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Published: 10 December 2022

Last updated: 28 January 2023

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1

Bertelsen, Thomas B., Asle Hoffart, Sondre Sverd Rekdal, and Rune Zahl-Olsen. "Bayes factor benefits for clinical psychology: review of child and adolescent evidence base." F1000Research 11 (September 23, 2022): 171. http://dx.doi.org/10.12688/f1000research.76842.2.

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Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice. Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors. Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors. Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials.

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2

Bertelsen, Thomas B., Asle Hoffart, Sondre Sverd Rekdal, and Rune Zahl-Olsen. "Bayes factor benefits for clinical psychology: review of child and adolescent evidence base." F1000Research 11 (February 11, 2022): 171. http://dx.doi.org/10.12688/f1000research.76842.1.

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Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice. Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors. Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors. Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials.

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3

O'Hagan, Tony. "Bayes factors." Significance 3, no. 4 (November 28, 2006): 184–86. http://dx.doi.org/10.1111/j.1740-9713.2006.00204.x.

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4

Kass, Robert E., and Adrian E. Raftery. "Bayes Factors." Journal of the American Statistical Association 90, no. 430 (June 1995): 773–95. http://dx.doi.org/10.1080/01621459.1995.10476572.

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5

Ramos-Vera, Cristian Antony. "A case of replication using the Bayes factor in the health sciences." Revista Virtual de la Sociedad Paraguaya de Medicina Interna 8, no. 1 (March 30, 2021): 189–90. http://dx.doi.org/10.18004/rvspmi/2312-3893/2021.08.01.189.

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6

Reschenhofer, Erhard. "Approximating the Bayes factor." Statistics & Probability Letters 30, no. 3 (October 1996): 241–45. http://dx.doi.org/10.1016/0167-7152(95)00226-x.

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7

Aitkin, Murray. "Posterior Bayes Factors." Journal of the Royal Statistical Society: Series B (Methodological) 53, no. 1 (September 1991): 111–28. http://dx.doi.org/10.1111/j.2517-6161.1991.tb01812.x.

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8

Zhuo, Xiande, Ling Fan, Di Hu, and Huangda Zhu. "Multifactor optimization of MICP base on BP model." Journal of Physics: Conference Series 2200, no. 1 (February 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2200/1/012003.

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Abstract Microbial-induced calcite precipitation (MICP) can be used to cement soil and produce new biomaterials. The formation of this material is affected by many factors, such as physical, chemical, biological factors, many studies focused on the effect of a single factor on the strength ignoring the synergy between factors. Back-propagation neural network (BPNN) can be used as a multi-factor nonlinear prediction model and an analysis method. 140 MICP grout tests in the literature were summarized to15 factors affecting UCS and act as BPNN input data. At last, five key factors were elected based on weight analysis of well-trained BPNN. On this basis, a simple strength model composed of those factors is established, which can well predict the strength of MICP grouting soil with practical convenience. Key factors and strength prediction models help popularize MICP for engineering applications and optimize grouting experiments.

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9

Alarcón-Moyano, Jessica, and Silvia Matiacevich. "Development of bioactive/active additives based on essential oils: A review." Medicinal Plant Communications 3, no. 4 (November 30, 2020): 60–67. http://dx.doi.org/10.37360/mpc.20.3.4.12.

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The use of bioactive/active additives has been increasing in recent years, especially those derived from medicinal plants such as essential oils. However, due to essential oil oxidation it is necessary to protect it by encapsulation techniques such as: emulsion, spray- and/or freeze-drying as the most economical techniques. On the other hand, an important factor is to determine the appropriate wall material to obtain a prolonged or controlled release in the food or in the organism. Therefore, several factors affect the release of the compounds such as the type, amount of wall material and/or combination of wall materials. Therefore, the knowledge of all the aforementioned factors is important in order to make an adequate selection for the development of a bioactive/active additive based on essential oils.

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10

Iqbal, Zafar, Heung-No Lee, and Saeid Nooshabadi. "Highly Reliable Decision-Making Using Reliability Factor Feedback for Factory Condition Monitoring via WSNs." Wireless Communications and Mobile Computing 2018 (October 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/8058624.

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Cooperation among sensors in a wireless sensor network, deployed for industrial monitoring in an indoor scenario, is a topic of interest in the smart factory and smart city research. The indoor wireless communication channel is very harsh and the observations of all the sensors cannot be sent reliably to the base station. Failure to transmit correct sensing results to the base station may result in false alarms or missed detection of events. Therefore, we propose a cooperation scheme for the wireless sensors to send the data reliably to the base station. Our aim is to increase the reliability of the received information, reduce the probability of error, lower the overall power consumption, and keep the latency to an acceptable low level. We propose a reliability factor feedback algorithm to adjust the weight of unreliable sensors in the decision-making process. The proposed scheme is analyzed based on its latency, power consumption, and packet delivery ratio. Our results show significant improvement in the reliability of the received data, improved packet delivery, and reduced false alarm ratio for full repetition and cluster head-based cooperation. The power consumption and latency in data transmission are also kept to an acceptable low level.

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11

van Ravenzwaaij, Don, and Alexander Etz. "Simulation Studies as a Tool to Understand Bayes Factors." Advances in Methods and Practices in Psychological Science 4, no. 1 (January 2021): 251524592097262. http://dx.doi.org/10.1177/2515245920972624.

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When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.

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12

RAFTERY, ADRIAN E. "Bayes Factors and BIC." Sociological Methods & Research 27, no. 3 (February 1999): 411–27. http://dx.doi.org/10.1177/0049124199027003005.

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13

Kass, Robert E. "Bayes Factors in Practice." Statistician 42, no. 5 (1993): 551. http://dx.doi.org/10.2307/2348679.

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14

Gomez-Villegas, M. A., and E. Gomez Sanchez-Manzano. "Bayes factor in testing precise hypotheses." Communications in Statistics - Theory and Methods 21, no. 6 (January 1992): 1707–15. http://dx.doi.org/10.1080/03610929208830873.

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15

Aitkin, Murray. "Simpson's paradox and the Bayes factor." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60, no. 1 (February 1998): 269–70. http://dx.doi.org/10.1111/1467-9868.00124.

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16

Dittrich, Dino, Roger Th A. J. Leenders, and Joris Mulder. "Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses." Sociological Methods & Research 48, no. 3 (October 12, 2017): 642–76. http://dx.doi.org/10.1177/0049124117729712.

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Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing procedures that do not have these limitations. We propose Bayes factors based on an empirical and a uniform prior for the network effect, respectively, first. Next, we develop a fractional Bayes factor where a default prior is automatically constructed. Simulation results suggest that the first two Bayes factors show superior performance and are the Bayes factors we recommend. We apply the recommended Bayes factors to three data sets from the literature and compare the results to those coming from classical analyses using p values. R code for efficient computation of the Bayes factors is provided.

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17

Grisaro, Hezi Y., and Idan E. Edri. "Numerical investigation of explosive bare charge equivalent weight." International Journal of Protective Structures 8, no. 2 (March 31, 2017): 199–220. http://dx.doi.org/10.1177/2041419617700256.

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Peak overpressure and impulse are the most important parameters in the explosive performance estimation. Available models commonly consider trinitrotoluene explosive as the standard charge. In this article, the trinitrotoluene equivalency factor is studied through verified one-dimensional numerical simulations. The equivalency factors for impulse and overpressure are different and found to be constant with the scaled distance (3–40 m/kg1/3), which means that a unique value for the equivalency factor is suitable for the equivalency factor calculation for each distance. Comparison of the equivalency factor with available models shows that it strongly depends on the internal energy ratio of the explosive in interest, relative to trinitrotoluene, and a new formula for equivalency factor for overpressure is proposed. A verified simulation method is presented in which the explosive is modeled with an equivalent “air bubble” with the same internal energy and ideal gas equation of state. A new approach using the energy flux density calculation is presented to calculate the equivalency factor for impulse and overpressure from a single gauge measurement of overpressure-time history, at a specific distance.

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18

Hasriyani, Hasriyani, Suharyo Hadisaputro, Kamilah Budhi, Mexitalia Setiawati, and Henry Setyawan. "Berbagai Faktor Risiko Kejadian Bayi Berat Lahir Rendah (BBLR) (Studi di Beberapa Puskesmas Kota Makassar)." Jurnal Epidemiologi Kesehatan Komunitas 3, no. 2 (August 15, 2018): 91. http://dx.doi.org/10.14710/j.e.k.k.v3i2.4027.

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Background: Low-birth-weight (LBW) as community health problem have remained as the main cause of prenatal morbidity and mortality. In Indonesia, the occurrence rate of LBW has reached 10,2%. LBW has caused by multifactors such as maternal factor, fetus factor, and environmental factor. The aim of the study was to elaborate if maternal factor and environmental factor are the risk factors contributing to LBW occurrence.Method: The study was based on case control design. The number of samples was 138 respondents who were categorized into 69 cases and 69 controls fulﬁlling the criteria of inclusion and exclusion. The cases were mothers with babies having birth weight <2500 grams regarding the gestational age, and the controls were mothers with babies having birth weight ≥2500 grams regarding the gestational age. Sampling method to the cases was conducted by consecutive sampling while sampling to the controls was conducted by simple random sampling with matching to sex and location.Results: Maternal factors observed as risk factors contributing to LBW are low nutritional status (OR=5.357; 95%CI=1.985-14.457), illness pregnancy (OR=3.976; 95%CI=1.669-9.476). Maternal factors and environmental factors observed as non-risk factors to LBWN are age < 20 years old or age > 35 years, the spacing of pregnancy < 2 years, the of family members smoking at home, the of a food taboos culture, ANC status < 4x , low social economy status.Conclusion: Low nutritional status, illness pregnancy are risk factors contributing to LBW occurrence. The probability of LBW occurrence when those risk factors observed are 68,87%.

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19

Hasriyani, Hasriyani, Suharyo Hadisaputro, Kamilah Budhi, Mexitalia Setiawati, and Henry Setyawan. "Berbagai Faktor Risiko Kejadian Bayi Berat Lahir Rendah (BBLR) (Studi di Beberapa Puskesmas Kota Makassar)." Jurnal Epidemiologi Kesehatan Komunitas 3, no. 2 (August 15, 2018): 91. http://dx.doi.org/10.14710/jekk.v3i2.4027.

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Background: Low-birth-weight (LBW) as community health problem have remained as the main cause of prenatal morbidity and mortality. In Indonesia, the occurrence rate of LBW has reached 10,2%. LBW has caused by multifactors such as maternal factor, fetus factor, and environmental factor. The aim of the study was to elaborate if maternal factor and environmental factor are the risk factors contributing to LBW occurrence.Method: The study was based on case control design. The number of samples was 138 respondents who were categorized into 69 cases and 69 controls fulﬁlling the criteria of inclusion and exclusion. The cases were mothers with babies having birth weight <2500 grams regarding the gestational age, and the controls were mothers with babies having birth weight ≥2500 grams regarding the gestational age. Sampling method to the cases was conducted by consecutive sampling while sampling to the controls was conducted by simple random sampling with matching to sex and location.Results: Maternal factors observed as risk factors contributing to LBW are low nutritional status (OR=5.357; 95%CI=1.985-14.457), illness pregnancy (OR=3.976; 95%CI=1.669-9.476). Maternal factors and environmental factors observed as non-risk factors to LBWN are age < 20 years old or age > 35 years, the spacing of pregnancy < 2 years, the of family members smoking at home, the of a food taboos culture, ANC status < 4x , low social economy status.Conclusion: Low nutritional status, illness pregnancy are risk factors contributing to LBW occurrence. The probability of LBW occurrence when those risk factors observed are 68,87%.

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20

Finki Dona Marleny and Mambang. "COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 3, no. 1 (April 20, 2018): 13–17. http://dx.doi.org/10.20527/jtiulm.v3i1.23.

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Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification approach. this paper will be compared k-nearest neighbor algorithm and Naive Bayes classifier to classify asphyxia factor. Naive Bayes uses the concept of Bayes’ Theorem which assuming the independency between predictors. Basically, Bayes theorem is used to compute the subsequent probabilities. Analysis of the two algorithms has been done on several parameters such as Kappa statistics, classification error, precision, recall, F-measure and AUC. We achieved the best classification accuracy with KNN algorithm, 92,27%, for k=4. are lower than the rates achieved with Naïve Bayes 83,19%.

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21

Yang, Wen Xia, Qiang Gu, and Zhen Sen Song. "Response Modification Factor for Y-Eccentric-Braced Steel Frame Structures." Advanced Materials Research 250-253 (May 2011): 2285–90. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2285.

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In current seismic design procedure, structural base shear is calculated according to the linear elastic response spectra divided by response modification factorR. The response modification factor is important to the reliability and economy of building seismic design. In this paper, the response modification factors of Twelve Y-eccentric braced steel frames with various stories and spans lengths were evaluated by capacity spectrum method based on the global capacity envelops obtained from an improved pushover analysis and incremental dynamic analysis. According to the results, an appropriate formula of the response modification factor for the Y-eccentric braced steel frames was suggested.

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22

Abanto Merino, Luis Alfredo, and Heyner Yuliano Márquez Yauri. "The Canvas model studied the impact factor and its visualization in the EBSCO database, Scopus, Scielo, 2017 -2021." SCIÉNDO 25, no. 3 (September 30, 2022): 325–31. http://dx.doi.org/10.17268/sciendo.2022.040.

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This manuscript is based on a bibliographic review that supports the research with concepts and definitions from different authors. The idea is to document different aspects of theresearch, the objective of this study is to know the field of science, engineering, arts, medicine, course management and/or business in general, the Canvas business model is It applies to any field. More specifically, this study aims to answer the following question: How much is the Canvas business model applied by business sector in jobs published between 2017 and 2021? The method used consisted of compiling and reviewing scientific publications filtered from databases, particularly EBSCO, Scopus and Scielo, using keywords related to the titles of the books. The result of the selected documents was 76, made up of 209 authors, leaving 71 documents in a state of debugging, the results of which will appear in various tables and figures. In conclusion, the author argues that the business model is a tool that can be used in different fields because it is a simple and flexible method that hopes to solve problems, deals with opportunities and demands, because it is very active and creative.

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23

Villa, Cristiano, and Stephen G. Walker. "An objective Bayes factor with improper priors." Computational Statistics & Data Analysis 168 (April 2022): 107404. http://dx.doi.org/10.1016/j.csda.2021.107404.

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24

Massaro, Dominic W., Michael M. Cohen, Christopher S. Campbell, and Tony Rodriguez. "Bayes factor of model selection validates FLMP." Psychonomic Bulletin & Review 8, no. 1 (March 2001): 1–17. http://dx.doi.org/10.3758/bf03196136.

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25

Taplin, Ross H. "A ROBUST BAYES FACTOR FOR LINEAR MODELS." Australian New Zealand Journal of Statistics 47, no. 4 (December 2005): 449–62. http://dx.doi.org/10.1111/j.1467-842x.2005.00408.x.

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26

Wang, Min, and Xiaoqian Sun. "Bayes factor consistency for unbalanced ANOVA models." Statistics 47, no. 5 (October 2013): 1104–15. http://dx.doi.org/10.1080/02331888.2012.694445.

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27

Koopman, S. J., and G. Mesters. "Empirical Bayes Methods for Dynamic Factor Models." Review of Economics and Statistics 99, no. 3 (July 2017): 486–98. http://dx.doi.org/10.1162/rest_a_00614.

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28

Schmalz, Xenia, and Claudio Mulatti. "Busting a myth with the Bayes Factor." Mental Lexicon 12, no. 2 (December 31, 2017): 263–82. http://dx.doi.org/10.1075/ml.17009.sch.

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Abstract Psycholinguistic researchers identify linguistic variables and assess if they affect cognitive processes. One such variable is letter bigram frequency, or the frequency with which a given letter pair co-occurs in an orthography. While early studies reported that bigram frequency affects visual lexical decision, subsequent, well-controlled studies not shown this effect. Still, researchers continue to use it as a control variable in psycholinguistic experiments. We propose two reasons for the persistence of this variable: (1) Reporting no significant effect of bigram frequency cannot provide evidence for no effect. (2) Despite empirical work, theoretical implications of bigram frequency are largely neglected. We perform Bayes Factor analyses to address the first issue. In analyses of existing large-scale databases, we find no effect of bigram frequency in lexical decision in the British Lexicon Project, and some evidence for an inhibitory effect in the English Lexicon Project. We find strong evidence for an effect in reading aloud. This suggests that, for lexical decision, the effect is unstable, and may depend on item characteristics and task demands rather than reflecting cognitive processes underlying visual word recognition. We call for more consideration of theoretical implications of the presence or absence of a bigram frequency effect.

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29

Mulder, Joris, and Jean-Paul Fox. "Bayes Factor Testing of Multiple Intraclass Correlations." Bayesian Analysis 14, no. 2 (June 2019): 521–52. http://dx.doi.org/10.1214/18-ba1115.

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30

Macci, Claudio, and Silvia Polettini. "Bayes factor for non-dominated statistical models." Statistics & Probability Letters 53, no. 1 (May 2001): 79–89. http://dx.doi.org/10.1016/s0167-7152(01)00035-9.

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31

Robert, Christian P. "The expected demise of the Bayes factor." Journal of Mathematical Psychology 72 (June 2016): 33–37. http://dx.doi.org/10.1016/j.jmp.2015.08.002.

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32

Xiao, Dianyan, Jincheng Zhuang, and Qi Cheng. "Factor base discrete logarithms in Kummer extensions." Finite Fields and Their Applications 53 (September 2018): 205–25. http://dx.doi.org/10.1016/j.ffa.2018.06.008.

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33

Walker, Stephen. "A NOTE ON BAYES FACTORS." Australian New Zealand Journal of Statistics 47, no. 3 (September 2005): 325–28. http://dx.doi.org/10.1111/j.1467-842x.2005.00397.x.

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34

de Rijke, Johan E. "Factors affecting cryopump base pressure." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 8, no. 3 (May 1990): 2778–81. http://dx.doi.org/10.1116/1.576667.

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35

Lindley, Dennis V. "Some comments on Bayes factors." Journal of Statistical Planning and Inference 61, no. 1 (May 1997): 181–89. http://dx.doi.org/10.1016/s0378-3758(96)00189-9.

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36

George Assaf, A., and Mike Tsionas. "Bayes factors vs. P-values." Tourism Management 67 (August 2018): 17–31. http://dx.doi.org/10.1016/j.tourman.2017.11.011.

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37

Santis, Fulvio De, and Fulvio Spezzaferri. "Bayes factors and hierarchical models." Journal of Statistical Planning and Inference 74, no. 2 (November 1998): 323–42. http://dx.doi.org/10.1016/s0378-3758(98)00109-8.

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38

Young, Karen D. S., and Lawrence I. Pettit. "On priors and Bayes factors." Journal of Econometrics 75, no. 1 (November 1996): 113–19. http://dx.doi.org/10.1016/0304-4076(95)01772-0.

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39

Sawcer, Stephen. "Bayes factors in complex genetics." European Journal of Human Genetics 18, no. 7 (February 24, 2010): 746–50. http://dx.doi.org/10.1038/ejhg.2010.17.

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40

Vidakovic, Brani. "Nonlinear Wavelet Shrinkage with Bayes Rules and Bayes Factors." Journal of the American Statistical Association 93, no. 441 (March 1998): 173–79. http://dx.doi.org/10.1080/01621459.1998.10474099.

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41

Choi, Bo-Hye, and Seung-Yeong Song. "Insulation Performance Comparison of Curtain Wall Systems with Existing Pipe Frames and Truss-Shaped Insulation Frames." Energies 14, no. 15 (August 2, 2021): 4682. http://dx.doi.org/10.3390/en14154682.

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The purpose of this study was to compare insulation performance between a base case that applied the existing steel pipe frame and an alternative case that reduced thermal bridging by applying a truss-shaped insulation frame (TIF) to a back frame type curtain wall. Insulation performance was compared by obtaining the effective U-factor and the lowest indoor surface temperature through a three-dimensional steady-state heat transfer simulation. In addition, mock-up tests were performed to compare the U-factors of the base case and alternative case. The simulation results showed that the effective U-factor of the alternative case was 36% lower than in the base case, a significant heat loss reduction. The lowest indoor surface temperature of the alternative case was 0.5 °C higher than in the base case, showing that the surface condensation risk also decreased. In the mock-up test results, the alternative case U-factor was 33% lower than in the base case, confirming the associated large heat loss reduction. For the base case, both the effective U-factor by simulation and the U-factor by the mock-up test were much higher than the design U-factor according to the Korean Design Standard, which neglects thermal bridging, indicating a significantly increased heat loss caused by this factor. For the alternative case, however, both U-factors were similar to the design U-factor.

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42

Wang, Fenglei, Shaochun Ma, Haonan Xing, Jing Bai, Jinzhi Ma, Yezhen Yang, and Jiwei Hu. "Base Cutting Energy Consumption for Sugarcane Stools Using Contra-Rotating Basecutters." Transactions of the ASABE 64, no. 1 (2021): 221–30. http://dx.doi.org/10.13031/trans.13997.

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HighlightsThis study focused on the base cutting energy consumption for sugarcane stools instead of single stalks, thus being more consistent with actual field harvesting.The energy consumption increased with increasing rotational speed (RS) and stool diameter (SD), while it decreased with increasing tilt angle (TA) and feed rate (FR).Each pair of levels of each factor was compared using Duncan’s multiple range test. Three factors (RS, SD, and FR) had significant effects on energy consumption at 95% confidence level, while one factor (TA) had no significant effect.The order of influence and the optimal combination of the four factors to minimize the energy consumed during base cutting were determined.Abstract. Previous studies on contra-rotating basecutter designs based on supported cutting have mainly focused on the base cutting energy consumption for single sugarcane stalks instead of sugarcane stools. However, in the actual base cutting process, a basecutter typically cuts multiple sugarcane stalks (in one sugarcane stool) simultaneously. Therefore, this study investigated how the rotational speed (RS) and tilt angle (TA) of the cutting discs, the sugarcane stool diameter (SD), and the feed rate (FR) affected the energy consumed when cutting cane stools using a contra-rotating cutting platform. Four single-factor experiments and an orthogonal experiment were performed using a Taguchi orthogonal experimental design, and each group was replicated five times. The results of the single-factor experiments showed that the energy consumption was proportional to RS and SD, while it was negatively correlated with TA and FR. The significance of the difference between each pair of levels of each factor was investigated using Duncan’s multiple range test. According to the results of the orthogonal experiment, RS, SD, and FR had significant influences on the base cutting energy consumption at the 95% confidence level; however, TA had no significant influence. The order of influence of the four factors was SD > FR > RS > TA (18.45 > 18.39 > 12.91 > 9.06), and the optimal factor-level combination for minimizing the cutting energy was RS2, TA4, SD1, and FR3 (200 rpm disc RS, 20° disc TA, 60 mm SD, and 1.0 m s-1 FR). An understanding of the relationships between energy consumption and its influencing factors can serve as a valuable reference for researchers seeking to optimize the design of contra-rotating basecutters, which could lead to increased energy efficiency and a reduction in energy consumption during sugarcane harvesting. Keywords: Contra-rotating basecutter, Energy consumption, Orthogonal experiment, Single-factor experiment, Sugarcane stools, Supported cutting.

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Nadir, Ali F., Jabbar H. Juber, and Abdul Ridha H. Subber. "The diffusion coefficient and calibration factors for indoor radon measurement in bare and twin cup modes." Journal of Zankoy Sulaimani - Part A 18, no. 1 (August 30, 2015): 49–56. http://dx.doi.org/10.17656/jzs.10450.

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Noe, DA, WR Bell, PM Ness, and J. Levin. "Plasma clearance rates of coagulation factors VIII and IX in factor- deficient individuals." Blood 67, no. 4 (April 1, 1986): 969–72. http://dx.doi.org/10.1182/blood.v67.4.969.969.

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Abstract The plasma clearance rates of factors IX and VIII were determined in patients with hemophilia A and B who had received factor replacement by prolonged, continuous infusion of factor concentrates. The clearance rates were calculated by dividing the factor infusion rates by the steady-state plasma factor activities corrected for base-line factor activities. The mean factor IX clearance rate in eight factor IX- deficient patients was 233 mL/h (range 159 to 340 mL/h). The mean normalized clearance rate was 3.4 mL/h/kg. The mean factor VIII clearance rate in eight factor VIII-deficient patients was 294 mL/h (range 229 to 361 mL/h) and the mean normalized rate was 5.0 mL/h/kg. Both factors show linear relationships between the factor infusion rates and the steady-state plasma factor activities achieved.

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Noe, DA, WR Bell, PM Ness, and J. Levin. "Plasma clearance rates of coagulation factors VIII and IX in factor- deficient individuals." Blood 67, no. 4 (April 1, 1986): 969–72. http://dx.doi.org/10.1182/blood.v67.4.969.bloodjournal674969.

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The plasma clearance rates of factors IX and VIII were determined in patients with hemophilia A and B who had received factor replacement by prolonged, continuous infusion of factor concentrates. The clearance rates were calculated by dividing the factor infusion rates by the steady-state plasma factor activities corrected for base-line factor activities. The mean factor IX clearance rate in eight factor IX- deficient patients was 233 mL/h (range 159 to 340 mL/h). The mean normalized clearance rate was 3.4 mL/h/kg. The mean factor VIII clearance rate in eight factor VIII-deficient patients was 294 mL/h (range 229 to 361 mL/h) and the mean normalized rate was 5.0 mL/h/kg. Both factors show linear relationships between the factor infusion rates and the steady-state plasma factor activities achieved.

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Faulkenberry, Thomas J. "Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation." Biometrical Letters 55, no. 1 (June 1, 2018): 31–43. http://dx.doi.org/10.2478/bile-2018-0003.

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Summary Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.

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Björkqvist, Kaj, Marianne Lindström, and Marina Pehrsson. "Attribution of Aggression to Acts: A Four-Factor Model." Psychological Reports 87, no. 2 (October 2000): 525–30. http://dx.doi.org/10.2466/pr0.2000.87.2.525.

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Factors determining why acts are regarded as aggressive were investigated in a sample of 303 subjects (140 males, 163 females, M age = 17.7 yr., SD = 1.4). A model was tested, according to which people base their attribution of aggression to acts on four factors: (1) intention (yes/no), (2) reason (attack/defense), (3) injury (death/injury/no injury), and (4) mode (weapon/bare hands/nonphysical means). All four factors contributed to the explanation of attribution of aggression, and the order of importance was the one mentioned above, intention being most important. The interaction between intention and reason was the second most important, contributing more than reason alone. The findings are consistent with the severity of punishment in sentences for different crimes of violence.

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GURD, BRUCE, MALCOLM SMITH, and REW SWAFFER. "FACTORS IMPACTING ON ACCOUNTING LAG: AN EXPLORATORY STUDY OF RESPONDING TO TQM." British Accounting Review 34, no. 3 (September 2002): 205–21. http://dx.doi.org/10.1006/bare.2002.0198.

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Engkus, Engkus. "OPTIMALISASI POTENSI RETRIBUSI PASAR BALE ENDAH KABUPATEN BANDUNG." MEDIA BINA ILMIAH 14, no. 2 (November 8, 2019): 2087. http://dx.doi.org/10.33758/mbi.v14i2.362.

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This research is motivated by problems related to market levies collection in the Baleendah Market in Bandung Regency, where the potential for such fees has not been maximally explored due to problems in the collection process. Therefore, researchers are interested in analyzing the potential market levies in the Baleendah Market in the hope that the maximum potential of existing levies can be explored so that it will provide additional income for local treasury. The purpose of this study is to determine the potential market charges that are received by Bandung Regency, and to find out what factors are obstacles to the achievement of retribution. In addition, a trend analysis is also conducted to predict the receipt of market charges in the Baleendah Market in the coming year. This study uses the theory of Market Levy Potential theory from Mahmudi and the trend analysis theory from Budiyowono. The method used in this study is a qualitative research method using a descriptive approach. The selection of informants uses a purposive technique. Data collection techniques used were observation, interviews, and documentation. To test the validity of the data, researchers used triangulation. The results of this study indicate that the Baleendah Market did not reach the target market levies that have been set. That happens because of the many inhibiting factors that influence it, the first is the trader factor, the second is the road access factor, and natural factors. Then from the results of trend analysis shows the decline in market retribution receipts each year from 2020 to 2024.

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Kim, Seong W., and Jinheum Kim. "Asymptotic equivalence between the default Bayes factors and the ordinary Bayes factors with intrinsic priors." Journal of the Korean Statistical Society 45, no. 4 (December 2016): 518–25. http://dx.doi.org/10.1016/j.jkss.2016.03.002.

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