Archive

REAL-TIME MONITORING SYSTEM OF POWER TRANSFORMER USING IoT AND GSM

Authors:

Jehan Parvez, Salman Khan, Imran Khan

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00006

Abstract:

The power transformer is the most important and expensive element in the power system. It is used to change the voltage levels at different stages in a power system. The foremost responsibility of the utility grid is to ensure smooth and reliable availability of power through the transformer. But there are different abnormal conditions that can occur in the transformer such as overheating, overexcitation, abnormal frequency, overload, abnormal voltage, open circuit, and breaker failure. These abnormal conditions reduce the life, efficiency, and performance of the transformer, as a result, the overall reliability of the power system gets decreased. Moreover, in case of any failure of the power transformer, the consumers will suffer a severe power outage and consequently, a massive economic loss will occur. During abnormal conditions, the health of a transformer is deteriorating, and it is very important, that the operator should act quickly and accurately in terms of any abnormality occurred. For this purpose, need a proper health monitoring system that should properly monitor the health of the transformer and take proper action to prevent it from greater damages. The proposed system is user-friendly, flexible, reliable, and presenting more functionalities with almost 10 times lower cost than the existing system. This research work has developed a low-cost GSM and internet of things (IoT) based indigenous prototype for transformer monitoring that will be able to early inform the relevant staff through SMS and web data for the different abnormal conditions.

Keywords:

Transformer,Health,Monitoring,GSM,IoT,

Refference:

I. A. Küchler, High Voltage Engineering: Fundamentals-Technology-Applications. Springer, 2017.
II. A. M. Elmashtoly and C.-K. Chang, “Prognostics Health Management System for Power Transformer with IEC61850 and Internet of Things,” Journal of Electrical Engineering & Technology, vol. 15, no. 2, pp. 673-683, 2020.
III. G. Arun, R. Arunkumar, K. K. Kumar, P. Muthupattan, and G. Kannayeram, “GSM BASED SINGLE PHASE DISTRIBUTION TRANSFORMER MONITORING AND CONTROL,” Journal of Critical Reviews, vol. 7, no. 12, pp. 637-640, 2020.
IV. I. Aniebiet and I. S. Fidelis, “Design and Implementation of Gsm Enabled Remote Sensor for Monitoring Power Transformer Operation,” American Journal of Electrical and Computer Engineering, vol. 4, no. 2, pp. 62-71, 2020.
V. J. Jiang, R. Chen, M. Chen, W. Wang, and C. Zhang, “Dynamic fault prediction of power transformers based on hidden Markov model of dissolved gases analysis,” IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1393-1400, 2019.
VI. M. Ghiasi, N. Ghadimi, and E. Ahmadinia, “An analytical methodology for reliability assessment and failure analysis in distributed power system,” SN Applied Sciences, vol. 1, no. 1, pp. 1-9, 2019.
VII. M. Subba Rao, SakilaGopal Reddy, K. Sai Janardhan, Sangu Harish Reddy. : ‘DESIGN OF SINGLE LINE TO THREE LINE POWER CONVERTER’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 822-835. DOI : 10.26782/jmcms.2020.08.00067.
VIII. Maheswari Muthusamy, A.K. Parvathy. : ‘ARTIFICIAL INTELLIGENCE TECHNIQUES-BASED LOW VOLTAGE RIDE THROUGH ENHANCEMENT OF DOUBLY FED INDUCTION WIND GENERATOR’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-3, March (2020) pp 125-139. DOI : 10.26782/jmcms.2020.03.00010
IX. P. Mercy, N. U. Maheswari, S. D. Devi, and V. Dhamodharan, “Wireless protection and monitoring of power transformer using PIC,” IJCSMC, vol. 4, no. 3, pp. 0634-640, 2015.
X. R. V. Jadhav, S. S. Lokhande, and V. N. Gohokar, “Monitoring of transformer parameters using Internet of Things in Smart Grid,” in 2016 International Conference on Computing Communication Control and automation (ICCUBEA), 2016: IEEE, pp. 1-4.
XI. Y. Sun et al., “A temperature-based fault pre-warning method for the dry-type transformer in the offshore oil platform,” International Journal of Electrical Power & Energy Systems, vol. 123, p. 106218, 2020.

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GASOLINE CONSUMPTION PREDICTION VIA DATA MINING TECHNIQUE

Authors:

Soma Gholamveisy

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00007

Abstract:

Due to the increasing dependence of human life on energy, it plays a crucial role in the functioning of the various economic sectors of the countries, potentially and actually. Fuel products, especially gasoline, given their importance in the transportation sector, play major roles in the economic growth and development of countries. Hence, the authorities in each country have to control the fuel supply and demand parameters accurately with a more accurate prediction of fuel consumption and proper planning in the direction of consumption. The purpose of this study is to find appropriate methods and approaches for forecasting gasoline consumption in Tehran using data mining methods. For this purpose, daily consumption data of gasoline stations were collected in 5 different regions of Tehran during the period of 2008-2013. Then, these numbers were predicted on a daily, weekly, monthly, and seasonal basis for analyzing the consumption at different time intervals. The standardization method was also used to match the scales. After data pre-processing, gasoline consumption was predicted using the multi-layer perceptron (MLP) neural network method. The gasoline consumption forecast was evaluated based on the mean squared error (MSE), mean, and mean absolute error (MAE) criteria. The results indicate that the artificial neural network (ANN) can accurately predict gasoline consumption in five different regions of Tehran.

Keywords:

data mining,gasoline consumption,ANN-MLP,prediction,

Refference:

I. Elnaz Siami-Irdemoosaa Saeid R.Dindarloo, 2015 “Prediction of fuel consumption of mining dump trucks: A neural networks approach” Applied Energy.Volume 151, 1 August 2015, Pages 77-84.

II. Fatemeh Rahimi-Ajdadi Yousef Abbaspour-Gilandeh, 2011. Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption, Measurement,Volume 44, Issue 10, December 2011, Pages 2104-2111.
III. G. E. Nasr E.A. Badr C.Joun, Backpropagation neural networks for modeling gasoline consumption, Energy Conversion and Management.Volume 44, Issue 6, April 2003, Pages 893-905.
IV. Karisa M. Pierce Janiece L. Hope Kevin J. Johnson Bob W.Wright Robert E.Synovec 2005” Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis”, Journal of Chromatography A Volume 1096, Issues 1–2, 25 November Pages 101-110.
V. Mohanad Aldhaidhawi, Muneer Naji, Abdel Nasser Ahmed. : ‘EFFECT OF IGNITION TIMINGS ON THE SI ENGINE PERFORMANCE AND EMISSIONS FUELED WITH GASOLINE, ETHANOL AND LPG’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 390-401. DOI : 10.26782/jmcms.2020.06.0003.
VI. Necla Kara .Togun Sedat Baysec, 2010” Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks” Applied Energy,Volume 87, Issue 1, January 2010, Pages 349-355.

VII. Pierhuigi Barbieri. (2001) Robust cluster analysis for detecting physico-chemical typologies of freshwater from wells of the plain of friuli. Analytica Chimica Acta,, pp.161-170.
VIII. Răzvan Andonie. (2010) “Extreme Data Mining: Inference from Small Datasets”; International Journal of Computers Communications & Control, 5: 280-291.
IX. Reza Babazadeh ,2017”A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption”, RAIRO-Oper. Res.Volume 51, Number 3, July-September 2017.

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EFFECTS OF TRAFFIC LOAD, TEMPERATURE AND MATERIAL PROPERTIES ON RUTTING IN FLEXIBLE PAVEMENTS

Authors:

Muhammad Asim, Haseeb Ullah, Haider Khan, Muhammad Yahya

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00008

Abstract:

Rutting (permanent deformation) is one of the most common and serious kinds of damage to flexible pavement, particularly in countries with high summer temperatures. Rutting also occurs when there is a lot of traffic and the use of poor materials. Pavement engineering is greatly influenced by the use of materials such as asphalt and cement in modern times. To study the effect of load, high temperature, and materials properties on rutting damage of flexible pavement this paper is the best approach to all these concerned issues related to rutting. Abaqus ver.6.12.1 has been used to simulate flexible pavement under different loading and thermal conditions. Three models have been developed in this paper, the first model simulated against traffic loading only, the second model shows combined traffic and thermal loading while the third model related with the change of materials property in terms of Young’s modulus (E).

Keywords:

Flexible Pavements,FEM,Rutting,Traffic loads,Temperature,

Refference:

I. Abd-Ali, M. S. (2013). A Finite Element Model for Rutting Prediction of Flexible Pavement Considering Temperature Effect. Engineering and Technology Journal, 31(21 Part (A) Engineering).
II. Abd-Ali, M. S. (2013). A Finite Element Model for Rutting Prediction of Flexible Pavement Considering Temperature Effect. Engineering and Technology Journal, 31(21 Part (A) Engineering).
III. Abed, A. H., & Al-Azzawi, A. A. (2012). Evaluation of rutting depth in flexible pavements by using finite element analysis and local empirical model. American Journal of Engineering and Applied Sciences, 5(2), 163-169.
IV. Abu Al-Rub, R. K., Darabi, M. K., Huang, C. W., Masad, E. A., & Little, D. N. (2012). Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements. International Journal of Pavement Engineering, 13(4), 322-338.
V. Ahmed, Z., Jabbar, A., Ghassan, E. G., & Masood, A. K. K. SURFACE DEFORMATION OF FLEXIBLE PAVEMENT WITH DIFFERENT BASE LAYER USING FINITE ELEMENT ANALYSIS.
VI. Ali, B., Sadek, M., & Shahrour, I. (2008). Elasto-Viscoplastic Finite Element Analysis of the Long-Term Behavior of Flexible Pavements: Application to Rutting. Road materials and pavement design, 9(3), 463-479.
VII. Al-Khateeb, L. A., Saoud, A., & Al-Msouti, M. F. (2011). Rutting prediction of flexible pavements using finite element modeling. Jordan Journal of Civil Engineering, 5(2), 173- 190.
VIII. Alkaissi, Z. A. (2020). Effect of high temperature and traffic loading on rutting performance of flexible pavement. Journal of King Saud University-Engineering Sciences, 32(1), 1-4.
IX. Alkaissi, Z. A., & Al-Badran, Y. M. (2018). FINITE ELEMENT MODELING OF RUTTING FOR FLEXIBLE PAVEMENT. Journal of Engineering and Sustainable Development, 22(3), 1-13.
X. Hossain, M. I., Mehta, R., Shaik, N. A., Islam, M. R., & Tarefder, R. A. (2016). Rutting Potential of an Asphalt Pavement Exposed to High Temperatures. In International Conference on Transportation and Development 2016 (pp. 1194-1205).
XI. Hulsey, J. L., Ahmed, M. J., & Connor, B. (2008). SOLVING PLASTIC DEFORMATION PROBLEMS FOR ANCHORAGE FLEXIBLE PAVEMENTS.
XII. Khodary, f., & mashaan, n. Behaviour of different pavement types under traffic loads using finite element modelling.
XIII. Leonardi, G. I. O. V. A. N. N. I. (2014). Finite element analysis of airfield flexible pavement. Archives of Civil Engineering, 60(3).
XIV. Nazarian, S., & Boddapati, K. M. (1995). Pavement-falling weight deflectometer interaction using dynamic finite-element analysis. Transportation Research Record, 1482, 33.
XV. Siang, A. J. L. M., Wijeyesekera, D. C., Mei, L. S., & Zainorabidin, A. (2013). Innovative laboratory assessment of the resilient behaviour of materials (rigid, elastic and particulates). Procedia Engineering, 53, 156-166.
XVI. Ullah Irfan, Dr. Rawid Khan, Manzoor Elahi, Ajab Khurshid. : ‘CHARACTERIZATION OF THE NONLINEAR BEHAVIOR OF FLEXIBLE ROAD PAVEMENTS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-12, December (2020) pp 111-126. DOI :0.26782/jmcms.2020.12.00010

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A FRAMEWORK BASED ON BLOCKCHAIN FOR ELECTORAL VOTING SYSTEM

Authors:

Tarun Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00001

Abstract:

Electoral voting system is the pillar to maintain the democratic freedom of any country. The fair and transparent organization of election is the basic need of the country. Many countries are basically using one of two ways to conduct election either using ballot paper or using electronic voting machines. Each one has its own pros and cons. The fast, trust and e-voting is the need of future. In recent years, blockchain technology is rapidly adopted in various fields by various organizations. The Decentralized and cryptographic algorithms are the major reason behind this. Considering the increasing issue of security, trust in the traditional Voting System and future requirements, this paper proposes a framework for E-Voting system based on blockchain technology. This paper discusses the network architecture for blockchain technology, framing the processing casting votes and counting of votes. The analysis of various issues and challenges in electoral system is carried out in context of the proposed framework. This framework may improve the security and decreases the cost of hosting nationwide elections

Keywords:

Blockchain Technology,E-Voting,SHA256,P2P Networks,E-Voting,

Refference:

I. Eastlake 3rd, D., Jones, P.: US secure hash algorithm 1 (SHA1). (2001).
II. Ferrer, E.C.: The blockchain: a new framework for robotic swarm systems. arXiv Prepr. arXiv1608.00695. (2016).
III. Foundation, Po.et – proof of existence on the top of bitcoin blockchain, https://www.po.et, last accessed on August 13, 2018.
IV. Herbert, J., Litchfield, A.: A novel method for decentralised peer-to-peer software license validation using cryptocurrency blockchain technology. In: Proceedings of the 38th Australasian Computer Science Conference (ACSC 2015). p. 30 (2015).
V. King, S., Nadal, S.: Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. self-published Pap. August. 19, (2012).
VI. Lemieux, V.L.: Trusting records: is Blockchain technology the answer? Rec. Manag. J. 26, 110–139 (2016).
VII. Litchfield, A., Herbert, J.: ReSOLV: Applying Cryptocurrency Blockchain Methods to Enable Global Cross-Platform Software License Validation. Cryptography. 2, 10 (2018).
VIII. Mallick, S., Pandey, R., Neupane, S., Mishra, S., Kushwaha, D.S.: Simplifying Web Service Discovery & Validating Service Composition. In: Services (SERVICES), 2011 IEEE World Congress on. pp. 288–294 (2011).
IX. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system. (2008).
X. Shiela David, R. Aroul Canessane. : ‘FOOD SAFETY USING RFID TAGS IN BLOCKCHAIN TECHNOLOGY’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 299-306. DOI : 10.26782/jmcms.2020.08.00028.
XI. Underwood, S.: Blockchain beyond bitcoin. Commun. ACM. 59, 15–17 (2016).
XII. Yogesh Sharma, B. Balamurugan. : ‘A SURVEY ON PRIVACY PRESERVING METHODS OF ELECTRONIC MEDICAL RECORD USING BLOCKCHAIN’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-2, February (2020) pp 32-47. DOI : 10.26782/jmcms.2020.02.00004.

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COMPARATIVE STUDY OF HEAVY METALS IN THE MUSCLE OF TWO EDIBLE FINFISH SPECIES IN AND AROUND INDIAN SUNDARBANS

Authors:

Shyama Prasad Bepari, Prosenjit Pramanick, Sufia Zaman, Abhijit Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00002

Abstract:

We analyzed the concentrations of zinc, copper, and lead in the muscle of two commercially important finfish species namely, Pampus argenteus and Scatophagus argus in and around the World Heritage site of Indian Sundarbans from 8th  to 15th  July 2021 using an Atomic Absorption Spectrophotometer. The sequence of bioaccumulation of the selected metals is as per the order Zn > Cu > Pb irrespective of the species. The degree of metal accumulation showed variation between the species with the highest value in Scatophagus argus followed by Pampus argenteus, which may be related to the difference in their food habit or degree of exposure to ambient media contaminated with heavy metals.

Keywords:

Heavy metals,Pampus argenteus,Scatophagus argus,bioaccumulation,

Refference:

I. Boudouresque, C. F., Verlaque, M., “Biological pollution in the Mediterranean Sea: invasive versus introduced macrophytes”, Marine Pollution Bulletin, vol. 44, pp: 32-38, 2002
II. Castro, H., Aguilera, P. A., Martinez, J. L., Carrique, E. L., “Differentiation of clams from fishing areas an approximation to coastal quality assessment”, Environmental Monitoring and Assessment, vol. 54, pp: 229-237, 1999
III. Cloete, C. E., Watling, R. J., “South African Marine Pollution Monitoring Programme 1979-1982”, Pretoria National Programme for Environmental Sciences, Report No. 51, 1981
IV. De Groot, A. J., Salomons, W., Allersma, E., “Processes affecting heavy metals in estuarine sediments”, In: Burton J.D. and Liss P.S. eds. Estuarine Chemistry, Academic Press, London, pp: 131-153, 1976
V. Gallagher, K. A., Wheeler, A. J., Orford, J. D., “An assessment of the heavy metal pollution of two tidal marshes on the north-west coast of Ireland”, Biology and Environment: Proceedings of the Royal Irish Academy, vol. 96B no. 3, pp: 177-188, 1996
VI. Heyvaert, A. C., Reuter, J. E., Sloton, D. G., Goldman, C., “Paleo-limnological reconstruction of historical atmospheric Pb and Hg deposition at lake Tahoe, California-Nevada”, Environmental Science and Pollution Research, vol. 34, pp: 3588-3597, 2000
VII. Iyengar, G. V., “Milestones in Biological trace elements research”, Science of the Total Environment, vol. 1, pp: 100, 1991.
VIII. Jegalakshimi Jewaratnam, Zarith Sofea Khalidi. : ‘BIOSORPTION OF COPPER (II), ZINC (II) AND NICKEL (II) FROM AQUEOUS MEDIUM USING AZADIRACHTA INDICA (NEEM) LEAF POWDER’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-6 November-December (2019) pp 533-545. DOI : 10.26782/jmcms.2019.12.00037
IX. Keller, E. A., “Environmental geology”, MacMillan Publishing Company, New York, 1992
X. Laliberte, C., Dewailly, E., Gugras, S., Ayotte, P., Weber, J. P., Sauve, L., Benedetti, J. L., “Mercury contamination in fishermen of the lower north shore of the Gulf of St. Lawrence (Quebec, Canada)”, In: Vernet J.P. ed. Impact of Heavy Metals in the Environment, Elsevier, Amsterdam, pp: 15-28, 1992
XI. Malina, G., “Ecotoxicological and Environmental problems Associated with the Former Chemical Plant in Tarnowskie Gory, Poland”, Toxicology, vol. 205, pp: 157-172, 2004
XII. Milu, V., Leroy, J. L., Peiffert, C., “Water Contamination Downstream from a Copper Mine in the Apuseni Mountains, Romania”, Environmental Geology, vol. 42, pp: 773-782, 2002
XIII. Mitra, A., Choudhury, A., “Heavy metal concentrations in oysters Crassostrea cucullata of Sagar Island, India”, Indian Journal of Environmental Health, NEERI, vol. 35, pp: 139-141, 1993
XIV. Okafor, E. C., Opuene, K., “Correlations, partitioning and bioaccumulation of trace metals between different segments of Taylor Creek, southern Nigeria”, Environmental Science and Technology, vol. 3, no. 4, pp: 381-389, 2006
XV. Otte, M. L., Kearns, C. C., Doyle, M. O., “Accumulation of arsenic and zinc in the rhizosphere of wetland plants”, Bulletin of Environmental Contamination and Toxicology, vol. 55, pp: 154-161, 1995
XVI. Pattnaik, S., Vikram Reddy, M., Singh, P., “Concentration of Cadmium, lead and zinc and their leaching in municipal solid waste dumping sites at Bhubaneswar city (Orissa)”, Proceedings of the National Academy of Scienecs, vol. 76(B), no. III, pp: 251-258, 2006
XVII. Pekey, H., “Heavy Metal Pollution Assessment in Sediments of the Izmit Bay, Turkey”, Environmental Monitoring and Assessment, vol. 123, pp: 219-231, 2006
XVIII. Rajkhowa, I., “Action in aquaculture-opportunities in aquatic specialization”, Business Today (May 22 Issue): 131, 2005
XIX. Rayms-Keller, A., Olson, K. E., McGaw, M., Oray, C., Carlson, J. O., Beaty, B. J., “Effect of heavy metals on Aedes Aegypti (Diptera culicidea) Larvae”, Ecotoxicology and Environmental Safety, vol. 39, pp: 41-47, 1998
XX. Reddy, M. S., Mehta, B., Dave, S., Joshi, M., Karthikeyan, L., Sarma, V. K. S., Basha, S., Ramachandraiah, G., Bhatt, O., “Bioaccumulation of heavy metals in some commercial fishes and crabs of the Gulf of Cambay, India”, Current Science, vol. 92, pp: 1489-1491, 2007
XXI. Santhanam, R., Ramanathan, N., Jegatheesan, G., In: Coastal aquaculture in India, CBS Publishers and Distributors, New Delhi, India, 1990
XXII. Skvarla, J., “A study on the trace metal speciation in the Ruzin reservoir sediment”, Acta Montanistica Slovaca, Rocnik, vol. 3, no. 2, pp: 177-182, 1998
XXIII. Stoffers, P., Glasby, G. P., Wilson, C. J., Davis, K. R., Walter, P., “Heavy metal pollution in Wellington Harbour”, New Zealand Journal of Marine and freshwater Research, vol. 20, pp: 495-512, 1986
XXIV. Strickland, J. D. H., Parsons, T. R., “A practical handbook of sea water analysis”, Fisheries Research Board of Canada, Ottawa Bulletin, 2nd Edition. 167, 1972
XXV. Tarifeno-Silva, E., Kawasaki, L., Yn, D. P., Gordon, M. S., Chapman, D. J., “Aquacultural approaches to recycling dissolved nutrients in secondarily treated domestic waste waters: Uptake of dissolved heavy metals by artificial food chains”, Water Resource, vol. 16, pp: 59-65, 1982
XXVI. UNEP, “Pollution and marine environment in the Indian Ocean”, UNEP regional Seas Report and Studies, 13, 1982
XXVII. Williams, J. P., Bubbs, J. M., Lester, J. N., “Metal accumulation within salt marsh environments – a review”, Marine Pollution Bulletin, vol. 28, pp: 277-290, 1994
XXVIII. Winkler, L. W., “The determination of dissolved oxygen”, Berichte der Deutschen Chemischen Gesellschaft, vol. 21, pp: 2843–2846, 1988
XXIX. www.wrc.org.za

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STABILITY ENHANCEMENT OF ALUMINUM-AIR BATTERY

Authors:

Syed Mazhar Shah, Muhammad Noman

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00003

Abstract:

A comparative analysis is presented for an aluminum-air battery with a carbon-coated and non-coated anode made of 4N pure aluminum with the purpose to enhance the stability of the battery. The carbon coating was proven to be quite effective which lasted almost two times more than the non-coated cell with little to almost no effect on the electrochemical behavior. A method was also proposed to limit the self-discharge electrode corrosion of the aluminum-air battery by limiting the oxygen supply to the cell from atmospheric air. The blockage of the air supply limits the oxidation-reduction reaction necessary for cell operation. For that purpose, the cell was tested in vacuum condition for 25 days which showed quite impressive results when compared with the cell kept in a non-vacuum room condition. It had retained its potential as well as resisted the corrosion quite well with almost negligible weight loss and byproduct accumulation.

Keywords:

Aluminum-air batteries,Carbon-coated,Oxidation-reduction,

Refference:

I. Bassam Ali Ahmed, Fathi Abdulsahib Alshamma. : ‘INVESTIGATING THE INFLUENCE OF COMBINED STRESSES ON DYNAMIC CRACK PROPAGATION IN THIN PLATE’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 507-523. DOI : 10.26782/jmcms.2020.08.00046
II. Bin, H. and G.J.R.m. LIANG, Neutral electrolyte aluminum air battery with open configuration. 2006. 25(6): p. 360-363.
III. Egan, D., et al., Developments in electrode materials and electrolytes for aluminium–air batteries. 2013. 236: p. 293-310.
IV. Hopkins, B.J., Y. Shao-Horn, and D.P.J.S. Hart, Suppressing corrosion in primary aluminum–air batteries via oil displacement. 2018. 362(6415): p. 658-661.
V. Levy, N.R., M. Auinat, and Y.J.E.S.M. Ein-Eli, Tetra-butyl ammonium fluoride–an advanced activator of aluminum surfaces in organic electrolytes for aluminum-air batteries. 2018. 15: p. 465-474.
VI. Liu, Y., et al., A comprehensive review on recent progress in aluminum–air batteries. 2017. 2(3): p. 246-277.
VII. Mohamad, A.J.C.S., Electrochemical properties of aluminum anodes in gel electrolyte-based aluminum-air batteries. 2008. 50(12): p. 3475-3479.
VIII. Mori, R.J.R.a., A new structured aluminium–air secondary battery with a ceramic aluminium ion conductor. 2013. 3(29): p. 11547-11551.
IX. Pino, M., et al., Performance of commercial aluminium alloys as anodes in gelled electrolyte aluminium-air batteries. 2015. 299: p. 195-201.
X. Pino, M., et al., Carbon treated commercial aluminium alloys as anodes for aluminium-air batteries in sodium chloride electrolyte. 2016. 326: p. 296-302.
XI. Wang, Q., et al., Performances of an Al–0.15 Bi–0.15 Pb–0.035 Ga alloy as an anode for Al–air batteries in neutral and alkaline electrolytes. 2017. 7(42): p. 25838-25847.
XII. Zhang, Z., et al., All-solid-state Al–air batteries with polymer alkaline gel electrolyte. 2014. 251: p. 470-475.

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VEHICLE LICENSE PLATE DETECTION: A SURVEY

Authors:

Tarun Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00004

Abstract:

Automatic Number Plate Recognition (ANPR) is an image processing technique that is used to extract the symbols (characters and digits) embedded on the number (license) plate to identify the vehicles. Huge numbers of ANPR techniques have been proposed by various researchers in the past. Most of the ANPR techniques are designed for restricted conditions due to the diversity of the license plate styles, environmental conditions etc. Not every technique is suited for all kinds of conditions. In general, the ANPR technique comprises of the following three stages; license plate detection (LPD); character segmentation; and character recognition. There exist a wide variety of techniques for carrying out each of the steps of the ANPR. Some score over others. This paper presents a State-of-the-Art survey of the various leading LPD techniques that exist today and an attempt has been made to summarize these techniques based on pros and cons and their limitations. Each technique is classified based on the features used at each stage of LPD. This survey shall help provide future direction towards the development of efficient and accurate techniques for ANPR. It shall also assist in identifying and shortlisting the methodologies that are best suited for the particular problem domain.

Keywords:

Automatic number plate recognition (ANPR),license plate detection (LPD),Edge detection,Texture detection,

Refference:

I. A. A. WANG, L. MAN, B. WANG, Y. XIAO, W. PAN, AND X. LU, “FUZZY-BASED ALGORITHM FOR COLOR RECOGNITION OF LICENSE PLATES,” PATTERN RECOGNITION LETTERS, VOL. 29, NO. 7, PP. 1007–1020, 2008.
II. “http://code.google.com/p/tesseract-ocr,” 2012.
III. A. Capar and M. Gokmen, “Concurrent segmentation and recognition with shape-driven fast marching methods,” in 18th International Conference on Pattern Recognition (ICPR’06), 2006, vol. 1, pp. 155–158.
IV. A. Rabee and I. Barhumi, “License plate detection and recognition in complex scenes using mathematical morphology and support vector machines,” in IWSSIP 2014 Proceedings, 2014, pp. 59–62.
V. B. R. Lee, K. Park, H. Kang, H. Kim, and C. Kim, “Adaptive local binarization method for recognition of vehicle license plates,” in International Workshop on Combinatorial Image Analysis, 2004, pp. 646–655.
VI. C. Busch, R. Domer, C. Freytag, and H. Ziegler, “Feature based recognition of traffic video streams for online route tracing,” in Vehicular Technology Conference, 1998. VTC 98. 48th IEEE, 1998, vol. 3, pp. 1790–1794.
VII. C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, “A license plate-recognition algorithm for intelligent transportation system applications,” IEEE Transactions on Intelligent transportation systems, vol. 7, no. 3, pp. 377–392, 2006.
VIII. C. Patel, A. Patel, and D. Patel, “Optical character recognition by open source OCR tool tesseract: A case study,” International Journal of Computer Applications, vol. 55, no. 10, 2012.
IX. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 5, pp. 603–619, 2002.
X. D. Llorens, A. Marzal, V. Palazón, and J. M. Vilar, “Car license plates extraction and recognition based on connected components analysis and HMM decoding,” in Iberian Conference on Pattern Recognition and Image Analysis, 2005, pp. 571–578.
XI. D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2431–2438, 2005.
XII. D.-J. Kang, “Dynamic programming-based method for extraction of license plate numbers of speeding vehicles on the highway,” International Journal of Automotive Technology, vol. 10, no. 2, pp. 205–210, 2009.
XIII. E. R. Lee, P. K. Kim, and H. J. Kim, “Automatic recognition of a car license plate using color image processing,” in Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 1994, vol. 2, pp. 301–305.
XIV. F. Martin, M. Garcia, and J. L. Alba, “New methods for automatic reading of VLP’s (Vehicle License Plates),” in Proc. IASTED Int. Conf. SPPRA, 2002, pp. 126–131.
XV. G. Henrich, “A simple computational method for reducing streak artifacts in CT images,” Computerized tomography, vol. 4, no. 1, pp. 67–71, 1980.
XVI. G. Li, R. Zeng, and L. Lin, “Research on vehicle license plate location based on neural networks,” in First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC’06), 2006, vol. 3, pp. 174–177.
XVII. G.-S. Hsu, J.-C. Chen, and Y.-Z. Chung, “Application-oriented license plate recognition,” IEEE transactions on vehicular technology, vol. 62, no. 2, pp. 552–561, 2013.
XVIII. H. Caner, H. S. Gecim, and A. Z. Alkar, “Efficient embedded neural-network-based license plate recognition system,” IEEE Transactions on Vehicular Technology, vol. 57, no. 5, pp. 2675–2683, 2008.
XIX. H. E. Kocer and K. K. Cevik, “Artificial neural networks based vehicle license plate recognition,” Procedia Computer Science, vol. 3, pp. 1033–1037, 2011.
XX. H. Liu and X. Ding, “Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes,” in Eighth International Conference on Document Analysis and Recognition (ICDAR’05), 2005, pp. 19–23.
XXI. I. Paliy, V. Turchenko, V. Koval, A. Sachenko, and G. Markowsky, “Approach to recognition of license plate numbers using neural networks,” in Proc. IEEE Int. Joint Conf. Neur. Netw, 2004, vol. 4, pp. 2965–2970.
XXII. I. Rish, “An empirical study of the naive Bayes classifier,” in IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, vol. 3, no. 22, pp. 41–46.
XXIII. J. A. Sethian, “A fast marching level set method for monotonically advancing fronts,” Proceedings of the National Academy of Sciences, vol. 93, no. 4, pp. 1591–1595, 1996.
XXIV. J. Jiao, Q. Ye, and Q. Huang, “A configurable method for multi-style license plate recognition,” Pattern Recognition, vol. 42, no. 3, pp. 358–369, 2009.
XXV. J.-M. Guo and Y.-F. Liu, “License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques,” IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1417–1424, 2008.
XXVI. K. K. Kim, K. Kim, J. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, 2000, vol. 2, pp. 614–623.
XXVII. K. Kanayama, Y. Fujikawa, K. Fujimoto, and M. Horino, “Development of vehicle-license number recognition system using real-time image processing and its application to travel-time measurement,” in Vehicular Technology Conference, 1991. Gateway to the Future Technology in Motion., 41st IEEE, 1991, pp. 798–804.
XXVIII. K. Miyamoto, K. Nagano, M. Tamagawa, I. Fujita, and M. Yamamoto, “Vehicle license-plate recognition by image analysis,” in Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON’91., 1991 International Conference on, 1991, pp. 1734–1738.
XXIX. K.-B. Kim, S.-W. Jang, and C.-K. Kim, “Recognition of car license plate by using dynamical thresholding method and enhanced neural networks,” in International Conference on Computer Analysis of Images and Patterns, 2003, pp. 309–319.
XXX. L. Juan and O. Gwun, “A comparison of sift, pca-sift and surf,” International Journal of Image Processing (IJIP), vol. 3, no. 4, pp. 143–152, 2009.
XXXI. L. Luo, H. Sun, W. Zhou, and L. Luo, “An efficient method of license plate location,” in 2009 First International Conference on Information Science and Engineering, 2009, pp. 770–773.
XXXII. L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE transactions on image processing, vol. 2, no. 2, pp. 176–201, 1993.
XXXIII. L. Zheng, X. He, B. Samali, and L. T. Yang, “An algorithm for accuracy enhancement of license plate recognition,” Journal of computer and system sciences, vol. 79, no. 2, pp. 245–255, 2013.
XXXIV. M. S. Landy and J. R. Bergen, “Texture segregation and orientation gradient,” Vision research, vol. 31, no. 4, pp. 679–691, 1991.
XXXV. M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, “Saudi Arabian license plate recognition system,” in Geometric Modeling and Graphics, 2003. Proceedings. 2003 International Conference on, 2003, pp. 36–41.
XXXVI. M.-C. Su, H.-H. Chen, and W.-C. Cheng, “A neural-network-based approach to optical symbol recognition,” Neural processing letters, vol. 15, no. 2, pp. 117–135, 2002.
XXXVII. M.-L. Wang, Y.-H. Liu, B.-Y. Liao, Y.-S. Lin, and M.-F. Horng, “A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks,” in International Conference on Computational Collective Intelligence, 2010, pp. 63–70.
XXXVIII. N. Singh, “A Smart Framework for Identifying Road Traffic Violators,” in International Conference on “Computing for Sustainable Global Development, 2015.
XXXIX. P. Comelli, P. Ferragina, M. N. Granieri, and F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Transactions on Vehicular Technology, vol. 44, no. 4, pp. 790–799, 1995.
XL. P. Jackway, “Improved morphological top-hat,” Electronics Letters, vol. 36, no. 14, pp. 1194–1195, 2000.
XLI. P. Kulkarni, A. Khatri, P. Banga, and K. Shah, “Automatic Number Plate Recognition (ANPR) System for Indian conditions,” in Radioelektronika, 2009. RADIOELEKTRONIKA’09. 19th International Conference, 2009, pp. 111–114.
XLII. Q. Gao, X. Wang, and G. Xie, “License plate recognition based on prior knowledge,” in 2007 IEEE International Conference on Automation and Logistics, 2007, pp. 2964–2968.
XLIII. R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, “The mahalanobis distance,” Chemometrics and intelligent laboratory systems, vol. 50, no. 1, pp. 1–18, 2000.
XLIV. R. Smith, “An overview of the Tesseract OCR engine,” 2007.
XLV. S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose, “A novel adaptive morphological approach for degraded character image segmentation,” Pattern Recognition, vol. 38, no. 11, pp. 1961–1975, 2005.
XLVI. S. Rasheed, A. Naeem, and O. Ishaq, “Automated Number Plate Recognition using hough lines and template matching,” in Proceedings of the World Congress on Engineering and Computer Science, 2012, vol. 1, pp. 24–26.
XLVII. S. Yohimori, Y. Mitsukura, M. Fukumi, N. Akamatsu, and N. Pedrycz, “License plate detection system by using threshold function and improved template matching method,” in Fuzzy Information, 2004. Processing NAFIPS’04. IEEE Annual Meeting of the, 2004, vol. 1, pp. 357–362.
XLVIII. S.-L. Chang, L.-S. Chen, Y.-C. Chung, and S.-W. Chen, “Automatic license plate recognition,” IEEE transactions on intelligent transportation systems, vol. 5, no. 1, pp. 42–53, 2004.
XLIX. T. D. Duan, T. H. Du, T. V. Phuoc, and N. V. Hoang, “Building an automatic vehicle license plate recognition system,” in Proc. Int. Conf. Comput. Sci. RIVF, 2005, pp. 59–63.
L. T. Shuang-tong and L. Wen-ju, “Number and letter character recognition of vehicle license plate based on edge Hausdorff distance,” in Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT’05), 2005, pp. 850–852.
LI. T.-H. Wang, F.-C. Ni, K.-T. Li, and Y.-P. Chen, “Robust license plate recognition based on dynamic projection warping,” in Networking, Sensing and Control, 2004 IEEE International Conference on, 2004, vol. 2, pp. 784–788.
LII. W. Devapriya, C. N. K. Babu, and T. Srihari, “Indian License Plate Detection and Recognition Using Morphological Operation and Template Matching,” Evolution, vol. 3427.
LIII. W. Jia, H. Zhang, and X. He, “Region-based license plate detection,” Journal of Network and computer Applications, vol. 30, no. 4, pp. 1324–1333, 2007.
LIV. W. Zhou, H. Li, Y. Lu, and Q. Tian, “Principal visual word discovery for automatic license plate detection,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4269–4279, 2012.
LV. X. Shi, W. Zhao, and Y. Shen, “Automatic license plate recognition system based on color image processing,” in International Conference on Computational Science and Its Applications, 2005, pp. 1159–1168.
LVI. Y. Cheng, J. Lu, and T. Yahagi, “Car license plate recognition based on the combination of principal components analysis and radial basis function networks,” in Signal Processing, 2004. Proceedings. ICSP’04. 2004 7th International Conference on, 2004, vol. 2, pp. 1455–1458.
LVII. Y. S. Soh, B. T. Chun, and H. S. Yoon, “Design of real time vehicle identification system,” in Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on, 1994, vol. 3, pp. 2147–2152.
LVIII. Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. von Deneen, and P. Shi, “An algorithm for license plate recognition applied to intelligent transportation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 830–845, 2011.
LIX. Y. Yoon, K.-D. Ban, H. Yoon, and J. Kim, “Blob detection and filtering for character segmentation of license plates,” in Multimedia Signal Processing (MMSP), 2012 IEEE 14th International Workshop on, 2012, pp. 349–353.
LX. Y.-R. Wang, W.-H. Lin, and S.-J. Horng, “A sliding window technique for efficient license plate localization based on discrete wavelet transform,” Expert Systems with Applications, vol. 38, no. 4, pp. 3142–3146, 2011.
LXI. Z. Liu, A. Liu, C. Wang, and Z. Niu, “Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification,” Future Generation Computer Systems, vol. 20, no. 7, pp. 1119–1129, 2004.
LXII. Z. Qin, S. Shi, J. Xu, and H. Fu, “Method of license plate location based on corner feature,” in 2006 6th World Congress on Intelligent Control and Automation, 2006, vol. 2, pp. 8645–8649.

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LOCKDOWN: PREVALENCE OF MENTAL ILLNESS DURING COVID-19 IN DHAKA, BANGLADESH

Authors:

Farjana Islam Aovi, Shopnil Akash, Sarah Islam, Abhijit Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00005

Abstract:

Mental and physical health has been smashed up due to SARS-2-CoV-19 across the world for about the last couple of years, which leads to producing mental stress and strain. Even though patients and healthcare staffs provide psychiatric treatment, the psychological health of the overall population often demands concern which causes psychosocial stressors, impacting both the spread of the disease and the incidence of emotional distress and psychological disorder. The study aimed to identify the psychological condition and demand as wee as the coping process of the population of the capital city-Dhaka of Bangladesh. For collecting data with these categories, the online portals, like facebook vote, Google met, LinkedIn, were used for both male and female gender. Among the participants, 35% people were depressed, in grief 4% people, 25% people were suffering from Anxiety,13% people were facing Insomnia problems and 7% people were facing Trauma. Our survey also revealed that 21%sample acknowledged to open up lockdown, on the other hand, 31% of people were consistently strongly agreed on the government decision. 34.8% of people spent their time during lockdown using Facebook, 26% on online classes, work from home were 14%, and the other 26% people were utilizing their lockdown time by watching YouTube and other social sites. This study puts together a towering contribution to developing an assessment of mental health profile during SARS-2-CoV-19 and lockdown in Dhaka.

Keywords:

Lockdown,Coping,Pandemic,Conceivable,Emotional,psychologically,Quarantine,

Refference:

I. Adla Rajesh, R. Shashi Kumar Reddy, M. Shiva Chander. : ‘SIGNIFICANT CHANGES IN INDIA DURING LOCK DOWN PERIOD WITH AN IMPACT OF COVID-19’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-8, August (2020) pp 8-16. DOI: 10.26782/jmcms.2020.08.00002
II. A. Alison, “COVID’s mental-health toll: how scientists are tracking a surge in depression,” Nature, vol. 590, pp. 194-195, 2021
III. A. Chowdhury, “When COVID moved work online, it created an opportunity for countries in the global south” November 13th, 2020.
IV. A. H. S. Khan, Mst Sadia; Hossain, Sahadat; Hasan, M Tasdik; Ahmed, Helal Uddin; Sikder, Md Tajuddin, “The impact of COVID-19 pandemic on mental health & wellbeing among home-quarantined Bangladeshi students: a cross-sectional pilot study,” Journal of affective disorders, vol. 277, pp. 121-128, 2020.
V. A. I. S. Bhuiyan, Najmuj; Pakpour, Amir H; Griffiths, Mark D; Mamun, Mohammed A, “COVID-19-related suicides in Bangladesh due to lockdown and economic factors: case study evidence from media reports,” International Journal of Mental Health and Addiction, pp. 1-6, 2020.
VI. A. R. Al Zubayer, Md Estiar; Islam, Md Bulbul; Babu, Sritha Zith Dey; Rahman, Quazi Maksudur; Bhuiyan, Md Rifat Al Mazid; Khan, Md Kamrul Ahsan; Chowdhury, Md Ashraf Uddin; Hossain, Liakat; Habib, Rahat Bin, “Psychological states of Bangladeshi people four months after the COVID-19 pandemic: an online survey,” Heliyon, vol. 6, p. e05057, 2020.
VII. C. C. Cheng, Mike WL, “Psychological responses to outbreak of severe acute respiratory syndrome: a prospective, multiple time‐point study,” Journal of personality, vol. 73, pp. 261-285, 2005.
VIII. “Child marriage up 13% during Covid-19 pandemic in Bangladesh,” Dhaka Tribune, March 28th, 2021.
IX. E. K. H. A. Emon, Ashrafur Rahman; Islam, M Shahanul, “Impact of COVID-19 on the institutional education system and its associated students in Bangladesh,” Asian Journal of Education and Social Studies, pp. 34-46, 2020.
X. H. H. C. Khachfe, Mohamad; Sammouri, Julie; Salhab, Hamza; Makki, Bassel Eldeen; Fares, Mohamad, “An epidemiological study on COVID-19: a rapidly spreading disease,” Cureus, vol. 12, 2020.
XI. J.-Y. Y. Li, Zhi; Wang, Qiong; Zhou, Zhi-Jian; Qiu, Ye; Luo, Rui; Ge, Xing-Yi, “The epidemic of 2019-novel-coronavirus (2019-nCoV) pneumonia and insights for emerging infectious diseases in the future,” Microbes and infection, vol. 22, pp. 80-85, 2020.
XII. M. H. Opu, “Private university students demand holding off tuition fees, online classes, exams,” Dhaka Tribune, May 5th, 2020.
XIII. M. M. K. Rahman, Saadmaan Jubayer; Sakib, Mohammed Sadman; Chakma, Salit; Procheta, Nawwar FatimaMamun, Zahid Al; Arony, Anuva; Rahman, Farzana; Rahman, Md Moshiur, “Assessing the psychological condition among general people of Bangladesh during COVID-19 pandemic,” Journal of Human Behavior in the Social Environment, vol. 31, pp. 449-463, 2021.
XIV. M. R. I. Karim, Mohammad Tarikul; Talukder, Bymokesh, “COVID-19′ s impacts on migrant workers from Bangladesh: In search of policy intervention,” World Development, vol. 136, p. 105123, 2020.
XV. R. Pozarycki, “New York’s mental health crisis during COVID-19 pandemic impacting people of color at higher rates: report.”
XVI. S. N. Sakib, “Bangladesh: Suicide claims more lives than coronavirus.”
XVII. T. A. Rahman, Ramim, “Combatting the impact of COVID-19 school closures in Bangladesh,” 2021.
XVIII. T. N. Kawashima, Shuhei; Tanoue, Yuta; Yoneoka, Daisuke; Eguchi, Akifumi; Shi, Shoi; Miyata, Hiroaki, “The relationship between fever rate and telework implementation as a social distancing measure against the COVID-19 pandemic in Japan,” Public Health, vol. 192, pp. 12-14, 2021.

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ADAPTATION OF MACHINE LEARNING TECHNIQUES WITH ITS CHALLENGES IN THE FIELD OF MEDICINE

Authors:

Asim Ali, Said ul Abrar, Safyan Ahmed, Sheeraz Ahmed, Ubaid Ullah, Muhammad Habib Ullah, Muhammad Tayyab

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00006

Abstract:

An affected person notices an effortless rash over his shoulder but does not get treatment. His spouse suggests he visit the hospital for a physician after few months, who will provide treatment a seborrhea keratosis. Afterward, when the patient went through a colonoscopy screening, a black shaded macule on his shoulder was noticed by a nurse and advises him to evaluate it. Then he takes it to a dermatologist after one month and takes a biopsy specimen for the lesion. Through which they find out a non-dangerous near to cancer but not cancer symptoms. A second reading of the biopsy specimen was suggested by the dermatologist. After that, they started to do the treatment by systematic chemotherapy. One friend who was a physician told the patient why he is not giving a try to immunotherapy.

Keywords:

colonoscopy,Machine learning,Medicine,Health System,immunotherapy,

Refference:

I. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv. September 1, 2014
II. Bakris G, Sorrentino M. Redefining hypertension assessing the new blood-pressure guidelines. N Engl J Med 2018; 378:497-9.
III. Berwick DM, Gaines ME. How HIPAA harms care, and how to stop it. JAMA 2018;320:229-30.
IV. Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for de-tection of critical findings in head CT scans: a retrospective study. Lancet 2018; 392:2388-96.
V. Clark J. Google turning its lucrative Web search over to AI machines. Bloom-berg News. October 26, 2015..
VI. Ehteshami Bejnordi B, Veta M, Jo-hannes van Diest P, et al. Diagnostic as-sessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318:2199-210.
VII. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.
VIII. Escobar GJ, Turk BJ, Ragins A, et al. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J Hosp Med 2016; 11:Suppl 1:S18-S24.
IX. Galloway CD, Valys AV, Petterson FL, et al. Non-invasive detection of hyperkale-mia with a smartphone electrocardio-gram and artificial.
X. Good fellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge, MA: MIT Press, 2016.
XI. Grinfeld J, Nangalia J, Baxter EJ, et al. Classification and personalized prognosis in myeloproliferative neoplasms. N Engl J Med 2018;379:1416-30.
XII. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of dia-betic retinopathy in retinal fundus photo-graphs. JAMA 2016;316:2402-10.
XIII. Institute of Medicine. Crossing the quality chasm: a new health system for the twenty-first century. Washington, DC: National Academies Press, 2001.
XIV. Institute of Medicine. To err is human: building a safer health system. Washing-ton, DC: National Academies Press, 2000.
XV. Johnson M, Schuster M, Le QV, et al. Google’s multilingual neural machine translation system: enabling zero-shot translation. arXiv. November 14, 2016 (http://arxiv.org/abs/1611.04558).
XVI. Kannan A, Chen K, Jaunzeikare D, Rajkomar A. Semi-supervised learning for information extraction from dialogue. In: Interspeech 2018. Baixas, France: Inter-national Speech Communication Associa-tion, 2018:2077-81.
XVII. Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treat-able diseases by image-based deep learn-ing. Cell 2018;172(5):1122-1131.e9.
XVIII. Kiranjit Kaur, Munish Saini. : ‘HEART DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES: A SYSTEMATIC REVIEW’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-5, May (2020) pp 112-126. DOI : 10.26782/jmcms.2020.05.00010.
XIX. Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating ma-chine learning models for diabetic reti-nopathy. Ophthalmology 2018;125:1264-72.
XX. Lasic M. Case study: an insulin over-dose. Institute for Healthcare Improvement (http://www.ihi.org/education/IHIOpenSchool/resources/Pages/Activities/AnInsulinOverdose.aspx).
XXI. Liu Y, Kohlberger T, Norouzi M, et al. Artificial intelligence-based breast cancer nodal metastasis detection. Arch Pathol Lab Med 2018 October 8 (Epub ahead of print).
XXII. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps dur-ing colonoscopy: a prospective study. Ann Intern Med 2018;169:357-66.
XXIII. Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA 2014;311:1406-15.
XXIV. National Academies of Sciences, Engi-neering, and Medicine. Improving diag-nosis in health care. Washington, DC: National Academies Press, 2016.
XXV. Obermeyer Z, Lee TH. Lost in thought — the limits of the human mind and the future of medicine. N Engl J Med 2017; 377:1209-11.
XXVI. Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018; 2:158-64.
XXVII. Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning for electronic health records. arXiv. January 24, 2018 (http://arxiv.org/abs/1801.07860).
XXVIII. Rajkomar A, Yim JWL, Grumbach K, Parekh A. Weighting primary care patient panel size: a novel electronic health record-derived measure using machine learning. JMIR Med Inform 2016;4(4):e29.
XXIX. Schwartz WB. Medicine and the com-puter — the promise and problems of change. N Engl J Med 1970;283:1257-64.
XXX. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine — where do we stand? N Engl J Med 1987; 316:685-8.

XXXI. Steiner DF, MacDonald R, Liu Y, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol 2018;42:1636-46.
XXXII. Ting DSW, Cheung CY-L, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017;318:2211-23.
XXXIII. Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smart-watch. JAMA Cardiol 2018;3:409-16.
XXXIV. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25(1):44-56.
XXXV. Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019 February 27 (Epub ahead of print).

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EFFECT OF LOCKDOWN DUE TO COVID-19 PANDEMIC ON AIR QUALITY IN THE INDUSTRIAL CITY OF EASTERN INDIA

Authors:

Rajrupa Ghosh

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00007

Abstract:

The lockdown due to coronavirus (COVID-19) was forced in India from March, 25 to May 3 2020 as precautionary actions in contradiction of the diffusion of infectious virus. The objective of this study is to analyse the changes in air quality between pre and during the lockdown in Asansol, the “coal mining city” of Eastern India is characterized by high pollution levels due to several industries leading to human discomfort and even health problems. Secondary data of seven parameters like CO, SO2, NO2, PM2.5, PM10, NH3, and O3 have been collected from the website of the Central Pollution Control Board, India and AQI were calculated as per the calculator provided by CPCB. The result displays a significant reduction of seven parameters from 33.31 % (SO2) to 60.44 % (PM2.5) due to the shut down of all manufacturing units and transportation throughout the lockdown period. The air quality index (AQI) was also upgraded from a very poor to a satisfactory state during this period. Plants are the main carbon sink, so, a green belt project proposal for this polluted city has been recommended to improve air quality management. This lockdown (temporarily) showed some vaccine effect on the air quality, but this is totally against economic growth.

Keywords:

COVID-19,lockdown,Air quality index (AQI),Industrial city,Eastern India,

Refference:

I. Arabi YM, Deeb AM, Al-Hameed F, Mandourah Y, Almekhlafi GA, Sindi AA, AlOmari A, Shalhoub S, Mady A, Alraddadi B, Amotairi AA, Khatib K, Abdulmomen A, Qushmag I, Solaiman O, Al-Aithan AM, Al-Raddadi R, Ragab A, AA, Kharaba A, Jose J, Dabbagh T, Fowler RA, Balkhy HH, Merson I, Hayden FG (2019) Saudi critical care trials group. Macrolides in critically ill patients with Middle East Respiratory Syndrome. Int J Infect Dis 81:184–190.
II. Ashour HM, Elkhatib WF, Rahman MM, Elshabrawy HA (2020) Insight into the recent 2019 novel coronavirus (SAR-CoV-2) in light of past human coronavirus outbreaks. Pathogens 9 (3): E186.
III. Banerjee D, Agarwalla NL (2006) Dispersion Modelling for a Chemical Manufacturing Plant. Indian J Air Pollut Control 6 (1): 29-39.
IV. Banerjee D et al. (2005) Analysis of air quality in Asansol City. Environ Pollut Control J 8 (6): 54-60
V. Bera B, Bhattacharjee S, Shit PK, Sengupta N, Saha S (2020) Significant impacts of COVID-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environmental health. Environ Dev Sustain https://doi.org/10.1007/s10668-020-00898-5
VI. Burnett RT, Stieb D, Brook JR, Cakmak S, Dales R, Raizenne M, Vincent R, Dann T (2004) Associations between short-term changes in nitrogen dioxide and mortality in Canadian cities. Arch Environ Health 59: 228–236.
VII. Choudhury P, Banerjee D (2009) Biomonitoring of Air Quality in the Industrial Town of Asansol using the Air Pollution Tolerance Index Approach. Res J Chem Environ Vol. 13: (1).
VIII. CPCB (2020) Impact of lockdown (25th March to 15th April) on air quality. Ministry of Environment, Forest and Climate Change, Govt. of India, Delhi, 1–62. https://cpcb.nic.in/latest-cpcb.php.
IX. Das K, Patial B (2020) The synergy between philosophy and science, need of the contemporary society. Int J Humanities Soc Sci Res 6 (1): 45–51.

X. Das, P., Mandal, I., Debanshi, S., Mahato, S., Talukdar, S., Giri, B., Pal, S., (2021). Short term unwinding lockdown effects on air pollution. J. Clean.Prod.126514.https://doi.org/10.1016/j.jclepro.2021.126514.Tyrrell DAJ, Almeida JD, Berry DM, Cunningham CH, Hamre D, Hofstad MS, Mallucci L and McIntosh K (1968) Coronaviruses. Nature 220: 650.
XI. Dey M (2013) A Contingent Valuation Approach to Estimate the Maximum Willingness-to-pay for Improved Air Quality in Asansol, Industrial Area of West Bengal. Int J Trend Econ Manag Tech Vol. II: Issue IV.
XII. Dong L, Hu S, Gao J (2020) Discovering drugs to treat coronavirus disease 2019 (COVID19). Drug Discov Ther 14 (1): 58–60.
XIII. Eroglu H (2020) Effects of Covid-19 outbreak on environment and renewable energy sector. Environ Dev Sustain https://doi.org/10.1007/s10668-020-00837-4.
XIV. ESA 2020a. ESA (2020) https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/ Air_pollution_drops_in_India_following_lockdown.
XV. ESA 2020b. ESA (2020) https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/ Coronavirus_lockdown_leading_to_drop_in_pollution_across_Europe.
XVI. Gautam S (2020a) COVID-19: air pollution remains low as people stay at home. Air. Qual. Atmos. Hlth. https://doi.org/10.1007/s11869-020-00842-6.
XVII. Gautam S, Hens L (2020) SARS-CoV-2 pandemic in India: what might we expect? Environ. Dev. Sustain. 22: 3867–3869. https://doi.org/10.1007/s10668-020-00739-5
XVIII. Hasnain, A.; Hashmi, M.Z.; Bhatti, U.A.; Nadeem, B.; Wei, G.; Zha, Y.; Sheng, Y. Assessment of Air Pollution before, during and after the COVID-19 Pandemic Lockdown in Nanjing, China. Atmosphere 2021, 12, 743. https://doi.org/10.3390/ atmos12060743.
XIX. He G, Pan Y, Tanaka T (2020a) COVID-19, City Lockdown, and Air Pollution: Evidence from China. MedRxiv, 2020.03.29.20046649. https://doi.org/10.1101/ 2020.03.29.20046649
XX. He L, Zhang S, Hu J, Li Z, Zheng X, Cao Y, Xu G, Yan M, Wu Y (2020b) On-road emission measurements of reactive nitrogen compounds from heavy duty diesel trucks in China. Environ Pollut 262: 114280. https://doi.org/10.1016/j. envpol.2020.114280.
XXI. He MZ, Kinney PL, Li T, Chen C, Sun Q, Ban J et al. (2020c) Short- and intermediate-term exposure to NO2 and mortality: a multi-county analysis in China. Environ Poll 261: 114165.
https://doi.org/10.1016/j.envpol.2020.114165.

XXII. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497–506. https://doi.org/10.1016/ S0140-6736(20)30183-5
XXIII. Huang X, Ding A, Gao J, Zheng B, Zhou D, Qi X, et al. (2020) Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. EarthArXiv. https:// doi.org/10.31223/osf.io/hvuzy.
XXIV. Isaifan RJ (2020) The dramatic impact of coronavirus outbreak on air quality: has it saved as much as it has killed so far? 6 (3): 275–288. https://doi.org/10.22034/ gjesm.2020.03.01.
XXV. Islam, S.; Tusher, T.R.; Roy, S.; Rahman, M. Impacts of nationwide lockdown due to COVID-19 outbreak on air quality in Bangladesh: A spatiotemporal analysis. Air Qual. Atmos. Health 2021, 14, 351–363.
XXVI. Jribi S, Ismai HB, Doggui D, Debbabi K (2020) COVID-19 virus outbreak lockdown: What impacts on household food wastage? Environ Dev Sustain 22: 3939– 3955. https://doi.org/10.1007/s10668-020-00740-y
XXVII. Kaur M, Nagpal AK (2017) Evaluation of air pollution tolerance index and anticipated performance index of plants and their application in development of green space along the urban areas. Environ Sci Pollut R 24: 18881–18895. https://doi.org/10.1007/s1135 6-017-9500-9
XXVIII. Kerimray A, Baimatova N, Ibragimova OP, Bukenov B, Kenessov B, Plotitsyn P, et al (2020) Assessing air quality changes in large cities during COVID-19 lockdowns: the impacts of traffic free urban conditions in Almaty, Kazakhstan Sci Total Environ 730: 1–8. https:// doi.org/10.1016/j.scitotenv.2020.139179.
XXIX. Koken PJ, Piver WT, Ye F, Elixhauser A, Olsen LM, Portier CJ (2003) Temperature, air pollution and hospitalization for cardiovascular diseases among elderly people in Denver. Environ Health Perspect 111 (10): 1312–1317.
XXX. Lal, S.; Naja, M.; Subbaraya, B. (2000) Seasonal variations in surface ozone and its precursors over an urban site in India. Atmos. Environ., 34, 2713–2724.
XXXI. Lau H, Khosrawipour V, Kocbach P, Mikolajczyk A, Schubert J, Bania J, et al. (2020) The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China. J Travel Med 37: 1–14. https://doi.org/10.1093/jtm/taaa037
XXXII. Le Tertre A, Medina S, Samoli E, Forsberg B, Michelozzi P, Boumghar A, Vonk JM, Bellini A, Atkinson R, Ayres JG, Sunyer J, Schwartz J, Katsouyanni K (2002) Short-term effects of particulate air pollution on cardiovascular diseases in eight European cities. J Epidemiol Community Health 56: 773–779.

XXXIII. Li L, Li Q, Huang L, Wang Q, Zhu A, Xu J, et al. (2020) Air quality changes during the COVID19 lockdown over the Yangtze River Delta Region: an insight into the impact of human activity pattern changes on air pollution variation. Sci Total Environ 732: 1–11. https://doi. org/10.1016/j.scitotenv.2020.139282.
XXXIV. Lu, X.; Zhang, L.; Liu, X.; Gao, M.; Zhao, Y.; Shao, J. (2018) Lower tropospheric ozone over India and its linkage to the South Asian monsoon. Atmos. Chem. Phys. Discuss. 18, 3101–3118.
XXXV. Mahato S, Pal S, Ghosh KG (2020) Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India Sci Total Environ 730: 139086
XXXVI. Mandal, I., Pal, S., (2020). COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas. Sci. Total Environ. 139281.
XXXVII. Mate A, Killian JA, Wilder B, Charpignon M, Awasthi A, Tambe M, et al. (2020) Evaluating COVID-19 Lockdown Policies for India: a Preliminary Modeling Assessment for Individual States. SSRN, 3575207. https://doi.org/10.2139/ssrn.3575207.
XXXVIII. Mitra A, Chaudhuri TR, Mitra A, Pramanick P, Zaman S, Mitra A, Chaudhuri TR, Mitra A, Pramanick P, Zaman S (2020) Impact of COVID-19 related shutdown on atmospheric carbon dioxide level in the city of Kolkata. Sci Educ 6: 84–92. https://sites.google.com/site/pjsciencea.
XXXIX. Muhmmad S, Long X, Salman M (2020) COVID-19 pandemic and environmental pollution: a blessing in disguise? Sci Total Environ 728:1–5. https://doi.org/10.1016/j.scito tenv.2020.138820.
XL. NASA (2020). NASA, 2020. https://earthsky.org/earth/satellite-images-air-pollution-india-covid19.
XLI. Otmani A, Benchrif A, Tahri M, Bounakhla M, Chakir EM, Bouch MEl, Krombi M (2020) Impact of Covid-19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco). Sci Total Environ 73: 139-541 10.1016/j.scitotenv.2020.139541
XLII. Paital B, Das K, Parida SK (2020) Inter nation social lockdown versus medical care against COVID-19, a mild environmental insight with special reference to India Sci Total Environ 728: 138914.
XLIII Sharma P, Dhar A (2018) Effect of hydrogen supplementation on engine performance and emissions. Int J Hydrog Energy 43: 7570–7580.
XLIII. Sharma S, Zhang M, Gao J, Zhang H, Kota SH (2020) Effect of restricted emissions during COVID-19 on air quality in India Sci Total Environ 728: 1–8. https://doi. org/10.1016/j.scitotenv.2020.138878.
XLIV. Thorpe AJ, Harrison RM (2008) Sources and properties of non-exhaust particulate matter from road traffic: a review. Sci Total Environ 400: 270–282.

XLV. Tobías A, Carnerero C, Reche C, Massagué J, Via M, Minguillón MC, Alastuey A, Querol X (2020) Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic Sci Total Environ 726: 138540. https:// doi.org/10.1016/j.scitotenv.2020.138540.
XLVI. Zhou F, Yu T, Du R, et al. (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet, S0140–6736(20): 30566–30573. https://doi.org/10.1016/s0140-6736(20)30566-3.
XLVII. Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al., (2020). Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin BioRxiv https://doi.org/10.1101/2020.01.22.914952.
XLVIII. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. (2020) A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382: 727–733. https://doi. org/10.1056/NEJMoa2001017

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CELLULAR MOBILE COMMUNICATION REVIEW

Authors:

Mehre Munir, Mubashar Javed, Muhammad Umer Javed

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00008

Abstract:

Mobile communication is continuously one of the hottest areas that are developing at a booming speed, with advanced techniques emerging in all the fields of mobile and wireless communications. This thesis deals with the comparative study of wireless cellular technologies namely First Generation, Second Generation, Third Generation, and Fourth Generation. A cellular network or mobile network is a radio network distributed over land areas called cells, each served by at least one fixed-location transceiver, known as a cell site or base station. In a cellular network, each cell uses a different set of frequencies from neighboring cells, to avoid interference and provide guaranteed bandwidth within each cell. The First Generation were referred to as cellular, which was later shortened to "cell", Cell phone signals were based on analog system transmissions,  and First Generation devices were comparatively less heavy and expensive. Second Generation phones deploy GSM technology. Global System for Mobile communications or GSM uses digital modulation to improve voice quality but the network offers limited data service. The Third Generation revolution allowed mobile telephone customers to use audio, graphics and video applications. Fourth Generation is short for fourth-generation cell phones or/and hand held devices.

Keywords:

Cellular network,First Generation,Second Generation,Third Generation,Fourth Generation,

Refference:

I. Amit Kumar, Dr. Yunfei Liu ,Dr. Jyotsna Sengupta, Divya, “Evolution of Mobile Wireless Communication Networks 1G to 4G”, International Journal of Electronics & Communication Technology, IJECT Vol. 1, Issue 1, Dece- mber 2010.
II. F. Williams, Ericsson, “Fourth generation mobile,” in ACTS Mobile Summit99, Sorrento, Italy, June 1999.
III. Fumiyuki Adachi, “Wireless past and Future: Evolving Mobile Communication Systems”. IEICE Trans. Findamental, Vol. E84-A, No.1, January 2001.
IV. H. Huomo, Nokia, “Fourth generation mobile,” in ACTS Mobile Summit99, Sorrento, Italy, June 1999.
V. Jamil.M.” 4G: The Future Mobile Technology” , in TENCON 2008 IEEE Region 10 Confererence, 19-21 Nov. 2008
VI. Jun-Zhao Sun, Jaakko Sauvola, and Douglas Howie, “Features in Future: 4G Visions From a Technical Perspective,”in IEEE, 2001.
VII. Kamarularifin Abd Jalil, Mohd Hanafi Abd. Latif, Mohamad Noorman Masrek, “Looking Into The 4G Features”, MASAUM Journal of Basic and Applied Sciences Vol.1, No. 2 September 2009.
VIII. Kamarularifin Abd Jalil, Mohd Hanafi Abd. Latif, Mohamad Noorman Masrek, “Looking Into The 4G Features”, MASAUM Journal of Basic and Applied Sciences Vol.1, No. 2 September 2009
IX. Mobile Technology: Evolution from 1G to 4G, Electronics for You, June 2003.
X. Mishra, Ajay K. “Fundamentals of Cellular Network Planning and Optimization, 2G/2.5G/3G…Evolution of 4G”, John Wiley and Sons, 2004
XI. Mishra, Ajay K. “Fundamentals of Cellular Network Planning and Optimization, 2G/2.5G/3G…Evolution of 4G”, John Wiley and Sons, 2004.

XII. Nabeel ur Rehman, Asad Asif,Junaid Iqbal, “3G Mobile Communication Networks” in Explore Summer 2006.
XIII. Pereira, Vasco & Sousa, Tiago. “Evolution of Mobile Communications: from 1G to 4G”, Department of Informatics Engineering of the University of Coimbra, Portugal 2004.
XIV. “Transition to 4G: 3GPP Broadband Evolution to IMT-Advanced”, Rysavy Research/3G Americas. [Online] Available: www.rysavy.com/PR/3GA_ PR_2010_09.pdf
XV. Third Generation (3G) Wireless White Paper, Trillium Digital Systems, Inc. March 2000.

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SECURE COMMUNICATION USING THE SYNCHRONIZATION OF TWO FRACTIONAL-ORDER CHAOTIC SYSTEMS WITH ORDER CHANGES USING THE FINITE-TIME OPTIMAL CONTROL APPROACH

Authors:

Ali Soleimanizadeh, Mohammad Ali Nekui

DOI NO:

https://doi.org/10.26782/jmcms.2021.10.00009

Abstract:

In this paper synchronization problem for two different fractional-order chaotic systems has been investigated. Based on fractional calculus, optimality conditions for this synchronization have been achieved. Synchronization Time and control signals are two factors that are optimized. After that, the synchronization method is applied in secure communication. Finally using the simulation example, the performance of the proposed method for synchronization and chaotic masking is shown.

Keywords:

Fractional calculus,Secure communication,Chaotic masking,

Refference:

I. A. A. Koronovskii, O. I. Moskalenko, and A. E. Hramov: On the use of chaotic synchronization for secure communication. Physics-Uspekhi 52 (2009), 12131238.
II. Awad El-Gohary, Optimal synchronization of Rssler system with complete uncertain parameters, Chaos, Solitons and Fractals, Volume 27, Issue 2, January 2006, Pages 345-355, ISSN 0960-0779.
III. Foroogh Motallebzadeh, Mohammad Reza Jahed Motlagh, Zahra Rahmani Cherati, Synchronization of different-order chaotic systems: Adaptive active vs. optimal control, Communications in Nonlinear Science and Numerical Simulation, Volume 17, Issue 9, September 2012, Pages 3643-3657, ISSN 1007-5704.
IV. G. Peng, Y. Jiang, F. Chen, ”Generalized projective synchronization of fractional order chaotic systems”, Phys. A 387 (2008) 37383746.
V. Ge Z, Yi C. ”Chaos in a nonlinear damped Mathieu system in a nano resonator system and in its fractional order systems”. Chaos Soliton Fract 2007;32(1):4261.
VI. Jian-Bing Hu, Guo-Ping Lu, Shi-Bing Zhang, Ling-Dong Zhao , Lyapunov stability theorem about fractional system without and with delay, 20 (2015) 905-913.
VII. K. B. Arman, K. Fallahi, N. Pariz, and H. Leung: A chaotic secure communication scheme using fractional chaotic systems based on an extended fractional Kalman filter. Comm. Nonlinear Sci. Numer. Simul. 14 (2009), 863879.
VIII. L. A. B. Torres and L. A. Aguirre: Transmitting information by controlling nonlinear oscillators. Physica D 196 (2004), 387406.
IX. L.M. Pecora, T.L. Carroll, ”Synchronization in chaotic systems”, Phys. Rev. Lett. 64 (1990) 821824.
X. Lin Pan , Wuneng Zhou b, Long Zhou a, Kehui Sun ”Chaos synchronization between two different fractional-order hyperchaotic systems”. Commun Nonlinear Sci Numer Simulat 16 (2011) 26282640
XI. M. J. Chen, D. P. Li, and A. J. Zhang: Chaotic synchronization based on nonlinear state observer and its application in secure communication. J. Marine Sci. Appl. 3 (2004), 6470.
XII. M.S.Tavazoei,M.Haeri, A necessary condition for double scroll attractor existence in fractional-order systems, Physics Letters A367(2007)102113.
XIII. Manabe, S., Early development of fractional order control, DETC2003/VIB-48370, in Proceedings of DETC03, ASME 2003 Design Engineering Technical Conference, Chicago, Illinois, September 26, 2003.
XIV. Momani S, Odibat Z. ”Numerical comparison of methods for solving linear differential equations of fractional order”. Chaos Soliton Fract 2007;31(5):124855.
XV. Odibat ZM, Momani S. ”Application of variational iteration method to nonlinear differential equations of fractional order”.Int J Nonlinear Sci Numer Simul 2006;7(1):2734
XVI. R. M. Guerra and W. Yu: Chaotic synchronization and secure communication via sliding mode observer. Internat. J. Bifurcation Chaos 18 (2008), 235243.
XVII. T.E. Duncan, Y. Hu and B. Pasik-Duncan, Stochastic calculus for fractional Brownian motion, I. Theory, SIAM J. Control Optim. 38 (2000) 582-612.
XVIII. Y. Hu and B. ksendal, Fractional white noise calculus and applications to finance, Infinite Dim. Anal. Quantum Probab. Related Topics 6 (2003), 1-32.

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MULTI-WAVE COVID-19 PANDEMIC DYNAMICS IN ICELAND IN TERMS OF DOUBLE SIGMOIDAL BOLTZMANN EQUATION (DSBE)

Authors:

Pinaki Pal, Asish Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2021.11.00001

Abstract:

The world is facing multi-wave transmission of COVID-19 pandemics, and investigations are rigorously carried out on modeling the dynamics of the pandemic. Multi-wave transmission during infectious disease epidemics is a big challenge to public health. Here we introduce a simple mathematical model, the double sigmoidal-Boltzmann equation (DSBE), for analyzing the multi-wave Covid-19 spread in Iceland in terms of the number of cumulative cases. Simulation results and the main parameters that characterize multi waves are derived, yielding important information about the behavior of the multi-wave pandemics over time. The result of the current examination reveals the effectiveness and efficacy of DSBE for exploring the Covid 19 dynamics in Iceland and can be employed to examine the pandemic situation in different countries undergoing multi-waves.

Keywords:

Cumulative Case,Daily Infection Rate,Double Sigmoidal Boltzmann Equation,Multi-wave Covid-19 Pandemic,Simulation,

Refference:

I. Asish Mitra. : ‘COVID-19 IN INDIA AND SIR MODEL.’ J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 1-8. DOI : 10.26782/jmcms.2020.07.00001
II. Asish Mitra. : ‘MODIFIED SIRD MODEL OF EPIDEMIC DISEASE DYNAMICS: A CASE STUDY OF THE COVID-19 CORONAVIRUS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-2, February (2021) pp 1-8. DOI : 10.26782/jmcms.2021.02.00001
III. Castro, R.D.; Marraccini, P. Cytology, biochemistry and molecular changes during coffee fruit development. Brazilian Journal of Plant Physiology, v.18, n.1 p.175-199, 2006. Available from: <http://dx.doi.org/10.1590/S1677-04202006000100013>. Accessed: Aug. 24, 2016.

IV. Centers for Disease Control and Prevention, “Cases of coronavirus disease (COVID-19) in the U.S.,” 2020. [cited 2020, Apr 7]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html.

V. Chowell, G., Hincapie-Palacio, D, Ospina, J, Pell, B, Tariq, A, Dahal, S, Moghadas, S, Smirnova, A, Simonsen, L, Viboud, C, “Using phenomenological models to characterize transmissibility and forecast patterns and final Burden of Zika epidemics,” PLoS Curr. (2016). https://doi.org/10.1371/currents.outbreaks.f14b2217c902f453d9320a43a35b9583.

VI. Chowell, G, “Fitting dynamic models to epidemic outbreaks with quantified uncertainty: a primer for parameter uncertainty, identifiability, and forecasts,” Infect. Dis. Model. 2(3), 379–398 (2017). https://doi.org/10.1016/j.idm.2017.08.001.

VII. Chowell, G, Tariq, A, Hyman, J M, “A novel sub-epidemic modeling framework for short-term forecasting epidemic waves,” BMC Med. 17(1), 1–18 (2019). https://doi.org/10.1186/s12916-019-1406-6.

VIII. Chowell, G, Luo, R, Sun, K, Roosa, K, Tariq, A, Viboud, C, “Real-time forecasting of epidemic trajectories using computational dynamic ensembles,” Epidemics. 30, 100379 (2020). https://doi.org/10.1016/j.epidem.2019.100379.

IX. Dingyu Xue, “Solving applied mathematical problems with MATLAB,” Chapman & Hall/CRC.

X. Elliott Sober, The Principle of Parsimony, Brit. J. Phil. Sci. 32 (1981), 145-156 DOI: 10.1093/bjps/32.2.145 • Source: OAI

XI. Fernandes TJ, Pereira AA, Muniz JA (2017) Double sigmoidal models describing the growth of coffee berries. Ciência Rural 47:1–7. https://doi.org/10.1590/0103-8478cr20160646

XII. https://www.worldometers.info/coronavirus/

XIII. Laviola, B.G. et al. Nutrient accumulation in coffee fruits at four plantations altitude: calcium, magnesium and sulfur. Revista Brasileira de Ciência do Solo, v.31, n.6, p.1451- 1462, 2007. Available from: <http://dx.doi.org/10.1590/S0100- 06832007000600022>. Accessed: Aug. 24, 2016.

XIV. Mendes, P.N. et al. Difasics logistic model in the study of the growth of Hereford breed females. Ciência Rural, v.38, n.7, p.1984-1990, 2008. Available from: <http://dx.doi.org/10.1590/ S0103-84782008000700029>. Accessed: Aug. 24, 2016.

XV. Mischan, M.M. et al. Inflection and stability points of diphasic logistic analysis of growth. Scientia Agricola, v.72, n.3, p.215- 220, 2015. Available from: <http://dx.doi.org/10.1590/0103-9016- 2014-0212>. Accessed: Aug. 24, 2016.

XVI. Morais, H. et al. Detailed phenological scale of the reproductive phase of Coffea arabica. Bragantia, v.67, n.1, p.693-699, 2008. Available from: <http://dx.doi.org/10.1590/S0006- 87052008000100031>. Accessed: Aug. 24, 2016.

XVII. Pinaki Pal, Asish Mitra, The Five Parameter Logistic (5PL) Function and COVID-19 Epidemic in Iceland, J. Mech.Cont. & Math. Sci., 16, 1-12, 2021.

XVIII. Santoro, K.R. et al. Growth curve parameters for Zebu breeds raised at Pernambuco State, Northeastern Brazil. Revista Brasileira de Zootecnia, v.34, n.6, p.2262-2279, 2005. Available from: <http://dx.doi.org/10.1590/S1516-35982005000700013>. Accessed: Aug. 24, 2016.

XIX. Vasquez, J.A. et al. Evaluation of non-linear equations to model different animal growths with mono and bisigmoid profiles. Journal of Theoretical Biology, v.314, p.95-105, 2012. Available from: <http:// dx.doi.org/10.1016/j.jtbi.2012.08.027>. Accessed: Aug. 24, 2016.

XX. Viboud, C, Simonsen, L, Chowell, G, “A generalized growth model to characterize the early ascending phase of infectious disease outbreaks,” Epidemics 15, 27–37 (2016). https://doi.org/10.1016/j.epidem.2016.01.002.

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NUMERICAL EXPERIMENTS FOR NONLINEAR BURGER’S PROBLEM

Authors:

Jawad Kadhim Tahir

DOI NO:

https://doi.org/10.26782/jmcms.2021.11.00002

Abstract:

The article contains the results of computational experiments for the non-homogeneous Burger's problem and finding its solution by using the non-classical variational-Cole-Hopf transformation approach. On using exact linearization via Cole-Hopf transformation, as well as the application of the non-classical variational approach, then the non-homogeneous Burger's problem has been solved. The solution which is obtained by this approach is in a compact form so that the original nonlinear solution is easy to be approximated. The accuracy of this method is dependent on the types of selected basis and its number.

Keywords:

Burger's problem,numerical solution,Cole-Hopf transformation,non-classical variational.,

Refference:

I. Ames, W. F., “Nonlinear Partial Differential Equations in engineering”, Academic pres, Inc., London, 1965.
II. Bateman, H., “Some recent Researches on the Motion of Fields”, Mon. Weather rev., 1915.
III. Chern, I. L., “Long-Time effect of Relaxation for Hyperbolic conservation Laws”, Communications in mathematical Physics, 1995.
IV. Chern, I. L., “Multiple-Mode Diffusion waves for Viscous Nonstrictly Hyperbolic Conservation Laws”, Communications in mathematical Physics, 1991.
V. Chern, I. L. and Tai-Ping Liu, “Convergence of Diffusion Waves of Solutions for Viscous Conservation Laws”, Communications in mathematical Physics, 1987.
VI. Cole, J. D., “On a Quasi-Linear Parabolic Equation Occurring in Aerodynamics”, Quart. Appl. Math., 1951.
VII. DeLillo, S., “The Burger’s Equation Initial-Boundary Value Problems on the Semiline”, Springer-Verlag, Berlin, Heidelberg, 1990.
VIII. Eschedo, M. and Zua Zua, E., “Long-Time Behaviour for a Convection-Diffusion Equation in Higher Dimension”, SIAM J. Math. Anal., 1997.
IX. Flether, C. A. J., “Burger’s Equation; a Model for all reasons in Numerical Solutions of Partial Differential Equations”, J. Noyle, ed., North-Holland, Amsterdam, New York, 1982.
X. Jawad K., Tahir. : ‘DEVELOPING MATHEMATICAL MODEL OF CROWD BEHAVIOR IN EXTREME SITUATIONS’. J. Mech. Cont.& Math. Sci., Special Issue, No.- 8, April (2020) pp 155-164. DOI : 10.26782/jmcms.spl.8/2020.04.00012
XI. Hopf, E., “The Partial Differential Equation ut + uux  uxx”, Comm. Pure and Applied Math., 1950.
XII. Lighthill, M. J., “Viscosity Effects in Sound Waves of Finite Amplitude”, C.U.P., Cambridge, 1956.
XIII. Magri, F., “Variaional Formulation for Every Linear Equation”, Int. J. Engng. Sci., Vol.12, pp.537-549, 1974.
XIV. Miller, J. C., Bernoff, A. J.; Rate of Convergence to Self-Similar Solutions of Burger’s Equation, Stud. Appl. Math. 111, 29-40, 2003.
XV. Moran, J. P. and S. F. Shen, “On the Formulation of Weak Plane Shock Waves by Impulsive Motion of a Piston”, Journal of Fluid Mechanics, 1966.
XVI. Nguyen, V. Q., “A Numerical study of Burger’s Equation with Robin Boundary Conditions”, M.Sc. Thesis, Virginia Polytechnic Institute and state University, 2001.
XVII. Radhi, A. Z., “Non-Classical Variational Approach to Boundary Problem in Heat Flow and Diffusion”, M.Sc. Thesis, al-Nahrain University, 1993.
XVIII. Wang, W. and Roberts, A. J.; Diffusion Approximation for Self-Similarity of Stochastic Advection in Burger’s Equation. Communication in Mathematical Physics, Vol.333, pp.1287-1316, 2014.
XIX. Whithman, G. B., “Linear and Nonlinear Waves”, John Wiley and Sons, 1974.
XX. W. A. Shaikh, A. G. Shaikh, M. Memon, A. H. Sheikh, A. A. Shaikh. : ‘NUMERICAL HYBRID ITERATIVE TECHNIQUE FOR SOLVING NONLINEAR EQUATIONS IN ONE VARIABLE’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-7, July (2021) pp 57-66. DOI : 10.26782/jmcms.2021.07.00005

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RESPONDENT ANALYSIS IN CONTEXT TO IMPACT OF CLIMATE CHANGE ON THE REGULATING SERVICES OF MANGROVE VEGETATION

Authors:

Dipak Kanti Paul, Prosenjit Pramanick, Sufia Zaman, Abhijit Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2021.11.00003

Abstract:

The mangrove ecosystem in the lower Gangetic delta is noted for providing several regulatory services. The major regulatory services include erosion, natural disaster, Phytoremediation, carbon sequestration, siltation, and sea-level rise.  Here, we have attempted to develop a mechanism of assessing and ranking the magnitude of regulatory services offered by Sundarban mangroves based on stakeholder’s views on the subject. The respondents were categorized into five major classes namely policy level worker, researcher, fisherman, agriculturist, and local inhabitant. About 295 respondents belonging to these 5 categories were asked about the types of regulatory services and their respective magnitude by ranking the services between 1 and 6. Finally, based on data generated, three separate Combined Mangrove Regulating Service Scale (CMRSS) were assessed for three sectors (western, central, and eastern) of Indian Sundarbans. The basic root for such assessment is contrasting variations between these three sectors based on geographical features, salinity, and biodiversity. The present approach of analysis can be a road map to identify and empirically score the regulatory services of mangroves.

Keywords:

Mangrove ecosystem,Lower Gangetic delta,Respondents,Regulatory services of mangroves,Mangrove Regulating Service Scale (MRSS),

Refference:

I. Banerjee, K., Roy Chowdhury, M., Sengupta, K., Sett, S., Mitra, A, “Influence of anthropogenic and natural factors on the mangrove soil of Indian Sundarbans wetland”, Archive of Environmental Science, vol. 6, pp: 80 – 91, 2012
II. Bryant, D., Burke, L., McManus, J. W., Spalding, M., “Reefs at risk”, A Map-Based Indicator of Potential Threats to the World’s Coral Reefs, 1998
III. Chaudhuri, A. B., Choudhury, A, “Mangroves of the Sundarbans”, The World Conservation Union, Dhaka, 1994
IV. Kappel, C. V., “Losing pieces of the puzzle: threats to marine, estuarine, and diadromous species”, Frontiers in Ecology and the Environment, vol. 3, pp: 275–282, 2005
V. Mitra, A., “Estuarine Pollution in the Lower Gangetic Delta”, Published by Springer, ebook ISBN 978-3-319-93305-4; Hardcover ISBN 978-3-319-93304-7, DOI: https://doi.org/10.1007/978-3-319-93305-4, vol. XVI, pp: 371, 2020a
VI. Mitra, A., “In: Sensitivity of Mangrove ecosystem to changing Climate”, Springer, DOI: 10.1007/978-; 81-322-1509-7, pp: 323, 2013
VII. Mitra, A., “Mangrove Forests in India”, Published by Springer, ebook ISBN 978-3-030-20595-9, Hardcover ISBN 978-3-030-20594-2, DOI: https://doi.org/10.1007/978-3-030-20595-9, vol. XV, pp: 361, 2020b
VIII. Mitra, A., Banerjee, K., Sengupta, K., Gangopadhyay, A., “Pulse of climate change in Indian Sundarbans: A myth or reality?”, National Academy of Science Letter, vol. 32 (1 & 2), pp: 19-25, 2009
IX. Mitra, A., Sengupta, K., Banerjee, K., “Standing biomass and carbon storage of above-ground structures in dominant mangrove trees in the Sundarbans”, Forest Ecology and Management (ELSEVIER DOI:10.1016/j.foreco.2011.01.012), vol. 261 (7), pp: 1325 -1335, 2011
X. Mitra, A., Zaman, S., “Basics of Marine and Estuarine Ecology”, Springer, ISBN 978-81-322-2705-2, 2016
XI. Mitra, A., Zaman, S., “Blue carbon reservoir of the blue planet”, published by Springer, ISBN 978-81-322-2106-7 (Springer DOI 10.1007/978-81-322-2107-4), 2015
XII. Pal, N., Saha, A., Biswas, P., Zaman, S., Mitra, A., “Loss of carbon sinks with the gradual vanishing of Heritiera fomes from Indian Sundarbans”, Research Article 4. In: Environmental Coastguards – Understanding mangrove Ecosystem and Carbon Sequestration (Climate Change Series 3). Edited by Abhijit MItra, J. Sundaresan, Kakoli Banerjee and Suresh Kumar Agarwal. Published by CSIR-National Institute of Science Communication And Information Resources (NISCAIR), New Delhi, ISBN: 978-81-7236-352-9, 2017, 202 –206, 2016
XIII. Raha, A. K., Mishra, A. V, Das, S., Zaman, S., Ghatak, S., Bhattacharjee, S., Raha, S., Mitra, A., “Time Series Analysis of forest and tree cover of West Bengal from 1988 to 2010, using RS/GIS, for monitoring afforestation programmes”, The journal of Ecology (Photon), vol. 108, pp: 255-265, 2014
XIV. TNC (The Nature Conservancy), “The five-S framework for site conservation: A practitioner’s handbook for site conservation planning and measuring conservation success”, The Nature Conservancy, Arlington, Virginia, USA, 2000
XV. Trivedi, S., Mitra, A., Gupta, A., Chaudhuri, A., Neogi, S., Ghosh, I., Choudhury, A., “Inter-relationship between physico-chemical parameters and uptake of pollutants by estuarine plants Ipomea pescarpes”, Proceedings of the seminer: On our environment: Its challenges to development projects, American Society of Civil Engineers – India Section, pp. SC-1 – SC-6, 1994

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