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Journal articles on the topic 'General two-stage model'

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Published: 4 June 2021
Last updated: 4 February 2022

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1

Lohr, Sharon L. "TWO-stage accurate estimation in the general linear model." Sequential Analysis 12, no. 1 (January 1993): 1–23. http://dx.doi.org/10.1080/07474949308836268.

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2

Kim, Jin-Hyung, and Mijung Kim. "Two-stage multinomial logit model." Expert Systems with Applications 38, no. 6 (June 2011): 6439–46. http://dx.doi.org/10.1016/j.eswa.2010.11.057.

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3

Kim, Mijung. "Two-stage logistic regression model." Expert Systems with Applications 36, no. 3 (April 2009): 6727–34. http://dx.doi.org/10.1016/j.eswa.2008.08.063.

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4

Jaśkowski, Piotr. "Two-stage model for order discrimination." Perception & Psychophysics 50, no. 1 (January 1991): 76–82. http://dx.doi.org/10.3758/bf03212206.

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5

Kim, Yong-soo, and Chan-bock Lee. "Two-step two-stage fission gas release model." Journal of Nuclear Materials 374, no. 3 (March 2008): 431–36. http://dx.doi.org/10.1016/j.jnucmat.2007.10.002.

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6

Hsu, Yu-Sheng. "General linear hypotheses in a two-stage least squares estimation model." Economics Letters 36, no. 3 (July 1991): 275–79. http://dx.doi.org/10.1016/0165-1765(91)90032-g.

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7

Jiang, Shuang, Shugang Wang, Xu Jin, and Tengfei Zhang. "A general model for two-stage vapor compression heat pump systems." International Journal of Refrigeration 51 (March 2015): 88–102. http://dx.doi.org/10.1016/j.ijrefrig.2014.12.005.

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8

Cook, L. M. "A two–stage model for Cepaea polymorphism." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1375 (October 29, 1998): 1577–93. http://dx.doi.org/10.1098/rstb.1998.0311.

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The history of the study of snails in the genus Cepaea is briefly outlined. Cepaea nemoralis and C. hortensis are polymorphic for genetically controlled shell colour and banding, which has been the main interest of the work covered. Random drift, selective predation and climatic selection, both at a macro– and micro–scale, all affect gene frequency. The usual approach to understanding maintenance of the polymorphism, has been to look for centripetal effects on frequency. Possible processes include balance of mutation pressure and drift, heterozygote advantage, relational balance heterosis, frequency–dependent predation, multi–niche selective balance, or some combination of these. Mutational balance is overlaid by more substantial forces. There is some evidence for heterosis. Predation by birds may protect the polymorphism, and act apostatically to favour distinct morphs. Although not substantiated for Cepaea , many studies show that predators behave in the appropriate manner, while shell colour polymorphisms in molluscs occur most commonly in species exposed to visually searching predators. It is not known whether different thermal properties of the shells help to generate equilibria. Migration between colonies is probably greater than originally thought. The present geographical range has been occupied for less than 5000 generations. Climatic and human modification alter snail habitats relatively rapidly, which in turn changes selection pressures. A simple simulation shows that migration coupled with selection which fluctuates but is not centripetal, may retain polymorphism for sufficiently long to account for the patterns we see today. There may therefore be a two–stage basis to the polymorphism, comprising long–term but weak balancing forces coupled with fluctuating selection which does not necessarily balance but results in very slow elimination. Persistence of genetic variants in this way may provide the conditions for evolution of a balanced genome.
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9

Zhang, Jun, Yuan Hao Wang, Ying Yi Li, and Feng Guo. "A Two-Stage Combination Model for Wind Power Forecasting." Applied Mechanics and Materials 737 (March 2015): 9–13. http://dx.doi.org/10.4028/www.scientific.net/amm.737.9.

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With the wind farm data from the southeast coast this paper builds a two-stage combination forecasting model of output power based on data preprocessing which include filling up missing data and pre-decomposition. The first stage is a composite prediction of decomposed power sequence in which a time series and optimized BP neural network predict the general trend and the correlation of various factors respectively. The second stage is BP neural network with its input is the results of first stage. The effectiveness and accuracy of the two-stage combination model are verified by comparing the mean square error of the combination model and other models.
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10

Schneider, Susan M. "A two-stage model for concurrent sequences." Behavioural Processes 78, no. 3 (July 2008): 429–41. http://dx.doi.org/10.1016/j.beproc.2008.02.014.

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11

Aleskerov, Fuad, and Yetkin Çinar. "‘q-Pareto-Scalar’ Two-Stage Extremization Model and its Reducibility to One-Stage Model." Theory and Decision 65, no. 4 (November 17, 2007): 325–38. http://dx.doi.org/10.1007/s11238-007-9084-9.

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12

Wilcox, Rand R. "On a Stein-type two-stage procedure for the general linear model." British Journal of Mathematical and Statistical Psychology 38, no. 2 (November 1985): 222–26. http://dx.doi.org/10.1111/j.2044-8317.1985.tb00838.x.

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13

Huang, Sheng-Yao, Chia-En Ko, Gin-Shin Chen, I.-Fang Chung, and Feng-Yi Yang. "Focused Ultrasound Simultaneous Irradiation/MRI Imaging, and Two-Stage General Kinetic Model." PLoS ONE 9, no. 6 (June 20, 2014): e100280. http://dx.doi.org/10.1371/journal.pone.0100280.

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14

Li, Tzuu-Hseng S., Nai Ren Guo, and Chia Ping Cheng. "Design of a two-stage fuzzy classification model." Expert Systems with Applications 35, no. 3 (October 2008): 1482–95. http://dx.doi.org/10.1016/j.eswa.2007.08.045.

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15

Hou, Wentao, Fengjun Zhao, Xiuqing Liu, and Robert Wang. "General Two-Stage Model-Based Three-Component Hybrid Compact Polarimetric SAR Decomposition Method." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 4647–60. http://dx.doi.org/10.1109/jstars.2021.3065431.

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16

Yan, Cheng, Xiuli Shen, and Fushui Guo. "Novel Two-Stage Method for Low-Order Polynomial Model." Mathematical Problems in Engineering 2018 (July 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/8156390.

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One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.
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17

Ding, Li, and Jalesh Mahbubani. "The two‐stage decision model of vertical integration." Management Decision 51, no. 2 (March 2013): 306–20. http://dx.doi.org/10.1108/00251741311301830.

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18

Qiu, Zhaoquan, and Youli Qu. "A Two-Stage Model for Chinese Grammatical Error Correction." IEEE Access 7 (2019): 146772–77. http://dx.doi.org/10.1109/access.2019.2940607.

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19

Wen, Jian Ming, and Qi Feng. "Two-Stage Stochastic Model on Rattling Vibration with Amplitude Modulation." Shock and Vibration 11, no. 5-6 (2004): 693–702. http://dx.doi.org/10.1155/2004/685892.

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Rattling vibration in gear boxes of automobiles is an annoying problem. It may be generated in those gear wheels of a car transmission system, which are unloaded. In recent years, very general models have been developed to analyze the rattling phenomenon. One of them, in consideration of plays being the consequence of tolerances, backlashes and others, is modeled as an impulsive system that consists of some unloaded gears being able to rattle. Modern research has shown that the chaotic vibration may occur on a rattling system. In this paper, a stochastic modulated rattling system with two-stage was studied. A discrete stochastic model described by mean map was established using non-Gaussian technique. By the analysis of the example this model can reveal chaotic stochastic behaviors.
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20

Stoyanova, Krassimira, and Vassil Guliashki. "Two-stage portfolio risk optimisation based on MVO model." International Journal of Reasoning-based Intelligent Systems 12, no. 1 (2020): 70. http://dx.doi.org/10.1504/ijris.2020.10026715.

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21

Stoyanova, Krassimira, and Vassil Guliashki. "Two-stage portfolio risk optimisation based on MVO model." International Journal of Reasoning-based Intelligent Systems 12, no. 1 (2020): 70. http://dx.doi.org/10.1504/ijris.2020.105011.

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22

Song, Tangnyu, Guohe Huang, Xiong Zhou, and Xiuquan Wang. "An inexact two-stage fractional energy systems planning model." Energy 160 (October 2018): 275–89. http://dx.doi.org/10.1016/j.energy.2018.06.158.

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23

Moolgavkar, Suresh H. "Biologically motivated two-stage model for cancer risk assessment." Toxicology Letters 43, no. 1-3 (October 1988): 139–50. http://dx.doi.org/10.1016/0378-4274(88)90025-2.

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24

Borges, A. "Energy demand in Portuguese manufacturing: A two-stage model." Energy 17, no. 1 (January 1992): 61–77. http://dx.doi.org/10.1016/0360-5442(92)90033-v.

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25

Kristan, Matej, Stanislav Kovacic, Aleš Leonardis, and Janez Pers. "A Two-Stage Dynamic Model for Visual Tracking." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 6 (December 2010): 1505–20. http://dx.doi.org/10.1109/tsmcb.2010.2041662.

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26

Li, G. C. "TISEM: A Two-Stage Interval-Stochastic Evacuation Management Model." Journal of Environmental Informatics 12, no. 1 (September 2008): 64–74. http://dx.doi.org/10.3808/jei.200800125.

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27

Ahedo, E., and F. I. Parra. "A model of the two-stage Hall thruster discharge." Journal of Applied Physics 98, no. 2 (July 15, 2005): 023303. http://dx.doi.org/10.1063/1.1957129.

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28

Chen, Yujie, Pengbiao Zhao, and Julin Chen. "CarfRCNN: A Two-stage Effective Model For Instance Segmentation." Journal of Physics: Conference Series 1884, no. 1 (April 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/1884/1/012005.

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29

Cung, Khanh D., Anqi Zhang, and Seong-Young Lee. "FL2-2 Numerical Study on Soot Precursor of JP-8 Surrogate under Diesel Conditions Using a Two-Stage Lagrangian (TSL) Model(FL: Fuels,General Session Papers)." Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines 2012.8 (2012): 337–42. http://dx.doi.org/10.1299/jmsesdm.2012.8.337.

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30

Hirata, Daisuke. "A model of a two-stage all-pay auction." Mathematical Social Sciences 68 (March 2014): 5–13. http://dx.doi.org/10.1016/j.mathsocsci.2013.12.002.

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31

Shi, Xiao. "Efficiency Bounds for Two-Stage Production Systems." Mathematical Problems in Engineering 2018 (July 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/2917537.

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Traditional data envelopment analysis (DEA) models find the most desirable weights for each decision-making unit (DMU) in order to estimate the highest efficiency score as possible. These efficiency scores are then used for ranking the DMUs. The main drawback is that the efficiency scores based on weights obtained from the standard DEA models ignore other feasible weights; this is due to the fact that DEA may have multiple solutions for each DMU. To overcome this problem, Salo and Punkka (2011) deemed each DMU as a “Black Box” and developed models to obtain the efficiency bounds for each DMU over sets of all its feasible weights. In many real world applications, there are DMUs that have a two-stage production system. In this paper, we extend the Salo and Punkka’s (2011) model to a more common and practical case considering the two-stage production structure. The proposed approach calculates each DMU’s efficiency bounds for the overall system as well as efficiency bounds for each subsystem/substage. An application for nonlife insurance companies has been discussed to illustrate the applicability of the proposed approach and show the usefulness of this method.
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32

Xu, Jiaxuan, Guohe Huang, Zoe Li, and Jiapei Chen. "A two-stage fuzzy chance-constrained water management model." Environmental Science and Pollution Research 24, no. 13 (March 30, 2017): 12437–54. http://dx.doi.org/10.1007/s11356-017-8725-y.

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33

TANAKA, Hidehiko. "Simulation of Initial-Stage Sintering with Two-Sphere Model." Journal of the Ceramic Society of Japan 105, no. 1220 (1997): 294–98. http://dx.doi.org/10.2109/jcersj.105.294.

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34

Cantrell, Robert Stephen, Chris Cosner, and Salomé Martínez. "Persistence for a Two-Stage Reaction-Diffusion System." Mathematics 8, no. 3 (March 11, 2020): 396. http://dx.doi.org/10.3390/math8030396.

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In this article, we study how the rates of diffusion in a reaction-diffusion model for a stage structured population in a heterogeneous environment affect the model’s predictions of persistence or extinction for the population. In the case of a population without stage structure, faster diffusion is typically detrimental. In contrast to that, we find that, in a stage structured population, it can be either detrimental or helpful. If the regions where adults can reproduce are the same as those where juveniles can mature, typically slower diffusion will be favored, but if those regions are separated, then faster diffusion may be favored. Our analysis consists primarily of estimates of principal eigenvalues of the linearized system around ( 0 , 0 ) and results on their asymptotic behavior for large or small diffusion rates. The model we study is not in general a cooperative system, but if adults only compete with other adults and juveniles with other juveniles, then it is. In that case, the general theory of cooperative systems implies that, when the model predicts persistence, it has a unique positive equilibrium. We derive some results on the asymptotic behavior of the positive equilibrium for small diffusion and for large adult reproductive rates in that case.
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35

Sinha, N., J. T. G. Brown, and R. H. S. Carpenter. "Task Switching as a Two-Stage Decision Process." Journal of Neurophysiology 95, no. 5 (May 2006): 3146–53. http://dx.doi.org/10.1152/jn.01184.2005.

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Saccades represent decisions, and the study of their latency has led to a neurally plausible model of the underlying mechanisms, LATER (Linear Approach to Threshold with Ergodic Rate), that can successfully predict reaction time behavior in simple decision tasks, with fixed instructions. However, if the instructions abruptly change, we have a more complex situation, known as task switching. Psychologists' explanations of the phenomena of task switching have so far tended to be qualitative rather than quantitative, and not intended to relate particularly clearly to existing models of decision making or to likely neural implementations. Here, we investigated task switching using a novel saccadic task: we presented the instructions by stimulus elements identical to those of the task itself, allowing us to compare decisions about instructions with decisions in the actual task. Our results support a relatively simple model consisting of two distinct LATER processes in series: the first detects the instruction, the second implements it.
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36

Luo, Dang, Manman Zhang, and Huihui Zhang. "Two-stage grey cloud clustering model for drought risk assessment." Grey Systems: Theory and Application 10, no. 1 (November 15, 2019): 68–84. http://dx.doi.org/10.1108/gs-06-2019-0021.

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Purpose The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province. Design/methodology/approach The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed. Findings This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods. Practical implications This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures. Originality/value The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.
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37

Sadeghi-Dastaki, Mohsen, and Abbas Afrazeh. "A two-stage skilled manpower planning model with demand uncertainty." International Journal of Intelligent Computing and Cybernetics 11, no. 4 (November 12, 2018): 526–51. http://dx.doi.org/10.1108/ijicc-04-2018-0048.

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Purpose Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed. Design/methodology/approach To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method. Findings The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research. Originality/value Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.
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38

Tan, Minghuan, Jing Jiang, and Bing Tian Dai. "A BERT-Based Two-Stage Model for Chinese Chengyu Recommendation." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–18. http://dx.doi.org/10.1145/3453185.

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In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this article, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the re-trained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on ChID and CCT datasets and find that it can achieve the state of the art on both datasets. Ablation studies show that both stages of training are critical for the performance gain.
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39

Dozov, I., S. Temkin, and N. Kirov. "Two-stage model for molecular orientational relaxation in liquid crystals." Liquid Crystals 8, no. 5 (November 1990): 727–38. http://dx.doi.org/10.1080/02678299008047384.

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40

Lu, Chao, and Haifang Cheng. "Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/9931796.

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Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.
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41

Li, Gang, Hong-Dong Ma, Rong-Yue Liu, Meng-Di Shen, and Ke-Xin Zhang. "A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest." Entropy 23, no. 5 (May 8, 2021): 582. http://dx.doi.org/10.3390/e23050582.

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Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusions: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.
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42

Dai, Zhaoyi, Fangfu Zhang, Amy T. Kan, Gedeng Ruan, Fei Yan, Narayan Bhandari, Zhang Zhang, et al. "Two-Stage Model Reveals Barite Crystallization Kinetics from Solution Turbidity." Industrial & Engineering Chemistry Research 58, no. 25 (June 4, 2019): 10864–74. http://dx.doi.org/10.1021/acs.iecr.9b01707.

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43

Piceno-Díaz, Ennio R., Luis A. Ricardez-Sandoval, Miguel A. Gutierrez-Limon, Hugo O. Méndez-Acosta, and Héctor Puebla. "Robust Nonlinear Model Predictive Control for Two-Stage Anaerobic Digesters." Industrial & Engineering Chemistry Research 59, no. 52 (December 15, 2020): 22559–72. http://dx.doi.org/10.1021/acs.iecr.0c03809.

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44

Tang, Xiaoquan, and Long Zhang. "Two-Stage Backward Elimination Method for Neural Networks Model Reduction." Journal of Physics: Conference Series 1267 (July 2019): 012087. http://dx.doi.org/10.1088/1742-6596/1267/1/012087.

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45

Adli Aminuddin, Adam Shariff, Nur Izzati Jaini, Maznah Mat Kasim, and Mohd Kamal Mohd Nawawi. "A two stage data envelopment analysis model with undesirable output." Journal of Physics: Conference Series 890 (September 2017): 012110. http://dx.doi.org/10.1088/1742-6596/890/1/012110.

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46

Liu, Pingshan, and Ziming Zeng. "Theory and Application of Logistic-SVM Two-Stage Hybrid Model." Journal of Physics: Conference Series 1746 (January 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1746/1/012017.

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47

Luo, Min, Fan Yang, Sheng-xin Huang, Zhi-peng Kuang, Xiao-ling Luo, Yuan-dong Li, Ji-ning Wu, and Yu-an Xie. "Two-Stage Model of Chemically Induced Hepatocellular Carcinoma in Mouse." Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics 20, no. 11 (October 1, 2012): 517–28. http://dx.doi.org/10.3727/096504013x13747716581336.

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48

Su, Chun, and Xiao-tian Yang. "Two-stage optimization model for renewing warranty considering warranty deadline." Journal of Central South University 26, no. 10 (October 2019): 2845–53. http://dx.doi.org/10.1007/s11771-019-4218-0.

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49

Owusu, Richard K., and Nathalie Berthalon. "A test for the two-stage thermoinactivation model for chymotrypsin." Food Chemistry 48, no. 3 (January 1993): 231–35. http://dx.doi.org/10.1016/0308-8146(93)90132-y.

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50

Zhang, Dali, Huifu Xu, and Yue Wu. "A two stage stochastic equilibrium model for electricity markets with two way contracts." Mathematical Methods of Operations Research 71, no. 1 (February 18, 2009): 1–45. http://dx.doi.org/10.1007/s00186-009-0283-8.

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