S. No

Page No.

1.

Authors:

Paper Title:

Abstract: The food we consumes provide nourishment and gives energy to our body, it gives us the ability to do daily activities and help improves our health in direct as well as indirect ways. A healthy and fresh diet is the most important way to keep ourselves fit. The food items kept at room temperature undergo rapid bacterial growth and chemical changes in food. Taking unhealthy food leads to bad health, and can cause different food borne diseases. The purpose to use biosensor and electrical sensors is to determine the freshness of food. A smart system which can detect the freshness of household food like dairy items, meat, and fruits. The identification and selection of pH sensor, Moisture sensor, and Gas sensor to develop a smart food freshness detector ensures the freshness of food and tells whether to eat it or bin it. An android application is developed to select the type of food to be checked.

Keywords: Food Freshness; pH Sensor; Moisture Sensor; Gas Sensor; Arduino Uno

References:

1. "Norovirus food poisoning", Foodborneillness.com,2018.[Online]. Available: http://www.foodborneillness.com/norovirus_food_poisoning/. [Accessed: 28- Jun- 2018].

2. "http://time.com", Time, 2018. [Online]. Available: http://time.com/3768003/351000-people-die-of-food-poisoning-globally-every-year/. [Accessed: 28- Jun- 2018].
3. Omid, M. Khojastehnazhand, A. Tabatabaeefar, “Estimating volume and mass of fruit by image processing technique”, Volume 100, Issue 2, September 2010
4. W. Gardner, P.N. Bartlett, "A brief history of electronic noses ," Sens. & Actuators B 18–19 (1994) 211–220
5. US, "FOODsniffer", com, 2018. [Online]. Available: http://www.myfoodsniffer.com. [Accessed: 25- Jun- 2018]. 

6. Ee Lim Tan, Wen Ni Ng, Ranyuan Shao, Brandon D. Pereles and Keat Ghee Ong,” A Wireless, Passive Sensor for Quantifying Packaged Food Quality”, Full Research Paper
7. “Importance of pH”,2018. [Online]. Available: http://www.sperdirect.com/public/the-importance-of-ph-in-foodquality-and-production/. [Accessed: 24- Jun- 2018].
8. Helmenstine, “What Is the pH of Milk?,”ThoughtCo.[Online].Available:https://www.thoughtco.com/what-is-the-ph-of-milk-603652.[Accessed: 28-Jun-2018].
9. Review Paper: Materials and Techniques for In Vivo pH Monitoring - IEEE  Journals & Magazine. (2017)
10. "Water in Meat and Poultry", usda.gov, 2018. [Online].Available:https://www.fsis.usda.gov/wps/portal/fsis/topics/food-safety-education/get-answers/food-safety-fact-sheets/meat-preparation/water-in-meat-and-poultry/ct_index. [Accessed: 24- Jun- 2018].
11. Dudley, R. (2004). Ethanol, fruit ripening, and the historical origins of human alcoholism in primate frugivory. Integrative and comparative biology, 44(4), 315-323.
12. Electrochemical Gas Sensor Module, C2H4 sensor, ethylene gas sensor, environment sensor-Winsen Electronics. (2018). Winsen-sensor.com
13. Campbell, “Is Yogurt Alkaline or Acidic?,” LIVESTRONG.COM, 03-Oct-2017. [Online].Available: https://www.livestrong.com/article/483061-is-yogurt-alkaline-or-acidic/. [Accessed: 28-Jun-2018].
14. Shiv Ram Dubey, Anand Singh Jalal, “Application of Image Processing in Fruit and Vegetable Analysis: A Review”, this article is published by Journal of Intelligent Systems, De Gruyter The online version DOI: 10.1515/jisys-2014-0079

1-4

2.

Authors:

Paper Title:

Abstract: Construction industry is labour intensive compared to other sectors with a range of 25-30 %. According to Wibowo (2002), the industry comprises of three major inputs namely labour, equipment and materials. Labour is therefore unpredictable in nature compared to other inputs (materials and equipment) which are affected and determined by the current market rates. Therefore, proper labour management is required all through the construction process; this can be achieved by introduction of effective management models for use in the construction industry. The research sought to develop an affective labour management model which can be used to increase productivity. The research used questionnaires and interviews to seek information from the practicing construction personnel who expressed their views and gave their opinions concerning labour management. The study found out that most practitioners are aware of the labour management models and their contribution in increasing productivity and some admitted that they have not used the models due to their complexity. The study used inferential statistics to generate correlation, which aimed to examine and describe the association and relationship between individual factors and their relationship to labour productivity. All factors affecting productivity were grouped in to five thematic coefficients which were used to create a model. The five coefficients are Labour planning (plan), Training of workforce (train), Motivation of labour (motivate), Mechanization of labour (mech) and availability of raw materials (raw). The model developed is:
Productivity = βplan + βtrain + βmotivate + βmech + βraw + βplan: βmech + β0 + ɛi
Logistic odds were assigned to each individual coefficient in order to give the model a simpler meaning; the odds generated were as shown below.
Productivity = 3.29plan + 1.31train + 0.85motivate + 2.7mech + 0.93raw + (3.29plan: 2.7mech) + constant (intercept)

Keywords: Labour, Labour Management Model, Labour Productivity, Production Efficiency.

References:

1. Abbot, C. and Carson, C. (2012), “A review of productivity analysis of the New Zealand construction industry”, Australasian Journal of Construction Economics and Building, Vol. 12 No. 3, pp. 1-15
2. AbouRizk, S., Knowles, P. and Hermann, U. (2001), “Estimating labor productivity for industrial construction activities”, Journal of Construction Engineering and Management, Vol. 127 No. 6, pp. 502-511. ”
3. Allmon, E., Haas, C.T., Borcherding, J.D., Allmon, E. and Goodrum, P.M. (2000), “US construction labor productivity trends, 1970-1998”,Journal of Construction Engineering and Management, Vol. 126 No. 2, pp. 97-104.
4. ARCOM (2013), “ARCOM abstracts”, Association of Researchers in Construction Management, available at: www.arcom.ac.uk/abstracts.php (accessed 15 March 2013).
5. Barg, J., Rurparathna, R., Mendis, D., AND Hewage, K. (2014). "Motivating Workers in Construction." Journal of Construction Engineering, 10.1155/2014/703084, 1-11.Online publication date: 1-Jan-2014.
6. Bernstein, H.M. (2003), “Measuring productivity: an industry challenge”, Civil Engineering,Vol. 73 No. 12, pp. 46-53.
7. Borcherding, J.D., and Alarcon, L.F. (1991). "Quantitative effects on construction productivity." The Construction Lawyer, 11(1), 1, 36-48.
8. Bryman,H. (2012). “Social research methods 4th edition”. New York: Oxford University Press Inc.
9. Chan, A., Scott, D., and Chan, A. (2004). ”Factors Affecting the Success of a Construction Project.” J. Constr. Eng. Manage., 130(1), 153–155
10. Chan, D. and Kumaraswamy, M. (2002), “Compressing construction duration: lessons learned from Hong Kong Building Projects”, International Journal of Project Management, Vol. 20 No. 1, pp. 23-35
11. Chan, E. and Raymond, Y. (2003), “Cultural considerations in international construction contracts”, Journal of Construction Engineering and Management, Vol. 129 No. 4, pp. 375-381.
12. Chang, C. and Yoo, W. (2013). "A Case Study on Productivity Analysis and Methods Improvement for Masonry Work." Journal of the Korea Institute of Building Construction, 10.5345/JKIBC.2013.13.4.372, 372-381. Online publication date: 20-Aug-2013.
13. Chia, F.C., Skitmore, M., Runeson, G. and Bridge, A. (2012), “An analysis of construction productivity in Malaysia”, Construction Management and Economics, Vol. 30 No. 12, pp. 1055-1069.
14. Dai, J., Goodrum, P.M. and Maloney, W.F. (2009), “Construction craft workers’ perceptions of the factors affecting their productivity”, Journal of Construction Engineering and Management, Vol. 135 No. 3, pp. 217-26.
15. Dissanayake, M., Fayek, R.A., Russell, A.D. and Pedrycz, W. (2005), “A hybrid neural network for predicting construction labour productivity”, Proceeding of ASCE International Conference on Computing in Civil Engineering, 12-15 July, Cancun, Mexico.
16. Dozzi, S.P. and AbouRizk, S. (1993), “Productivity in construction”, Institute for Research in Construction, National Research Council, Ottawa, ON.
17. Draper, N.R., and Smith, H. (1981). Applied Regression Analysis. John Wiley & Sons, New York, N.Y.
18. Elazouni, A.M., Ali, A.E. and Abdel-Razek, R.H. (2005), “Estimating the acceptability of new formwork systems using neural networks”, Journal of Construction Engineering and Management, Vol. 131 No. 1, pp. 33-41.
19. El-Gohary, K. and Aziz, R. (2013). "Factors Influencing Construction Labor Productivity in Egypt." Journal of Management in Engineering,10.1061/(ASCE)ME.1943-5479.0000168, 1-9. Online publication date: 1-Jan-2014.
20. Enshassi, A., Lisk, R., Sawalhi, I. and Radwan, I. (2003), “Contributors to construction delays in Palestine”, The Journal of American Institute of Constructors, Vol. 27 No. 2, pp. 45-53
21. Enshassi, A., Mohammed, S. and Abu Mosa, J. (2008), “Risk management in building projects: contractors’ perspective”, Emirates Journal for Engineering Research, Vol. 13 No. 1, pp. 29-44.
22. Frimpongs, Y., Oluwoye, J. and Crawford, L. (2003), “Causes of delay and cost overruns in construction of groundwater projects in a developing countries; Ghana as a case study”, International Journal of Project Management, Vol. 21 No. 5, pp. 321-6

23. Goodrum, P. and Haas, C. (2002), “Partial factor productivity and equipment technology change at activity level in US construction industry”, Journal of Construction Engineering and Management, Vol. 128 No. 6, pp. 463-472.
24. Goodrum, P.M. and Haas, C.T. (2002), “Partial factor productivity and equipment technology change at activity level in US construction industry”, Journal of Construction Engineering and Management, Vol. 128 No. 6, pp. 463-72.
25. Han, S.H., Park, S.H., Jin, E.J., Kim, H. and Seong, Y.K. (2008), “Critical issues and possible solutions for motivating foreign construction workers”, Journal of Management in Engineering, Vol. 24 No. 4, pp. 217-26.
26. Hanna, A., Chang, C., Sullivan, K. and Lackney, J. (2008), “Impact of shift work on labor productivity for labor intensive contractor”, Journal of Construction Engineering and Management, Vol. 134 No. 3, pp. 197-204.
27. Hanna, A.S., Chang, C.-K., Sullivan, K.T. and Lackney, J.A. (2008), “Impact of shift work on labor productivity for labor intensive contractor”, Journal of Construction Engineering and Management, Vol. 134 No. 3, pp. 197-204
28. Hanna, A.S., Taylor, C.S. and Sullivan, K.T. (2005), “Impact of extended overtime on construction labor productivity”, Journal of Construction Engineering and Management, Vol. 131 No. 6,pp. 734-9.
29. Horner, R.M.W. and Duff, A.R. (2001), More for Less – a Contractor’s Guide to Improving Productivity in Construction, CIRIA, London
30. Horner, R.M.W. and Duff, A.R. (2001), More for less – a Contractor’s Guide to Improving Productivity in Construction, CIRIA, London.
31. Kaming, P., Olomolaiye, P., Holt, G. and Harris, F.C. (1997), “Factors influencing construction time and cost overruns on high-rise projects in Indonesia”, Journal of Construction Management and Economics , Vol. 15 No. 1, pp. 83-94.
32. Kim, S. and Kim, Y. (2001), “A study on the construction labor productivity model using neuro-fuzzy network”, Conference of the Architectural Institute of Korea, Ansan, Korea, Vol. 21 1 pp. 493-6.
33. Koushki, P.A., Al-Rashid, K. and Kartam, N. (2005), “Delays and cost increases in the construction of private residential projects in Kuwait”, Journal of Construction Management and Economics, Vol. 23 No. 3, pp. 285-94
34. Le Ma Chunlu Liu Anthony Mills , (2016)," Construction labor productivity convergence: a conditional frontier approach ", Engineering, Construction and Architectural Management, Vol. 23 Iss 3 pp. 283 301
35. Li, Y. and Liu, C. (2010), “Malmquist indices of total factor productivity changes in the Australian construction industry”, Construction Management and Economics, Vol. 28 No. 9, pp. 933-945.
36. Lo, T., Fung, I. and Tung, K. (2006), “Construction delay in Hong Kong civil engineering projects”, Journal of Construction Engineering and Management, Vol. 132 No. 6, pp. 636-49.
37. Loosemore, M. (2014), “Improving construction productivity: a subcontractor’s perspective”, Engineering, Construction and Architectural Management, Vol. 21 No. 3, pp. 245-260.
38. Lowe, J.L. (2003). Construction Economics, www.callnetuk.come/home/johnlowe70/
39. Lu, M., AbouRizk, S.M. and Hermann, U.H. (2000), “Estimating labor productivity using probability inference neural network”, Journal of Computing in Civil Engineering, Vol. 14 No. 4, pp. 241-8.
40. Martin Loosemore , (2014)," Improving construction productivity: a subcontractor's perspective ",Engineering, Construction and Architectural Management, Vol. 21 Iss 3 pp. 245 – 260
41. Mugenda, O. M. & Mugenda, A. G. (1999). Research Methods: Qualitative and Quantitative Approach. Nairobi-Kenya: ARTS Press.
42. Odeh, A. and Battaineh, H.T. (2002), “Causes of construction delay: traditional contracts”, International Journal of Project Management, Vol. 20 No. 1, pp. 67-73
43. Oglesby, Clarkson, Henry Parker, and Gregory Howell. 1989. Productivity improvement in construction. New York: McGraw-Hill.
44. Ozorhon, B., Arditi, D., Dikmen, I. and Birgonul, M.T. (2007), “Effect of host country and project conditions in international construction joint ventures”, International Journal of Project Management, Vol. 25 No. 8, pp. 799-806
45. Portas, J. and AbouRizk, S. (1997), “Neutral network model for estimating construction productivity”, Journal of Construction Engineering and Management, Vol. 123 No. 4, pp 399-410
46. Rojas, E.M. and Aramvareekul, P. (2003), “Labor productivity drivers and opportunities in the construction industry”, Journal of Management in Engineering, Vol. 19 No. 2, pp 78-82
47. Seeley, L.H. (1995), Building Economics, Macmillan, London.
48. Song, L. and AbouRizk, S. (2008), “Measuring and modeling labor productivity using historical data”, Journal of Construction Engineering and Management, Vol. 134 No. 10, pp 786-94
49. Sonmez, R. and Rowings, J.E. (1998), “Construction labor productivity modeling with neural networks”, Journals of Construction Engineering and Management, Vol. 124 No. 6, pp 498-504
50. Thomas, H R. and Ahmet S. Sakaran. 1994. Forecasting labor productivity using factor model. Journal of construction Engineering and management.
51. Thomas, H. and Yiakoumis, I. (1987), “Factor model of construction productivity”, Journal of Construction Engineering and Management, Vol. 113 No. 4, pp. 623-639.
52. Wibowo, M.A. and Mawdesley, M.J. (2002). “Systems Modeling to Evaluate the Effects of Labour Intensive Construction, Proceeding International Conference on Advancement in Design, Construction, Construction Management and Maintenance of Building Structures.”
53. Yi, W. and Chan, A.P.C. (2014), “Critical review of labor productivity research in construction journals”, Journal of Management in Engineering, Vol. 30 No. 2, pp. 214-225
54. Yu, J. and Lee, H. (2002), “Productivity management system for construction projects”, Journal of the Architectural Institute of Korea, Vol. 18 No. 7, pp. 103-13.
55. Zayed, T.M. and Halpin, D.W. (2005), “Productivity and cost regression models for pile construction”, Journal of Construction Engineering and Management, Vol. 131 No. 7,pp. 779-89.
56. Zhu, K. and Lin, L. (2004), “A stage-by-stage factor control frame work for cost estimation of construction projects”

5-11

3.

Authors:

Paper Title:

Abstract: The current navigation system used in ships are still manual for various operations like data acquisition and processing. An autonomous navigator must be installed on the ship when the requirement is to maneuver the ship without any assistance. Such navigators accepts the data from different sensors to gauge the locations of obstacles present in water. Our work aims at developing a prototype model of the ship that is capable of autonomously sailing and navigating its own way through the obstacles present around it. The operation of the ship involves data acquisition and decision making in real time. The operation of the ship is also simulated in MATLAB using Fuzzy Logic. The electronic system designed for the ship has excellent scalability and can be used for the larger ships as well with modifications. The final system consists of both hardware and software making the ship completely autonomous.

Keywords: Autonomous Navigator, Data Acquisition, Fuzzy Logic, MATLAB, Prototype, Scalability. 

References:

1. Shashank Garg, Rohit Kumar Singh, Rajiv Kapoor, “AUTONOMOUS SHIP NAVIGATION SYSTEM,” Texas Instruments India Educators' Conference, DOI 10.1109/TIIEC.2013.60, 300 – 305.
2. Sang-Min Lee, Kyung-Yub Kwon, and Joongseon Joh, “A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREG Guidelines,” International Journal of Control, Automation, and Systems Vol.2, No. 2, June 2004.
3. P. Perera • J. P. Carvalho • C. Guedes Soares, “Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions,” pp 16:84–99, 2011.
4. Qiuhong LU , Shaoyuan LI , GuozhengYAN, “A Positioning and Navigation Algorithm of Autonomous Mobile Robot,” 2011.
5. Zeng X, Ito M, Shimizu E, “Building an automatic control system of manoeuvring ship in collision situation with genetic algorithms,” Proceedings of the 2001 American control conference, Arlington, VA, USA, pp 2852–2853, 2001.
6. AlYahmedi, A. S., El-Tahir, E., Pervez, T., “Behavior based control of a robotic based navigation aid for the blind,” Control & Applications Conference, July 13-July 15, 2009.
7. Cox, I. J., “Blanche — An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle,” IEEE Transactions on Robotics and Automation, vol. 7, no. 2, April, pp. 193-204, 1991.
8. Perera LP, Carvalho JP, Guedes Soares C, “Decision making system for  the collision avoidance of marine vessel navigation based on COLREGs  rules and regulations,” Proceedings of 13th congress of international maritime association of Mediterranean, Istanbul, Turkey, pp 1121–1128, 2009.
9. Hasegawa K, “Advanced marine traffic automation and management system for congested waterways and coastal areas,” Proceedings of international conference in ocean engineering (ICOE2009), Chennai, India, pp 1–10, 2009.

12-16

4.

Authors:

Paper Title:

Abstract: The continuous demand for improved and efficient health and safety management have put pressure to construction project managers, thereby creating a lot of management challenges that require an integrated process to be tackled. Hence, this research sought to assess the impact of health and safety management on construction projects success in Nairobi County. A survey to investigate health and safety management factors was delimited to 45 on-going commercial/ mixed urban development projects each worth more than Kshs100 million in Westlands constituency, Nairobi County. Owing to the fact that the population was reasonably small, a census was deemed suitable for this study. The survey achieved 80% rate of return of questionnaires from the construction project managers and data analysis was carried out using both descriptive and inferential (through correlation analysis) statistical methods. Results from the study were presented in form of tables and figures in a comprehensive manner. The findings indicated that, there is no well-defined site management system in the Kenyan construction industry and most sites are run through intuition and processes that involves a lot of paper work (checklists). This study therefore, recommends use of Oracle prime Projects Cloud Service, radio frequency identification device (RFID) technology, drones and Autodesk Navisworks software in construction health and safety management.

Keywords: Construction Health and Safety Management, Project Success. 

References:

1. Cheng & Li. (2004). Construction safety management: an exploratory study from China. Construction Innovation, Pp. 229–241.
2. Kibe, K. (2016). Assessment of health and safety management on construction sites in Kenya: a case of construction projects in Nairobi County. Nairobi: Jomo Kenyatta University of Agriculture and Technology.
3. Mugenda & Mugenda. (2003). Research Methods: Qualitative and Quantitative Approach. Nairobi, Kenya: Acts Press.
4. Muir, B. (2005). Challenges facing today’s construction manager. Newark, Delaware: University of Delaware.
5. Muiruri and Mulinge. (2014). Health and safety management on construction projects sites in Kenya: A case study of construction projects in Nairobi County. FIG Congress, (p. 14). Kuala Lumpur, Malaysia.
6. Nyabioge et al. (2018). Construction site management and its influence on project implementation in Nairobi County. International Journal of Soft Computing and Engineering (IJSCE), Volume-8 Issue-2, Pp. 7-12.
7. Olubunmi et al. (2014). Diversity among construction professionals: A study of their perception of construction site management practices. Akure, Nigeria: Federal University Of Technology.
8. (2017). Oracle prime projects cloud service. Retrieved from Oracle web site: http://www.oracle.com
9. Parsons, L. (2017). How drones can help monitor health and safety on site. Retrieved from BIM plus web site: http://www.bimplus.co.uk
10. Spillane et al. (2012). Confined site construction: An empirical analysis of factors impacting health and safety management. Journal of Engineering, Design and Technology, Vol. 10 No. 3, Pp. 397-420.
11. Wong et al. (2014). An integrated 5D tool for quantification of construction process emissions and accident identification. The 31st international symposium on automation and robotics in construction and mining (p. 5). Hong Kong: Hong Kong Polytechnic University.

17-19