. Possible effects of impulsiveness and tonality are discussed. The method proposed explicitly includes correction factors for the former, while the latter is shown to be insignificant in this context. The method is cross-checked with existing methods based on frequency weightings, and its performance is objectively quantified using synthetic indicators.

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26

Hu, Eric, Linda Zou, and Craig Langston. "A New Interlink Decision Making Index for Making Multi-criteria Decision." Construction Economics and Building 4, no. 2 (2012): 33–42. http://dx.doi.org/10.5130/ajceb.v4i2.2929.

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Multi-criteria decisions usually require measurement or evaluation of performance in different units and their mix by application of weighting factors. This approach lads to potential manipulation of the results as a direct consequence of the applied weightings. In this paper a mechanism that is the brain child of the authors, has been proposed to overcome this problem. It is known as the Interlink Decision Making Index (IDMI) and has all the desired features: simple, interlink (all criteria) and automatically guaranteed dominant influence of critical criteria (i.e. no human weighting needed).

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27

Ferdian, Yuda Ariza, Rahayu Sulistyorini, and Muhammad Karami. "analisis penentuan wilayah Blackspot terjadinya kecelakaan lalu lintas di jalan soekarno-hatta lampung." Jurnal Rekayasa Sipil dan Desain 7, no. 2 (2019): 247–54. https://doi.org/10.23960/jrsdd.v7i2.1170.

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Traffic accidents need to get greater attention. Road accidents always have an impact on damage and losses for vehicles involved in accidents and road users around them. The cause of traffic accidents is due to unfavorable traffic conditions. The condition is divided into 4 factors which include human factors, vehicle factors, environmental factors and road factors.The accident data used in this study are secondary data obtained from the Bandar Lampung Police Department in 2015 to 2017. The methods used are Frequency Method, Highways Weighting Method, INDII-Aus Aid Weighting Method and KSI Met

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28

Bolat, Pelin, Gizem Kayisoglu, Emine Gunes, Furkan Eyup Kızılay, and Soysal Ozsogut. "Weighting Key Factors for Port Congestion by AHP Method." Journal of ETA Maritime Science 8, no. 4 (2020): 252–73. http://dx.doi.org/10.5505/jems.2020.64426.

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29

Cowtan, Kevin. "Likelihood weighting of partial structure factors using spline coefficients." Journal of Applied Crystallography 38, no. 1 (2005): 193–98. http://dx.doi.org/10.1107/s0021889804031474.

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A method for the weighting of structure factors from an incomplete and inaccurate model is described which relies on the fitting of smooth spline functions of resolution. The use of smooth spline functions avoids the problems of discontinuities introduced when performing calculations in resolution shells. The complexity of the functions to be fit may be varied by changing the number of spline parameters. This approach is used to investigate the stability of the problem when data are limited.

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30

Yang, Xiaogen, Max Deinzer, Cindy Lederer, and Mina McDaniel. "Statistical Analysis with Weighting Factors: Hop Aroma in Beer." Journal of the American Society of Brewing Chemists 52, no. 4 (1994): 155–62. http://dx.doi.org/10.1094/asbcj-52-0155.

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31

Luo, M., B. Cao, Q. Ouyang, and Y. Zhu. "Indoor human thermal adaptation: dynamic processes and weighting factors." Indoor Air 27, no. 2 (2016): 273–81. http://dx.doi.org/10.1111/ina.12323.

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32

Turan, Hakan. "The Weighting of Factors Affecting Credit Risk in Banking." Procedia Economics and Finance 38 (2016): 49–53. http://dx.doi.org/10.1016/s2212-5671(16)30175-7.

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33

Ronis, David L., and Edmund R. Lipinski. "Value and uncertainty as weighting factors in impression formation." Journal of Experimental Social Psychology 21, no. 1 (1985): 47–60. http://dx.doi.org/10.1016/0022-1031(85)90005-8.

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34

Jung, Ki Mook, and Sigon Kim. "Evaluation factors weighting for investment priority of Tranfer center." Journal of The Korean Society For Urban Railway 7, no. 3 (2019): 373–80. http://dx.doi.org/10.24284/jkosur.2019.9.7.3.373.

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35

ZERDALİ, EMRAH, MERT ALTINTAŞ, ALİ BAKBAK, and ERKAN MEŞE. "Computationally efficient predictive torque control strategies without weighting factors." Turkish Journal of Electrical Engineering and Computer Sciences 30, no. 7 (2022): 2554–67. http://dx.doi.org/10.55730/1300-0632.3955.

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36

Soares, Sebastião R., Laurence Toffoletto, and Louise Deschênes. "Development of weighting factors in the context of LCIA." Journal of Cleaner Production 14, no. 6-7 (2006): 649–60. http://dx.doi.org/10.1016/j.jclepro.2005.07.018.

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37

Gribanova, Ekaterina B., and Dmitry V. Leonov. "Development of conditional optimization models taking into account weighting factors of relative importance." Proceedings of Tomsk State University of Control Systems and Radioelectronics 27, no. 3 (2024): 100–108. https://doi.org/10.21293/1818-0442-2024-27-3-100-108.

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The article examines the possibilities of generative textual artificial intelligence to determine the weighting coefficients of the relative importance of features. For the first time, the modification of conditional optimization models was performed in order to take into account the weighting coefficients of relative importance obtained on the basis of the randomization model.

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38

Alik, Rozana, Nik Rumzi Nik Idris, and Norjulia Mohamad Nordin. "Enhanced FS-PTC: Dynamic Weighting Factors for Optimal Flux and Torque Control." ELEKTRIKA- Journal of Electrical Engineering 22, no. 3 (2023): 31–36. http://dx.doi.org/10.11113/elektrika.v22n3.464.

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Recently, Finite State-Predictive Torque Control (FS-PTC) of induction motors has gained significant interest in high-performance motor drive applications. The effectiveness of FS-PTC relies on the successful minimization of a cost function achieved by selecting an appropriate voltage vector. Typically, the cost function for FS-PTC is composed of errors between the predicted and reference values of torque and flux; hence a weighting factor, \mathbit{\lambda}, is normally employed to establish different priorities between torque and flux. However, determining the optimal is a complex undertakin

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39

Jiang, Hao, and Wen Qiang Li. "Improved Algorithm Based on TFIDF in Text Classification." Advanced Materials Research 403-408 (November 2011): 1791–94. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1791.

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Traditional feature weighting algorithm TFIDF doesn’t take some other factors which impact the feature weight into consideration, so this paper discusses the factors in details and proposes a new feature weighting algorithm called NTFIDF combined with these factors and TFIDF. Experiment on the KNN classifier shows that NTFIDF is better than TFIDF in text classification.

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40

Oztas, Saniye Karaman, and Leyla Tanacan. "Development of Local Weighting Factors in the Context of LCIA." Advanced Materials Research 935 (May 2014): 293–96. http://dx.doi.org/10.4028/www.scientific.net/amr.935.293.

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Life Cycle Impact Assessment (LCIA) is a phase of the Life Cycle Assessment (LCA) in order to quantify various environmental impacts based on the inventory analysis. Weighting although is not the mandatory element of LCIA is the element in which local data becomes important. Potential environmental impacts and the importance of particular impacts can be quite significant among the countries or regions. Determination of the importance degree is possible by weighting of the selected environmental impact categories. Therefore, this study aimed to develop local weighting factors (WFs) by taking th

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41

Gao, Tian Bao, Wen Li Shi, and Teng Li. "Weighted Grey Relation Evaluation Model on Colliery Safety Factors." Advanced Materials Research 524-527 (May 2012): 803–7. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.803.

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Aiming at the shortage of existing grey relation model, the colliery safety factor evaluation index system is built with the weighted grey relation evaluation model which combines the subjective weighting process and the objective weighting process. The analysis conclusions of the four collieries of Zao Zhuang Coal Mining Co. Ltd show that the evaluation of colliery safety factors based on the weighted grey relation evaluation can make a good division and evaluation for various colliery safety factors, and also eliminate the gray characteristics the colliery safety system owns effectively, thu

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42

Zapp, Neal, Frank Cucinotta, and W. Atwell. "A comparison of quality factors and weighting factors for characterizing astronaut radiation exposures." Advances in Space Research 30, no. 4 (2002): 965–74. http://dx.doi.org/10.1016/s0273-1177(02)00161-8.

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43

Safarishaal, Masoud, and Mohammad Sarvi. "Optimizing weighted voltage mode control for enhanced output cross-regulation in multi-output DC/DC converters." Future Technology 3, no. 1 (2024): 32–39. http://dx.doi.org/10.55670/fpll.futech.3.1.4.

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Weighted voltage mode control is a widely used method for regulating multiple output DC-DC converters, but inconsistent outcomes are often observed in design due to the complexity of the weighting variable optimization. This study proposes an optimization-based approach to accurately estimate the optimal weighting factors for improved output voltage regulation in multiple output forward DC-DC converters. Three optimization algorithms, the Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are compared for their speed and accuracy in es

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44

Zhang, Yuzhe, Zhenbin Zhang, Oluleke Babayomi, and Zhen Li. "Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives." Symmetry 15, no. 6 (2023): 1219. http://dx.doi.org/10.3390/sym15061219.

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The rapid growth of energy demand requires progressive energy generation. This, together with the demand for higher efficiency and flexibility, has promoted the application of power electronics in energy systems. During the past decade, model predictive control (MPC) of power electronics has witnessed significant advancements in both dynamic performance and optimal control of the multi-objective terms. Several of these terms can have equal control priorities, resulting in a symmetrical cost function; however, most objectives have different priorities and require weighting factors to resolve th

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45

Mumtaz, Ghina Fadhilla, Suyitno Suyitno, and Sifriyani Sifriyani. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING OF POVERTY RATES IN TROPICAL RAINFOREST AREAS OF KALIMANTAN." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 903–16. https://doi.org/10.30598/barekengvol19iss2pp903-916.

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When applied to spatial panel data, the Geographically Weighted Panel Regression (GWPR) model is a localized version of the linear regression model. The Fixed Effect Model (FEM) inside estimator is used as a global model in this investigation. The purpose of this research is to obtain a GWPR model and identify the variables that affect the proportion of the impoverished in 56 districts and cities located in Kalimantan's humid tropical forest region between 2019 and 2022. The Weighted Least Square (WLS) approach, which provides geographic weighting in addition to the Least Square method, is use

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46

Ohkubo, Kazuaki, and Hiroaki Sobagaki. "The Calculation of Weighting Factors for Calculation of Tristimulus Values." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 77, no. 6 (1993): 364–70. http://dx.doi.org/10.2150/jieij1980.77.6_364.

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47

Fikse, W. F., and G. Banos. "Weighting Factors of Sire Daughter Information in International Genetic Evaluations." Journal of Dairy Science 84, no. 7 (2001): 1759–67. http://dx.doi.org/10.3168/jds.s0022-0302(01)74611-5.

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48

Kuwabara, Kouhei, Tohru Mochida, Katsunori Nagano, and Kazumi Shimakura. "Fundamental Study of Weighting Factors for Calculating Mean Skin Temperature." Journal of the Human-Environment System 9, no. 1 (2006): 35–42. http://dx.doi.org/10.1618/jhes.9.35.

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49

Park, Jinkyun, Wondea Jung, Jaewhan Kim, and Jaejoo Ha. "Step Complexity Measure for Emergency Operating Procedures - Determining Weighting Factors." Nuclear Technology 143, no. 3 (2003): 290–308. http://dx.doi.org/10.13182/nt03-a3418.

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50

Xu, X. G., and W. D. Reece. "Sex-Specific Tissue Weighting Factors for Effective Dose Equivalent Calculations." Health Physics 70, no. 1 (1996): 81–86. http://dx.doi.org/10.1097/00004032-199601000-00012.

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