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  • Vietnam National University Journal of Science  (3)
  • 1
    Online Resource
    Online Resource
    Vietnam National University Journal of Science ; 2023
    In:  VNU Journal of Science: Medical and Pharmaceutical Sciences Vol. 39, No. 3 ( 2023-09-24)
    In: VNU Journal of Science: Medical and Pharmaceutical Sciences, Vietnam National University Journal of Science, Vol. 39, No. 3 ( 2023-09-24)
    Abstract: Objectives: This study aims to describe changes in hemodynamics and homeostasis after continuous renal replacement therapy (CRRT) in septic shock children without acute kidney injury. Methods: An observational study was conducted in the pediatric intensive care unit (PICU) at Vietnam National Children’s Hospital from January 2018 to June 2022. Children aged under 18 years old with septic shock and without acute kidney injury who required CRRT were analyzed on demographic factors, baseline clinical and laboratory results, changes in hemodynamics and homeostasis at 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 4 days and 5 days after CRRT initiation. Results: A total of 125 children (male, 57.6%) were enrolled in the study. The median age was 11.5 months (IQR: 5 – 29). Overall PICU mortality rate at day 28 was 40.8%. Among survivors, there was a statistically significant improvement in heart rate, mean blood pressure, vasoactive inotropic score (VIS), and pH at all mentioned periods after CRRT initiation. Lactatemia statistically significantly decreased after 24 hours of CRRT initiation (p 〈 0.05). Among non-survivors, there was a statistically significant improvement in heart rate at all the periods and in mean blood pressure at 6 hours and 12 hours, while there was no improvement in VIS, pH, and lactatemia. Conclusion: CRRT played an important role in stabilizing hemodynamics and homeostasis in septic shock children without acute kidney injury.        
    Type of Medium: Online Resource
    ISSN: 2588-1132 , 2615-9309
    Language: Unknown
    Publisher: Vietnam National University Journal of Science
    Publication Date: 2023
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  • 2
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    Online Resource
    Vietnam National University Journal of Science ; 2019
    In:  VNU Journal of Science: Earth and Environmental Sciences Vol. 35, No. 2 ( 2019-06-29)
    In: VNU Journal of Science: Earth and Environmental Sciences, Vietnam National University Journal of Science, Vol. 35, No. 2 ( 2019-06-29)
    Abstract: Abstract: In this study, the equations for estimating the number of tropical cyclones (TCs) at a 6-month lead-time in the Vietnam East Sea (VES) have been developed and tested. Three multivariate linear regression models in which regression coefficients were determined by different methods, including 1) method of least squares (MLR), 2) minimum absolute deviation method (LAD), 3) minimax method (LMV). The artificial neural network model (ANN) and some combinations of the above regression models were also used. The VES was divided into the northern region above 15ºN (VES_N15) and the southern one below that latitude (VES_S15). The number of TCs was calculated from the data of the Japan Regional Specialized Meteorological Center (RMSC) for the period 1981-2017. Principal components of the 14 climate indicators were selected as predictors. Results for the training period showed that the ANN model performed best in all 12 times of forecasts, following by the ANN-MLR combination. The poorest result was obtained with the LMV model. Results for the independent dataset showed that the number of adequate forecasts based on the MSSS scores decreased sharply compared to the training period and the models generated generally similar errors. The MLR model tended to give out the best results. Better-forecast results were obtained in the VES_N15 region followed by the VES and then the VES_S15 regions. Keywords: Tropical cyclone, Seasonal prediction, Vietnam East Sea (VES). References: [1] W. Landsea Christopher, Gerald D. Bell, William M. Gray, Stanley B. Goldenberg, The extremely active 1995 Atlantic hurricane season: Environmental conditions and verification of seasonal forecasts, Mon. Wea. Rev. 126 (1998) 1174-1193[2] W. Landsea Christopher, William M. Gray, Paul W. Mielke, Jr, Kenneth J. Berry, Seasonal Forecasting of Atlantic hurricane activity, Weather. 49 (1994) 273-284.[3] M. Gray William, Christopher W. Landsea, Paul W. Mielke, Predicting Atlantic basin seasonal tropical cyclone activity by 1 June, Weather and Forecasting. 9 (1994) 103-115.[4] Neville Nicholls, Chris Landsea, Jon Gill, Recent trends in Australian region tropical cyclone activity, Meteorol. Atmos. Phys. 65 (1998) 197-205.[5] Elsner, James B., Kam-biu Liu, Bethany Kocher, Spatial Variations in Major U.S., Hurricane Activity: Statistics and a Physical Mechanism, J. Climate. 13 (2000) 2293–2305.[6] J. C. L. Chan, Jiuen Shi, Cheukman Lam, Seasonal Forecasting of Tropical Cyclone Activity over the Western North Pacific and the South China Sea. Departmentof Physics and Materials Science, City University of Hong Kong, Kowloon, Hong Kong, China, (1998).[7] J. C. L. Chan, J. E. Shi and C. M. Lam, Seasonal forecasting of tropical cyclone activity over the Western North Pacific and the South China Sea, Wea. Forecasting. 13 (1998) 997-1004.[8] J. C. L. Chan, Tropical cyclone activity over the Western North Pacific associated with El Niño and La Niña events, J. Climate. 13 (2000) 2960-2972.[9] Pao-Shin Chu, Xin Zhao, Chang-Hoi Ho, Hyeong-Seog Kim, Mong-Ming Lu, Joo-Hong Kim, Bayesian forecasting of seasonal typhoon activity: A track-pattern oriented categorization approach, J.Climate. 23 (2010) 6654-6668[10] M. Lu, P.-S. Chu, and Y.-C. Lin, Seasonal prediction of tropical cyclone activity near Taiwan using the Bayesian multivariate regression method, Wea. Forecasting. 25 (2010) 1780–1795.[11] H. J Kwon, W.-J. Lee, S.-H.Won, and E.-J. Cha, Statistical ensemble prediction of the tropical cyclone activity over the Western North Pacific.Geophys. Res. Lett. 34 (2007) L24805. doi:10.1029/2007GL032308[12] J. C. L. Chan, Tropical cyclone activity in the Western North Pacific in relation to the stratospheric quasi-biennial oscillation, Mon. Wea. Rev. 123 (1995) 2567-2571.[13] J. C. L. Chan, Prediction of annual tropical cyclone activity over the Western North Pacific and the South China Sea, Int’l J. Climatol. 15 (1995) 1011-1019.[14] J. C. L. Chan, J. E. Shi and C. M. Lam, Seasonal forecasting of tropical cyclone activity over the Western North Pacific and the South China Sea, Wea.Forecasting. 13 (1998) 997-1004.[15] J.C.L. Chan, J.E. Shi, K.S. Liu, 2001: Improvements in the seasonal forecasting of tropical cyclone activity over the Western North Pacific. Wea. Forecasting, 16, 491-498.[16] J. Klotzbach Philip, Recent developments in statistical prediction of seasonal Atlantic basin tropical cyclone activity, Journal compilation C (2007) Blackwell Munksgaard. DOI: 10.1111/j.1600-0870.2007.00239.x[17] W. Zhang, Y. Zhang, D. Zheng, F. Wang, and L. Xu, Relationship between lightning activity and tropical cyclone intensity over the northwest Pacific, J. Geophys. Res. Atmos. 120 (2015). doi:10.1002/2014JD022334.[18] Phan Van Tan, On the tropical cyclone activity in the Northwest Pacific basin and South China sea in relationship with ENSO, Journal of Science, Vietnam National University, Hanoi, t.XVIII, No1, (2002) 51-58. (In English)[19] Nguyễn Văn Tuyên, Xu hướng hoạt động của xoáy thuận nhiệt đới trên Tây Bắc Thái Bình Dương và biển Đông theo các cách phân loại khác nhau, Tạp chí KTTV. số 559 (2007) tr.4-10.[20] Đinh Bá Duy, Ngô Đức Thành; Phan Văn Tân, 2016, Mối quan hệ giữa ENSO và số lượng, cấp độ Xoáy thuận Nhiệt đới trên khu vực Tây Bắc - Thái Bình Dương, Biển Đông giai đoạn 1951-2015, VNU Journal of Science: Earth and Environmental Sciences, [S.l.], v. 32, n. 3S, sep. (2016) ISSN 2588-1094.[21] Đinh Bá Duy, Ngô Đức Thành, Nguyễn Thị Tuyết, Phạm Thanh Hà, Phan Văn Tân, Đặc điểm hoạt động của Xoáy thuận Nhiệt đới trên khu vực Tây Bắc Thái Bình Dương, Biển Đông và vùng trực tiếp chịu ảnh hưởng trên lãnh thổ Việt Nam giai đoạn 1978-2015, VNU Journal of Science: Earth and Environmental Sciences, [S.l.], v. 32, n. 2, (2016) ISSN 2588-1094.[22] Đinh Văn Ưu, Đánh giá quy luật biến động dài hạn và xu thế biến đổi số lượng bão và áp thấp nhiệt đới trên khu vực Tây Thái Bình Dương, Biển Đông và ven biển Việt Nam, Tạp chí Khoa học ĐHQGHN, Khoa học Tự nhiên và Công nghệ. 25 3S, (2009) 542-550.[23] Nguyễn Văn Hiệp và nnk, Đặc điểm hoạt động của bão ở Tây Bắc Thái Bình Dương và Biển Đông qua số liệu Ibtracs, Tuyển tập báo cáo tại Hội thảo khoa học năm 2016 của Viện Khoa học KTTV & BĐKH, (2006) tr. 9-14.[24] Vũ Thanh Hằng, Ngô Thị Thanh Hương,, Phan Văn Tân, Đặc điểm hoạt động của bão ở vùng biển gần bờ Việt Nam giai đoạn 1945-2007, Tạp chí Khoa học ĐHQGHN, Khoa học Tự nhiên và Công nghệ 26, Số 3S, pp 344‐353, 2010[25] Nguyễn Văn Tuyên, Khả năng dự báo hoạt động mùa bão biển Đông Việt Nam: Phân tích các yếu tố dự báo và nhân tố dự báo có thể (Phần I), Tạp chí KTTV, (số 568) tháng 4 năm 2008, tr.1-8.[26] Nguyễn Văn Tuyên, 2008: Khả năng dự báo hoạt động mùa bão biển Đông Việt Nam: Phân tích các yếu tố dự báo và nhân tố dự báo có thể (Phần II). Tạp chí KTTV, số 571, tháng 7 năm 2008, tr.1-11.[27] Phan Văn Tân, 2009-2010, Nghiên cứu tác động của biến đổi khí hậu toàn cầu đến các yếu tố và hiện tượng khí hậu cực đoan ở Việt Nam, khả năng dự báo và giải pháp chiến lược ứng phó. Đề tài cấp Nhà nước, mã số KC08.29/06-10.[28] https://www.jma.go.jp/jma/jma-eng/jma-center/ rsmc-hp-pub-eg/besttrack.html. [29] https://www.esrl.noaa.gov/ psd/data/climateindices/ list/[30] T. Ngo-Duc, J. Matsumoto, H. Kamimera, and H.H. Bui, Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia–ThuBon River Basin in Central Vietnam using an artificial neural network. Hydrological Research Letters. 7(4), (2013) 85-90. doi:10.3178/hrl.7.85.[31] J. C. L. Chan, J. E. Shi and C. M. Lam, Seasonal forecasting of tropical cyclone activity over the Western North Pacific and the South China Sea, Wea. Forecasting. 13 (1998) 997-1004.[32] E. S. Blake, W. M. Gray, Prediction of August Atlantic Basin Hurricane Activity. Wea. Forecasting. 19 (2004) 1044-1060.[33] P. J. Klotzbachi, W. M. Gray, Extended range forecast of Atlantic seasonal Hurricane activity and U. S. landfall strike probability for 2008, (2007) http://hurricane.atmos. colostate.edu/Forecasts.
    Type of Medium: Online Resource
    ISSN: 2588-1094 , 2615-9279
    Language: Unknown
    Publisher: Vietnam National University Journal of Science
    Publication Date: 2019
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  • 3
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    Online Resource
    Vietnam National University Journal of Science ; 2021
    In:  VNU Journal of Science: Computer Science and Communication Engineering Vol. 37, No. 1 ( 2021-02-02)
    In: VNU Journal of Science: Computer Science and Communication Engineering, Vietnam National University Journal of Science, Vol. 37, No. 1 ( 2021-02-02)
    Abstract: In this paper, we analyze an underlay two-way decode-and-forward scheme in which secondary relays use successive interference cancellation (SIC) technology to decode data of two secondary sources sequentially, and then generate a coded signal by superposition coding (SC) technology, denoted as SIC-SC protocol. The SIC-SC protocol is designed to operate in two time slots under effects from an interference constraint of a primary receiver and residual interference of imperfect SIC processes. Transmit powers provided to carry the data are allocated dynamically according to channel powers of interference and transmission, and a secondary relay is selected from considering strongest channel gain subject to increase in decoding capacity of the first data and decrease in collection time of channel state information. Closed-form outage probability expressions are derived from mathematical manipulations and verified by performing Monte Carlo simulations. An identical scheme of underlay two-way decodeand-forward relaying with random relay selection and fixed power allocations is considered to compare with the proposed SIC-SC protocol, denoted as RRS protocol. Simulation and analysis results show that the non-identical outage performances of the secondary sources in the proposed SIC-SC protocol are improved by increasing the number of the secondary relays and the interference constraint as well as decreasing the residual interference powers. Secondly, the performance of the nearer secondary source is worse than that of the farther secondary source. In addition, the proposed SIC-SC protocol outperforms the RRS comparison protocol, and effect of power allocations through channel powers is discovered. Finally, derived theory values are precise to simulation results. Keywords: Successive interference cancellation, superposition coding, power allocation, underlay cognitive radio, non-orthogonal multiple access, outage probability. References [1] Popovski, H. Yomo, Physical Network Coding in Two-Way Wireless Relay Channels, presented at 2007 IEEE International Conference on Communications (ICC), Glasgow, 2007, pp. 707-712. https://doi.org/10.1109/ICC.2007.121. [2] Cao, X. Ji, J. Wang, S. Zhang, Y. Ji, J. Wang, Security-Reliability Tradeoff Analysis for Underlay Cognitive Two-Way Relay Networks, IEEE Transactions on Wireless Communications 18(12) (2019) 6030-6042. https://doi.org/10.1109/ TWC.2019.2941944. [3] Mitola, G.Q. Maguire, Cognitive radio: making software radios more personal, IEEE Personal Communications 6(4) (1999) 13-18. https://doi. org /10.1109/98.788210. [4] M.C. Chu, H. Zepernick, Performance Optimization for Hybrid Two-Way Cognitive Cooperative Radio Networks With Imperfect Spectrum Sensing, IEEE Access 6 (2018) 70582-70596. https://doi.org/10.1109/ICC.2007.121. [5] Ho-Van, T. Do-Dac, Security Analysis for Underlay Cognitive Network with Energy-Scavenging Capable Relay over Nakagami-m Fading Channels, Wireless Communications and Mobile Computing 2019 1-16. https://doi.org/ 10.1155/2019/5080952. [6] Zhang, Z. Zhang, J. Xing, R. Yu, P. Zhang, W.Wang, Exact Outage Analysis in Cognitive Two-WayRelay Networks With Opportunistic Relay SelectionUnder Primary User’s Interference, IEEE Transactionson Vehicular Technology 64(6) (2015) 2502-2511. https://doi.org/10.1109/2014.2346615. [7] T. Duy, H.Y. Kong, Exact outage probability of cognitive two-way relaying scheme with opportunistic relay selection under interference constraint, IET Communications 6(16) (2012), 2750-2759. https://doi.org/ 10.1049/iet-com. 2012.0235. [8] V. Toan, V.N.Q. Bao, Opportunistic relaying for cognitive two-way network with multiple primary receivers over Nakagami-m fading, presented at 2016 International Conference on Advanced Technologies for Communications (ATC), Hanoi city, 2016, pp.141-146. https://doi.org/1109/ATC.2016.7764762. [9] V. Toan, V.N.Q. Bao, H. Nguyen-Le, Cognitive two-way relay systems with multiple primary receivers: exact and asymptotic outage formulation, IET Communications 11(16) (2017) 2490-2497. https://doi.org/10.1049/iet-com.2017. 0400. [10] V.Toan, V.N.Q. Bao, K.N. Le,Performance analysis of cognitive underlay two-wayrelay networks with interference and imperfect channelstate information, EURASIP Journal on WirelessCommunications and Networking 2018 53 (2018).https://doi.org/10.1186/s13638-018-1063-z. [11] Solanki, P.K. Sharma, P.K. Upadhyay,Adaptive Link Utilization in Two-Way SpectrumSharing Relay Systems Under Average Interference Constraints, IEEE Systems Journal 12(4) (2018) 3461-3472. https://doi.org/10.1109/ JSYST.2017.2713887. [12] Yue, Y. Liu, S. Kang, A. Nallanathan, Y. Chen, Modeling and Analysis of Two-WayRelay Non-Orthogonal Multiple Access Systems, IEEETransactions on Communications 66(9) (2018) 3784-3796. https://doi.org/10.1109/TCOMM. 2018.2816063. [13] Zou, B. He, H. Jafarkhani, An Analysis of TwoUser Uplink Asynchronous Non-orthogonal MultipleAccess Systems, IEEE Transactions on WirelessCommunications 18(2) (2019) 1404-1418. https://doi.org/10.1109/TWC.2019.2892486. [14] Yang, Z. Ding, P. Fan, N. Al-Dhahir, TheImpact of Power Allocation on Cooperative Nonorthogonal Multiple Access Networks With SWIPT,IEEE Transactions on Wireless Communications 16(7) (2017) 4332-4343. https://doi.org/10.1109/TWC.2017.2697380. [15] N. Son, T.T. Duy, K. Ho-Van, SIC-Coding Schemes for Underlay Two-Way Relaying Cognitive Networks, Wireless Communications and Mobile Computing 2020, pp.1-24. https://doi.org/ 10.1155/2020/8860551. [16] F. Kader, M.B. Shahab, S.Y. Shin, ExploitingNon-Orthogonal Multiple Access in Cooperative RelaySharing, IEEE Communications Letters 21(5) (2017) 1159-1162. https://doi.org/1109/LCOMM.2017.2653777. [17] Yue, Y. Liu, S. Kang, A. Nallanathan, Z. Ding,Spatially Random Relay Selection for Full/Half-DuplexCooperative NOMA Networks, IEEE Transactions onCommunications 66(8) (2018) 3294-3308. https://doi.org/10.1109/TCOMM. 2018.2809740. [18] Liu, Z. Ding, M. Elkashlan, J. Yuan,Nonorthogonal Multiple Access in Large-Scale UnderlayCognitive Radio Networks, IEEE Transactions onVehicular Technology 65(12) (2016)10152-10157. https://doi.org/10.1109/ TVT.2016.2524694. [19] Song, W. Yang, Z. Xiang, N. Sha, H. Wang, Y.Yang, An Analysis on Secure Millimeter Wave NOMACommunications in Cognitive Radio Networks, IEEE Access 8 (2020), 78965-78978. https://doi.org/10.1109/ACCESS.2020.2989860. [20] Ding, T. Song, Y. Zou, X. Chen, L. Hanzo,Security-Reliability Tradeoff Analysis of Artificial NoiseAided Two-Way Opportunistic Relay Selection, IEEE Transactions on Vehicular Technology 66(5) (2017) 3930-3941. https://doi.org/10.1109/TVT.2016.2601112. [21] Zheng, M. Wen, F. Chen, J. Tang, F. Ji, SecureNOMA Based Full-Duplex Two-Way Relay Networkswith Artificial Noise against Eavesdropping, presented at 2018IEEE International Conference on Communications(ICC), Kansas City, 2018,pp.1-6. https://doi.org/ 10.1109/ICC.2018.8422946. [22] N. Son,H.Y. Kong, Exact Outage Analysisof Energy Harvesting Underlay Cooperative CognitiveNetworks, IEICE Transactions on Communications E98-B(4) (2015),pp.661-672. https://doi.org/10.1587/transcom.E98.B.661. [23] Tourki, K.A. Qaraqe, M. Alouini, OutageAnalysis for Underlay Cognitive Networks UsingIncremental Regenerative Relaying, IEEE Transactions on Vehicular Technology 62(2) (2013) 721-734. https://doi.org/10.1109/TVT. 2012.2222947. [24] Papoulis, S.U. Pillai, Probability, randomvariables and stochastic processes, 4th ed., McGrawHill, New York, 2002. [25] Pei, T. Zhifeng, L. Zinan, E. Erkip, S.Panwar, Cooperative wireless communications: a cross-layer approach, IEEE Wireless Communications 13(4) (2006) 84-92. https://doi.org/10.1109/2006.1678169. [26] Ghasemi, E.S. Sousa, Fundamental limitsof spectrum-sharing in fading environments, IEEETransactions on Wireless Communications 6(2) (2007) 649-658. https://doi.org/10.1109/TWC. 2007.05447. [27] M. Peha, Approaches to spectrum sharing, IEEECommunications Magazine 43(2) (2005) 10-12. https://doi.org/10.1109/MCOM.2005. 1391490. [28] Kim, S. Lim, H. Wang, D. Hong, Optimal PowerAllocation and Outage Analysis for Cognitive FullDuplex Relay Systems, IEEE Transactions on Wireless Communications 11(10) (2012) 3754-3765. https://doi.org/10.1109/TWC. 2012.083112.120127. [29] N. Son,T.T. Duy, Performance analysisof underlay cooperative cognitive full-duplexnetworks with energy-harvesting relay, ComputerCommunications 122 (2018) 9-19. https://doi.org/1016/j.comcom.2018.03.003. [30] V. Nguyen, T. Do, V.N.Q. Bao, D.B.d.Costa, B. An, On the Performance of MultihopCognitive Wireless Powered D2D Communications inWSNs, IEEE Transactions on Vehicular Technology 69(3) (2020) 2684-2699. https://doi.org/10.1109/TVT.2020.2963841. [31] Ruan, Y. Li, C. Wang, R. Zhang, H.Zhang, Energy Efficient Power Allocation for DelayConstrained Cognitive Satellite Terrestrial NetworksUnder Interference Constraints, IEEE Transactions on Wireless Communications 18(10) (2019) 4957-4969. https://doi.org/10.1109/TWC. 2019.2931321. [32] Gao, S. Zhang, Y. Su, M. Diao, M. Jo, Joint Multiple Relay Selection and Time Slot Allocation Algorithm for the EH-Abled Cognitive Multi-User Relay Networks, IEEE Access 7 (2019) 111993- 112007. https://doi.org/10.1109/2019.2932955. [33] Arezumand, H. Zamiri-Jafarian, E. Soleimani-Nasab, Exact and Asymptotic Analysis of Partial Relay Selection for Cognitive RF-FSO Systems With Non-Zero Boresight Pointing Errors, IEEE Access 7 (2019) 58611-58625. https://doi.org/1109/ACCESS.2019.2914480. [34] N. Son, H.Y. Kong, Energy-Harvesting Relay Selection Schemes for Decode-and-Forward Dual-Hop Networks, IEICE TRANSACTIONS on Communications E98-B(12) (2015) 2485-2495. https://doi.org/10.1587/transcom.E98.B.2485. [35] N. Nguyen, T.H. Quang Minh, P.T. Tran, M. Voznak, T.T. Duy, T.-L. Nguyen, P.T. Tin, Performance enhancement for energy harvesting based two-way relay protocols in wireless ad-hoc networks with partial and full relay selection methods, Ad Hoc Networks 84 (2019) 178-187. https://doi.org/10.1016/j.adhoc.2018.10.005. [36] Pan, Z. Li, Z. Wang, F. Zhang, Joint Relay Selection and Power Allocation for the Physical Layer Security of Two-Way Cooperative Relaying Networks, Wireless Communications and Mobile Computing, 2019, pp. 1-7. https://doi.org/10.1155/2019/1839256. [37] A. Nasir, Z. Xiangyun, S. Durrani, R.A. Kennedy, Relaying Protocols for Wireless Energy Harvesting and Information Processing, IEEE Transactions on Wireless Communications 12(7) (2013) 3622-3636. https://doi.org/10.1109/TWC. 2013.062413.122042. [38] I. Gradshteyn, I.M. Ryzhik, A.Jeffrey, D. Zwillinger, Table of integral, series and products, 7th ed., Elsevier, Amsterdam, 2007. [39] Haiyan, L. Zan, S. Jiangbo, G. Lei, Underlay cognitive relay networks with imperfect channel state information and multiple primary receivers, IET Communications 9(4) (2015) 460-467. https://doi.org/10.1049/iet-com.2014.0429. [40] Zhong, Z. Zhang, Opportunistic Two-Way Full-Duplex Relay Selection in Underlay Cognitive Networks, IEEE Systems Journal 12(1) (2018) 725-734.https://doi.org/10.1109/JSYST. 2016.2514601.
    Type of Medium: Online Resource
    ISSN: 2588-1086 , 2615-9260
    Language: Unknown
    Publisher: Vietnam National University Journal of Science
    Publication Date: 2021
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