We present a method of calculating measures for centrality and betweenness in complex networks based on properties of spreading processes on networks. The model is designed for influence spreading and other similar processes. The modelling is based on temporal spreading in a topological network structure via all the possible paths from source nodes to target nodes. The contributions of different paths to the measures are computed and dependencies of the paths are taken into account using probability theory. The method is not limited to network structures near the source nodes, instead, the measures can be calculated for different sizes of networks. When the spreading process is modelled with time dependencies, weighting factors describing the activity of nodes is an essential aspect of the theory. Typically, the measures for passive and active networks have different values, for example, rankings of the most influential nodes may not be the same. Furthermore, we show how to model processes where revisits along different routes are allowed. The method is demonstrated along the lines of an artificial network designed for illustrations. After that the method is also applied to a real-world social network.

[1]. F. Agneessens, S. P. Borgatti, and M. G. Everett, "Geodesic based centrality: Unifying the local and the global," Social Networks vol. 49, pp. 12-26, 2017.
[2]. J. Bae and S. Kim, "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A vol. 395, pp. 549-559, 2014.
[3]. P. Basaras, D. Katsaros, and L. Tassiulas, "Detecting Influential Spreaders in Complex, Dynamic Networks," Computer, vol. 46:04, pp. 24-29, 2013.
[4]. F. Bauer and J. T. Lizier, "Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach," EPL (Europhysics Letters), vol. 99:6, 2012.
[5]. S. P. Borgatti, "Centrality and network flow," Social Networks vol. 27, pp. 55-71, 2005.

The purpose of this study is to establish the content of heavy metals in agricultural lands in the Obiliq and Vitia municipalities in Kosovo. The study was carried out within the span of 2015. Sampling was done on agricultural lands in Vitia and Obiliq areas, which were under cultivation with Capsicum Annum culture. Elements subject to analysis included: As, Cr, Ni, Pb and Zn. Heavy metals were analyzed according to Atomic Absorber Spectrophotometry (AAS) at Technologies Transfer Center in Fushë-Kruje. The analysis have demonstrated lower values of As with a minimum and maximum values set at 39.88 mg/kg and 53.43 respectively as well as higher values of Zn at a minimum of 107.29 and a maximum of 736.35 mg/kg. The average content of heavy metals appears in this order: Zn> Cr> Ni>Pb>As. Considering the perennial cultivation of these lands with Capsicum Annum and the enduring impact of industrial activities in the area, especially in Obiliq, it is necessary to track down and analyze the content of heavy metals and the quality of vegetables consumed by the population.

[1]. Corinne P.R., Fang-Jie Zhao y Steve McGrath P. Phytotoxicity of nickel in a range of European soils: Influence of soil properties, Ni solubility and specia¬tion. Environ. Pollut. 145, pp. 596-605. (2006).
[2]. DAESB. Departamenti i Analizave Ekonomike dhe Statistikave Bujqesore, Ministria e Bujqesise, Pylltarise dhe Zhvillimit Rural, Kosovo, (Raport Vjetor 2015).
[3]. García I. y Dorronsoro C. Contaminación por metales pesados. En Tecnología de Suelos. Universidad de Granada. Departamento de Edafología y Química Agrícola. http://edafologia.ugr.es. (2005).
[4]. Kabata-Pendias A. y Pendias H. Trace elements in soil and plants. Boca Raton Arbor, London, 365 pp. (1991).
[5]. Lucho C.A., Prieto F., Del Razo L.M., Rodríguez R. y Poggi H. Chemical fractionation of boron and heavy metals in soils irrigated with waste water in central Mexico. Agric. Ecosyst. Environ. 108, pp. 57-71. (2005).

On the basis of boost inversion ability this paper compare the performance of different control methods applied on Switched Inductor Z-Source Inverter (SLZSI) .Control methods : Simple Boost , Maximum Boost and Maximum Constant Boost are generated for same value of modulation index. The operating principle and analysis of inverter are presented. To verify the performance of the circuit, SLZSI is simulated on SIMULINK/MATLAB environment.

[1]. F. Gao, P. C. Lob, R. Teodorescu, and F. Blaabjerg, "Diode-assisted buck-boost voltage-source inverters," IEEE Trans. Power Electron., vol. 24, no. 9, pp. 2057-2064, Sept. 2009.
[2]. F. Z. Peng, "Z-source inverter," IEEE Trans. Ind. Applicat., vol. 39, no. 2, pp.504–510, Mar./Apr. 2003.
[3]. Florescu, O. Stocklosa, M. Teodorescu, C. Radoi, D.A. Stoichescu and S.Rosu. "The advantages, limitations and
disadvantages of Z-source inverter " Semiconductor Conference (CAS), 2010 International.
[4]. J. Anderson and F. Z. Peng, "Four quasi-Z-source inverters," in Proc. IEEE PESC'08, Jun. 2008, pp. 2743–2749.
[5]. M. Zhu, K. Yu, and F. L. Luo, "Switched-inductor Z-source inverter," IEEE Trans. Power Electron., vol. 25, no. 8, pp. 2150–2158, Aug. 2010.

Placement of bins is an important aspect of the solid waste management process. Optimization of bin placement results in adequate number of bins as per the waste generation capacity of the locality, ease of access due to proper spatial distribution of the bin by the waste generators as well as waste collectors. In the present study the bin optimization for the city of Rohtak has been done using Geospatial inputs from remote sensing imageries and local solid waste generation survey and census. Solid waste generation was estimated for 488 blocks of the city taking population and socioeconomic conditions of each block in consideration while determining the waste generation. Bins of 3000 Liter volume having a calculated capacity of 990Kg were used as collection depots for deposition of solid waste collected from house to house through rickshaw trolleys...............

[1]. Abdulai, H., Hussein, R., Bevilacquq, E and Storring, M., 2015. GIS Based Mapping and Analysis of Municipal Solid Wase Collection System in Wa, Ghana. Journal of Geographic Information System, 7(85-94). http://dx.doi.org/10.2436/igis.2015.72008
[2]. Chalkies, C. Lasaridi, K., 2009. A GIS based model for the optimization of municipal solid waste collection: the case study of Nikea, Athens, Greece. In: Proceedings of the WSEAS Transactions on Environment and Development, 640-650.
[3]. CPHEEO, 2015. Manual on Solid Waste Management (CPHEEO), New Delhi, Ministry of Urban Development.
Moud.gov.in/advisory/swm_manual.
[4]. Gopangi, A., Guntuka, D and Bhole, V., 2014. GIS & GPS in micro-spatial location analysis of solid waste disposal sites. Coordinates, 1-9.
[5]. Ghose, M.K., Dikshit, A.K and Sharma, S.K., 2006. A GIS based transportation model for solid waste disposal- a case study on Asansol municipality, Waste Management, 26(11):1287-1293.

Concrete mix design is complicated and time consuming, experience based and uncertain task. Most of the time to achieve the desired strength, one has to depend upon past experience in mix design process and some sort of trial and error methods the final acceptance come after quality control test results. In mix design min task is proportioning the ingredients of concrete ( water , cement , coarse aggregates , fine aggregates ) to chive desired strength. The ANN model is based on 7 input parameters such as cement, sand, coarse aggregate, and water and fineness modulus. ANN is used to reduce number of trials need to be perform in laboratory as well as in field. ANN is very helpful in saving lot of time cost of materials as well as labour. ANN gives higher accuracy. with the help of ANN we can predict of concrete mix for desired strength.

[1] Paratibha Aggarwal, Rafat Siddiq, Yogesh Aggarwal, Surinder M Gupta "Self-Compacting Concrete - Procedure for Mix Design" Leonardo Electronic Journal of Practices and Technologies, January-June 2008.
[2] Paratibha Aggarwal, Yogesh Aggarwal, "Prediction of Compressive Strength of SCC Containing Bottom Ash using Artificial Neural Networks" Academy of Science, Engineering and Technology 53 2011
[3] M. Jamil, M.F.M. Zain, H.B.Basri, "Neural Network Simulator Model foR Optimization in High Performance Concrete Mix Design" European Journal of Scientific Research
[4] L. M. Snell, J. Van Rockel and N .D. Wallace, Predicting early concrete strength, Concrete International, 11(12),43-47.1989.

[5] S. Haykin, Neural Networks - A Comprehensive Foundation, Prentice Hall INC, Englewood Cliffs N, J, 07632, 1991 BP neural networks, J chin ceram soc ,Vol27 (4), pp. 408-414,1991

Multi Level Inverters are the emerging power electronic converters in the present scenario. To realize them many more topologies have been proposed. Out of which diode clamped topology is the basic topology to model them. When multiple sources are available this topology is not preferable. This paper proposes a multi source topology for modeling a seven level inverter and compares its performance with neutral point topology.

[1] Rodriguez, J.-S. Lai, and F. Z. Peng, "Multilevel inverters: A survey of topologies, controls, and applications," IEEE Trans. Ind. Electro, vol. 49, no. 4, pp. 724-738, Aug. 2002.
[2] L. G. Franquelo, J. L. Rodriguez, J. Leon, S. Kouro, R. Portillo, and M. A. Prats, " The age of multilevel converters arrives," IEEE Ind. Electron. Mag., vol.2, no. 2, pp. 28-39, Jun. 2008.
[3] H. Abu-Rub, J. Holtz, J. Rodriguez, and G. Baoming, "Mediumvoltage multilevel converters - state of the art, challenges, and requirements in industrial application," IEEE Trans. Ind. Electr. vol. 57, no. 8, pp. 2581-2596, Aug. 2010
[4] M. Zahra, M. Jafari, Md. R. Islam and J. Zhu, "A comparative study on characteristics of major topologies of voltage source multilevel inverters," IEEE Innovative Smart Grid Tech., pp. 612-617, 2014
[5] T. Murali Krishna, C. Bhargav, A new hybrid Multi Level Inverter to improve the performance of induction motor, International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), 2015 , pp.264-268

Smart fluids also known as Magnetorheological fluids (MR Fluids), behave like a Newtonian fluid without the presence of a magnetic field but in few seconds change its rheological properties when an external magnetic field appears. In this research, Smart Fluids were synthesized, carbonyl iron particles with different grades were used, oil and water emulsions were employed as a continuous phase and silicon dioxide as surfactant. The influence of the properties of soft magnetic particles over Smart fluids was investigated by using a Transmission Electron Microscope (TEM) to see the morphology and X-Ray Diffraction to confirm the quality in phases. Magnetic properties in particles and fluids were determined by Vibrating Sample Magnetometer (VSM)................

[1]. Rabinow, J. The magnetic fluid clutch. AIEE trans., 1948.67,1308-1315
[2]. I. Bica, Y.D. Liu, H.J. ChoiPhysical characteristics of magnetorheological suspensions and their applications. J. Ind. Eng. Chem., 19 (2) (2013), pp. 394-406
[3]. Bica I. 2004. "Magnetorheological Suspension Electromagnetic Brake". Journal of Magnetism and Magnetic Materials, 270:321- 326.
[4]. Wang and G. Meng. Magnetorheological fluid devices: principles, characteristics and applications in mechanical engineering. IMechE 2001. Vol. 215-part L.
[5]. D.C. Jiles. 2003. Recent advances and future directions in magnetic materials. Acta materialia.51:5907-5939.

An increasing concern on the depletion of fossil fuel and its rising demand for global energy has encouraged many researches on alternative renewable biofuel. Vegetable oil is noticeable to be a potential source of energy that can substitute fossil fuels because of its similar properties to diesel fuel and it is readily available. However, the high viscosity of vegetable oil limits its application in diesel engines. This study investigated the effect of low volume canola-vegetable oil blends with petroleum diesel on performance and emission of a 2 cylinder light-duty direct injection (DI) diesel engine at various engine speeds: low idling speed (1000 rpm), speed for maximum torque (2100 rpm) and speed for maximum power (3000 rpm) for different load conditions at each speed: low (≈20%), medium (≈50%) and high (≈80%)...............

[1]. Y. Hua, M. Omar, and C. Nolasco-hipolito, "The potential of waste cooking oil-based biodiesel using heterogeneous catalyst derived from various calcined eggshells coupled with an emulsification technique : A review on the emission reduction and engine performance," Renew. Sustain. Energy Rev., vol. 47, pp. 589–603, 2015.
[2]. M. Saleh, H. Abdelfattah, O. Sayed, M. Abu-elyazeed, E. Abd, and M. Ahmed, "On biodiesels from castor raw oil using catalytic pyrolysis," Energy, vol. 143, pp. 950–960, 2018.
[3]. M. Atapour and H. Kariminia, "Characterization and transesterification of Iranian bitter almond oil for biodiesel production," Appl. Energy, vol. 88, issue 7, pp. 2377–2381, 2011.
[4]. Y. Hemmat, B. Ghobadian, M. Loghavi, S. Kamgar, and E. Fayyazi, "Biodiesel Fuel Production From Residual Animal Fat as An Inedible And Inexpensive Feedstock," Int. Res. J. Appl. Basic Sci., vol. 5 (1), pp. 84–91, 2013.
[5]. Md. A Ruhul, Md. J Abedin, S.M.A. Rahman, B.H.H. Masjuki, A. Alabdulkarem, Md. A. Kalam, S. Islam, "Impact of fatty acid composition and physicochemical properties of Jatropha and Alexandrian laurel biodiesel blends: An analysis of performance and emission characteristics," J. Clean. Prod., vol. 133, pp. 1181–1189, 2016.