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Efficiency of Public and Nonpublic Primary Health Care Providers in Poland

References Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273-292. Farrell, M. J. (1957). The measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3), 253-290. Folland, S., Goodmand, A. C., & Stano, M. (2013). The Economics of Health and Healthcare. New Yersey, USA: Pearson

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Efficiency Analysis with non parametric method: Illustration of the Tunisian ports

REFERENCES 1. Ablanedo-Rosas, J.H. and Ruiz-Torres, A.J. (2009), Benchmarking of Mexican ports with data envelopment analysis. International Journal of Shipping and Transport Logistics 1(3): 276-294. 2. Al-Eraqi, A., Mustafa, A., Khader, A. and Barros, C. (2008), Efficiency of Middle Eastern and East African seaports: Application of DEA using window analysis. European Journal of Scientific Research 23(4): 597-612. 3. Al-Eraqi, A.S., Mustafa, A. and Khader, A.T. (2010), An extended DEA windows analysis: Middle East and East African seaports

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The Production Efficiency and Specific Energy Consumption During Processing of Corn Extrudates with Fresh Vegetables Addition

blends by extrusion processing. Journal of Food Engineering, 84 (2), 231-242. Baik, B. K., Powers, J., Nguyen, L. T. (2004). Extrusion of regular and waxy barley flours for production of expanded cereals. Cereal Chemistry, 8 , 94-99. Connell, P. M., Finkelstein, S. R., Scott, M. L., Vallen, B. (2018). Negative associations of frozen compared with fresh vegetables. Appetite , 127 , 296-302. Harper, J. M. (1981). Extrusion of Foods . CRC Press, Boca Raton, FL, USA. Korotkiy, I. A., Korotkaya, E. V., Kireev, V. V. (2016). Energy efficiency

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Seaport efficiency and productivity based on Data Envelopment Analysis and Malmquist Productivity Index

References 1. G. N. Okeudo, Measurement of Efficiency Level in Nigerian Seaport after Reform Policy Implementation. Case Study of One and Rivers Seaport, Nigeria, IOSR Journal of Business and Management, Volume 12, Issue 5 (Jul. - Aug. 2013), pp. 46-55. 2. K.P.B. Cullinane, D.-W. Song, R. Gray, A stochastic frontier model of the efficiency of major container terminals in Asia: assessing the influence of administrative and ownership structures. Transportation Research A: Policy and Practice 36, 2002, pp. 743

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Benchmarking of contributory organisations within the framework of technical efficiency

-hours and client-hours, client satisfaction rates, number of employees and their attendance, occupancy rate of facilities. According to Malíková (2011) , several methods and techniques can be used to measure the quality and efficiency of social services such as quality standards of social services; supervision, intervision; TQM (Total Quality Management); ABC (Activity Based Costing), Balanced Scorecard; controlling; benchmarking; complaints; self-assessment and others. The methods range from simple metrics to complex metrics such as Balanced Scorecard. This is a system

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Integration of Travel Agencies with other Supply Chain Members: Impact on Efficiency

R eferences Alamdari, Fariba. (2002). Regional development in airlines and travel agents relationship. Journal of Air Transport Management, 8 (5), 339-348. doi: 10.1016/s0969-6997(02)00014-5 Assaf, A., Barros, C. P., & Josiassen, A. (2010). Hotel efficiency: A bootstrapped metafrontier approach. International Journal of Hospitality Management, 29 (3), 468-475. doi: 10.1016/j.ijhm.2009.10.020 Assaf, A. George, Barros, Carlos Pestana, & Dieke, Peter U. C. (2011). Portuguese tour operators: A fight for survival. Journal of Air Transport

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Process Efficiency and Energy Consumption During the Extrusion of Potato and Multigrain Formulations

, P., Krzaczek, P. (2015). The influence of moisture content of barley on the flaking process. Agricultural Engineering, 1 (153), 5-14. Bouasla, A., Wójtowicz, A., Juśko, S., Zidoune, M.N. (2017). Energy consumption and process efficiency as affected by extrusion-cooking conditions and recipe formulation during the production of gluten-free rice-legumes products, Agricultural Engineering, 21 (1), 39-46. Camacho-Hernández, I.L., Zazueta-Morales, J.J., Gallegos-Infante, J.A., Aguilar-Palazuelos, E., Rocha-Guzmán, N.E., Navarro-Cortez, R.O., Jacobo

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The Technical Efficiency of Tunisian Ports: Comparing Data Envelopment Analysis and Stochastic Frontier Analysis Scores

REFERENCES [1] Aigner, D., Lovell, C. & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics. 6(1), 21–37. DOI: 10.1016/0304-4076(77)90052-5 [2] Al-Eraqi, A., Adli, M. & Khader, M. & Barros, C.P. (2008). Efficiency of Middle Eastern and East African Seaports: Application of DEA Using Window Analysis. European journal of scientific research. 23 (4), 597-612. DOI: 10.1504/IJSTL.2009.027533. [3] Battese, G.E. & Corra, G.S. (1977). Estimation of a production frontier model: With

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The Energy Efficiency of Electric Energy as a Traction Used in Transport

References Database of real vehicle fuel consumption; Decarbonisation Pathways, publication of Eurelectric (iniciative of energy industry in Europe, December 2018; Electropedia – Battery and Energy Technologies – Energy Efficiency; European standard EN 16 258:2012 Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers

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Vessel Crowd Movement Pattern Mining for Maritime Traffic Management


The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

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