Archive

Abstract:

Water is not only necessary for life but it plays vital role in the social accompanied with economic growth of that specific country especially developing countries. Increasing population and rapid urbanization accompanied with climate change may reduce the supply of fresh water globally in twenty-first century. This study aims to understand current household water use and water use pattern in different five houses of different five places of KPK Pakistan for five months, to improve the efficiency of house hold water use, to encourage sustainable use and conservation of water resources. For the provision of new fresh water facilities. It’s necessary for water supply system planners to comprehend current water consumption behaviors of inhabitant, and how they use water of the new facility in future. The water consumption pattern is differs for the nations and societies and dependent on factors which may vary consumption on daily, weekly, monthly and yearly basis. These factors are availability of water source, economic, cultural, seasonal, climatic, and approachability to these water sources.

Daily water consumption pattern for five different houses in different areas of KPK for five months were found by carefully examining the time taken by pump to fill the Household overhead tanks. But in order to increase reliability of the acquired data the pumps were allowed to fill the tank till water flow for one minute at overflow pipe of the water tank was not recorded. During the period of research (March 2018 to July 2018) it was concluded that the average consumption in Charsadda (urban), Charsadda (rural), Mardan (urban), Mardan (rural) and Kohat (urban) was 102.84, 61.81, 105.99, 66.44 and 100 litres per captia per day (LPCD) respectively. Interestingly it was also observed that the trends of water consumption were almost the same in urban and rural areas of different districts of KPK

Keywords:

Daily water Consumption Pattern,Peak factors,LPCD,Overflow pipe,Flow rate,

Refference:

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V. Howard, G., Bartram, J., Water, S., & World Health Organization. (2003). Domestic water quantity, service level and health.

VI. Kumpel, E., Woelfle‐Erskine, C., Ray, I., & Nelson, K. L. (2017). Measuring household consumption and waste in unmetered, intermittent piped water systems. Water Resources Research, 53(1), 302-315.

VII. Mead, N. (2008). Investigation of domestic water end use.

VIII. Sadr, S. M., Memon, F. A., Jain, A., Gulati, S., Duncan, A. P., Hussein, W. E., & Butler, D. (2016). An Analysis of Domestic Water Consumption in Jaipur, India.

IX. Shankhwar, A. K., Ramola, S., Mishra, T., & Srivastava, R. K. (2015). Grey water pollutant loads in residential colony and its economic management. Renewables: Wind, Water, and Solar, 2(1), 5.

X. Singh, O., &Turkiya, S. (2013). A survey of household domestic water consumption patterns in rural semi-arid village, India. GeoJournal, 78(5), 777-790.

XI. Tabassum, R., Arsalan, M. H., & Imam, N. (2016) Estimation of Water Demand For Commercial Units in Karachi City

DISCRETIZATIONS OF A FRACTIONAL ORDER LOGISTIC EQUATION ARISING FROM A SIMPLE SI-TERRORIST MODEL

Authors:

Asep K. Supriatna, Asep Sholahuddin, Hennie Husniah

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00006

Abstract:

The simplest model of terrorist growth model consists of two subpopulations, namely the susceptible subpopulation (S) and the militant or infected and infectious subpopulation (I). The model is governed by a coupled of differential equation reflecting the growth of the susceptible and infected subpopulations. Assuming a constant human population, the system can be reduced to a logistic differential equation. In this paper a fractional order delayed logistic equation is discussed and the discretization in the form of piecewise constant argument is used to find the solution. We use the first and the second order discretization method in the numerical scheme and investigate the effect of the fractional order in the growth of the underlying population modelled by the equation. We found that in general the discretization method can mimic the behavior of the original logistic equation for some parameters. However, destabilizing effect may occur depending on the combination of the values of related parameters, such as the fractional order, the intrinsic growth rate, and the piecewise constant argument parameter.

Keywords:

SI-terrorist model,logistic differential equation,fractional order,piecewise constant argument,

Refference:

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SHORT TERM RAIN FORECASTING FROM RADIOMETRIC BRIGHTNESS TEMPERATURE DATA

Authors:

Kausik Bhattacharyya, Manabendra Maiti , Salil Kumar Biswas, Md Anoarul Islam, Ayan Kanti Pradhan, Pradip Kumar Ghosh, Judhajit Sanyal

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00007

Abstract:

Prediction of rainfall is important in terms of the impact of a rain event on various systems such as communication systems. Traditional approaches used to predict rain events are often sensitive to fluctuations in the datasets on which the predictions are made. The present paper therefore develops a robust machine learning based technique for accurate short term rain forecasting, based on experimentally collected data. Ground based microwave radiometer allows continuous monitoring of ambient temperature, water vapour and liquid water, and other hydrometeors through measurement of radiometric brightness temperature at different frequencies in clear and cloudy weather conditions. The radiometric brightness temperature outputs at 23.834 and 30 GHz are used to establish a relation where data trends which are precursors to rain events can be identified using this parameter. Spline equations are modeled by partitioning the dataset. The predictability of the occurrence of precipitation and the rainfall intensity has been studied based on the rise of brightness temperature from clear to cloudy weather conditions. The rise of brightness temperature at 23.834 and 30 GHz show that the precursory variations of this parameter preceding rain events are observable from 29 to 47 minutes prior to precipitation depending upon the nature of rainfall patterns. The data collected empirically displays trends that are used in this paper to provide a clear forecast of short term precipitation. Spline regression based machine learning models incorporating monthly trends, proposed in this paper improve the accuracy of prediction of short term rain events.

Keywords:

Radiometer,brightness temperature,microwave,propagation,rain,weather forecasting,

Refference:

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V. Chan, P.W., & Tam, C.M., “Performance and application of a multi-wavelength, ground-based microwave radiometer in rain now-casting”, 9th IOAS-AOLS of AMS., 2005.
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A NEW STRATEGY FOR PHASE SWAPPING LOAD BALANCING RELYING ON A META-HEURISTIC MOGWO ALGORITHM

Authors:

Ibrahim H. Al-Kharsan, Ali. F. Marhoon, Jawad Radhi Mahmood

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00008

Abstract:

The significant spread of the single-phase loads in the consumer homes make the distribution network suffering from many dangerous problems like the load unbalancing. This problem comes because the single-phase devices continuously plugged in and out to different phases each time. This paper cared about this problem and solved it efficiently by the new meta-heuristic algorithm called GWO that applied for the first time to solve the load balancing issue. The algorithm has the ability based on the smart meter included swapping mechanism to disconnects the appropriate home phases from their initial connection to specific feeders and reconnected again to other feeders for satisfying the balancing in the secondary distribution network. The algorithm adapted to reaching the balancing with a minimal number of swaps and take care of the online PVs if the consumer likes to buy energy to the national utility. It distributed all the PVs in a manner that not cause a balancing problem or lead to a stability issue. The proposed method has been applied to some unbalanced areas with random data generated in MATLAB to confirm the efficacy of the proposed algorithm.

Keywords:

Load balancing,Gray Wolf algorithm,phase swapping, solar energy,swapping factor,unbalanced feeders,

Refference:

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IX. M. M. A. Salama, “Multi-Objective Optimization for the Operation of an Electric Distribution System With a Large Number of Single Phase Solar Generators,” vol. 4, no. 2, pp. 1038–1047, 2013.
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XI. P. V. Prasad, S. Sivanagaraju, and N. Sreenivasulu, “Network reconfiguration for load balancing in radial distribution systems using genetic algorithm,” Electr. Power Components Syst., vol. 36, no. 1, pp. 63–72, 2008.
XII. R. A. Hooshmand and S. Soltani, “Fuzzy optimal phase balancing of radial and meshed distribution networks using BF-PSO algorithm,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 47–57, 2012.
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A NOVEL 3D-CNN BASED FEATURE EXTRACTION BASED CLASSIFICATION FOR DIABETIC RETINOPATHY (DR) DETECTION

Authors:

Shaik Akbar, Divya Midhunchakkaravarthy

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00009

Abstract:

Diabetic retinopathy (DR) is one of the eye diseases that results in vision loss if not diagnosed earlier. The automated computer aided models on the DR images help in accurate treatment disease prevention. Microaneurysms (MA) and red spots are the indicators of DR for disease diagnosis. Many DR classification approaches have been proposed in the literature with deep learning framework and non-linear functionality. Also, these models are not applicable to large feature space due to high true negative rate. To optimize these problems, a hybrid feature selection based deep learning classifier is used to detect the MA and red spots disease severity on the large image dataset. In this paper, a new feature extraction approach is implemented to find the essential positive bag features to the deep learning framework. A hybrid SVM classification model is used to classify the disease patterns with high true positive rate. Experimental results are simulated on different DR image class labels; results show that the hybrid deep learning classification model is better than the traditional models under various statistical metrics on large dataset.

Keywords:

Diabetic Retinopathy,Deep learning,Feature Extraction,Classification,

Refference:

I. B. Dashtbozorg, J. Zhang, F. Huang, and B. M. ter Haar Romeny, “Retinal Microaneurysms Detection Using Local Convergence Index Features,” IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3300–3315, Jul. 2018.
II. J. Xu et al., “Automatic Analysis of Microaneurysms Turnover to Diagnose the Progression of Diabetic Retinopathy,” IEEE Access, vol. 6, pp. 9632–9642, 2018.
III. K. M. Adal, P. G. van Etten, J. P. Martinez, K. W. Rouwen, K. A. Vermeer, and L. J. van Vliet, “An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 6, pp. 1382–1390, Jun. 2018.
IV. L. Zhou, Y. Zhao, J. Yang, Q. Yu, and X. Xu, “Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images,” IET Image Processing, vol. 12, no. 4, pp. 563–571, 2018.
V. M. Leeza and H. Farooq, “Detection of severity level of diabetic retinopathy using Bag of features model,” IET Computer Vision, vol. 13, no. 5, pp. 523–530, 2019.
VI. P. Costa, A. Galdran, A. Smailagic, and A. Campilho, “A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images,” IEEE Access, vol. 6, pp. 18747–18758, 2018.
VII. R. Bhoopalan and S. Sundaramoorthy, “Efficient approach for the automatic detection of haemorrhages in colour retinal images,” IET Image Processing, vol. 12, no. 9, pp. 1540–1544, 2018.
VIII. R. F. Mansour, “Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey,” IEEE Reviews in Biomedical Engineering, vol. 10, pp. 334–349, 2017.
IX. R. Pires, S. Avila, H. F. Jelinek, J. Wainer, E. Valle, and A. Rocha, “Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 193–200, Jan. 2017.
X. S. Lahmiri, “High-frequency-based features for low and high retina haemorrhage classification,” Healthcare Technology Letters, vol. 4, no. 1, pp. 20–24, 2017.
XI. S. Manivannan, C. Cobb, S. Burgess, and E. Trucco, “Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification,” IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1140–1150, May 2017.
XII. S. Qummar et al., “A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,” IEEE Access, vol. 7, pp. 150530–150539, 2019.
XIII. S. Sil Kar and S. P. Maity, “Gradation of diabetic retinopathy on reconstructed image using compressed sensing,” IET Image Processing, vol. 12, no. 11, pp. 1956–1963, 2018.
XIV. Seoud, T. Hurtut, J. Chelbi, F. Cheriet, and J. M. P. Langlois, “Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening,” IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1116–1126, Apr. 2016.
XV. W. Cao, N. Czarnek, J. Shan, and L. Li, “Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods,” IEEE Transactions on NanoBioscience, vol. 17, no. 3, pp. 191–198, Jul. 2018.
XVI. W. Zhou, C. Wu, D. Chen, Y. Yi, and W. Du, “Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method,” IEEE Access, vol. 5, pp. 2563–2572, 2017.
XVII. Zeng, H. Chen, Y. Luo, and W. Ye, “Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network,” IEEE Access, vol. 7, pp. 30744–30753, 2019.

PREDICTION OF GENDER FROM FACIAL IMAGE USING DEEP LEARNING TECHNIQUES

Authors:

Ramalakshmi K, T. Jemima Jebaseeli, Venkatesan R

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00010

Abstract:

Gender recognition is a process of recognizing a person’s gender from their facial image using deep learning. The posed variation, illumination, and occlusion are some of the factors that affect in recognizing faces. These are reduced by increasing the accuracy of prediction. The network used for training the system is Convolutional Neural Network (CNN). For improving accuracy, the faces are detected and cropped from the image. Face detection is done using Open CV which detects the face by the frontal features of the face. This is done during training the network. The dataset used for training has cropped images. The proposed system predicts the person’s gender without compromising accuracy.

Keywords:

Gender recognition,convolutional neural network,VGGNet,

Refference:

I. Amit Dhomne, Ranjit Kumar and Vijay Bhan. Gender Recognition through Face using Deep Learning. Procedia Computer Science. 2018; 132: 2-10.
II. Biao Shi, HuaijuanZang, RongshengZheng and ShuZhan.An efficient 3D face recognition approach using Frenet feature of iso-geodesic curves. Journal of Visual Communication and Image Representation.2019; 59: 455-460.
III. Dhriti. K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion.International Journal of Computer Applications. 2012; 60(14): 0975 – 8887.

IV. DongshunCui, GuanghaoZhang, KaiHu, WeiHan and Guang-BinHuan, Face recognition using total loss function on face database with ID photos. Optics & Laser Technology. 2019; 110: 227-233.

V. Gil Levi and Tal Hassner. Age and Gender Classification Using Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2015.

VI. Hui-Cheng Lian, Bao-Liang Lu, ErinaTakikawa, and Satoshi Hosoi. Gender Recognition Using a Min-Max Modular Support Vector Machine. Lecture Notes in Computer Science. 2006; 210-215.
VII. HuiZhi and Sanyang Liu.Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation.2019; 58: 495-502.

VIII. Kevin Santoso and Gede Putra Kusuma.Face Recognition using Modified OpenFace. Procedia Computer Science.2018; 135: 510-517.

IX. MaafiriAyyad andChougdali Khalid.New fusion of SVD and Relevance Weighted LDA for face recognition, Procedia Computer Science.2019; 148: 380-388.

X. MaafiriAyyad andChougdali Khalid.New fusion of SVD and Relevance Weighted LDA for face recognition, Procedia Computer Science.2019; 148: 380-388.
XI. Rai P and Khanna P. Gender Classification Techniques: A Review. Advances in Computer Science, Engineering & Applications. Advances in Computer Science, Engineering & Applications. 2012; 51-59.

XII. Ranjeet Singh and Mohit Kumar Goel. Gender Classification Techniques-From Machine Learning to Deep Learning. International Journal of Computer Technology and Applications. 2016; 9(41): 77-88.
XIII. RupaliSandipKute,VibhaVyas and AlwinAnuse. Component-based face recognition under transfer learning for forensic applications.Information Sciences.2019; 476: 176-191.

XIV. Samik Banerjee and Sukhendu Das.LR-GAN for degraded Face Recognition.Pattern Recognition Letters.2018; 116: 246-253.
XV. SwaroopGuntupalliJandM. Ida Gobbini. Reading Faces: From Features to Recognition.Spotlight. 2017; 21(12): 915-916.

XVI. Vito Santarcangelo, Giovanni Maria Farinella and SebastianoBattiat. Gender Recognition: Methods, Datasets and Results. Conference: International Workshop on Video Analytics for Audience Measurement. 2015.
XVII. YangLi, WenmingZheng and ZhenCuiTong Zhang.Face recognition based on recurrent regression neural network. Neurocomputing. 2018; 297: 50-58.
XVIII. Yan Liang, Yun Zhang andXian-Xian Zeng.Pose-invariant 3D face recognition using half face. Signal Processing: Image Communication. 2017; 57: 84-90.
XIX. Yihua Chen, Ali Rahimi and Luca Cazzanti. Similarity-based Classification: Concepts and Algorithms, Journal of Machine Learning Research. 2009; 747-776.

XX. ZahraddeenSufyanu, FatmaSusilawatiMohamad, Abdulganiyu Abdu Yusuf, AbdulbasitNuhu Musa and RabiuAbdulkadir. Feature Extraction Methods for Face Recognition.International Review of Applied Engineering Research. 2016; 5(3): 5658-5668.

A COMPREHENSIVE STUDY ON MINING COMPETITORS TOWARDS HANDLING UNSTRUCTURED DATASET

Authors:

Safeena Nasreen, R. Vijaya Prakash, Seena Naik Korra

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00011

Abstract:

In the existing very competitive organization situation, there is a demand to evaluate the competitive attributes as well as aspects of a thing that most affect its competition. The evaluation of competition regularly uses the consumer opinions in terms of evaluations, rankings, as well as the mother lode of relevant information, 's coming from the web and also various other sources. Within this paper, a professional meaning of the very competitive mining is explains along with its similar jobs. Lastly, the paper provides the challenges and also usefulness in the competition mining activities with optimum renovations. We feature skillful strategies for examining strength insignificant poll datasets as well as take care of the symbolic problem of finding the greatest k opponents of a provided point. Once and for all, our team determine the nature of our outcomes and the convenience of our method using several datasets coming from different locations.

Keywords:

Mining competitors,structured dataset,unstructured dataset,

Refference:

I. Anusha Medavaka, “Enhanced Classification Framework on Social Networks” in “Journal of Advances in Science and Technology”, Vol. IX, Issue No. XIX, May-2015 [ISSN : 2230-9659]
II. Anusha Medavaka, “Monitoring and Controlling Local Area Network Using Android APP” in “International Journal of Research”, Vol. 7, Issue No. IV, April-2018 [ISSN : 2236-6124]
III. Anusha Medavaka, Dr. P. Niranjan, P. Shireesha, “USER SPECIFIC SEARCH HISTORIES AND ORGANIZING PROBLEMS” in “International Journal of Advanced Computer Technology (IJACT)”, Vol. 3, Issue No. 6 [ISSN : 2319-7900]
IV. Anusha Medavaka, P. Shireesha, “A Survey on TraffiCop Android Application” in “Journal of Advances in Science and Technology”, Vol. 14, Issue No. 2, September-2017 [ISSN : 2230-9659]
V. B. H. Clark as well as D. B. Montgomery, “Managerial Identification of Competitors,” Journal of Marketing, 1999.
VI. G. Pant and O. R. Sheng, “Web footprints of companies: Using on-line isomorphism for competition id,” Information Systems Research, vol. 26, no. 1, pp. 188– 209, 2015.
VII. J. F. Porac as well as H. Thomas, “Taxonomic mental styles in competitor definition,” The Academy of Management Review, 2008.
VIII. K. Xu, S. S. Liao, J. Li, as well as Y. Song, “Mining comparative opin- ions coming from consumer assessments for very competitive intellect,” Decis. Help Syst., 2011.
IX. M. Bergen as well as M. A. Peteraf, “Competitor identity as well as competitor study: a vast- based managing method,” Managerial and Decision Economics, 2002.
X. Q. Wan, R. C.-W. Wong, and Y. Peng, “Finding top-k successful products,” in ICDE, 2011.
XI. W. T. Few, “Managerial competition identification: Integrating the classification, economical and also business identification perspectives, “DoctoralDissertaion, 2007.
XII. X.Q. Wan, R. C.-W. Wong, I. F. Ilyas, M. T. O ¨ zsu, as well as Y. Peng, “Creating affordable items,” PVLDB, vol. 2, no. 1, pp. 898– 909, 2009.

IMPLEMENTATION OF BRT SYSTEM IN PESHAWAR CITY(PAKISTAN) : A METHODOLOGY FOR CASE STUDY ANALYSIS

Authors:

Raza Hussain, Mudasir Hussain, Mushahid Hussain, Ram Chand, Sohail Asghar

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00012

Abstract:

Large cities have increasing mobility problems due to a large number of vehicles on the streets and roads, which results in traffic jams and thus a waste of time and money. An alternative to improve traffic is to prioritize the public transportation system. Many cities across the world have recently launched ambitious programs of BRT system implementation with varying success. And Peshawar is also one of them which has launched a BRT system in 2017. This paper presents the planning management and importance of Bus rapid transit (BRT) for the city like Peshawar as it is more cost-effective mode, and to examine the level of accessibility, perception/acceptability of users on the operations of BRT in Peshawar in terms of distances, safety, affordability, reliability, travel time and waiting time at the BRT bus stops. Creating a safe corridor by combining the existing physical infrastructure and the use of communications systems in all components of the BRT system is to improve passenger safety factors in urban trips. For the study, data for all the cities have been collected from different sources such as local authority websites, organizations involved in the projects, published reports and studies, and the media.

Keywords:

Bus Rapid Transit,passengers,Corridor,Stations,Planning and Management,Peshawar city,

Refference:

I. Ali, Z., Shah, S. A. A., & Hussain, A. (2012). Growing Traffic in Peshawar: An Analysis of Causes and Impacts. South Asian Studies.
II. Asim Farooq, AamirJavaid, Dr. Astrid Karl Peshawar Local Public Transport Strategy and Organization (Pakistan) © 2015 IJEDR | Volume 3, Issue 4 | ISSN: 2321-9939

III. DesMobi, 2016. Preliminary Design Report, Peshawar Bus Rapid Transit Project.

IV. EPA, 1997: Guidelines for Sensitive and Critical Areas. Pakistan Environmental Protection Agency, Government of Pakistan, October

V. GoP, 1997: Sectoral Guidelines for Environmental Reports: – Environmental Protection Agency, Government of Pakistan, October

VI. GoP, 1997: Pakistan Environmental Protection Act 1997: – Government of Pakistan, October

VII. GoP, 1997: Policy Guidelines for Preparation and Review of Environmental Reports: – Environmental Protection Agency, Government of Pakistan, November

VIII. GoP, 1997: Policy and Procedures for filling, review and approval of Environmental Assessment Environmental Protection Agency, Government of Pakistan, November
IX. Government of Khyber Pakhtunkhw. (2015). Khyber Pakhtunkhwa in Figures 2014. Retrieved from http://kpbos.gov.pk/files/1425491934.pdf.
X. L.Da-ming, J. Xiao-jing, “General Introduction of Bus Rapicl Transport Intelligent System of Beijing South-Center Corridor”. In: Conf. Communication and Transportation Systems Engineering and Information, China, 2005
XI. Samina Islam, 2016, Land Acquisition and Resettlement Plan for Peshawar BRT

XII. Survey of Pakistan, 1997. Atlas of Pakistan. Director Map Publication, Survey of Pakistan, Rawalpindi.

XIII. World Bank. 2001. OP 4.12 – Involuntary Resettlement. The World Bank Operational

XIV. World Bank. 2003. Social Analysis Sourcebook, Incorporating Social Dimensions into Bank-Supported Projects. Social Development Department, The World Bank. December 2003.

XV. World Bank. 2005. OP 4.10 – Indigenous People. The World Bank Operational Manual. The World Bank. July 2005

THE BEHAVIOR OF FUME SILICA AND BAGASSE ASH IN CONCRETE

Authors:

Adeed Khan, Waqas Khalid, Mohammad Adil, Muhammad Hasnain, Zeeshan Haider, Mudasir Hussain

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00013

Abstract:

The nanotechnology has added new trends in concrete. By virtue of it has enhanced the concrete properties. The study is associated with the application of nano silica (Fume silica) and Bagasse Ash. The reason for the tests conducted was to discover impacts of fumesilica (FS) & B-A on the quality features of concrete. B-A & fume-Silica were used to examine whether these nano materials are capable to enhance the concrete bonds or they are weak. The tests when conducted, the nano material B-A, replaced cement by10 percent & 20 percent & (1, 2 & 3)percent of fume-Silica was added by weight. The tests when conducted, showed impressive increase in early age compressive strength and steady increase in overall compressive strength. The increased strength was due to the percentage addition nano materials. The FESEM micrographs illustrated that the nano materials have hardened the concrete bonds up to certain addition by weight and a gradual decrease was seen when the amounts of nano materials exceeded than the required ratios.

Keywords:

Concrete,Fume silica,Compressive strength,Bagasse Ash (B-A),

Refference:

I. Bartos, P. J., Sonebi, M., &Tamimi, A. K. (Eds.). (2002). Report 24: workability and rheology of fresh concrete: compendium of tests–report of RILEM Technical Committee TC 145-WSM (Vol. 24). RILEM publications.

II. Feynman, R.P. (1960). There’s plenty of room at the bottom. Engineering and Science 23, pp. 22-36.

III. Garboczi, E.J. (2009). Concrete nanoscience and nanotechnology: Definitions and applications. Nanotechnology in Construction 3, pp. 81-88.

IV. Hammond, G. P., & Jones, C. I. (2008). Embodied energy and carbon in construction materials. Proceedings of the Institution of Civil Engineers-Energy, 161(2), 87-98.

V. Kline, J., &Barcelo, L. (2012, May). Cement and CO 2, a victim of success!. In Cement Industry Technical Conference, 2012 IEEE-IAS/PCA 53rd (pp. 1-14). IEEE.

VI. Nelson, E.B. and Guillot, D. (2007). Well Cementing. Second edition, Schlumberger Ltd., Sugar Land, Texas, U.S.A., pp. 1-773.

VII. Olivier, J. G., Peters, J. A., &Janssens-Maenhout, G. (2012). Trends in global CO2 emissions 2012 report. The Hague, pp. 1-64.

VIII. Proske, T., Hainer, S., Rezvani, M., &Graubner, C. A. (2013). Eco-friendly concretes with reduced water and cement contents—Mix design principles and laboratory tests. Cement and Concrete Research, 51, 38-46.

IX. Sobolev, K., Flores, I., Hermosillo, R. (2006). Nanomaterials and nanotechnology for high-performance cement composites. In Proceedings of ACI Session on “Nanotechnology of Concrete: Recent Developments and Future Perspectives”. American Concrete Institute, 7 November, Denver, U.S.A., pp. 91-118.

X. Taniguchi, N. (1974). On the basic concept of nano-technology. In Proceedings of International Conference on Production Engineering Tokyo, Part II, Vol. 2, Japan, Society of Precision Engineering, pp. 18-23.

XI. Van den Heede, P., & De Belie, N. (2012). Environmental impact and life cycle assessment (LCA) of traditional and ‘green ‘concretes: literature review and theoretical calculations. Cement and Concrete Composites, 34(4), 431-442

DETECTION OF EXHALE ANALYSIS THROUGH ARDUINO AND INTEGRATED SENSORS

Authors:

S.Venkatesulu, SeenaNaik Korra, E.Sudarshan, A. Rajeshwar Rao

DOI NO:

https://doi.org/10.26782/jmcms.2020.02.00014

Abstract:

Nowadays population growth increasing exponentially and also health diseases increase parallel due to environment and manmade things. A common person live healthy to required things are air, food, water environmental conditions and our body functioning. Due to these above parameters health issues are affecting and medical Diagnosis is too costly. The proposed analysis is helping out to every common people with their exhale; through their exhale they can analyze present environmental and body circumstances. The basic analysis is exhale air quality index comparing some gases like carbon oxide, nitrogen, argon and oxygen. The detection of exhale and analysis forms a major application in therapeutic field. It helps in detecting the deviation real time by using Arduino and integrated sensors. The developed system systematically monitors the gases Parameters to get down various consequences so that early detection of dieses symptoms is possible. Detection of abnormality may lead to avoidance of chronic respiratory diseases

Keywords:

Exhale,Arduino,Integrated Sensors,Gases ,Detection of Abnormality,

Refference:

I. H.Donal Brooke Jenkins, Ideal and Real Gases. Dalton’s Law of Partial Pressures.

II. http://www.e-how.com

III. IvacBozic, DjordjeKlisic, Andrej Savic, Detection of Breath Phases, Serbian Journal of Electrical Engineering, Vol. 6, No.3, 389-398, December2009.

IV. K. SeenaNaik and E. Sudarshan ,” Smart Healthcare Monitoring System using Raspberry Pi on IoT Platform” ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. Vol. 14, No. 4, February 2019. ISSN 1819-6608.

V. Laura Boccanfuso and Jason M. O’Kane, Remote Measurement of Breath Analysis in Real-time Using a High Precision, Single point, Infrared Temperature Sensor, Biomedical Robotics and Biomechatronics (BioRob), 4th IEEE RAS & EMBS Internation Conference, 2012.

VI. MittapelliNikitha, RajeshwarRaoArabelli, Smart Monitoring and Controlling of home Appliances using Internet of Things, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019.

VII. Paul S.Monks and Kerry A.Wills, Breath Analysis, Education in Chemistry, July 2010.

VIII. Phil Corbishley and Esther Rodriguez-Villegas, Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System, IEEE Transactions on Biomedical Engineering, Vol .55, No.1, January 2008.

IX. RajeshwarRaoArabelli , D. Rajababu, Transformer Optimal Protection using Internet of Things, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-11, September 2019.