Archive

Abstract:

This study presents a U shape wideband antenna for small wireless applications. With half ground plane the patch antenna is slotted into U shape which resulted a wide bandwidth response with gain ranging from 4.1dB to 2.5dB. A patch antenna was constructed and was introduced with slots and was modified into monopole with half ground plane. The antenna has been simulated into CST 2015. The U shaped antenna with different parameter results showed its efficient structure. The proposed antenna can be used for GSM, WiMax and other small wireless applications.

Keywords:

Gain, Directivity,U shaped,partial ground plane,efficiency,

Refference:

I.Balanis, Constantine A. “Antenna theory: A review.” Proceedings of theIEEE 80.1 (1992): 7-23

II.Kuo, Yen-Liang, and Kin-Lu Wong. “Printed double-T monopoleantenna for 2.4/5.2 GHz dual-band WLAN operations.” IEEEtransactions on antennas and propagation 51.9 (2003): 2187-2192.

III.Liang, J., Chiau, C. C., Chen, X., & Parini, C. G. (2005). Study of a printed circular disc monopole antenna for UWB systems. IEEE transactions on antennas andpropagation, 53(11), 3500-3504.

IV.Saad Hassan Kiani, Khalid Mahmood and Ahsan Altaf, “A Linear Arrayfor Short Range Radio Location and Application Systems” InternationalJournal of Advanced Computer Science and Applications(IJACSA),9(4), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090420

V.Saad Hassan Kiani, Khalid Mahmood, Ahsan Altaf and Alex J. Cole,“Mutual Coupling Reduction of MIMO Antenna for Satellite Servicesand Radio Altimeter Applications” International Journal of AdvancedComputer Science and Applications(IJACSA), 9(4), 2018.http://dx.doi.org/10.14569/IJACSA.2018.090405

VI.SONG, Z. H., QIU, J. H., ZHANG, S. H., LIU, Z. H., & YANG, C. T.(2003). Study of A Planar Equiangular Spiral Antenna and the RelevantWideband Balun [J]. Guidance and Fuze, 2, 009

VII.YANG, Xiao-dong, Peng CHEN, and Hao TONG. “A broadbandstacked microstrip antenna with half U-slot coupling [J].” Journal ofHarbin Engineering University 3 (2008): 020.

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Abstract:

A huge quantity of waste material are produced i-e marble powder, shredded rubber, fly ash, stone dust, lime stone dust etc. from different sources. Modification of asphalt with such type of additives not only proved as cost effective and environmental friendly but can also improve asphalt properties. As asphalt is one the most expensive material used in the construction of flexible pavement and modification of asphalt with additives can make the pavement construction more economical. In this research an effort is made for the utilization of some waste materials such as fly ash and shredded rubber as an additive to improve the properties of asphalt i.e. ductility, penetration grade, flash and fire point of asphalt and marshal stability of asphalt mix. The asphalt was partially replaced with fly ash and shredded rubber in different percentages i.e. 0% (control) 3%, 5% and 7% by weight to bitumen. Total 90 specimen were prepared and were taken into laboratory for further investigation. The results showed that addition of 0% (control), 3%, 5% and 7% fly ash had no effect on flash and fire point of bitumen otherwise the addition of 3%, 5% and 7% of shredded rubber increased the flash point of bitumen from 191cº to 195cº, 200cº and 204cº and fire point from 198cº to 206cº, 208cº and 212cº respectively. The penetration test result showed that addition of fly ash and Shredded rubber up to 7 % decreases the value grade but the overall grade of the bitumen remained same, which was 60/70. Moreover the ductility value decreases with increase in percentage of fly ash and shredded rubber. Marshall Stability value of asphalt mix also increased with increase in percentage of fly ash and shredded rubber.

Keywords:

Shredded Rubbe,Fly ash, Ductility test,Flash and Fire point of bitumen,Marshall Stability test,

Refference:

I.Churchill, E.V. and Amirkhanian, S.N., 1999. Coal ash utilization in asphalt concrete mixtures. Journal of Materials in Civil Engineering, 11(4), pp.295-301.

II.Liu, S., Cao, W., Fang, J. and Shang, S., 2009. Variance analysis and performance evaluation of different crumb rubber modified (CRM) asphalt. Construction and Building Materials, 23(7), pp.2701-2708.

III.Mistry, R. and Roy, T.K., 2016. Effect of using fly ash as alternative filler in hot mix asphalt. Perspectives in Science, 8, pp.307-309.

IV.Navarro, F.J., Partal, P., Martınez-Boza, F., Valencia, C. and Gallegos, C., 2002. Rheological characteristics of ground tire rubber-modified bitumens. Chemical Engineering Journal, 89(1), pp.53-61.

V.Oliver, J.W., 2000. Rutting and fatigue properties of crumbed rubber hot mix asphalts. Road Materials and Pavement Design, 1(2).

VI.Tons, E., Goetz, R.O. and Razi, M., 1983. Fly ash as asphalt reducer in bituminous base courses. University of Michigan, College of Engineering, Department of Civil Engineering.

VII.Wulandari, P. S., & Tjandra, D. (2017). Use of crumb rubber as an additive in asphalt concrete mixture (Doctoral dissertation, Petra Christian University).

VIII.Xiao, F., Amirkhanian, S.N., Shen, J. and Putman, B., 2009. Influences of crumb rubber size and type on reclaimed asphalt pavement (RAP) mixtures. Construction and Building Materials, 23(2), pp.1028-1034.

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Abstract:

Building information modelling (BIM)Building information modelling (BIM) is a new way of approaching the design and documentation of building projects. The objective of BIM is not only to model and manage graphics, but also information – information that allows the automatic generation of drawings and reports, design analysis, schedule simulation, facilities management, and more – ultimately enabling the building team to make better-informed decisions. This thesis documents the modelling of a real life structure (Qayyum Stadium) as well as implies interoperability checks between BIM tool and SAP2000 analysis software. The Pavilion of Qayyum Stadium is located in Saddar, Peshawar. The data like plans of the structure was acquired. The structure was modeled in BIM tool, Tekla Structures v20. The structure was then exported to SAP2000 for analysis. In SAP2000 Gap analysis was performed to determine the structural elements which were not translated by SAP2000 like curved slab, column beam joints. The component catalog option is an important asset in BIM tool, Tekla Structure, making it easy to counter various flaws during the reinforcement placing. The reinforcement detailing of the structure are done using Tekla Structures, Drawings are generated, quantity take offs are done, Clash Detection tool was applied, which is one of the important tool in Tekla Structure (BIM). Nowadays the Architecture, Engineering, Construction (AEC) sector has the intentions to deliver a product through formal procedures, which will not depend on design process. With the development in technology, many sectors(production and business) other than construction industry of production and business (outside of construction) are being modified and refurnished, due to which the construction industry lays behind. Presently construction process is same as it previously hundred years before, from the set of drawings. Mostly these drawings have errors and limitations which ultimately results in delays, increase in project cost and delay in project completion. These limitations and errors can be improved through Building Information Modelling tool.

Keywords:

Building information modelling (BIM),design analysis,(AEC) sector,SAP2000 analysis software,Tekla Structure,

Refference:

I.Aranda-Mena, G., Crawford, J., Chevez, A., &Froese, T. (2009). “Building information modelling demystified: does it make business sense to adopt BIM?”.International Journal of Managing Projects in Business, 2(3), 419-434.

II.Arayici, Y., Khosrowshahi, F., Ponting, A. M., &Mihindu, S. (2009). “Towards implementation of building information modelling in the construction industry”.

III.Azhar, S., Hein, M., and Sketo, B. (2008). “Building Information Modelling(BIM): Benefits, Risks and Challenges”. Proceedings of the 44th ASC Annual Conference (on CD ROM), Auburn, Alabama, April 2-5, 2008.

IV.Bernstein, P.G., and Pittman, J.H. (2005). “Barriers to the Adoption of Building information Modellingin the Building Industry”. Autodesk Building Solutions Whitepaper, Autodesk Inc., CA.

V.Building Information Modelling(BIM): A new paradigm for quality of life within Architectural, Engineering and Construction (AEC) industry By RoshanaTakim* Mohd Harris, Abdul Hade Nawawi.URL www.sciencedirect.com

VI.Ding, L., Drogemuller, R., Akhurst, P., Hough, R., Bull, S. and Linning, C. (2009). “Towards sustainable facilities management”. In P. Newton, K. Hampson, & R. Drogmuller, Thechnology, Design and Process Innovation in the Built Environment. pp. 373-392. Taylor & Francis.

VII.Eastman, C., Teicholz, P., Sacks, R. and Liston,K. (2011). BIM Handbook, a Guide to Building Information Modelling2nd Ed. Hoboken: John Wiley & Sons, Inc.

VIII.Fischer, M., Kunz, J. (November 12, 2006). “The Scope and Role of Information Technology in Construction” [WWW document]. URL http://cife.stanford.edu/online.publications/TR156.

IX.Integration of Agents in the Construction of a Single-Family House through use of BIM TechnologyFaustino PatiñoCambeiro*, ItziarGoicoecheaCastaño, MaríaFenolleraBolíbar, Javier Rodríguez. URL www.sciencedirect.com

X.Is BIM Adoption Advantageous for Construction Industry of Pakistan? By Masood,R.a*Kharal, M.K.N.bNasir, A.R.c URL www.sciencedirect.com

XI.Tekla Structures Official website. URL http://tekla.com/international/Pages/Default.aspx

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Abstract:

Current trends in software development have shown a strong move towards autonomous and rational mechanism for the human societal growth. Customer behavior analysis and its knowledge have always been given its due importance in research community to develop real life practical solutions. In this scenario a real-world phenomenon of customer buying habits is tested through observations lying in the database and is experimented and validated through association mining. On the flip side of the coin, the development of intellectual and evolutionary data mining tool for retail industries through the machine learning algorithm has always been proved to adequately respond to environment changes and improve its behavioral rules to derive intelligent quotient. A case study of Market basket analysis is simulated to imitate customer behavior in the dynamic environment to predict about rational and intelligent behavior for future business expansion.

Keywords:

Customer behavior analysis, Data mining, Intellectual Management,Neural Networks,Genetic algorithm,Retail industry,

Refference:

I.Ahmed, S.R.,”Applications of data mining in retail business”, Information Technology: Coding andComputing,vol. 2, 2004, pp.455-459.

II.AmandeepKaur, P.S.Grover, “Performance Efficiency Assessment for Software Systems” a chapter in “Advances in Intelligent Systems and Computing”book series, AISC, Volume 731, June 2018.

III.Ansari Azarnoush and Riasi Arash, “Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms”, International Journal of Business and Management; vol. 11, Canadian Center of Science and Education, June 2016, ISSN 1833-3850.

IV.Ismail, J., “The design of an e-Learning System: Beyond the hype”, Internet and Higher Education, vol. 4, 2002.

V.Ngai, E.W.T, Xiu, Li and Chau, D.C.K.,“Application of data mining techniques in customer relationship management: A literature review and classification”, Expert systems with applications, vol. 36,March 2009, pp. 2592-2602.

VI.Pillai Jyothi, “User centric approach to itemset utility mining in Market Basket Analysis”, International Journal on Computer Science and Engineering, Jan 2011.

VII.Sandhu Parvinder, Dalvinder S. and Panda S. N ,“Mining utility-oriented association rules: An efficient approach based on profit and quantity”, International Journal of the Physical Sciences,vol. 6, pp. 301-307, Jan 2011.

VIII.Vijaylakshmi S., Mohan V., Suresh Raja S., “Mining of users’ access behavior for frequent sequential pattern from web logs”, International Journal of Database Management System (IJDM), vol. 2, August 2010.

IX.Woo,J.Y.,Bae,S.M., andPark, S.C., “Visualization method for customer targeting using customer map”, Expert Systems with Applications,vol.28, 2005, pp.763-772.

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Abstract:

A standout amongst the most well-known kinds of vitality source utilized in residential is propane in which condensed gas contains. Despite the fact that the wellbeing issues are considered, spillage of gas has turned out to be basic mishap which can make harm human lives and property. This Paper displays a minimal effort, control effective brought together Gas Leakage Alerting System. The framework has two principle gadgets: the gas identifier and the alert unit. The gas finder that is found near the gas utilization point gas chamber is a battery worked gadget. There can be more than one locator in the frameworks, which can be independently distinguished in the framework. The caution unit distinguishes the alarms sent by the indicators and discharges the alert. And furthermore it sends messages to indicated people. The segments of the gadget have been picked thinking about the power utilization and the time interims have been determined concerning the present utilization.

Keywords:

Vitality Source,Alerting System,Control Utilization,Current Utilizati,

Refference:

I.Attia, Hussain A., and Halah Y. Ali. “Electronic Design of Liquefied Petroleum Gas Leakage Monitoring, Alarm, and Protection System Based on Discrete Components.” International Journal of Applied Engineering Research, vol. 11, no. 19, pp. 9721-9726, 2016.

II.Apeh, S. T., K.B. Erameh, and U. Iruansi. “Design and Development of Kitchen Gas Leakage Detection and Automatic Gas Shut off System.” Journal of Emerging Trends in Engineering and Applied Sciences, vol. 5, no. 3, pp. 222-228, 2014.

III.Ashish Shrivastava, Ratnesh Prabhaker, Rajeev Kumar, Rahul Verma, “GSM based gas leakage detection system.” International Journal of Emerging Trends in Electrical and Electronics, vol. 3, no. 2, pp. 42-45, 2013.

IV.B. Jolhe, P. Potdukhe and N. Gawai, “Automatic LPG Booking, Leakage Detection and Real Time Gas Measurement Monitoring System,” International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 4, pp. 1192 -1195, 2013.

V.B. Didpaye and S. Nanda, “Automated Unified System for LPG using Microcontroller and GSM Module,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 2, pp. 234 -237, February 2016..

VI .“Converting Occupational Exposure Limits from mg/m3 to ppm”, Canadian Centre for Occupational Health & Safety, 2009.

VII.“Flammable Gas Cylinders for Laboratory Use”,Various Small Gas Torches, 2003.

VIII.“Gascylinders inlabs” Aylward & Findlay,1988.

IX .Q.Instrument Services Limited, “An introduction to Gas Detection Oliver”,IGD, Q. Instrument Services Limited, Cork, 2006.

X.K. B. Vinoth, S. Kalaiyarasan, B. A. R. Denesu and T. Kanthavel, “Quadcopter Based Gas Detection System,” IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 11, no. 1, pp. 64 -68, 2016.

XI .Mahalingam, A., R. T. Naayagi, and N. E. Mastorakis. “Design and implementation of an economic gas leakage detector.” Recent Researches in Applications of Electrical and Computer Engineering, pp. 20-24, 2012

XII .P.Meenakshi Vidya, S.Abinaya, G.Geetha Rajeswari, N.Guna,”Automatic LPG detection and hazard controlling “.

XIII ,S. Ashish, P. Ratnesh, K. Rajeev and V. Rahul, “GSM Based Gas Leakage Detection System,” International Journal of Technical Research and Applications (IJTRA), vol. 1, no. 2, pp. 42 -45, 2013.

XIV.T. Soundarya and J. Anchitaalagammai, “Control and Monitoring Sytem for Liquefied Petroleum Gas (LPG) Detection andPrevention,” International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 3, no. 3, pp. 696 -700, 2014.

XV.“What is Gas Leak Detection Gas Monitors UK Europe”, 2012.

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Abstract:

In multimedia community, cross modal similarity search based hashing received extensive attention because of the effectiveness and efficiency of query. This research work contributes large scale dataset for weakly managed cross-media recovery, named Twitter100k. Current datasets namely Wikipedia, NUS Wide and Flickr30k, have two main restrictions. First, these datasets are deficient in content diversity, i.e., only some pre-characterized classes are secured. Second, texts in these datasets are written informal dialect, that leads to irregularity with practical applications. To overcome these disadvantages, the proposed method used Twitter100k dataset because of two major points, first, it has 100,000 content-image pairs that are randomly crawled from Twitter and it has no importance in the image classifications. Second, text in Twitter100k is written in informal language by the clients. Since strongly supervised strategies use the class labels that might be missing in practice, this paper mainly concentrates on weakly managed learning for cross-media recovery, in which only text-image sets misused during training. This paper proposed a Random Multimodal Deep Learning (RMDL) based Recurrent Neural Network (RNN) for cross-media retrieval. The variety of input data such as video, text, images etc. are used for cross-media recovery which can be accept by proposed RMDL in weakly dataset. In RMDL, the various input data can be classified by using RNN architecture. to improve the accuracy and robustness of the proposed method, RMDL uses the specific RNN structure i.e. Long Short-Term Memory (LSTM). In the experimental analysis, the results demonstrated that the proposed RMDL-based strategy achieved 78% of Cumulative Match Characteristic (CMC) compared to other datasets.

Keywords:

Cross modal similarity search,witter dataset,class labels,strong supervised methods, NUS Wide,Random Multimodal Deep Learning,

Refference:

I.Ahmad, Khaleel, Monika Sahu, Madhup Shrivastava, Murtaza Abbas Rizvi, and Vishal Jain., “An efficient image retrieval tool: query based image management system,” International Journal of Information Technology, pp. 1-9, 2018.

II.Ballan Lamberto, Tiberio Uricchio, Lorenzo Seidenari, and Alberto Del Bimbo,“A cross-media model for automatic image annotation”, In Proceedings of International Conference on Multimedia Retrieval, pp. 73, 2014.

III.Ding Guiguang, Yuchen Guo, and Jile Zhou,“Collective matrix factorizationhashing for multimodal data,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.

IV.Deng Cheng, Xu Tang, Junchi Yan, Wei Liu, and Xinbo Gao, “Discriminative dictionary learning with common label alignment for cross-modal retrieval,” IEEE Transactions on Multimedia, vol. 18, 2, pp. 208-218, 2016.

V.Ding, Kun, Bin Fan, Chunlei Huo, Shiming Xiang, and Chunhong Pan, “Cross-modal hashing via rank-order preserving,” IEEE Transactions on Multimedia, vol. 19, no. 3, pp. 571-585, 2017.

VI.Hauptmann, A. G., Yi Yang, and L. Zheng,“Person Re-identification: Past, Present and Future,” 2016.

VII.Hwang Sung Ju, and Kristen Grauman,“Reading between the lines: Object localization using implicit cues from image tags,” IEEE transactions on pattern analysis and machine intelligence vol. 34, no.6,pp. 1145-1158, 2012.

VIII.Jiang Bin, Jiachen Yang, Zhihan Lv, Kun Tian, Qinggang Meng, and Yan Yan, “Internet cross-media retrieval based on deep learning”, Journal of Visual Communication and Image Representation, vol.48, pp. 356-366, 2017.

IX.Kang Cuicui, Shiming Xiang, Shengcai Liao, Changsheng Xu, and Chunhong Pan, “Learning consistent feature representation for cross-modal multimedia retrieval,” IEEE Transactions on Multimedia, vol. 17, no. 3, pp. 370-381, 2015.

X.L. Malliga, and K. Bommanna Raja, “A Novel Content Based Medical Image Retrieval Technique with Aid of Modified Fuzzy C-Means Clustering (CBMIR-MFCM),” Journal of Medical Imaging and Health Informatics vol. 6, no. 3, pp. 700-709, 2016

XI.Pennington Jeffrey, Richard Socher, and Christopher Manning,“Glove: Global vectors for word representation,” Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014.

XII.Pascanu Razvan, Tomas Mikolov, and Yoshua Bengio,“On the difficulty oftraining recurrent neural networks,” International Conference on Machine Learning. 2013.

XIII.Rasiwasia Nikhil, Jose Costa Pereira, Emanuele Coviello, Gabriel Doyle, Gert RG Lanckriet, Roger Levy, and Nuno Vasconcelos,“A new approach to cross-modal multimedia retrieval,” In Proceedings of the 18th ACM international conference on Multimedia, pp. 251-260, ACM.

XIV.Rehman Sadaqat Ur, Shanshan Tu, Yongfeng Huang, and Obaid Ur Rehman, “A Benchmark Dataset and Learning High-Level Semantic Embeddings of Multimedia for Cross-Media Retrieval,” IEEE Access, vol. 6, pp. 67176-67188, 2018.

XV.SharmaAbhishek, Abhishek Kumar, Hal Daume, and David W. Jacobs,“Generalized multiview analysis: A discriminative latent space”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2160-2167, 2012.

XVI.Shen Fumin, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, and Zhenmin Tang,“Inductive hashing on manifolds,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1562-1569, 2013.

XVII.Song Jingkuan, Yi Yang, Zi Huang, Heng Tao Shen, and Jiebo Luo,“Effective multiple feature hashing for large-scale near-duplicate video retrieval,” IEEE Transactions on Multimedia, vol. 15, no. 8, pp. 1997-2008, 2013.

XVIII.Song Jingkuan, Yang Yang, Yi Yang, Zi Huang, and Heng Tao Shen, “Inter-media hashing for large-scale retrieval from heterogeneous data sources,” In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 785-796, 2015.

XIX.Wu Fei, Zhou Yu, Yi Yang, Siliang Tang, Yin Zhang, and Yueting Zhuang,“Sparse Multi-Modal Hashing,” IEEE Trans. Multimedia, vol. 16, no. 2, pp. 427-439, 2014.

XX.Xu Xing, Yang Yang, Atsushi Shimada, Rin-ichiro Taniguchi, and Li He,“Semi-supervised coupled dictionary learning for cross-modal retrieval in internet images and texts”, In Proceedings of the 23rd ACM international conference on Multimedia, pp. 847-850, 2015.

XXI.Yang Yi, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, and Yunhe Pan,“A multimedia retrieval framework based on semi-supervised ranking and relevance feedback,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 723-742, 2012.

XXII.Yang Yang, Zheng-Jun Zha, Yue Gao, Xiaofeng Zhu, and Tat-Seng Chua, “Exploiting web images for semantic video indexing via robust sample-specific loss,” IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1677-1689, 2014.

XXIII.Yang Yang, Hanwang Zhang, Mingxing Zhang, Fumin Shen, and Xuelong Li,“Visual coding in a semantic hierarchy,” In Proceedings of the 23rd ACM international conference on Multimedia pp. 59-68, 2015.

XXIV.Zhang Hong, Yun Liu, and Zhigang Ma “Fusing inherent and external knowledge with nonlinear learning for cross-media retrieval”, Neurocomputing, vol.119, pp.10-16, 2013.

XXV.Zha Zheng-Jun, Meng Wang, Yan-Tao Zheng, Yi Yang, Richang Hong, and Tat-Seng Chua,“Interactive video indexing with statistical active learning,” IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 17-27, 2014.

XXVI.Zheng Liang, Zhi Bie, Yifan Sun, Jingdong Wang, Chi Su, Shengjin Wang, and Qi Tian,“Mars: A video benchmark for large-scale person re-identification.”In European Conference on Computer Vision, pp. 868-884, Springer, 2016.

XXVII.Zhou Jile, Guiguang Ding, and Yuchen Guo,“Latent semantic sparse hashing for cross-modal similarity search,”In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.

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Abstract:

In a lot of papers the main focus is given to study finite difference schemes for one dimensional hyperbolic equation. Since this idea is valid for one dimensional hyperbolic equation, one can also consider finite difference schemes for two dimensional hyperbolic equations. It is convenient to apply Fourier transform to check stability analysis. The present paper studies stability analysis of finite difference schemes for two dimensional hyperbolic equations with constant coefficients [IV].

Keywords:

Hyperbolic equation,fourier transform,difference schemes, stability analysis ,

Refference:

I.A.M.Blokhin, R.D. Aloev “Energy integrals and thier applications to investigation of stability of difference schemes”. Novosibirsk, 1993. 224 p.

II.A.G.Kulikovskii, N.V.Pogorelov,A.Yu.Semenov “Mathematical problems of numerical solution of hyperbolic systems”. M.:Physics and mathematics publishers, 2001, 608 p.

III.A.M.Blokhin,I.G.Sokovikov “About one approach to formulation of difference schemes for quasi-linear equations of gas dynamics”. Siberian Mathematical Journal, 1999, V.40, No 6, Pp.1236-1243.

IV.A.Harten “On the symmetric form of systems of conservation laws with enthropy”. J. Comput. Phys. 1983. V. 49, No 1, p.151-164.

V.A. M. Blokhin and R. D. Aloev, “Energy Integrals and Their Applications to Investigation of Stability of Difference Schemes”, Novosibirsk, 1993. 224 p(in Russian).

VI.A. I. Vol’pert and S. I. Khudyaev, “On the Cauchy problem for composite systems of nonlinear differential equations”,Math. USSR Sb.,16 (1972), 517–544.

VII.R. D. Aloev, Z. K. Eshkuvatov, Sh. O. Davlatov and N. M. A. Nik Long, “Sufficient condition of stability of finite element method for symmetric t-hyperbolic systems with constant coefficients”,Computersand Mathematics with Applications,68 (2014), 1194–1204.

VIII.R. D. Aloev,A. M. Blokhin and M. U. Hudayberganov, “One class of stable difference schemes for hyperbolic system”, American Journal of Numerical Analysis,2(2014), 85–89.

IX.S. K. Godunov, “Equations of Mathematical Physics”, Nauka, Moscow, 1979 (in Russian).

X.S. K. Godunov, “An interesting class of quasi-linear systems”, Dokl. Akad. Nauk SSSR,139 (1961), 521–523. (in Russian).

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Abstract:

This study investigates the dynamic response | E*|, rutting susceptibility and fatigue resistance of the virgin HMA and HMA blended with RAP and further RAP with hydrated lime mixtures. Optimum binder contents were obtained using Marshal Mix design method and the samples for performance testing were prepared. The Superpave Gyratory Compactor (SGC) was used. The samples were then cored and trimmed to the specified dimensions. Using Asphalt mixture performance tester (AMPT), test was conducted at four different temperatures (4. 4, 21. 1, 37. 7 and 54. 4) and six different frequencies (0.1, 0.5,1,5,10 and 25). And flow tests were conducted at only one temperature of 54.4 C. The viscous properties of the mixture and the dynamic response indicators were brought into account to obtain the fatigue parameters to evaluate the fatigue resistance and from flow tests the rutting susceptibility was evaluated and the results showed that RAP and lime has weak resistance to fatigue but are less susceptible to permanent deformation. Master curves for all the mixtures were developed. HMA blended with RAP are very cost effective and environmentally friendly. The flow number results revealed that the virgin HMA accumulated more strains at less loading cycles as compared to the other mixes.

Keywords:

Dynamic Modulus,Superpave,Flow numbe, RAP, Lime, HMA,

Refference:

I.AASHTO, T.J.S.S.f.T.M., M.o. Sampling, and Testing, Standard method of test for determining dynamic modulus of hot-mix asphalt concrete mixtures. 2005.

II.Bari, J., Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. 2005, Arizona State University Tempe, AZ.

III.Bayane, B.M., et al., Dynamic Modulus Master Curve Construction Using Christensen-Anderson-Marasteanu (CAM) model. 2017. 7(1): p. 53-63.

IV.D, A. Standard practice for preparation of bituminous specimens using Marshall apparatus. 2010. American Society for Testing and Materials USA.

V.Dougan, C.E., et al., E*-dynamic modulus: test protocol-problems and solutions. 2003.

VI.Ekwulo, E.O., D.B.J.A.J.o.E.S. Eme, and Technology, Fatigue and rutting strain analysis of flexible pavements designed using CBR methods. 2009. 3(12).

VII.Flintsch, G.W., et al., Asphalt materials characterization in support of implementation of the proposed mechanistic-empirical pavement design guide. 2007, Virginia Center for Transportation Innovation and Research.

VIII.Gul, M.A., Laboratory characterization of HMA mixes subjected to indirect tensile fatigue test. 2015, NUST.

IX.Ma, T., et al., Using RAP material in high modulus asphalt mixture. 2015. 44(2): p. 781-787.

X.Olidis, C. and D. Hein. Guide for the Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures Materials Characterization: Is Your Agency Ready. in 2004 Annual Conference of the Transportation Association of Canada. 2004.

XI.Pellinen, T.K. and M.W.J.J.o.t.A.o.A.P.T. Witczak, Stress dependent master curve construction for dynamic (complex) modulus (with discussion). 2002. 71

XII.Seo, J., et al., Estimation of in situ dynamic modulus by using MEPDG dynamic modulus and FWD data at different temperatures. 2013. 14(4): p. 343-353

XIII.Specifications, N.G.J.L., Pakistan. National Highway Authority, prepared by SAMPAK International (Pvt.) Ltd. 1998.

XIV.Witczak, M. and O.J.T.R.R.J.o.t.T.R.B. Fonseca, Revised predictive model for dynamic (complex) modulus of asphalt mixtures. 1996(1540): p. 15-23.

XV.Witczak, M. and J.J.A.S.U.R.R. Bari, Tempe : Arizona State University, Development of a master curve (E*) database for lime modified asphaltic mixtures. 2004.

XVI.Yu, H. and S.J.R.N.T. Shen, An investigation of dynamic modulus and flow number properties of asphalt mixtures in Washington State. 2012. 2.

XVII.Ye, Q., S. Wu, and N.J.I.J.o.F. Li, Investigation of the dynamic and fatigue properties of fiber-modified asphalt mixtures. 2009. 31(10): p. 1598-1602.

XVIII.Zhang, Y., R. Luo, and R.L.J.J.o.M.i.C.E. Lytton, Characterizing permanent deformation and fracture of asphalt mixtures by using compressive dynamic modulus tests. 2011. 24(7): p. 898-906.

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Abstract:

Soil pH is one of the major factors to be considered before doing any cultivation. Farmers always tested their soil pH either in soil pH laboratory, soil pH color chart or sometimes with the help of an expert. But these methods need time, labor and expertness. In this paper, a digital Smartphone image-based method is presented which predicts the soil pH in a simple and accurate way. Soil images are captured with the help of Redmi 3S prime Smartphone and store all the images as soil dataset. Soil images are processed through the different steps of digital image processing including soil image enhancement, soil image segmentation, and soil image feature extraction. During the feature extraction, Hue, Saturation and Value of the soil image are calculated and store Saturation and Hue plus Saturation as an index for the feature vector of the soil images. Prediction of soil pH is done with the help of Linear Regression, Artificial Neural Network, and KNN Regression. The coefficient of the linear regression is 0.859 for the Saturation feature of the soil image. Again, the coefficient of linear regression is 0.823 for Hue plus Saturation. The regression coefficient for ANN is 0.94064 with Levenberg-Marquardt algorithm and 0.92932 with Scaled Conjugate Gradient Backpropagation Algorithm. The regression coefficient of KNN is 0.89326 for K=5 with an RMSE value 0.1311. It is found that ANN always gives a better result as compare to another one.

Keywords:

Soil pH, K Mean,HSV,Linear Regression, KNN,ANN,

Refference:

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Abstract:

With an increasing demand for electrical energy, it is certain that the production will also increase, especially in rapid developing countries like Pakistan. Rapid industrialization is carving for more electrical energy, investment and suitable space for its infrastructure. But this development has to be sustainable keeping in mind the increasing global temperature due to pollution. Pakistan is the six largest populations in the world and hence produces a lot of waste daily. As of now, most of the waste goes to the landfills and gets burnt there or decomposed, either way releasing greenhouse gases in the process and degrading the environment. The municipal waste management is a challenging process in developing countries because of non-availability of proper infrastructure. There are some methods to manage this waste, such as scientific landfills, Incineration, Biomethanation, Gasification, Pyrolysis and Plasma Arc Gasification. By gasification the solid waste is converted into synthesis gas which can be used for chemical industries, power generation, transportation and industrial heating etc. This process shrinks the solid waste to slag or ash which can either be used to manufacture eco bricks or can be disposed of on landfill. Thus saving a lot of place from land filling and if used for power generation it does not release any considerable harmful gases into the environment making it a sustainable process and partially renewable source of energy. This project will estimate the cost and procedure to setup gasification plant. In the study, the generation, composition, treatment and energy potential of solid waste have been studied. The technologies for waste-to-energy conversion have also been studied and the feasibility comparison of two leading technologies has been done.

Keywords:

Municipal Solid Waste,Gasification,aste-to-Energy,

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