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Abstract:

The positive impact of social capital on job search success has been supported in the literature, however the research community has not reached a consensus because social capital is not always good, especially in terms of bonding. This paper explores the role of bonding social capital on several dimensions of job search success. The partial least square structural equation model was used with input data from 400 undergraduates, obtained from a field survey in Ho Chi Minh City, Vietnam. The results confirm the positive role of bonding social capital on acquired job quality, job search cost, and job search convenience. Keywords: education, job search success, partial least square structural equation model (PLS-SEM), social capital

Keywords:

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Refference:

I.Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (pls-sem) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514-538.

II.Ali, Z., Sun, H., & Ali, M. (2017). The impact of managerial and adaptive capabilities to stimulate organizational innovation in smes: A complementary pls–sem approach. Sustainability, 9(12), 2157.

III.Brasher, E. E., & Chen, P. Y. (1999). Evaluation of success criteria in job search: A process perspective. Journal of Occupational and Organizational Psychology, 72(1), 57-70.

IV.Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, S95-S120. V.Delavari, M., & Badizadeh, A. (2018). The effect of social media use at work on employees’ performance by considering the mediating role of trust, shared vision, network ties and knowledge transfer: An empirical case study. Pacific Business Review International, 10(10), 110-119.

VI.Dinh, Q. H., Dufhues, T. B., & Buchenrieder, G. (2012). Do connections matter? Individual social capital and credit constraints in vietnam. The European Journal of Development Research, 24(3), 337-358.

VII.Dubos, R. (2017). Social capital: Theory and research: Routledge.

VIII.Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling: University of Akron Press.

IX.Franzen, A., & Hangartner, D. (2006). Social networks and labour market outcomes: The non-monetary benefits of social capital. European Sociological Review, 22(4), 353-368.

X.Granovetter, M. (2018). Getting a job: A study of contacts and careers: University of Chicago press.

XI.Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology, 1360-1380.

XII.Granovetter, M. S. (1995). Getting a job: A study of contacts and careers: University of Chicago Press.

XIII.Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (pls-sem): Sage Publications.

XIV.Hanifan, L. J. (1916). The rural school community center. The Annals of the American Academy of Political and Social Science, 67(1), 130-138.

XV.Hernández-Perlines, F.,& Rung-Hoch, N. (2017). Sustainable entrepreneurial orientation in family firms. Sustainability, 9(7), 1212.

XVI.Kang, S., & Na, Y. (2018). The effect of the relationship characteristics and social capital of the sharing economy business on the social network, relationship competitive advantage, and continuance commitment. Sustainability, 10(7), 2203.

XVII.Kaur, K. (2016). Impact of quality of work life on overall job satisfaction level and motivational level: A study of government universities in punjab. PacificBusiness Review International, 8(8), 125-140.

XVIII.Lin, N. (1999). Building a network theory of social capital. Connections, 22(1), 28-51.

XIX.Mardani, A., Streimikiene, D., Zavadskas, E. K., Cavalaro, F., Nilashi, M., Jusoh, A., & Zare, H. (2017). Application of structural equation modeling (sem) to solve environmental sustainability problems: A comprehensive review and meta-analysis. Sustainability, 9(10), 1814.

XX.Mortimer, J. T. (1975). Review of getting a job: A study of contacts and careers by mark s. Granovetter. Sociology of Work and Occupations, 2, 284-287.

XXI.Myeong, S., & Seo, H. (2016). Which type of social capital matters for building trust in government? Looking for a new type of social capital in the governance era. Sustainability, 8(4), 322.

XXII.Narayan, D., & Cassidy, M. F. (2001). A dimensional approach to measuring social capital: Development and validation of a social capital inventory. Current sociology, 49(2), 59-102.

XXIII.Newton, K. (2001). Trust, social capital, civil society, and democracy. International Political Science Review, 22(2), 201-214.

XXIV.OECD. (2013). Oecd employment outlook 2013: OECD publishing.

XXV.Pandit, R. K. (2017). Use of analytical tools for measuring tourist satisfaction in india: A critical review of scholarly doctoral theses. Pacific Business Review

XXVI.Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual review of sociology, 24(1), 1-24.

XXVII.Putnam, R. D. (1993). The prosperous community. The american prospect, 4(13), 35-42.

XXVIII.Rather, R., & Sharma, J. (2018). Brand loyalty with hospitality brands: The role of customer brand identification, brand satisfaction and brand commitment. PACIFIC BUSINESS REVIEW INTERNATIONAL.

XXIX.Saks, A. M. (2006). Multiple predictors and criteria of job search success. Journal of Vocational Behavior, 68(3), 400-415.

XXX.Sanchez, G. (2013). Pls path modeling with r. Berkeley: Trowchez Editions, 383.

XXXI.Sanchez, G., Trinchera, L., Sanchez, M. G., & FactoMineR, S. (2013). Package ‘plspm’. In: Citeseer.

XXXII.Stone, W., Gray, M., & Huges, J. (2004). Social capital at work how family, friends and civic ties relate to labour market outcomes. Retrieved from.

XXXIII.Szreter, S., & Woolcock, M. (2004). Health by association? Social capital, social theory, and the political economy of public health. International Journal of Epidemiology, 33(4), 650-667.

XXXIV.Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global goodness-of-fit index for pls structural equation modelling.Paper presented at the Proceedings of the XLII SIS scientific meeting.

XXXV.Van Beuningen, J., & Schmeets, H. (2013). Developing a social capital index for the netherlands. Social Indicators Research, 113(3), 859-886.

XXXVI.Van Nguyen, P., Nguyen, P. T., Thuy, Q. L. H., Nguyen, T., & Huynh, V. D. B. (2016). Calculating weights of social capital index using analytic hierarchy process. International Journal of Economics and Financial Issues, 6(3). XXXVII.Wang, Y. (2008). The effects of cumulative social capital on job outcomes of college graduates.Virginia Tech, XXXVIII.Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using pls path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.

XXXIX.Woolcock, M., & Narayan, D. (2000). Social capital: Implications for development theory, research, and policy. The world bank research observer, 15(2), 225-249.

XL.Zou, T., Su, Y., & Wang, Y. (2018). Examining relationships between social capital, emotion experience and life satisfaction for sustainable community. Sustainability, 10(8), 2651.

 

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Abstract:

Developed economies have realized construction health and safety issue and have improved the working site condition by continuously emphasizing on the issue. Sadly, the case is different in developing countries particularly in the Indian subcontinent where the injury and death rate is high due to poor health and safety conditions. The paper examines the current health and safety practices, legislations and the management of Health and safety of Pakistan, a country in the Indian subcontinent. The data reviewed is organized around developing countries and the culture affecting health and safety in these countries is discussed. Moreover, the secondary data focuses on health and safety management system, behavioral aspects of the stakeholders, general health conditions of workers associated to the construction industry and the construction industry of Pakistan is also discussed. For the achievement of objectives, both, qualitative and quantitative methodologies are adopted (i-e questionnaire survey and interviews). The questionnaire and the interviews mainly focus on the contractors, workers, designers and the clients. The findings from these methods indicates that majority of the respondents have a poor degree of health and safety awareness. It also reveals that there are general health problems faced by the workers, people are hesitant to record and report the accident at site and showed the key behavioral aspects affecting the health and safety.

Keywords:

OSHA,CDM,HSE,MSD,SME,PPE,

Refference:

I.Asia Monitor Resource Centre (AMRC) (2013). OHS Legal Resources Book, Hong Kong. Li, H., & Chen, Z. (2007). Environmental management in construction: a quantitative approach. Routledge.

II.Abbe, O.O., Harvey, C.M., Ikuma, L.H. and Aghazadeh, F., 2011. Modelling the relationship between occupational stressors, psychosocial/physical symptoms and injuries in the construction industry. International Journal of Industrial Ergonomics, 41(2), pp.106-117.

III.Awan, T. (2001). Pakistan Institute of Labour Education and Research (FILER), Occupational Health and Safety in Pakistan, Asian Labour Update (ALU) issue, Nathan Road, Kowloon, Hong Kong, No.39, pp.5-7.

IV.Awan, T., 2007, Occupational Health and Safety in Pakistan [Online]. Pakistan Institute of Labour Education and Research, Asian Labour Update Issue 39. V.Alhajeri, M. (2011). Health and safety in the construction industry: challenges and solutions in the UAE. PHD. Coventry University.VI.Arndt,

V., Rothenbacher, U., Daniel, U., Zscshenderlein, B., Schuberth, S. and Brenner, H., 2005. Construction work and risk of occupational disability: a. a ten year follow up of, 14, pp.559-566

VII .Asia Monitor Resource Centre. (2013). Pakistan Asia Monitor Resource Centre. [Online]

VII.Awan, T. (2001). OCCUPATIONAL HEALTH AND SAFETY IN PAKISTAN Asia Monitor Resource Centre. [Online] Asia Monitor Resource Centre.

VIII.Azhar, S. and Choudhry, R.M., 2016. Capacity building in construction health and safety research, education, and practice in Pakistan. Built Environment Project and Asset Management, 6(1), pp.92-105.

IX.Azim, A., 2010, Labour and Human Resource Statistics [Online]

X.Bryman, A., 1992. Quantitative and qualitative research: further reflections on their integration. Mixing methods: Qualitative and quantitative research, pp.57-78.

XI.C. F. Chabris and Simons, Daniel, “The invisible gorilla: And other ways our intuitions deceive us.” NewYork: Crown (2010).

XII.Choudhry, R., Fang, D. and Mohamed, S. (2007). Developing a Model of Construction Safety Culture. Journal of Management in Engineering, [online] 23(4), pp.207-212.

XIII.CH De Zwart, MHW Frings-Dresen, JC Van Duivenbooden, B., 1999. Senior workers in the Dutch construction industry: a search for age-related work and health issues. Experimental aging research, 25(4), pp.385-391.

XIV.Coble, R.J. and Haupt, T.C., 1999, March. Construction safety in developing countries. In 2nd International Conference of CIB on W (Vol. 99, pp. 903-908).

XV.Creswell, J.W., 2014. A concise introduction to mixed methods research. Sage Publications.

XVI.Devine, C.M., Jastran, M., Jabs, J., Wethington, E., Farell, T.J. and Bisogni, C.A., 2006. “A lot of sacrifices:” Work–family spillover and the food choice coping strategies of low-wage employed parents. Social science & medicine, 63(10), pp.2591-2603.

XVII.DiDomenico, A. and Nussbaum, M. (2011). Effects of different physical workload parameters on mental workload and performance. International Journal of Industrial Ergonomics, 41(3), pp.255-260.

XVIII.Dong, X.S., Wang, X., Fujimoto, A. and Dobbin, R., 2012. Chronic back pain among older construction workers in the United States: a longitudinal study. International journal of occupational and environmental health, 18(2), pp.99-109.

XIX.Carr, L.T., 1994. The strengths and weaknesses of quantitative and qualitative research: what method for nursing. Journal of advanced nursing, 20(4), pp.716-721.

XX.Endsley, M. (1995). Measurement of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), pp.65-84.

XXI.Eich, W.G., 1996. Safety practices of large construction firms (Doctoral dissertation, Monterey, California. Naval Postgraduate School).

XXII.Farooqui, R., Ahmed, S. and Lodhi, S. (2008). Assessment of Pakistani Construction Industry –Current Performance and the Way Forward. Journal for the Advancement of Performance Information and Value, [online] 1(1), pp.55-62.

XXIII.Farooqui, R., Arif, F. and Rafeeqi, S. (2008). Advancing and Integrating Construction Education, Research & Practice. In: First International Conference on Construction in Developing Countries. [Online] Karachi: Department of Civil Engineering, NED University of Engineering & Technology, pp.74-88.

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Abstract:

Architecture Evaluation is a means to reduce risk and save cost. It holds the key to success of the system being developed. Various evaluation methods exist which have specific objectives and basis and all contribute to enhance product quality. In this paper a Complexity UML Based Architecture Evaluation (CUBAE) approach is proposed to evaluate the architecture of a system built using CBSD approach. . The proposed approach estimates the complexity of the architecture from the UML representation of different views of the architecture. Earlier works on complexity measures of UML representations found in literature are used along with proposed measures for complexity calculation. This complexity measure may be used to assess and compare architecture representing the same system along with other measures like modifiability and different quality attributes used for evaluating the architecture.

Keywords:

CBSD,Architecture evaluation,UML,Complexity,Metrics,

Refference:

I. B. Xu, D. Kang and J.Lu, ―”A structural complexity measure for UML class diagrams”, International Conference on Computational Science (ICCS 2004), Krakow Poland, June 2004, pp.431-435.
II. D.Kang, B. Xu, J. Lu and W.C. Chu, ―”A complexity measure for ontology based on UML”, IEEE 10th International Workshop on Future Trends in Distributed Computing Systems (FTDCS 2004), Suzhou, China, May 2004, pp.222-228.
III. E. Bouwers, C. Lilienthal, J. Visser, and A.V. Deursen , “A Cognitive Model for Software Architecture Complexity “,Proceedings of the International Conference on Program Comprehension (ICPC), IEEEComputerSociety, 2010. Software Engineering Research Group Technical Reports: http://www.se.ewi.tudelft.nl/techreports/.
IV. J.D. Thomas , Ph.D. Thesis, “Architecture Assessment of InformationSystem Families”, Department of Technology Management, Eindhoven University of Technology, February 2002.
V. M. Marchesi, OOA metrics for the unified modeling languages. In Proceedings of 2ndEuromicro Conference on Software Maintenance and Reengineering (CSMR’98), Palazzo degli Affari, Italy, March, 1998,pp.67- 73.
VI. Nico Lassing, “Architecture-Level Modifiability Analysis”, Ph.D. thesis, Free University Amsterdam, February 2002.
VII. P. Kruchten,‖ Architectural Blueprints—The ―4+1” View Model of Software Architecture‖, IEEE Software 12 (6),November 1995, pp. 42-50.
VIII. P. Bengtsson, Ph..D. Thesis, “Architecture Level Modifiability Analysis‖, Department of Software Engineering and Computer Science, Blekinge Institute of Technology, Sweden 2002.
IX. R. Kazman, M. Klein, and P. Clements, “ATAM: Method for Architecture Evaluation”, CMU/ SEI- 200 0- TR-0 04,ES C- TR- 200 0- 004.
X. R. Kazman,G. Abowd, L. Bass, & M. Webb, “SAAM: A Method for Analyzing the Properties of Software Architectures”,81-90. Proceedings of the 16th International Conference on Software Engineering. Sorrento, Italy, May 1994.
XI. R. Kazman and M, Burth, “Assessing Architectural Complexity”, Proceedings of 2nd Euromicro Working Conference on Software Maintenance And Reengineering (CSMR 98), IEEE Computer Society Press, 1998.
XII. S.Sengupta, A. Kanjilal, “Measuring Complexity of Component Based Architecture : A Graph based Approach”, ACM SIGSOFT Software Engineering Notes, January 2011, DOI: 10.1145/1921532.1921546.
XIII. T.Yi,F. Wu,and C. Gan, “A Comparison of Metrics for UML Class Diagrams”, ACM SIGSOFT Software Engineering Notes, Vol 25, Sept’2004.
XIV. https://resources.sei.cmu.edu/asset_files/FactSheet/2018_010_001_515610.p df
XV. http://www.sei.cmu.edu/architecture/saturn/2006/OConnell.pdf
XVI. “CBAM: Cost Benefit Analysis Method”, http://www.sei.cmu.edu/ata/products_services/ cbam. html
XVII. https://softwareengineering.stackexchange.com/questions/233257/mappingbetween-41- architectural -view-model-uml dated 16/10/17
XVIII. www.fcgss.com, “Applying 4+1 View Architecture with UML 2”, White Paper, 2007.

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Abstract:

Global warming has been a severe threat to humanityand greenhouse gases emitted from power plants is one of the major causes of global warming. In this paper, we use machine learning to incorporate energy efficiency techniques to buildings by predicting the Heating and Cooling Load using eight input features.Heating load is the amount of heat per unit time that a building needs to maintain the temperature at an established level whereas Cooling load is the amount of heat per unit time that must be removed. Heating, cooling, and ventilation systems are used to handle heating and cooling load. We train four regression (linear regression, Lasso, Ridge, and Elastic-Net) and three gradient boosting models (GBM, XGBoost, and LightGBM) and test them to compare their performance using 768 rows of data of residential buildings. We observe that the gradient boosting models perform significantly better than the standard regression models for both Heating Load and Cooling Load. XGBoost achieves the highest R-squared score of 0.99 for Heating Load and 0.99 for Cooling Load. From the results of this study, we conclude that machine learning techniques can predict Heating Load and Cooling Load with high accuracy. The obtained Heating load and cooling load values can be used to install efficient heating, cooling and ventilation systems and thus reduce both energy consumption and money.

Keywords:

Energy efficiency,Heating Load,Cooling Load,Machine Learning,

Refference:

I.Al Fardan, A. S., Al Gahtani, K. S., and Asif, M. (2017). Demand side management solution through new tariff structure to minimize excessive load growth and improve system load factor by improving commercial buildings energy performance in Saudi Arabia. 2017 5th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2017, pages 302–308.

II.Bizjak, M., Zalik, B.,ˇ Stumberger, G., and Lukaˇc, N. (2018). Estimation andˇ optimisation of buildings’ thermal load using LiDAR data. Building and Environment, 128:12–21.

III.Borgstein, E. H., Lamberts, R., and Hensen, J. L. (2018). Mapping failures in energy and environmental performance of buildings. Energy and Buildings, 158:476–485.

IV.Caputo, P., Costa, G., and Ferrari, S. (2013). A supporting method for defining energy strategies in the building sector at urban scale. Energy Policy, 55:261 –270. Special section: Long Run Transitions to Sustainable Economic Structures in the European Union and Beyond.V.Cetin, K. S., Tabares-Velasco, P. C., and Novoselac, A. (2014). Appliance daily energy use in new residential buildings: Use profiles and variation in time-ofuse. Energy and Buildings, 84:716–726.

VI.Chen, T. and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System.VII.Cheng, V. and Steemers, K. (2011). Modelling domestic energy consumption at district scale: A tool to support national and local energy policies. Environmental Modelling Software, 26(10):1186 –1198.VIII.Dai, C., Zhang, H., Arens, E., and Lian, Z. (2017). Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions. Building and Environment, 114:1–10.IX.Deng, H., Fannon, D., and Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163:34–43.X.Dheeru, D. and KarraTaniskidou, E. (2017). UCI machine learning repository.XI.Flett, G. and Kelly, N. (2017). A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand. Energy and Buildings, 140:171–187.XII.Fonseca, J. A.and Schlueter, A. (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142:247 –265.XIII.Guo, Y., Li, G., Chen, H., Wang, J., and Huang, Y. (2017). A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods. Energy Procedia, 142:1003–1008.XIV.Gupta, N. and Shet, H. N. (2016). Analysis of Measures to Improve EnergyXV.Performance of a Commercial Building by Energy Modeling.2016 Online International Conference on Green Engineering and Technologies (IC-GET) Analysis, pages 1–4.XVI.Hamid, M. F. A., Ramli, N. A., and Syawal Nik Mohd Kamal, N. M. F. (2017). An analysis of energy performance of a commercial building using energy modeling. In 2017 IEEE Conference on Energy Conversion (CENCON), pages 105–110. IEEE.XVII.Holmegaard, E., Johansen, A., and Kjærgaard, M. B. (2016). Towards a metadata discovery, maintenance and validation process to support applications that improve the energy performance of buildings. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016.XVIII.Jaffal, I. and Inard, C. (2017). A metamodel for building energy performance. Energy and Buildings, 151:501–510.

XIX.Jeong, Y.-k., Kim, T., Nam, H.-S., and Lee, I.-w. (2016). Implementation of energy performance assessment system for existing building. 2016 International Conference on Information and Communication Technology Convergence (ICTC), (20142010102370):393–395.XX.Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 3146–3154. Curran Associates, Inc.XXI.Kim, J., Zhou, Y., Schiavon, S., Raftery, P., and Brager, G. (2018). Personal comfort models: Predicting individuals’ thermal preferenceusing occupant heating and cooling behavior and machine learning. Building and Environment, 129:96–106.XXII.Konis, K. and Annavaram, M. (2017). The Occupant Mobile Gateway: A participatory sensing and machine-learning approach for occupant-aware energy management. Building and Environment, 118:1–13.XXIII.Kwok, S. S. K., Yuen, R. K. K., and Lee, E. W. M. (2011). An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Building and Environment, 46(8):1681–1690.XXIV.Onose, B.-a. (2016). Control optimization for increasing energy performance of existing buildings. 2016 Eleventh International Conference on Ecological Vehicles and Renewable Energies (EVER), pages 1–4.XXV.Parise, G., Martirano, L., and Parise, L. (2014). Energy performance of buildings: An useful procedure to estimate the impact of the lighting control systems. Conference Record -Industrial and Commercial Power Systems Technical Conference, pages 1–7.XXVI.Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., and Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208(May):889–904.XXVII.Shimoda, Y., Fujii, T., Morikawa, T., and Mizuno, M. (2004). Residential enduse energysimulation at city scale. Building and Environment, 39(8):959 –967. Building Simulation for Better Building Design.XXVIII.Song, M., Niu, F., Mao, N., Hu, Y., and Deng, S. (2018). Review on building energy performance improvement using phase change materials. Energy and Buildings, 158:776–793.

XXIX.Talebi, B., Haghighat, F., and Mirzaei, P. A. (2017). Simplified model to predict the thermal demand profile of districts. Energy and Buildings, 145:213 –225.XXX.Talebi, B., Haghighat, F., Tuohy, P., and Mirzaei, P. A. (2018). Validation of a community district energy system model using field measured data. Energy, 144:694 –706.XXXI.Touzani, S., Granderson, J., and Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158:1533–1543.XXXII.Tsanas, A. and Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49:560–567.XXXIII.Tuominen, P., Holopainen, R.,Eskola, L., Jokisalo, J., and Airaksinen, M. (2014). Calculation method and tool for assessing energy consumption in the building stock. Building and Environment, 75:153 –160.XXXIV.Vujoˇsevi ́c, M. and Krsti ́c-Furundˇzi ́c, A. (2017). The influence of atrium on energy performance of hotel building. Energy and Buildings, 156:140–150.XXXV.Wang, Z., Wang, Y., and Srinivasan, R. S. (2018). A novel ensemble learning approach to support building energy use prediction. Energy and Buildings, 159:109–122.

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Abstract:

Pervious concrete pavement could be a distinctive and effective thanks to capture storm water and permit it to course into the bottom therefore recharging groundwater, reducing storm water runoff, and meeting U.S. Environmental Protection Agency (EPA) storm water laws. this technique has been counseled by independent agency and geotechnical engineers as a Best Management Practices (BMPs) for the management of storm water runoff. This pavement technology creates additional economical land use by eliminating the necessity for retention ponds, swales, and alternative storm water management devices. receptive surface treatments retain the water sub-surface because it bit by bit infiltrates into the soil; holding the storm water in multiple air voids or cells conjointly aiding in water quality through degradation of hydrocarbons into greenhouse emission and water, and retentive metals within the structure keeps them from the groundwater table Despite the employment of receptive systems for nearly thirty years within the USA, not tons of analysis has been performed on the long run absorption of contaminants within the concrete microstructure. many studies showcase the removal potency of those pavements within the 1st few years of service, stating it's shown higher than seventy five p.c potency in removal of contaminants, this investigation targeted on varied receptive concrete treatments decisive optimum strength, voids, infiltration and voids. in addition geochemical work on trace metal sorption, major component adverse effects and water quality edges was performed on existing tons on MTSU field.

Keywords:

concrete pavemen, water runoff,optimum strength,

Refference:

I.Construction and Maintenance Assessment of Pervious Concrete Pavements, RMC Foundation, January 2007, www.rmcfoundation.org

II.G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references).

III.Hydraulic Performance Assessment of Pervious Concrete Pavements for Storm water Management Credit, RMC Foundation,

IV.I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.

V.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892,pp.68–73.

VI.Kevern, J., Wang, K., Suleiman, M., and Schaefer, V. (2005). Mix Design Development.

VII.M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.

VIII.Pervious Concrete Construction: Methods and Quality ControlIX.Principles pervious Concrete Testing (Charles Mitchell P.E)X.R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.

XI.Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].

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Abstract:

This paper presents an inventory model for deteriorating items with a constant rate of deterioration and the demand rate is flexible which depends on the price, stock as well as the reliability of the products. This model allowing the shortage under inflation, and delay in payment is also taken into account. We consider situation of the credit period is less than or greater than the cycle time for settling the account. Numerical example is given for different cases and sensitivity analysis is carried out to analyze the effect of the parameters on the optimal solution.

Keywords:

Deterioration,Reliability,Credit period,Inflation,Delay payment,

Refference:

I.A. Guria, B. Das, S. Mondal and M. Maiti,“Inventory policy for an item with inflation induced purchasing price, selling price and demand with immediate part payment”, Applied Mathematical Modelling, 37 (1-2), 240-257, 2013.

II.BenkheroufL., Z.T. Balkhi, “On an inventory model for deteriorating items and time-varying demand”, Mathematical Methods of Operations Research, 45(2), 221-233, 1997.

III.C.J. Chung and H.M. Wee, “Scheduling and replenishment plan for an integrated deteriorating inventory model with stock dependent selling rate”, International Journal of Advanced Management Technology, 35 (7-8), 665-679, 2008.

IV.C.K. Jaggi, P.K. Kapur, S.K. Goyal and S.K. Goel,”Optimalreplenishment and credit policy in EOQ model under two-levels of trade credit policy when demand is influenced by credit period”, International Journal of System Assurance Engineering and Management, 3(4), 352-359, 2012.

V.E.A. Elsayed and C. Teresi, “Analysis of inventory systems with deteriorating items”, International Journal of Production research, 21(4), 449-460, 1983.

VI.G. Janakiram, S. Sridhar, J.G. Shanthikumar, “A comparison of the optimal costs of two canonical inventory systems”, Operations Research, 55(5), 866-875, 2007.

VII.G.A. Widyadana and H.M. Wee, “Optimal deteriorating items production inventory models with random machine breakdown and stochastic repair time”,Applied Mathematical Modelling, 35, 3495-3508, 2011.

VIII.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “A production inventory model with fuzzy coefficients using parametric geometric programming approach”, International Journal of Machine Learning and Cybernetics, 2(2), 99-105, 2011.

IX.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “Fuzzy parametric geometric programming with application in fuzzy EPQ model under flexibility and reliability consideration”, Journal of Information and Computing Science, 7(3), 223-234, 2012.

X.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “An EPQ model with imprecise space constraint based on intuitionistic fuzzy optimization technique”, Journal of Multiple-Valued Logic and Soft Computing, 19(5-6), 409-423, 2012.

XI.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “EPQ model with fuzzy coefficient of objective and constraint via parametric geometric programming”, International Journal of Operational Research, 17(4), 436-448, 2013.

XII.G.S. Mahapatra, S. Adak, T.K. Mandal and S. Pal, “Inventory model for deteriorating items with time and reliability dependent demand and partial backorder”, International Journal of Operational Research, 29 (3), 344-359, 2017.

XIII.H.C. Liao, C.H. Tsai and C.T. Su, “An inventory model with deteriorating items under inflation when delay in payment is permissible”, International Journal of Production Economics, 63, 207-214, 2000.

XIV.H.J. Chang and C.Y. Dye, “An EOQ model for deteriorating items with time vary demand and partial backlogging”, Journal of Operational Research Society, 50, 1176-1182, 2001.

XV.H.M. Wee and S.T. Law, “Replenishment and pricing policy for deteriorating items taking into account the time value of money”, International Journal of Production Economics, 71, 213-220, 2001.

XVI.N.H.Shah and H. Soni, “A Multi-Object Production Inventory Model with Backorder for Fuzzy Random Demand Under Flexibility and Reliability”,Journal of Mathematical Modelling and Algorithms, 10 (4), 341-356, 2011.

XVII.K.J. Chung and C.N. Lin, “Optimal inventory replenishment models for deteriorating items taking account of time discounting”, Computer and Operations Research, 28, 67-83, 2001.XVIII.K.J. Chung and P.S. Ting, “A heuristic for replenishment for deteriorating items with a linear trend in demand”, Journal of Operational Research Society, 44, 1235-1241, 1993.

XIX.K.L. Hou, “An inventory model for deteriorating items with stock-dependent consumption rate and shortage under inflation and time discounting”,European Journal of Operational research, 168, 463-474, 2006.

XX.J.J. Liao and K.N. Huang, “An inventory model for deteriorating items with two levels of trade credit taking account of time discounting”, Acta Application Mathematics, 110(1), 313-326, 2010.

XXI.J.M. Chen, “An EOQ model for deteriorating items withtime-proportional demand and shortages under inflation and time discounting”, International Journal of Production Economics, 55, 21-30, 1998.

XXII.J. Sicilia, L.A. San-Jose and J. Garcia-Laguna, “An inventory model where backordered demand ratio is exponentially decreasing with the waiting time”, Annals of operations research, 19 (1), 137-155, 2012.

XXIII.P.K. Tripathy, W.M.Wee and P.R. Majhi, “An EOQ model with process reliability consideration”, Journal of Operational Research Society, 54, 549-554, 2003.

XXIV.R.B. Misra, “Optimum production lot size model for a system with deteriorating inventory”, International Journal of Production Research, 13, 495-505, 1975.

XXV.R.I. Levin, C.P. McLaughlin, R.P. Lamone and J.F. Kottas, “Production/Operations Management: Contemporary Policy for Managing Operating System”, McGraw-Hill, New York.

XXVI.S. Khanra, S.K. Ghosh and K.S. Chaudhuri,”An EOQ model for a deteriorating item with time dependent quadratic demand rate under permissible delay in payment”, Applied Mathematics and Computation, 218, 1-9, 2011.

XXVII.S. Pal, A. Goswami and K.S. Chaudhuri, “A deterministic inventory model for deteriorating items with stock dependent demand rate”, International Journal of Production Economics, 32, 291-99, 1993.

XXVIII.S. Pal, G.S. Mahapatra and G.P. Samanta, “An EPQ model of ramp type demand withWeibull deterioration under inflation and finite horizon in crisp and fuzzy environment”, International Journal of Production Economics, 156, 159-166, 2014.

XXIX.S. Pal, G.S. Mahapatra and G.P. Samanta, “An Inventory Model of Price and Stock dependent Demand Rate with Deterioration under Inflation and Delay in payment”, International Journal of System Assurance Engineering and Management, 5(4), 591-601, 2014.

XXX.S. Pal, G.S. Mahapatra and G.P. Samanta, “A production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness”, Economic Modelling, 46, 334-345, 2015.

XXXI.S. Pal, G.S. Mahapatra, G.P. Samanta, “A Three-Layer Supply Chain EPQ Model for Price-and Stock-Dependent Stochastic Demand with Imperfect Item Under Rework”, Journal of Uncertainty Analysis and Applications, 4 (1), 10, 2016.

XXXII.S. Pal, and G.S. Mahapatra, “A manufacturing-oriented supply chain model for imperfect quality with inspection errors, stochastic demand under rework and shortages”, Computers & Industrial Engineering, 106, 299-314, 2017.

XXXIII.S.H. Kim, M.A. Cohen and S. Netessine, “Performance contracting in after-sales service supply chains”, Management Science, 53 (12), 1843-1858, 2007.

XXXIV.S.K. Goyal, “EOQ under conditions of permissible delay in payments”, Journal of Operation Research Society, 36, 335-338, 1985.

XXXV.S.K. Manna, K.S. Chaudhuri, “An EOQ model with ramp type demand rate time dependent deterioration rate, unit production cost and shortage”, European Journal of Operational research, 171, 557-566, 2006.

XXXVI.S.S. Sana and K.S. Chaudhuri, “A deterministic EOQ model with delays in payments and price discount offers”, European Journal of Operational research, 184, 509-533, 2008.

XXXVII.T. Jin and H. Liao,“Spare parts inventory control considering stochastic growth of an installed base”, Computers & Industrial Engineering, 56 (1), 452-460, 2009.

XXXVIII.T. Roy and K.S. Chaudhuri,“An EPLS model for a variable production rate with stock-pricesensitive demand and deterioration”,Yugoslav Journalof Operations Research, 21, 1-13, 2011.

XXXIX.T.K. Datta, A.K. Pal, “Deterministic inventory system for deteriorating items with inventory level-dependent demand rate and shortages”, Opsearch, 27, 213.224, 1990.

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Abstract:

There are different classes of the graph generation. Node is one of the important parts in graph which is associated with the metric space. The elements of the set are placed very close to each other. These elements are similar to each other having minor or unobservable difference. Hence, it is difficult to find them in a given set in several of applications. The application area finds at many branches like multimedia, computer science and pattern reorganization. Here, we are focused on metric space and its prediction. Also, we have discussed some methods with some examples and the view of all known proposals to organize metric spaces. There are a large number of solutions are available. The notations of a random metric space and tried to prove that space was isometric. The study is focused on universal and random distance matrices. The properties of universal metric space with the properties of distance metric were correlated. Latent metric was also considered. This review includes the different scenarios of metric space with the basic concepts and mathematical formulae.

Keywords:

Random objects,Random prediction,Metric space,Space theory,

Refference:

I.A. M. Vershik. “Random metric spaces and universality”.math. Rt,St. Petersburg Department of Steklov Institute of Mathematics (2004).

II.Arzhantseva G., Delzant T.,“Examples Of Random Groups”. 1-30 (2008).

III.Bartini P. O. di “Consider some total and hence unique copy of A.” Soviet Math. Dokl., vol. 163, no. 4, p. 861–864 (1965).

IV.Biau G and Scornet E, “A Random Forest Guided Tour”arXiv: 1511. 0574 [math.ST] (2015).

V.Caruana R, Niculescu-Mizil A, “Data Mining in Metric Space: An Empirical Analysis ofSupervised Learning Performance Criteria” KDD; DOI:10.1145/1014052-1014063,(2004).

VI.Edgar Chavez, Gonzalo Navarro, Recardo and Josh ,“Searching in metric spaces”Journal of Experimental Algorithmics (JEA), 16, Article No. 1.1 (2011).

VII.Sarkar P,Chakrabarti D, Moore AW,“Theoretical Justification of Popular Link Prediction Heuristics”IJCAI 201, Proceedings of the 22ndInternational Joint Conference on Artificial Intelligence, pp 2722-2727, Barcelona,Catalonia,Spain 16-22 (2011).

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Abstract:

Induction training program plays vital role for all new mathematics teachers who are recruited. It helps them to increase efficacy level to adhere to the educational life and facilitate the organization with effective performance. This study is the part of wider research to help to investigate evaluation of induction training program for mathematics teachers with the main objective of predicting future modality of professional development on the bases of induction training process. 150 participants, who experienced for induction training program, were selected for this study. These 150 teachers were having vast experience and background in mathematics and statistics. The data were gathered through questionnaire and observation to explore the approaches of induction program for development and its effectiveness. Data were analyzed through statistical techniques of t-test, correlation, ANOVA and regression. The analysis showed significant effect of induction training program on teachers’ development as whole. Induction training program found positive relation with professionalism and strongly predict the professional development of educational organization.

Keywords:

Induction Program,Professional Development,Training of Teachers,Future Modality,

Refference:

I. Bush, T. &Middlewood, D., (2005), “Leading and Managing People in Education”. Great Britain: SAGE Publications.

II. Chidambaram, V., Ramachandran, A., Thevar, S.S., (2013), “Study On Efficacy Of Induction Training Programme In Indian Railways Using Factor Analysis”, Verslas: Teorijairpraktika Business: Theory and Practice, Issn 1648-0627 print / Issn 1822-4202.

III. Davey, G., (2004), “Complete Psychology”,Dubai: Book Point Ltd. Hyman, Flanagan, & Smith. (1982). The Hand Book of SchoolPsychology. New York: John Wiley & Sons.

IV. Fideler, E., &Haselkorn, D., (1999), “Learning The Rope: Urban Teachers Induction Program And Practices In The United States”, Belmont, MA: Recruiting New teachersV. Golrick, L., (2002), “Issue Brief: Improving Teacher Evaluation To Improve Teacher Quality”. New York: NGA Center for Best Practices.

V. Hassel, E., (1999), “ProfessionalDevelopment: Learning From The Best”, Oak Brook, IL: North Central Regional Educational Laboratory.

VII. Hendricks, K., &Potgieter, J. L., (2012), “A Theory Evaluation Of An Induction Programme”,SA Journal Of Human Resource Management/SA TydskrifVirMenslikehulpbronbestuur, 10(3), Art. #421, 9 pages. http://dx.doi.org/10.4102/ sajhrm.v10i3.421.

VIII. Ingersoll, R., & Strong, M., (2011), “The Impact Of Induction And Mentoring Programs Fro Beginning Teachers: A Critical Review Of The Research.”Review of Education Research. Vol. 81(2), 201-233. doi: 10.3102/0034654311403323.

IX. Klein, H.J., & Weaver, N.A., (2000), “The Effectiveness Of An Organizational Level Orientation Training Program In The Socialization Of New Hires”. JournalOf Personnel Psychology, 53, 47–66. http://dx.doi.org/10.1111/j.1744-6570.2000.tb00193.x.

X. Lisa, A., Lim, Y.L, Lew,M.D.N., & Chew, A., (2013), “Impact Of An Intensive Professional Induction Programme OnTeacher Self-Efficacy & Approach To Teaching”,Joint 7th SELF Biennial International Conference and ERAS Conference, Singapore 2013.

XI. Marriam, S.B., (2001), “Andragogy And Self‐Directed Learning: Pillars Of Adult Learning Theory”. New Directions For Adult And Continuing Education, 2001 (89), 3-14.

XII. Moscato, D., (2005), “Using Technology To Get Employees Onboard. Human Resources” Magazine, April, 107–109.

XIII. Olivia, P.F., and Pawlas, G.E., (1997), “Supervision for Today’sSchools”, 5th ed., Longman, New York, NY.

XIV. Peterson, D.A., (1990), “A History Of The Education Of Older Learners. Introduction To Educational”, Gerontology, 1-21.

XV. Rossi, P., Lipsey, M.W., & Freeman, H.E., (2004), “Evaluation.A Systematic Approach”.(7th edn.). Thousand Oaks: Sage.

XVI. Ruhela S.P.,and Singh R.P., (1990), “Trends in Modern IndianEducation”, Uppal Publishing House: New Delhi 395–(1990)XVII. Shulman, L.S., (1987), “Knowledge and teaching: Foundationsof theNew Reform”. Harvard Educational Review. 57(1), 1-22.

XVIII. Smaldino, Sharon E., Lowther, Deborah L., Russel, James D. (2008),“Instructional Technology and Media for Learning”.Pearson Merrill/Prentice Hall.

XIX. Smith Thomas, M., & Ingersoll Richard, M., (2004), “What Are TheEffects Of Induction And Mentoring On Beginning TeachersTurnover?”, American Educational Research Journal. Fall 2004, Vol41, No.3, pp. 681-714.

XX. Wesson, M.J., &Gogus, C.I., (2005), “Shaking Hands With AComputer: An Examination Of Two Methods Of OrganizationalNewcomer Orientation”. Journal of Applied Psychology, 90(5), 10181026. http://dx.doi.org/10.1037/0021-9010.90.5.1018, PMid:16162074.

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Abstract:

Data plays a major role in today's Internet world.Analyzing historical data became easy due to advancement of analytical tools. Gathering data from social networking websites is a great challenge for today's data scientists. Many advancements and research has been conducted to gather streaming data(data generated every second) .Hadoop has provided acomponent called Apache Flume to ingest data into HDFS for processing using MapReduce. It has its own benefits,which made many analysis easy for social networking data,but Apache Flume requires a depthknowledge on configuration files and administration. Our work proposes a framework for real-time data streaming of Twitter data. Apache spark which is an enhancement of Hadoop in terms of speed and faster processing provides much more insight than Apache flume.Spark is an in-memory distributed computing engine to increase processing speed over MapReduce, Spark is considered one of the most advanced ecosystem component for Batch and near-real time processing. We in our paper are explaining in detail about data ingestion using Apache Spark and Scala IDE. In our work the data will be directly ingested from Twitter website through tokens and access keys provided,which will be explained in chapter 3,4. Our GUI can also help a user to tweet into Twitter directly without moving on to Twitter website. We have also provided an option to categorize tweet of specific persons using '#' tags.The data thus obtained can be used for statistical analysis and generating reports.

Keywords:

Apache Spark,Big Data,Flume,Hadoop,Map Reduce,Twitter data ingestion,

Refference:

I.Altti Ilari Maarala, Mika Rautiainen, Miikka Salmi, Susanna Pirttikangas and Jukka Riekki”, Low latency analytics for streaming traffic data with Apache Spark” IEEE InternationalConference on Big Data (2015).

II.Anand Gupta, Hardeo Kumar Thakur ” A Big Data Analysis Framework Using Apache Spark and Deep Learning”, IEEE International Conference on Data Mining Workshops (2017).

III.Babak Yadranjiaghdam, Seyedfaraz Yasrobi, Nasseh Tabrizi “Developing a Real-time Data Analytics Framework For Twitter Streaming Data”,IEEE 6th International Congress on Big Data (2017).

IV.Hassan Nazeer, Waheed Iqbal, Fawaz Bokhari, Shuja Ur Rehman Baig ” Real-time Text Analytics Pipeline UsingOpen-source Big Data Tools”,arXiv:1712.04344, Dec(2017).

V.Marouane Birjalia, Abderrahim Beni-Hssane, Mohammed Erritali “Analyzing Social Media through Big Data using InfoSphere BigInsights and Apache Flume “, The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks Elsevier (2017).

VI.Ramkrushna C. Maheshwar, D. Haritha “Survey on High Performance Analytics ofBigdata with Apache Spark”,International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) (2016).VII.Sangeeta “Twitter Data Analysis Using FLUME & HIVE on Hadoop Framework”,Special Issue on International Journal of Recent Advances in Engineering & Technology (IJRAET) V-4 I-2February (2016).

VIII.S. Cha and M. Wachowicz. “Developing a real-time data analytics framework using Hadoop”,IEEE International Congress on Big Data June (2015)

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Abstract:

The Blindness is frequently used to describe severe visual deterioration with or without residual vision. According to WHO (World Health Organisation) 30Million people are blind. In India only 6.8 Million people are blind, 46.2 Million people have low vision and 5.3Million people are visually diminished. There is a great dependency for any type of movement or walking within area or out of the particular area, they use only their natural senses such as touch or sound for identification. To gift a simplified and independent life for blind person, this project proposed which is light weight , compact , cost efficient and easy to handle.

Keywords:

ArduinoUNO,Ultrasonic sensor,Fire sensor,Rain sensor, Blind Stick,

Refference:

I.AlbertoRodriguez, et al., “Obstacle avoidance system for assisting visually impaired people”, in proceeding IEEE Intelligent Vehicles Symposium Workshop, 2012.

II.Alshbatat, Abdel Ilah Nour.”Auto1nated Mobility and Orientation System for Blind or Partially.”INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, 568-582, 2013.

III.C.S. Kher, Y.A. Dabhade, S.K Kadam., S.D. Dhamdhere and A.V. Deshpande “An Intelligent Walking Stick for the Blind.” International Journal of Engineering Research and General Science, vol. 3, number 1, pp. 1057-1062, 2015.

IV.Dambhara, S. & Sakhara, A., 2011. Smart stick for Blind: Obstacle Detection, Artificial vision and Real-time assistance via GPS. International Journal of Computer Applications® (IJCA).

V.Mahdi Safaa A , Muhsin Asaad H. and Al-Mosawi Ali I.”Using Ultrasonic Sensor fo Blind and Deaf persons combines Voice.”International Science Congress Association, 50-52,2012.

VI.Mohammad Hazzaz, et al., “Smart Walking Stick-an electronic approach to assist visually disable persons”, International Journal of Scientific & Engineering Research vol. 4, No. 10, 2013.

VII.Nandhini. N, Vinoth Chakkaravarthy.G , G.Deepa Priya,”Talking Assistance about Location Finding”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 3, Issue 2, February 2014.

VIII.Shinohara, K. ―Designing assistive technology for blind users‖ In Proceedings of the 8th International ACM SIGACCESS conference on Computers and accessibility, ACM, 293–294, 2006.

IX.S.Sai Santhosh,T. Sasiprabha,R.Jeberson,.”BLI-NAV 1Emmbedded Navigation System for Blind People.”IEEE,277-282,2010.

X.Sung Jae Kang, et al.” Development of an Intelligent Guide-Stick for the Blind”, Proceeding of the IEEE international Conference on Robotics & Automation, 2001.

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