Public higher education sector is under a growing pressure worldwide to increase efficiency and improve the quality of its activities. Limited financial resources as well as detailed regulations and supervision of their spending are the important features of the public higher education sector. Another important and debated issue is the division of public money among higher education institutions (HEI). It is therefore crucial to create stimuli for the rational management of public funds by HEI and for the quality improvement of HEI services. One of the proposed ways to achieve the desired result is the comparative efficiency assessment of HEI activities. Setting clear reference points for HEI, such assessment may be treated as a substitute for market competition. This paper describes a comparative efficiency study of 19 Polish universities of technology. Detailed analysis of potential input, output and environmental variables describing the HEI efficiency model was carried out. The study used the CCR-CRS output-oriented DEA model. It was assumed that HEI had more influence on achieved results than on the amount of their resources. The economies of scale were also studied in relation to the efficiency achieved. Sensitivity of the model to data errors was tested.
This paper presents a new scheme based on the fuzzy regression analysis for the estimation of peak load in distribution systems. In distribution system, bus load estimation is complicated because system load is usually monitored at only a few points. As a rule receiving nodes are not equipped with stationary measuring instruments so measurements of loads are performed sporadically. In general, the only information commonly available regarding loads, other than major distribution substations and equipment installations, is billing cycle customer kWh consumption. In order to model system uncertainty, inexactness, and random nature of customers' demand, a fuzzy system approach is proposed. This paper presents possibilities of application of the fuzzy set theory to power distribution system calculations. Unreliable and inaccurate input data have been modeled by means of fuzzy numbers. Trapezoidal and triangular forms of fuzzy numbers were used for description of input data. A regression model, expressing the correlation between a substation peak load and a set of customer features (explanatory variables), existing in the substation population, is determined. Simulation studies have been performed to demonstrate the efficiency of the proposed scheme on the basis of actual data obtained at two distribution system substations. The same data have been used for building standard linear regression models. Comparison of the performance of both methods has been done.
Construction of modern and durable asphalt and cement pavements requires high quality materials and suitable technologies that take into account sustainability concerns which are related to the environmental protection, mitigation and compensation for road construction effects on surface water and groundwater, soil, air, wildlife, landscape, vibration and noise. The objectives of this paper are to identify possible development directions of materials and technologies in road construction in the time perspective of approximately 30 years. In order to achieve that goal a nationwide Delphi survey with 150 invited experts was deployed. The study concluded that binding materials with improved viscoelastic range – and often with specific modifications – would continue to play a leading role. Furthermore, technologies that enable monitoring the state of road pavement condition in a continuous manner would be used to a greater range. Introduction of sensors into the pavement network would lead to the construction of “smart” roads while spreading of nanomaterial technology would improve the durability and reliability of road pavement construction.
Hourly load research data for residential customers is used to calculate diversity factors and KWHR-to-peak-RW factors.The customers are grouped into two classes based on their type of heating. The class-based diversity factors are a function of the number of customers, month and type of day.The class-based KWHRto-peak-KW factors are a function of the month and type of day. With the use of control samples, comparisons are made between estimated and measured peak KW. Estimated daily load profiles are also calculated and compared to measured daily load profiles.
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