Archive

Abstract:

Einstein was pioneer in his work of the theory of special relativity and the theory of general relativity. This paper took a famous equation from the theory of special relativity to have a comprehensive explanatory derivation from the equation of the theory of special relativity. This paper also explains why there is always constancy of the speed of light, the universal speed limit of the Universe, disregard of movement in or away of source that transmits light and the movement in or away of body that receives light. This paper also shows that Doppler Effect is a property of space – time. The Doppler Effect can explain why there is the constancy in the speed of light.

Keywords:

The theory of special relativity,the theory of general relativity,the space – time,Doppler Effect, the speed of light–the Universal speed limit of the Universe,

Refference:

I.Stephen Hawking, “The Beginning of Time”, A Lecture.

II.Roger Penrose, “Cycles of Time”, Vintage Books, London, pp. 50-56.

III.Stephen Hawking, “A Briefer History of Time”, Bantam Books, London, pp. 1-49.

IV.Stephen Hawking, “Black holes and Baby Universes and other essays”, Bantam Press, London 2013, ISBN 978-0-553-40663-4.

V.Stephen Hawking, “The Grand Design”, Bantam Books, London 2011

VI.Stephen Hawking, “A Brief History of Time”, Bantam Books, London 2011, pp. 156-157. ISBN-978-0-553-10953-5

VII.Stephen Hawking, “The Universe in a Nutshell”, Bantam Press, London 2013, pp. 58-61, 63, 82-85, 90-94, 99, 196. ISBN 0-553-80202-XVIII.Stephen Hawking, “A stubbornly persistent illusion-The essential scientific works of Albert Einstein”, Running Press Book Publishers, Philadelphia, London 2011.

IX.Stephen Hawking, “Stephen Hawking’s Universe: Strange Stuff Explained”, PBS site on imaginary time.

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Abstract:

With increase in use of fibres for strengthening of Reinforced Concrete (RC) beams, Textile Reinforced Mortar (TRM) is becoming a popular choice among the researchers and scientists. While Carbon, glass and PBO fibers have shown encouraging results for structural strengthening, use of basalt fibres have not been much explored for strengthening other than masonry. Limited small-scale data exists for the use of basalt fibres in TRM strengthening but data for full scale beams is scarce. This paper presents an experimental study of six full scale RC beams tested with a varying shear span to depth (a/d) ratio 3 through 6, where three beams served as control beams for various a/d ratios while the remaining three beams were strengthened with TRM using basalt fibres. TRM was provided at the tension face of the beams for strengthening in flexure along with U-shaped wraps. Results showed that TRM using basalt fibres is effective in improving the performance of RC beams in terms of serviceability, crack and deflection control, load carrying capacity, initial and post cracking stiffness, and ductility.

Keywords:

Reinforced concrete,Full scale beams, Strengthening,Textile Reinforced Mortar, Load-Deflection,Performance,

Refference:

I.ACI. “Guide to design and construction of externally bonded fabric-reinforced cementitious matrix (FRCM) systems for repair and strengthening concrete and masonry structures”,2013.

II.ACI Committee %J American Concrete Institute, F. H., MI. (2014). 318, “Building Code Requirements for Structural Concrete (ACI 318–14) and Commentary (ACI 318R–14). 519”,2014.

III.Al-Salloum, Y. A., Siddiqui, N. A., Elsanadedy, H. M., Abadel, A. A., & Aqel, “Textile-reinforced mortar versus FRP as strengthening material for seismically deficient RC beam-column joints”,Journal of Composites for Construction15(6), 920-933, 2011.

IV.Aljazaeri, Z. R., Janke, M. A., & Myers, “A novel and effective anchorage system for enhancing the flexural capacity of RCbeams strengthened with FRCMcomposites”,Composite Structures, 2018.

V.Ayub, T., Khan, S. U., & Memon, F. Ahmed,(2014).”Mechanical characteristics of hardened concrete with different mineral admixtures: a review”,The Scientific World Journal,2014.

VI.Azam, R., & Soudki, “FRCM strengthening of shear-critical RC beams”,Journal of Composites for Construction,18(5), 04014012, 2014.

VII.Bencardino, F., Spadea, G., Swamy, “The problem of shear in RC beams strengthened with CFRP laminates”, Construcion Building Materials,21(11), 1997-2006, 2007.

VIII.Bisby, L., & Williams, B. J.,”An introduction to FRP strengthening of concrete structures”. 4, 1-39, ISIS Educational Module, 2014.

IX.Bournas, D. A., Lontou, P. V., Papanicolaou, C. G., T.Triantafillou, “Textile-reinforced mortar (TRM) versus FRP confinement in reinforced concrete columns”, ACI Structural Journal104(6), 740-748, 2007.

X.Brückner, A., Ortlepp, R., Curbach, “Textile reinforced concrete for strengthening in bending and shear”, Materials & Structures39(8), 741-748, 2006.

XI.Carloni, C., Subramaniam, K. V., Savoia, M., & Mazzotti, “Experimental determination of FRP–concrete cohesive interface properties under fatigue loading”. 94(4), Composite Structures, 1288-1296, 2012.

XII.D’Antino, T., Carloni, C., Sneed, L., & Pellegrino, “Matrix–fiber bond behavior in PBO FRCM composites: A fracture mechanics approach”, Engineering Fracture Mechanics,117, 94-111, 2014.

XIII.Escrig, C., Gil, L., Bernat-Maso, E., Puigvert, “Experimental and analytical study of reinforced concrete beams shear strengthened with different types of textile-reinforced mortar”, Construction and Building Materials,83, 248-260, 2015.

XIV.Fib builletin:”Externally BondedFRPReinforcement for RC Structures”. (14), 51-58., 2001.

XV.Jiang, C., Fan, K., Wu, F., Chen, “Experimental study on the mechanical properties and microstructure of chopped basalt fibre reinforced concrete”, Materials & Design58, 187-193, 2014.

XVI.Khan, A., F.Masood, “Strengthening of Reinforced Concrete Beams With Textile Reinforced Mortar (TRM) in Flexure”,8th International Civil Engineering Congress (ICEC-2016), Karachi, Pakistan, 2016.

XVII.Khan, A., F.Masood, “Strengthening of shear deficient reinforced concrete beams with textile reinforced mortar (TRM)”.8th international conference on fibre-reinforced (FRP) composites in civil engineering, Hong Kong, China, 2016.

XVIII.Loreto, G., Babaeidarabad, S., Leardini, L., & Nanni, “RC beams shear-strengthened with fabric-reinforced-cementitious-matrix (FRCM) composite”, International Journal of Advanced Structural Engineering7(4), 341-352, 2015.

XIX.Ma, G., G.Li, “Experimental study of the seismic behavior of predamaged reinforced-concrete columns retrofitted with basalt fiber–reinforced polymer”, Journal of Composites for Construction,19(6), 04015016, 2015.

XX.Ombres, L., Mancuso, N., Mazzuca, S., & Verre, “Bond between Carbon Fabric-Reinforced Cementitious Matrix and Masonry Substrate”, Journal of Materials in Civil Engineering,31(1), 04018356, 2018.

XXI.Ombres, Luciano, “Structural performances of reinforced concrete beams strengthened in shear with a cement based fiber composite material”, Composite Structures,122, 316-329,2015.

XXII.Razaqpur, A. G., Shedid, M., & Isgor, Burkan, “Shear strength of fiber-reinforced polymer reinforced concrete beams subject to unsymmetric loading”, Journal of Composites for Construction,15(4), 500-512, 2010.

XXIII.Rostam, S., Bakker, R., Beeby, A., van Nieuwenburg, D., Schiessl, P., L.Sentler, “Durable Concrete Structures-CEB Design Guide”,Bulletin d’Information(182), 1992 J. Mech. Cont.& Math. Sci., Vol.-14, No.-3, May-June(2019) pp 65-82Copyright reserved © J. Mech. Cont.& Math. Sci.Fawwad Masoodet al.82.

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Abstract:

GLARE belongs to a family of fiber-metal laminates composed of alternate layers of prefabricated reinforced composites with unidimensional glass fibers and Aluminum 2024 sheets first invented for aeronautical applications.The dynamic response of structures, which are subjected to impact loading can be studied by employing equivalent mechanical systems consisting of springs and masses. It is then possible to derive the differential equations of motion using the equilibrium of forces, which are applied on the masses. In this research, a mathematical model of low velocity impact loading on Glass Fiber Reinforeced Aluninum Laminates was derived and simulated , as well as the dynamic effect of low energy impact with the simulation of finite element method (FEM) of on 4 types of GLARE were performed. Low velocity impact tests were conducted with drop-weight impact tower and the = central plate’s deflection, force- time history, velocity- time history and energy-time diagrams obtained from the mathematical model and simulation of finite element analisys were compared with the experimental data obtained from the drop weight impact tower. The comparison of the results shows that the results of simulation of finite element are 4% and the results of the 8% mathematical model differ with experimental results and mathemathical model can use for low velocity impact modelings.

Keywords:

Glare, low velocity impact,mathematical model,experimental tests,finite element model,

Refference:

I.A.Vlot, Glare-History of the Development of a New Aircraft Material, 1st edition, Kluwer, Dordrecht, The Netherlands, 2001.

II.Dadej K, Surowska B, Bieniaś J. Isostrain elastoplastic model for prediction of static strength and fatigue life of fiber metal laminates,International Journal of Fatigue 2018; doi: https://doi.org/10.1016/j.ijfatigue.01.009, 2018.

III.F. Bagnoli, M. Bernabei, D. Figueroa-Gordon, P. E. Irving, The response of………aluminum/GLARE hybrid materials to impact and to in-plane fatigue, Material Science and Engineering A, Vol,523,118–124, 2009.

IV.G. Caprino, G. Spataro and S. Del Luongo, Low-velocityimpact behavior of fiber glassaluminum laminates, Composites.Part A35,605–616, 2004.V.G. Caprino,

V. Lopresto and P. Iaccarino, A simple mechanistic model to predict macroscopic response of fireglass -aluminum laminates under low-velocity impact, Composites.Part A38,290–300, 2007.

VI.G. Wu, The Impact Properties and Damage Tolerance of Bidirectionally Reinforced Fiber Metal laminates, Journal of Material Science and Technology, Vol. 42, No3,948–957, 2005.

VII.K. Preusch, P. Linde, H. De Boer, C. Carmone, Modelling of fiber metal laminates shells applied to the inter rivet buckling phenomenon, European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), 115-132, 2004.

VIII.Lapczyk, J.A. Hurtado, Progressive damagemodeling in fiber-reinforced materials”. Composites: Part A, Vol. 38,2333–2341, 2007.

IX.Li H, Xu Y, Hua X, Liu C, Tao J. Bending failure mechanism and flexural properties of GLARE laminates with different stacking sequences, Composite Structures, doi: https://doi.org/10.1016/ j.compstruct.12, 68-77, 2017.

X.M. Hoo Fatt, C. Lin, D. Revilock and D. Hopkins, Ballistic impact of GLARE fiber-metal laminates, Composite Structure, 14,73–88, 2003.

XI.P. Linde, J. Pleitner, H. De Boer, C. Carmone, Modelling and simulation of fiber metal laminates, Abaqus User’s Conference, 2004.

XII.Park S.Y, Choi W.J, Choi C.H, Choi H.S. Effect of drilling parameters on hole quality and delamination of hybrid GLARE laminate, Composite Structures,; doi: https://doi.org/10.1016/j.compstruct..11.073, 2017.

XIII.R.C Alderliesten. Fatigue ra k propagation and delamination growth in the glare, Ph.D. thesis, Delft University of Technology, Delft,2005.

XIV.S. Hyoungseock, J. Hundley, H.T. Hahn, J. Yang , Numerical Simulation of Glass-Fibre Reinforced Aluminum Laminates withDiverse Impact Damage, AIAA Journal, Vol. 48, No. 3,676-687, 2010.

XV.T.J. Vries Blunt and sharp notch behavior of glare laminates. Ph.D Dissertation, Delft..University Press, 2001.

XVI.Y. Lui, B. Liaw, Effects of constituents and lay-up configuration on drop-weight tests of fiber-metal laminates, Applied Composite Materials, Vol. 17, 43–62, 2010.

XVII.Zarei H, Brugo T, Belcari J, Bisadi H, Minak G, Zucchelli A. Low velocity impact damage assessment of GLARE fiber-metal laminates interleaved by Nylon 6,6 nanofiber mats. Composite Structures.167,123–131, 2017.

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Abstract:

This study aims to identify and examine early mathematical concepts based on mathematical terminology and early mathematical skills among children and teachers of the National Child Development Research Centre (NCDRC). This study also investigates the pedagogical practices by the teachers on how to overcome vague terminology and children skills procedure in the teaching and learning of early mathematics. The study focuses on the teaching and learning of early Mathematics topics such as match, gather, separate and compare. Eight teacher participants are involved in 4 selected PERMATA Anak Negara Centres: Bercham, Besout 1, Teluk Intan and Sg. Siput. The findings have revealed that the children are given the opportunities to explain, defend, conclude, predict and suit their ways of understanding the mathematical concepts related to the chosen topics: match, gather, separate and compare. The children are also encouraged to show their understanding through various different ways including the critical thinking skills. As a result, this study has produced the Lexical Kit based on the Prosodic Semantic Theory for the learning and early Mathematics facilitation for pre-schools in Malaysia thus help teachers to make children understand the terminology concepts and early mathematical procedures clearly.

Keywords:

Children,lexical approach,mathematics terminology,procedure skills,PERMATA Anak Negara Centre,Prosodic Semantic,

Refference:

I.Abdul Latif Samian. (1997). Falsafah matematik sekolah rendah. Kuala Lumpur: Dewan Bahasa & Pustaka.

II.Abraham H. Maslow. (1970). Motivation and Personality. New York: Harper & Row Publisher.

III. Asmah Haji Omar. (1986). Bahasa dan alam pemikiran Melayu. Kuala Lumpur: Dewan Bahasa dan Pustaka.

IV.Aubrey, C., Godfrey, R. & Sarah, D. (2006). Early mathematics development and later achievement: Mathematics education research journal, 18 (1), 27-46.

V.Aunola, K. & Nurmi, J.E. (2004). Maternal affection moderates the impact of psychological control on a child’s mathematical performance. Journal of developmental psychology, 40, 965-978.

VI.Azizi Hj. Yahaya & Elanggovan. (2010). Kepentingan kefahaman konsep dalam matematik. Jurnal pendidik dan pendidikan, 14(2), 22-33.

VII.Barrouillet, P., Fayol, M. & Lathuliere, E. (1998). Selecting between competitors in multiplication tasks: An explanation of the errors produced by adolescents with learning disabilities. International journal of behavioral development, 21, 253-275.

VIII.Bryant, P. & Nunes, T. (2002). Learning and teaching mathematics: An international perspective. United Kingdom: Psychology Press Ltd.

IX.David C. Geary, Carmen O. Hamson & Mary, K. Hoard. (2000). Numerical and arithmetical cognition: A longitudinal study of process and concept deficits in children with learning disability. Journal of experimental child psychology, 77(2), 236-263.

X.Desoete, A., Stock, P., Schepens, A., Baeyens, D. & Roeyers, H. (2009). Classification, seriation and counting in grades 1,2 and 3 as two-year longitudinal predictors for low achieving in numerical facility and arithmetical achievement? Journal of psychoeducational assessment, 27(3), 252-264.

XI. Fuson, K. C. (1982). An analysis of the counting-on solution procedure in addition. Journal for research in mathematics education, 14(1), 67-81.

XII.Jordan, N. C., Kaplan, D., Locuniak, M. & Ramineni, C. (2007). Predicting first-grade math achievement from developmental number sense. Learning disabilities research & practice, 22(1), 36-46.

XIII.Kilpatrick, J. (1992). A history of research in mathematics education. Handbook of research on mathematics teaching and learning. New York: Macmillan Publishing.

XIV.Koponen, T., Aunola, K., Ahonen, T. & Nurmi, J.E. (2007). Cognitive predictors of single digit and procedural calculation sills and their covariation with reading skills. Journal of experimental child psychology, 97, 220-241.

XV.Page A. Smith. (2002). The organizational health of high schools and student proficiency in mathematics. International journal of educational management, 16 (2), 98-104.

XVI.Puteri Roslina Abdul Wahid. (2005). Bahasa Melayu dalam konteks masyarakat, budaya dan kuasa. Jurnal Melayu Antarabangsa, 4(2), 75-86.

XVII.Siegler, R. S. & Shrager, J. (1984). Strategy choice in addition and subtraction: How do children know what to do? Hillsdale: Erlbaum.

XVIII.Sophian, C. (1998). A developmental perspective on children’s counting. The development of mathematical skills. United Kingdom: Psychology Press Ltd.

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Abstract:

The objective of the paper is to develop an Area optimized Low power digital circuit for MAC (Multiply and Accumulate) operation. We developed implementations of the MAC to avoid using multipliers and prefer to use the combinational circuits like multiplexers. We analyze all the MAC digital circuits to find out the best digital circuit which consumes minimum area and power. MAC is basic building block of many Digital Signal Processing Applications like Noise Cancellation Circuits, Speech Processing, Image Processing, Video Processing, Artificial Neural Networks etc. We also give some suggestions on the system level solutions based on the MAC. The digital circuit which is developed by us will be compatible to FPGAs, as it is developed by the industry standard Synthesis tool i.e. Synopsys Synlipy pro synthesis tool. The MAC which we are developing can be placed in the FPGA Fabric and it can be interfaced to any processors like Cortex M3, Cortex M0, 805 1etc. The overall throughput decreases due high latency and increase in the processing time. So, all the MAC operations must be performed in the hardware by the MAC block developed by us as it is low power, low area and fast hardware.

Keywords:

Digital Signl Processing,MAC,

Refference:

I.Abdelgawad, A. (2013).Low power multiply accumulate unit (MAC) for future Wireless Sensor Networks. 2013 IEEE Sensors Applications Symposium Proceedings.doi:10.1109/sas.2013.6493571.

II.Comparison among Different Adders Prof. Rashmi Rahul Kulkarni, IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 5, Issue 6, Ver. I (Nov -Dec. 2015), PP 01-06 e-ISSN: 2319 –4200, p-ISSN No. : 2319 –4197

III.Jayanthi, A. N., &Ravichandran, C. S. (2013).Comparison of performance of high speed VLSI adders. 2013 International Conference on Current Trends in Engineering and Technology (ICCTET).doi:10.1109/icctet.2013.6675920

IV.Kapse, V., Jain, A., &Pattanaik, M. (2016).Design of anArea Efficient and Low Power MAC Unit.Smart Trends in Information Technology and Computer Communications, 276–284.doi:10.1007/978-981-10-3433-6_33.

V.Ramadass, Uma &Vijayan, Vidya&Mohanapriya, M & Paul, Sharon. (2012). Area, Delay and Power Comparison of Adder Topologies.International Journal of VLSI Design & Communication Systems.

VI.Shah, S., Al-Khalili, A. J., & Al-Khalili, D. (n.d.). Comparison of 32-bit multipliers for various performance measures.ICM 2000.Proceedings of the 12th International Conference on Microelectronics. (IEEE Cat. No.00EX453). doi:10.1109/icm.2000.916418V., Jain, A., &Pattanaik, M. (2016).Design of anArea Efficient and Low Power MAC Unit.Smart Trends in Information Technology and Computer Communications, 276–284.doi:10.1007/978-981-10-3433-6_33.

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Abstract:

Nowadays, cloud computing is an interesting research area among the researchers. It is an internet-based pool of heterogeneous resources. Cloud environment is very much reliable to make availability of resources when required to online users. Reliable computing services can be handled without any own infrastructures, so it would be considered as an alternate cost effective technique. Most of the organizations utilized the technique of cloud computing to host their applications. The service of the health care unit is the most essential service for the people. There is a necessity to store the sensitive information related to the patient’s medical history in a secure way. Therefore, the research and development in the Personal Health records and Electronic Health records is negligible area. Thus the most robust encryption and decryption should be encountered. One among the advanced technology in cloud computing is the maintenance of Electronic Health Records (EHR). The main objective of this paper is to propose and implement a methodology to exchange the health information about a particular person in a secured cloud environment. The medical information about a patient from distributed manner is also maintained in EHR by cloud environment. The stored information of the user provides the facility of collecting, sharing, exchanging and organizing that information through users. Therefore, an efficient approach for securing e-health cloud system using identity based cryptography techniques is presented in this research study.

Keywords:

Cloud computing,EHR,Data Privacy,Key Management,

Refference:

I.Aljawarneh SA & Yassein MOB (2016), ‘A Conceptual Security Framework for Cloud Computing Issues’, International Journal of Intelligent Information Technologies (IJIIT), Vol. 12,No. 2, pp. 12-24.

II.Almorsy M, Grundy J & Müller I (2016), ‘An Analysisof the Cloud Computing Security Problem’, arXiv preprint arXiv:1609.01107.

III.Dhirender Singh, R.K. Banyal, Arvind Sharma, ‘Cloud Computing Research Issues, Challenges, and Future Directions’, Emerging Trends in Expert Applications and Security, Advances in Intelligent Systems and Computing 841, Springer Nature Singapore Pte Ltd. 2018

IV.Fabian B, Ermakova T & Junghanns P (2015), ‘Collaborativeand Secure Sharing of Healthcare Data in Multi-Clouds’, Information Systems, Vol. 48, pp. 132-150.

V.Hu Y & Bai G (2014), ‘A Systematic Literature Review of Cloud Computing in e-Health’, arXiv preprint arXiv:1412.2494.

VI.Huang Q, Yue W, He Y & Yang Y(2018), ‘Secure Identity-Based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing’, IEEE Access, Vol. 6, pp. 36584-36594.

VII.Kalaiprasath R, Elankavi R & Udayakumar DR. (2017), ‘Cloud. Security and Compliance-A Semantic Approach in End to End Security’, International Journal of Mechanical Engineering and Technology (IJMET), Vol. 8,No.5.

VIII.Khan SS & Tuteja R (2015), ‘Security in Cloud Computing using Cryptographic Algorithms’, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3,No. 1, pp. 148-155.

IX.Li J, Zhang Y, Chen X & Xiang Y(2018), ‘Secure Attribute-Based Data Sharing for Resource-Limited Users in Cloud Computing’, Computers & Security, Vol. 72, pp. 1-12.

X.Liang K, Liu JK, Wong DS & Susilo W (2014), ‘An efficient Cloud-Based Revocable Identity-Based Proxy Re-Encryption Scheme for Public Clouds Data Sharing’, European Symposium on Research in Computer Security, pp. 257-272.

XI.Luna J, Taha A, Trapero R &Suri N. (2017), ‘Quantitative Reasoning about Cloud Security Using Service Level Agreements’, IEEE Transactions on Cloud Computing, Vol. 5,No. 3, pp. 457-471.

XII.Ma (2016), ‘Identity-based Encryption with Outsourced Equality Test in Cloud Computing’, Information Sciences, Vol. 328, pp. 389-402.

XIII.Samarati P, Di Vimercati SDC, Murugesan S & Bojanova I (2016), ‘Cloud Security: Issues and Concerns’, Encyclopedia on Cloud Computing, pp. 207-219.

XIV.Shen Q, Liang X, Shen XS, Lin X & Luo HY (2014), ‘Exploiting Geo-Distributed Clouds foraE-Health Monitoring System with Minimum Service Delay and Privacy Preservation’, IEEE Journal of Biomedical and Health Informatics, Vol. 18,No. 2, pp. 430-439.

XV.Xhafa F, Li J, Zhao G, Li J, Chen X & Wong DS (2015), ‘Designing Cloud-Based Electronic Health Record System with Attribute-Based Encryption’, Multimedia Tools and Applications, Vol. 74, No. 10, pp. 3441-3458

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Abstract:

The small perturbation method is widely used in attempting to model non-linear systems. Many systems nowadays in different domains exist as adaptive-parameter type models, where the control effort is not applied as in input to the system (as is usually the case) but as a change to the parameter within the system itself. This paper attempts to analyze a non-linear adaptive-parameter type system, using the small perturbation method for linearization. The Ward-Leonard DC Motor with thyristor field control is used as a “test bench” here as it is suited for being an adaptive parameter system. The results and inferences from this study can easily be generalized to a wide variety of systems in applied mathematics, general control systems, power systems, robotics etc.

Keywords:

Non-linear adaptive-parameter,Small perturbation approach,DC drive,Thyristorized W-L method,silicon-controlled rectifier,

Refference:

I. A. Bara, S. Dale, C. Rusu and H. Silaghi, “DC electrical drive control with fuzzy systems,” 2015 13th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, 2015, pp. 1-4. doi: 10.1109/EMES.2015.7158437.
II. A. Choudhary, S. A. Singh, M. F. Malik, A. Kumar, M. K. Pathak and V. Kumar, “Virtual lab: Remote access and speed control of DC motor using Ward-Leonard system,” 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), Kerala, 2012, pp. 1-7. doi: 10.1109/ICTEE.2012.6208666.
III. A.S.Avila Balula, M.S. “Nonlinear Control of an inverted pendulum”Thesis in engineering physics,and Technology, School of engineering University of Lisbon, Portugal September,2016.
IV. F. P. A. Vaccaro, M. Janusz and K. Kuhn, “Digital control of a Ward Leonard drive system,” 3D Africon Conference. Africon ’92 Proceedings (Cat. No.92CH3215), Ezulwini Valley, Swaziland, 1992, pp. 123-127. doi: 10.1109/AFRCON.1992.624433.
IV. G. A. Biacs and M. S. Adzic, “Modeling of the thyristor controlled rectifiers for control of Ward – Leonard system,” 2009 7th International Symposium on Intelligent Systems and Informatics, Subotica, 2009, pp. 193-196.doi: 10.1109/SISY.2009.5291167.
V. Meng Cno, X. jin & R.E. White, “Nonlinear state-variable method for solving physical based Li-Ion cell model with High frequency inputs” journal of Electrochimical Society, Feb 2017.
VI. Q. Zhong, “Speed-sensorless AC Ward Leonard drive systems,” SPEEDAM 2010, Pisa, 2010, pp. 1512-1517. doi: 10.1109/SPEEDAM.2010.5544901.

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Abstract:

This paper aims to model and forecasting on GDP data of Bangladesh for the period of 1960 to 2017. To test the stationarity of the series graphical method, correlogram and unit root test were used. The time series plot of GDP shows a non-stationary pattern and overall this is like exponential curvature shape. Hence the data have been differenced twice to convert the data from non-stationary to stationary. From the autocorrelation function (ACF) and partial autocorrelation function (PACF) we obtain the order of the time series model. The chosen model was autoregressive integrated moving average ARIMA (1, 2, 1). The model has been fitted on data to estimate the parameters of autoregressive and moving average components of ARIMA (1, 2, 1) model. For residual diagnostics, correlogram, Q-statistic, histogram, and normality test were used. Also, Chow test was used for stability testing. Using model selection criterion and checking model adequacy, wesee that the model is suitable in shape. It is found that the forecast values of GDP in Bangladesh are steadily improving over the next thirteen years.

Keywords:

GDP,ARIMA Modeling,Forecasting,Bangladesh,

Refference:

I.Box GEP, Gwilym MJ, Gregory CR. Time Series Analysis: Time Series Analysis Forecasting & Control. New Jersey: Prentice Hall, Englewood Cliffs; 1994.

II.Dickey DA, Fuller WA. Distributions of the Estimators for Autoregressive Time Series with a Unit Root. J Am Stat Assoc. 1979; 74(366),pp:427–481.

III.Dr. ChaidoDritsaki (2015). Forecasting Real GDP rate through Econometric Models: An Empirical Study from Greece. J of Internal Business and Economics, 3(1), pp: 13-19.

IV.Gujarati DN, Porter DC, Gunasekar S. Econometric Modeling: Specification and Diagnostic Testing. Basic Econometrics. 4th Edn. McGraw Hill International; 2003.

V.Hanke JE, Wichern DW. Business Forecasting. 8th Edn. Int J Forecast. 2005; 22(4), pp: 823–824.

VI.Imon AHMR. Box-Jenkins ARIMA Models: Introduction to Regression TimeSeries and Forecasting. NanitaProkash; 2017.VII.Jovanovic, B. &Petrovska M. (2010). Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting. Working Paper, National Bank of the Republic of Macedonia.

VIII.Ljung, G. M., & Box G. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika, 75(2), pp: 335-346.

IX.Maity, B., &ChatterjeeB. (2012). Forecasting GDP growth rates of India: An empirical study. IntJof Economics and Management Sciences, 1(9), pp: 52-58.

X.Ning, W., Kuan-jiang, B. and Zhi-fa, Y. (2010), Analysis and forecast of Shaanxi GDP based on the ARIMA model, Asian Agricultural Research, Vol.2 No. 1, pp. 34-41.

XI.Shahini, L. &Haderi S. (2013). Short term Albanian GDP forecast: One quarter to one year ahead. European Scientific Journal, 9(34),pp: 198-208.

XII.Wei Ning, BianKuan-Jiang. &Yuan Zhi-fa (2010).Analysis and forecast of Shaanxi GDP based on the ARIMA model. Asian Agricultural Research, 2(1), pp: 34-41.

XIII.Zakai, M. (2014). A time series modeling on GDP of Pakistan. J of Contemporary Issues in Business Research,3(4), pp: 200-210.

XIV.Zhang, H. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Working Paper, HögskolanDalarna University, Sweden.

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Abstract:

As the world thrives for its need to complete its energy demand and supply challenges, the state estimation in distribution systems remains a key factor at online observing and controlling in Energy Management Technology. As the world is advancing towards an advance era in order to fulfill its energy supply different sources whether traditional or renewable online monitoring of Distribution of state estimation is becoming more challenging and demandable. In this letter, a concept for state estimation is offered. The accountability for SE is surrogate to indigenous means in secondary substations. By means of past statistics and probabilistic models the substation bad statistics charts knowledge is gathered. Topology and observability analysis as well as bad data identification are performed Data not performing well is identified using topology tools is accomplished with a perfunctory that crosses the secondary substations of the primary substation feeders.

Keywords:

Electrical Distribution Network,Active elements,routing packets,Secondary substation, Primary Station,

Refference:

I.Breda, Jader FD, Jose CM Vieira, and Mario Oleskovicz. “Three-phase harmonic state estimation for distribution systems by using the svd technique.” 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016.

II.D. P. Buse, P. Sun, Q. H. Wu, and J. Fitch, “Agent-based substation automation,” IEEE Power Energy, vol. 1, no. 2, pp. 50–55, Mar.–Apr. 2003.

III.D. Falcao, F.Wu, and L. Murphy, “Parallel and distributed state estimation,” IEEE Trans. Power Syst., vol. 10, no.2, pp. 724–730, May 1995.

IV.D. V. Coury, J. S. Thorp, K. M. Hopkinson, and K. P. Birman, “Agent technology applied to adaptive relay setting for multi-terminal lines,” in Proc. IEEE Power Eng. Soc. Summer Meeting, July 16–20, 2000, pp. 1196–1201.

V.M. Shahidehpour and Y. Wang, Communication and Control in Electrical Power Systems. Piscataway, NJ: IEEE Press, 2003, p. 529.

VI.M. Lehtonen, M. Jalonen, A. Matsinen, J. Kuru, and V. Haapamäki, “A novel state estimation model for distribution automation,” in Proc. PSCCConf., Jun. 24–28, 2002.

VII.M. Amin, “National infrastructure as complex interactive networks,” in Automation, Control, and Complexity: An Integrated Approach. New York: Wiley, 2000, pp. 263–286.

VIII.M. Kezunovic, X. Xu, and D. Wong, “Improving circuit breaker maintenance management tasks by applying mobile agent software technology,” in Proc. IEEE Power Eng. Soc. Asia Pacific Transm. Distrib. Conf., Oct. 6–10, 2002, pp. 782–787.

IX.Primadianto, Anggoro, and Chan-Nan Lu. “A review on distribution system state estimation.”IEEE Transactions on Power Systems32.5 (2016): 3875-3883.

X.T. Hiyama, D. Zuo, and T. Funabashi, “Multi-agent based control and operation of distribution system with dispersed power sources,” in IEEE Power Eng. Soc. Asia Pacific Transm. Distrib. Conf., Oct. 6–10, 2002, pp. 2129–2133.

XI.T. Nagata and H. Sasaki, “A multi-agent approach to power system restoration,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 457–462, May 2002.

XII.Voltage Characteristics of Electricity Supplied by Public Distribution Systems. Brussels, Belgium: Cenelec, Nov. 1999.

XIII.Y. Liang, “Simulation of Top-Oil Temperature for Transformers,” Master’s thesis, Arizona State Univ., Tempe, AZ, Feb. 2001

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Abstract:

Power is a necessary tool for modern civilization. All the modern achievements and technology have made man achieve more and more day by day but use of fossil fuels tends to be limited. On other hand, technologies and techniques are being developed to use natural renewable sources in order to full fill power and energy demand. Distributed Generation is part of new renewable energy trend in which different grid resources are added to meet user end requirements. This paper presents an approach to limit the power storage from wind energy while working with voltage levels. The study is performed in mainly two levels. First the wind profile is studied with load requirements and then detailed control is performed for optimal power flow (OPF). It is found that storages can be changed via user requirement while also depending upon threshold of DG network.

Keywords:

Wind Energy,Energy Storage, Distributed Generation,Wind Energy Farm,Power Generation,Power flow,

Refference:

I.G. Carpinelli, G. Celli, S. Mocci, F. Mottola, F. Pilo, and D. Proto, ―Optimal integration of distributed energy storage devices in smart grids,‖ IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 985–995, 2013

.II.J. A. Martinez, F. de Leon,A. Mehrizi-Sani, M. H. Nehrir, C. Wang, and V. Dinavahi, ―Tools for analysis and design of distributed resources—Part II: Tools for planning, analysis and design of distribution networks with distributed resources,‖ IEEE Trans. Power Del., vol. 26, no. 3,pp. 1653–1662, Jul. 2011.

III.L. Alexio, G. Celli, E. Ghiani, J. Myrzik, L. F. Ochoa, and F. Pilo, ―A general framework for active distribution network planning,‖ in Proc. CIGRE Symp., 2013, pp. 1–8.

IV.L. F. Ochoa, C. Dent, and G. P. Harrison, ―Distribution network capacity assessment: Variable DG and active networks,‖ IEEE Trans. Power Syst., vol. 25, no. 1, pp. 87–95, Feb. 2010.

V.M. Nick, R. Cherkaoui, and M. Paolone, ―Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support,‖ IEEE Trans. Power Syst., vol. 29, no. 5, pp. 2300–2310, Sep. 2014.

VI.N. Etherden and M. H. J. Bollen, ―Dimensioning of energy storage for increased integration of wind power,‖ IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 546–553, 2013.

VII.N. Wade, P. Taylor, P. Lang, and J. Svensson, ―Energy storage for power flow management and voltage control on an 11 kV UK distribution network,‖ in Proc. Int. Conf. Electricity Distribution (CIRED), 2009, pp. 1–4.

VIII.R. A. F. Currie, G. W. Ault,C. E. T. Foote, and J. R. McDonald, ―Active power-flow management utilising operating margins for the increased connection of distributed generation,‖ IET Proc. Gener., Transm., Distrib., vol. 1, no. 1, pp. 197–202, 2007.

IX.J. P. Barton and D. G. Infield, ―Energy storage and its use with intermittent renewable energy,‖ IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 441–448, Jun., 2004.

X.S. Gill, I. Kockar, and G. W. Ault, ―Dynamic optimal power flow for active distribution networks,‖ IEEE Trans. Power Syst., vol. 29, no. 1, pp. 121–131, Jan. 2014.

XI.S. Carr, G. C. Premier, A. J. Guwy, R. M. Dinsdale, and J. Maddy, ―Energy storage for active network management on electricity distribution networks with wind power,‖ IET Renew. Power Gener., vol. 8, no. 3, pp. 249–259, 2014.

XII.S. W. Alnaser and L. F. Ochoa, ―Advanced network management systems: A risk-based AC OPF approach,‖ IEEE Trans. Power Syst., vol. 30, no. 1, pp. 409–418, Feb. 2015.

XIII.Y. V. Makarov, P. Du, M. C. W. Kintner-Meyer, C. Jin, and H. F. Illian, ―Sizingenergy storage to accommodate high penetration of variable energy resources,‖ IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 34–40, 2012.

XIV.Y. M. Atwa and E. F. El-Saadany, ―Optimal allocation of ESS in distribution systems with a high penetration of wind energy,‖ IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1815–1822, Nov. 2010.

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Abstract:

Analysis of human body movements for emotion prediction is necessary for social communication. Body movements, gestures, eye movements and facial expression are some non-verbal communication method used in many applications. Among them emotion prediction from body movements is commonly used because it convey the emotional states of person from different camera view. In this paper, human emotional states predict from full body movements using feed forward deep convolution neural network architecture and Block Average Intensity Value BAIV feature. Both model can be evaluated by emotion action dataset (University of YORK) with 15 types of emotions. The experimental result showed the better recognition accuracy of the feed forward deep convolution neural network architecture.

Keywords:

Emotion Recognition,Non-verbal communication,Body Movement,Human Computer Interaction (HCI),Deep Convolutional Neural Networks (DCNN),BAIVfeature,

Refference:

I.A.Krizhevsky, I. Sutskever, and G. E. Hinton, (2014),“Imagenet Classification With Deep Convolutional Neural Networks,” In Advances in neural information processing systems, pp. 1097–1105.

II.D.Tran, L. Bourdev, R. Fergus, L.Torresani and M. Paluri, (2015), “Learning Spatiotemporal features with 3d Convolutional networks”,IEEE International Conference on Computer Vision (ICCV), pp. 4489-4497.

III.Damel Rucha, Gurjar Aditya, Joshi Anuja, Nagre Kartik, (2015), “Human Body Skeleton detection and Tracking”, International Journal of Technical Research and Applications, Volume 3, Issue 6, pp.222-225.

IV.Daniel Holden, Jun Saito, Taku Komura. (2016) “A Deep Learning Framework for Character Motion Synthesis and Editing” SIGGRAPH ’16 Technical Paper, July 24 -28, Anaheim, CA, ISBN: 978-1-4503-4279-7/16/07.

V.Enrique Correa, Arnoud Jonker, Michael Ozo, Rob Stolk. (2016) “Emotion Recognition using Deep Convolutional Neural Networks”

VI.F. Zhu and L. Shao, (2014),“Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition,” International Journal of Computer Vision, Vol. 109, No. 1-2, pp. 42–59.

VII.F. Zhu and L. Shao, (2015),“Correspondence-Free Dictionary Learning for Cross-View Action Recognition,” In ICPR, pp. 4525–4530.

VIII.F. Zhu, L. Shao, J. Xie, and Y. Fang, (2016),“From Handcrafted to Learned Representations for Human Action Recognition: A Survey,” Image and Vision Computing.

IX.Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kami ́nska, Tomasz Sapi ́ nski, Sergio Escalera, and Gholamreza Anbarjafari (2015) “Survey on Emotional Body Gesture Recognition” Journal of IEEE Transactions on Affective Computing.

X.Gavrilescu, M., (2015) “Recognizing emotions from videos by studying facial expressions, body postures and hand gestures”, 23rdTelecommunication fourm TELFOR,pp. 720-723.

XI.H.Wang, C. Yuan,W. Hu, and C. Sun,(2012), “Supervised Class-Specific Dictionary Learning for Sparse Modeling in Action Recognition,” Pattern Recognition, Vol. 45, No. 11,pp. 3902–3911.

XII.Hatice Gunes, Caifeng Shan, Shizhi Chen, YingLi Tian. (2015) “Bodily Expression for Automatic Affect Recognition. Emotion Recognition: A Pattern Analysis Approach” Published by John Wiley & Sons, Inc.

XIII.Hazel Rose Markus, Shinobu Kitayama.(1991) “Culture and the self: Implementations for cognition, emotion, and motivation” Psychological Review,pp. 224-253.

XIV.Heike Brock. (2018) “Deep learning -Accelerating Next Generation Performance Analysis Systems” 12th Conference of the International Sports Engineering Association, Brisbane, Queensland, Australia, pp. 26–29

.XV.Hiranmayi Ranganathan, Shayok Chakraborty, Sethuraman Panchanathan.(2017) “Multimodal Emotion Recognition using Deep Learning Architectures” http://emofbvp.org/

XVI.J. Arunnehru, M. Kalaiselvi Geetha. (2017) “Automatic Human Emotion Recognition in Surveillance Video” Intelligent Techniques in Signal Processing for Multimedia Security, Springer-Verlag,pp. 321-342.

XVII.Lei Zhang, Shuai Wang, Bing Liu. (2018) “Deep Learningfor Sentiment Analysis: A Survey” https://arxiv.org/pdf/1801.07883.XVIII.Nourhan E, Pablo B, Parisi, Stefan Wermter, (2017),”Emotion recognition from body expressions with Neural Network Architecture”, Algorithm and Learning, HAI 2017, pp. 143-149.

XIX.Pablo Barros, Doreen Jirak, Cornelius Weber, Stefan Wermter. (2015) “Multimodal emotional state recognition using sequence-dependent deep hierarchical features” Neural Networks. 72, pp. 140–151.

XX.Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie Dagli, Thomas S. Huang. (2017) “How Deep Neural Networks Can Improve Emotion Recognition on Video Data” https://arxiv.org/pdf/1602.07377.pdf.

XXI.Prinzie, A., Van den Poel, D., (2012), Random Forests for multiclass classification: Random MultiNomial Logit. Expert Systems with Applications. Vol.34, 3, pp.1721–1732.

XXII.Samira Ebrahimi Kahou, Vincent Michalski, Kishore Konda, Roland Memisevic, Christopher Pal. (2015) “Recurrent Neural Networks for Emotion Recognition in Video” ICMI 2015, Seattle, WA, USA.

XXIII.Shirbhate Neha, Talele Kiran, (2016), “Human Body Language Understanding for Action detection using Geometric Features”,2ndInternational Conference on Contemporary Computing and Informatics, IEEE, pp.603-607.

XXIV.T. Guha and R. K.Ward, (2012),“Learning Sparse Representations for Human Action Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 34, No. 8, pp. 1576–1588.

XXV.Y. Du,W.Wang, and L.Wang, (2015),“Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110–1118.

XXVI.Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel,(1989), “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural computation,Vol. 1, No. 4, pp. 541–551.

XXVII.Yann LeCun, Yoshua Bengio, Geoffrey Hinton.(2015) “Deep learning” Nature, Vol. 521, pp. 436-444.

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Abstract:

In this contribution,We present a benchmark collection Inclusive of some needful real-world examples, which can be used to assessment and compare numerical methods for model reduction. In this paper the reduction method is explored for getting structure preserving reduced order model of a large-scale dynamical system, we have considered model order reduction of higher orderLTIsystems) with SISO and MIMO [XXXII] that aims at finding Error estimation using Approximation of both system. This enables a new evaluation of the error system Provided that the Observability Gramian of the original system has once been considered, an H∞and H2 error bound can be computed with negligible numerical attempt for any reduced model attributable to The reduced order model (ROM) of a large-scale dynamical system is necessary to effortlessness the analysis of the system using approximation Algorithms. The response evaluation is considered in terms ofresponse constraints and graphical assessments.the application of Approximation methodsis offered for arisingROMof the large-scaleLTI systems which includes benchmark problems. It is reported that the reduced order model using compare numerical methods is almost alike in performance to that of with original systems.all simulation resultshave been obtained via MATLAB based software (sssMOR toolbox).

Keywords:

Benchmarks Example,Order reduction,Error estimation,Krylov,Balanced Truncation,Modal method,

Refference:

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Abstract:

Face recognition is one of the most challenging issues in analyzing images. Face recognition technology is one of the fastest technologies that do the identification process without having the slightest disturbance to the person. Face recognition today has found many applications that can be used for faces recognition, military issues, legal issues, image retrieval, identification of protagonists, video images, and so on. Face recognition is considered as one of the smart computer analysis scenarios. There are always improvements in this area that make these improvements accurate in identifying facial expressions. Accordingly, the present paper seeks to study facial recognition using machine learning algorithms. Time information has useful features for recognizing facial expressions. However, a lot of effort is needed to manually design features. In this paper, to reduce these factors, a machine learning technique is selected, which is an automated tool that extracts useful features from raw data. Using machine learning methods can be considered as a more effective way. In this paper, a method based on machine learning algorithms for face recognition is presented. The proposed algorithms perform the unknown image by comparing it with known and stored images in databases and also obtaining information from a person familiar with the process of face recognition. The results show that the proposed method has high accuracy compared to other previous methods.

Keywords:

face recognition, machine learning al gorithms,image process,

Refference:

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Abstract:

The wide separation of using camera video surveillance and increasing the depending on these video to identify human identity. One of trending method to achieve this task is human gait recognition. In this paper, human gait recognized using three features include gait energy image (GEI) human body height and width. Features are easy to extract and archived high correlation to target class. Neural network Multi-Layer Perceptron used to build a recognition model to achieve 90 % accuracy.

Keywords:

human gait recognition,gait energy image,Neural network Multi-Layer Perceptron,

Refference:

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Abstract:

Because of the prolong use of the system, the performance (Output parameters of the system) can change and output of the system may start deteriorating from the desired value. If the performance of a system, based on control theory is not up to the expectations as per the desired specification, then some changes in the system are required to obtain the desired performance. The control system can be represented with a set of mathematical equations called system model which are used to answer questions via analysis and simulation. A model is a precise representation of a system dynamics which are the arrangement of physical elements and that physical elements are analyzed to make governing equations. Cardiovascular muscle senses the force generated due to the contraction and expansion of muscle wall .This can be well understood by the analytical approach of the transfer function generated by using a mechanical model of force displacement analogy. The efficiency of the work also lies in the measure of the movement of cardiovascular factors in the system. The mass of heart muscle varies with different age groups both for male and female. This work is based on the glimpses of changing transfer function with different age groups due to the variation of mass of heart muscle. Viscous drag has also been calculated considering different values of damping coefficient for a particular value of mass. For attending the optimality in the performance of the system one designed controller is used along with the derived transfer function in cascade arrangement. To get more stability of the system, damping coefficient is chosen for the system model considering less settling time and steady state error. The open loop transfer function in the forward path is simply the product of derived transfer function and designed transfer function of controller. The design emphasizes on the optimality in operation of the control process which has been determined by the performance index (PI) of the total process using integral square method.

Keywords:

Transfer Function, Steady State Error, Performance Index,Integral Square Error,

Refference:

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