The concept of refining crude palm kennel oil with the aim of reducing the non-glycerides, its colour pigment and odour using physical refining method (activated charcoal) is what this research is about. Carbonized charcoal was subjected to chemical activation process using standard potassium hydroxide for activation (KOH). Characterization of the crude palm kernel oil was carried out before and after refining with the activated charcoal.. Results obtained showed that specific gravity remained unchanged, 0.921.However, the free fatty acid (FFA) content of the oil reduced from 5.6mg (KOH)g to 2.81mg (KOH) g. other properties evaluated were iodine value, Acid value and moisture content.

Keywords: Refining, palm kernel, activated charcoal, free fatty acid, iodine value.

[1]. Berger, K.G. and Noraini, I. (2005): Formulation of zerotrans fatty acid shortening and margarines and other food fats with products of palm oil. Journal of American Oil Chemists' Society 86: 775-782.
[2]. Young F.V.K (1982). "The refining of palm oil", PORIM Technology, P 13-24, www.fullerserch.com, 2005.
[3]. Macrae, R. (1993) "Encyclopedia of food Science, Food technology and Nutrition" vol.7 Spectroscopy7, Harcourt Brace, Jovanovich publishers, Sandiego, pp 4676-4700.
[4]. Campbell, E. et. al., (1999), "Food fats and oil", Institute of shortening and edible oils available at. www.iseo.org/
[5]. Hermandez – Montoya, V., Garcia-Servin, J. And Bueno-Lopez, J.J (2010): Thermal Treatments and Activation Procedures in the preparation of Activated Carbon. ISBN 978-953-510197-0 Dof: 10.5772/3736

Cognitive Radio (CR) is an intelligent technology by which we can use the frequency spectrum of a wireless network more efficiently. Using this technology, the secondary user of a cognitive radio network can use the frequency band of primary user when it remains idle. As most of the wireless portable devices are battery operated, continuous energy supply is always critical issue. In this paper, we proposed an energy harvesting technique that can harvest more energy from RF signal than conventional energy harvesting system which leads to enhance the longevity of the cognitive user. Here we utilized a super cluster-based energy harvesting technique where we get the increased spectrum sensing time by the cognitive user in a cluster. For this, the cognitive users are able to harvest more energy from RF signal which contributes in enhancing the longevity of the cognitive user.

Keywords: Cognitive radio network, Clustering, Energy detection, Energy harvesting, Spectrum sensing.

[1]. J. Mitola and G. Q. Maguire, "Cognitive radios: making software radios more personal," IEEE Personal Commun., vol. 6, no. 4, pp. 13-18, 1999.
[2]. S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Trans. Communication, vol. 23, no. 2, pp. 201-220, 2005.
[3]. Z. Hasan, H. Boostanimehr, and V. K. Bhargava, "Green cellular networks: A survey, some research issues and challenges," IEEE Communications Surveys & Tutorials, vol. 13, no. 4, pp. 524–540, November 2011.
[4]. S. Park, J. Heo, B. Kim, W. Chung, H. Wang, and D. Hong, "Cognitive radio networks with energy harvesting," IEEE Transactions on Wireless communications, vol. 12, no. 3, pp. 1386–1397, March 2013.
[5]. S. H. Lee, R. Zhang, and K. B. Huang, "Opportunistic wireless energy harvesting in cognitive radio networks," IEEE Transactions on Wireless Communications", vol. 12, no. 9, pp. 4788–4799, September 2013.

Sawmill noise induced hearing deterioration has been modelled using measured noise levels at selected sawmills (Rumuosi, Ogbogoro and Mile 3) in Port Harcourt. Noise measurement was taken for one hour at six-minute interval using a Sound Level Meter and conducted three times daily (morning, afternoon and evening) for five days at each sawmill. Background noise at the studied sawmills ranged 53.68 – 56.92 dBA in the order of Mile 3 >Rumuosi>Ogbogoro. Equivalent continuous noise levels (Leq) ranged83.37 – 87.03 dBA (Rumuosi), 85.10 – 86.48dBA (Ogbogoro) and 85.38 – 86.46 dBA (Mile 3). Although these Leqvalues are less than the recommended standard of 90 dB per 8 hours daily, persistent exposure without ear protection could still result to devastating health effect on receptors. Thus, from the Leqresults, hearing deterioration prediction modelswere developed for the three studied sawmills with coefficient of determination (R2) range of 0.966 – 0.986 and p-value range of 0.999927 – 0.999999 (p>0.05), indicating excellent fit. Therefore, it is concluded that the developed models can adequately predict hearing deterioration due to exposure to the noise from the studied sawmills.

Keywords:Sawmill noise, Modeling, Induced hearing deterioration, Sound level meter.

[1]. WHO (World Health Organisation) Occupational noise: assessing the burden of disease from work-related hearing impairment at national and local levels. Environmental Burden of Disease Series. No.9 World Health Organization, Switzerland, 2005.
[2]. D. Baker, Noise: the invisible hazard. Department of Agricultural Engineering. University of Missouri, Columbia, 1997.
[3]. M. Yahaya,R. Chukwufumnanya, and E. Odunola, Assessment of noise levels generated in some feed mills in Ibadan, Nigeria. Research Journal in Engineering and Applied Sciences, 1(3), 2012, 156-159.
[4]. E. Agbalaga,A. Akpata, andS. Olali, Investigation of noise pollution levels of four selected sawmill factories in Delta State, Nigeria. Advances in Applied Acoustics Journal, 2(3), 2013, 83-90.
[5]. C. Boateng, andG. Amedofu, Industrial noise pollution and its effects on the hearing capabilities of workers: a study from sawmills, printing presses and corn mills. African Journal of Health Sciences, 11, 2004, 55-60

The illegal release of used engine oil (UEO) is an environmental hazard with global concerns. UEO contains heavy metals and hydrocarbons that could modify soil properties and gradually destroy soil usefulness. Thus, soil contaminated with UEO requires remediation to restore its usefulness. In this study, the biodegradation of UEO in soil was examined and modeled. Soil samples were contaminated with UEO and inoculated with cultured bacteria isolated from clogged drainage systems for 56 days. Experimental results indicated that Pseudomonas, Micrococcus, Bacillus cereus,ProvidenciaandAcinetobacterspecies actively participated in the biodegradation process. The percentage reduction of UEO was statistically highly significant (p<0.05) for all five bacterial species and found to be in the following order:Pseudomonas (94.67%) >Micrococcus (71.45%) >Bacillus cereus (31.00%) >Providencia (28.37%) >Acinetobacter (28.25%). The biodegradation data complied with first-order kinetic model. Accordingly, first-order kinetic models of the biodegradation of UEO in soil for the five active bacteria were developed. The models were used to fit the biodegradation of UEO in soil with coefficient of determination (R2) range of 0.6757 – 0.9078 and p-value range of 0.1508 – 0.6704(p>0.05), indicating that the developed models can adequately predict the biodegradation of UEO in soil with time by the respective bacteria..

Keywords:Modeling, Biodegradation, Bacteria, Used engine oil, Contaminated soil.

[1]. O.P.Abioye,P. Agamuthu, and A.R. Abdul-Aziz, Biodegradation of Used Motor Oil in Soil Using OrganicWaste Amendments. Biotechnology Research International, 2012, 1-8.
[2]. D.Klamman, Lubricants and Related Products, VerlagChemie, ISBN 0-89573-177-0, 1984.
[3]. S.K.Singh, R.K.Srivastava, and S. John, Studies on soil contamination due to used motor oil and itremediation. Canadian Geotechnical Journal, 46(9), 2009,1077-1083.
[4]. B.U. Dike,B.C.Okoro, N.N.Nwakwasi, and K.C.Agbo, Remediation of Used Motor Engine Oil Contaminated Soil: A Soil Washing Treatment Approach. Journal of Civil & Environmental Engineering, 3(1), 2013, 1-3.
[5]. W.C. Blodgett, Water-soluble mutagen production during the bio-remediation of oil-contaminated soil. Florida Scientist, 60(1), 2011, 28–36..

Attendance management is an important procedure in an educational institution as well as in business organizations.Most of the available methods are time consuming and manipulative. The traditional method of attendance management is carried out in handwritten registers. Other than the manual method, there exist biometric methods like fingerprint and retinal scan, RFID tags, etc. All of these methods have disadvantages, therefore, in order to avoid these difficulties here, we introduce a new method for attendance management using deep learning technology. Using deep learning we can easily train a data-set. Real-time face algorithms are used and recognized faces of students in real-time while attending lectures. This system aims to be less time-consuming in comparison to the existing system of marking attendance. The program and the training of the data are all done in Google collab. The face of the students is recognized and attendance is marked on an excel file. The program detects the faces and also crop the detected faces and store it in a file..

Keywords: Google Colabatory,Cosine Similarity,Face Detection,Face Encoding,FaceRecognition.

[1]. M. Yadav, A. Aggarwal and N. Rakesh, "Motion Based Attendence System in Real- Time Environment for Multimedia Application,"2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, 2018, pp. 332-336.
[2]. X. Qu, T. Wei, C. Peng and P. Du, "A Fast Face Recognition System Based on Deep Learning,"2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2018, pp. 289-292
[3]. T. Mantoro, M. A. Ayu and Suhendi, "Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods,"2018 6th International Conference on Multimedia Computing and Systems (ICMCS), Rabat, 2018, pp. 1-5.
[4]. M. Arsenovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "FaceTime — Deep learning-base face recognition attendance system,"2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, 2017, pp. 000053-000058.
[5]. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517.

High penetration of DFIG units in a weak harmonic grid may lead to stability issues due to high frequency resonance (HFR). These HFR occurs due to frequency mismatch and phase angle deviation. This work presents control of grid connected DFIG system with the HFR damping and harmonics suppressor. In order to improve the performance of the system and reduce harmonic content of the weak grid, the HFR needs to be effectively damped. In this paper, the proposed active damping control strategy implemented effectively both in the Rotor Side Converter (RSC) and in the Grid Side Converter (GSC), through the introduction of virtual positive capacitor by reshaping the DFIG system impedance and mitigate the high frequency resonance.

Keywords: ....

[1] M. Liserre, R. Cárdenas, M. Molinas, and J. Rodriguez, ―Overview of multi-MW wind turbines and wind parks,‖ IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1081–1095, Apr. 2011.
[2] Y. Song and F. Blaabjerg, "Overview of DFIG-Based Wind Power System Resonances Under Weak Networks," IEEE Trans. Power Electron., vol. 32, no. 6, pp. 4370-4394, Jun 2017.
[3] A. Adib, B. Mirafzal, X. Wang and F. Blaabjerg, "On Stability of Voltage Source Inverters in Weak Grids," IEEE Access, vol. 6, pp. 4427-4439, 2018.
[4] D. Yang, X. Wang, F. Liu, K. Xin, Y. Liu and F. Blaabjerg, "Adaptive Reactive Power Control of PV Power Plants for Improved Power Transfer Capability under Ultra-Weak Grid Conditions," IEEE Trans. Smart Grid., vol. PP, no. 99, pp. 1-1.
[5] M. Glinkowski, J. Hou and G. Rackliffe, "Advances in Wind Energy Technologies in the Context of Smart Grid," Proceedings of the IEEE, vol. 99, no. 6, pp. 1083-1097, June 2011.

In India, Covid-19 spread has set panic in the mindset of humans. In this study, the focus is on the districts of Delhi and Telangana. Delhi is third leading state with almost 1,07,051 confirmed cases (as on July 10, 2020) of Covid-19. Geoinformatics is the tool used to map hotspot areas in Delhi with Central Delhi (184) cases and Telangana where Hyderabad stands with 24,710 cases. In this study, coronavirus affected areas are mapped using GIS to monitor the viral infected areas within each city. Thus, helps to take the preventive measures for the virus spread. Thereby determines containment zone areas and integrates with socio-demographic data for assessment and making necessary action plan during lockdown situation. Spatial data is an excellent way for measuring the impact of Covid-19 pandemic disease over all the affected regions. In fact, it gives the glance of total visualization at a time over the whole world. On comparison of both curves of day-wise increase of Delhi and Telangana, Telangana state does not flattened as Delhi curve.

Keywords:Covid-19, Delhi, Telangana, Day-wise Cases, Mobility trends.

[1]. Chatterjee, P., Nagi, N., Agarwal, A., Das, B., Banerjee, S., Sarkar, S., Gupta, N., &Gangakhedkar, R. R. (2020). The 2019 novel coronavirus disease (COVID-19) pandemic: A review of the current evidence. Indian J Med Res Vol 151, pp 147-159 DOI: 10.4103/ijmr.IJMR_519_20
[2]. Gupta, R., Pal, K. S., & Pandey, G. (2020). A Comprehensive Analysis of COVID-19 Outbreak situation in India. medRxiv preprint doi: https://doi.org/10.1101/2020.04.08.20058347
[3]. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Li Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J., & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: pp 497-506 https://doi.org/10.1016/ S0140-6736(20)30183-5
[4]. Sohrabi, C., Alsafib, Z., O'Neilla, N.,Khanb, M., Kerwanc, A., Al-Jabirc, A., Iosifidisa, C., &Aghad, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery Vol 76 (2020) pp 71–76.
[5]. Lupia, T., Scabini, S., Pinna, S, M., Perri, G, D., Rosa, F, G, D., &Corcione, S. (2020). 2019 novel coronavirus (2019-nCoV) outbreak: A new challenge. Journal of Global Antimicrobial Resistance Vol 21. pp-22–27.

Solar photovoltaic (SPV) system offers an alternative to the conventional system of electricity generation. It helps to meet out the increasing energy demand nonetheless it limits the pollution as it is clean, green and emission free. It has been finding more attention of industries and academia in recent year. This paper presents a simulation study of the grid-connected solar PV system to evaluate the performance of the system in the different insolation. In this paper, the components of the SPV grid connected system are described. The study shows the behavior of the SPV system for different insolation and the bi-directional power flow capability through the grid as well. The performance analysis may be helpful for researcher of this field.

Keywords: Solar PV (SPV) system, Grid connected, Bi-directional, DC-DC converter, InC MPPT

[1]. https://powermin.nic.in/en/content/power-sector-glance-all-india [2]. https://www.ibef.org/industry/renewable-energy-presentation [3]. Lesourd, J. B. (2001). Solar photovoltaic systems: the economics of a renewable energy resource. Environmental Modelling & Software, 16(2), 147-156. [4]. Dincer, F. (2011). The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy. Renewable and Sustainable Energy Reviews, 15(1), 713-720. [5]. Parida, B., Iniyan, S., & Goic, R. (2011). A review of solar photovoltaic technologies. Renewable and sustainable energy reviews, 15(3), 1625-1636

These days the safety of an individual is at stake, it may be due to ill health or due to the increasing crimes such as the sexual assaults, molestation, abuse etc. So in order to prevent these to a certain extent, this paper proposes an automated wearable smart device to prevent the above mentioned cause, Stun-Gun also known as electroshock weapon is an incapacitating or shocking weapon which is used to protect the user from threatens like either from persons or from animals. This system designed here is aimed to deliver electric shock in the form of low voltage and low current pulses by which assails are frightened due to the adequate current pulses. The GSM and GPS are used to identify the victim's location when in need. The IP address of the LPC2148 is linked with the web server. The victim location is shared to the near by police station and to the pre registered mobile. While the speaker alerts the surroundings of the victim

Keywords:LPC2148, Stun-Gun,voice chip, GSM, GPS,

[1]. Dr. AntoBennet, M, SankarBabu G, Natarajan S, "Reverse Room Techniques for Irreversible Data Hiding", Journal of Chemical and Pharmaceutical Sciences 08(03): 469-475, September 2015.
[2]. Dr. AntoBennet, M ,Sankaranarayanan S, SankarBabu G, " Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis", Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.
[3]. D.G. Monisha, M. Monisha, G. Pavithra and R. Subhashini, "Women Safety Device and Application-FEMME", Indian Journal of Science and Technology, Vol9 (10), March 2016
[4]. GeethaPratyushaMiriyala, P.V.V.N.D.P Sunil, RamyaSreeYadlapalli, Vasantha Rama Lakshmi Pasam, TejawiKondapalli, AnushaMiriyala, "Smart Intelligent Security System for Women", International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 7, Issue 2, March-April 2016.
[5]. RashaTalal Hammed, Omar AbdulwahabeMohamad, NicolaeTapus, "Health Monitoring System Based on Wearable Sensors and Cloud Platform", 20th International Conference on System Theory, Control and Computing (ICTSCC), 2016

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers. We also discuss the two categories of the controller along with some popular available controller. For each controller , we discussed the architectural overview, design aspects and so on. This paper points to the major state-of-the-art controllers used in industry and academia. Our review work covers major popular controllers used in SDN paradigm.

Keywords: software defined networks (SDN), SDN controller.

[1]. McCauley, M. (2012). POX, http://www.noxrepo.org/
[2]. Nippon Telegraph and Telephone Corporation, RYU network operating system, 2012, http://osrg.github.com/ryu
[3]. OpenDaylight, Linux Foundation Collaborative Project, 2013,http://www.opendaylight.org\
[4]. Gude al, N. (2008). NOX: Towards an operating system networks. ACM SIGCOMM Computer Communication Review,vol. 38, no. 3, pp. 105–110.
[5]. Smith M. (2014). OpFlex control protocol, Internet Engineering Task Force, http://tools.ietf.org/html/draft-smith-opflex-00.

This paper describes an entropy equation, but one that should be used for measuring energy and not information. In relation to the human brain therefore, both of these quantities canbe used to represent the stored information. The human brain makes use of energy efficiency to form its structures, which is likely to be linked to the neuron wiring. This energy efficiency can also be used as the basis for a clusteringalgorithm, which is described in a different paper. This paper is more of a discussion about global properties, where the rules used for the clustering algorithm can also createthe entropy equation E = (mean * variance).This states that work is done through the energy released by the 'change' in entropy. The equation is so simplistic and generic that it can offer arguments for completely different domains, where the journey ends with a discussion about global energy properties in physics and beyond. A comparison with Einstein's relativity equation is made and also the audacious suggestion that a black hole has zero-energy inside.

Keywords: entropy, energy, self-organising, physics, human brain.

[1] Alpaydin, E. (2020). Introduction to machine learning. MIT press.
[2] Anderson, J.A., Silverstein, J.W., Ritz, S.A. and Jones, R.A. (1977) Distinctive Features, Categorical Perception, and Probability Learning: Some Applications of a Neural Model, Psychological Review, Vol. 84, No. 5.

[3] Einstein A. (1916), Relativity: The Special and General Theory(Translation 1920), New York: H. Holt and Company
[4] Feynman, R.P. (2005). The Feynman Lectures on Physics. 2 (2nd ed.). Addison-Wesley. ISBN 978-0-8053-9065-0.
[5] Goldstein, M. and Inge F. (1993). The Refrigerator and the Universe: Understanding the Laws of Energy. Harvard Univ. Press. ISBN 9780674753259.

In the present article ,the solution of a fractional integro-differential equation model associated with a RLC circuit with 0<α≤1 is derived.By introducing the fractional derivative in the sense of Caputo,homotopy analysis method is applied to find the analytic approximate solution of the model.The result obtained is shown graphically with the effect of varying the auxiliary parameter h and α. MSC 2010: 44XX(26A33), 34XX(34K28)

Keywords: Homotopy analysis method,mathematical modeling,RLCcircuit,integro-differential equation,caputo fractional derivative.

[1]. Carpinteri A., Mainardi F.(1997). Fractals and Fractional Calculus in Continuum Mechanics, Springer Verlag, Wien, New York.
[2]. Podlubny I.(1994).Fractional-order systems and fractional-order controllers, Report UEF-03-94, Slovak Academy of Sciences, Institute of ExperimentalPhysics, Kosice, Slovakia.
[3]. Podlubny I.(1999). Fractional Differential Equations, Academic Press, San Diego.
[4]. Oldham K.B., Spanier J.(1974). The Fractional Calculus, Academic Press, New York.
[5]. Miller K.S., Ross B.(1993). An Introduction to the Fractional Calculus and Fractional Differential Equations, Wiley, New York.