One of the basic parameters which describes road traffic is Annual Average Daily Traffic (AADT). Its accurate determination is possible only on the basis of data from the continuous measurement of traffic. However, such data for most road sections is unavailable, so AADT must be determined on the basis of short periods of random measurements. This article presents different methods of estimating AADT on the basis of daily traffic (VOL), and includes the traditional Factor Approach, developed Regression Models and Artificial Neural Network models. As explanatory variables, quantitative variables (VOL and the share of heavy vehicles) as well as qualitative variables (day of the week, month, level of AADT, the cross-section, road class, nature of the area, spatial linking, region of Poland and the nature of traffic patterns) were used. Based on comparisons of the presented methods, the Factor Approach was identified as the most useful.
In 2015 an important part of the official evaluation of Polish scientific journals was left to experts’ judgement. In this paper we try to establish which observable factors (with available data) are closely related to the outcome of experts’ evaluation of Polish journals in economic sciences. Using the multiple regression statistical model we show that only 5 variables (out of 17) significantly explain almost 50% of the empirical variance of the experts’ evaluation. The determinants of particular interest, not entering the formal criteria and not related to the impact on global science, are: the number of citations mainly in Polish journals and the affiliation with the Polish Academy of Sciences.
Landfill leachate makes a potential source of ground water pollution. Municipal waste landfill substratum can be used for removal of pollutants from leachate. Model research was performed with use of a sand bed and artificially prepared leachates. Effectiveness of filtration in a bed of specific thickness was assessed based on the total solids content. Result of the model research indicated that the mass of pollutants contained in leachate filtered by a layer of porous soil (mf) depends on the mass of pollutants supplied (md). Determined regression functions indicate agreement with empirical values of variable m′f. The determined regression functions allow for qualitative and quantitative assessment of influence of the analysed independent variables (m′d, l, ω) on values of mass of pollutants flowing from the medium sand layer. Results of this research can be used to forecast the level of pollution of soil and underground waters lying in the zone of potential impact of municipal waste landfill.
The method described in this work allows to determine the optimal distribution of pulses of digital signal as well as the non-linear mathematical model based on a multiple regression statistical analysis, which are specialized to an effective and low-cost testing of functional parameters in analog electronic circuits. The aim of this concept is to simplify the process of analog circuit specification validation and minimize hardware implementation, time and memory requirements during the testing stage. This strategy requires simulations of the analyzed analog electronic circuit; however, this effort is done only once – before the testing stage. Then, validation of circuit specification can be obtained after a quick, very low-cost procedure without time consuming computations and without expensive external measuring equipment usage. The analyzed test signature is a time response of the analog circuit to the stream of digital pulses for which distributions were determined during evolutionary optimization cycles. Besides, evolutionary computations assure determination of the optimal form and size of the non-linear mathematical formula used to estimate specific functional parameters. Generally, the obtained mathematical model has a structure similar to the polynomial one with terms calculated by means of multiple regression procedure. However, a higher ordered polynomial usage makes it possible to reach non-linear estimation model that improves accuracy of circuit parametric identification. It should be noted that all the evolutionary calculations are made only at the before test stage and the main computational effort, for the analog circuit specification test design, is necessary only once. Such diagnosing system is fully synchronized by a global digital signal clock that precisely determines time points of the slopes of input excitation pulses as well as acquired output signature samples. Efficiency of the proposed technique is confirmed by results obtained for examples based on analog circuits used in previous (and other) publications as test benchmarks.
Cost estimation in the pre-design phase both for the contractor as well as the investor is an important aspect from the point of view of budget planning for a construction project. Constantly growing commercial market, especially the one of public utility constructions, makes the contractor, at the stage of development the design concept, initially estimate the cost of the facade, e.g. office buildings, commercial buildings, etc., which are most often implemented in the form of aluminum-glass facades or ventilated elevations. The valuation of facade systems is of an individual calculation nature, which makes the process complicated, time-consuming, and requiring a high cost estimation work. The authors suggest using a model for estimating the cost of facade systems with the use of statistical methods based on multiple and stepwise regression. The data base used to form statistical models is the result of quantitative-qualitative research of the design and cost documentation of completed public facilities. Basing on the obtained information, the factors that shape the costs of construction façade systems were identified; which then constitute the input variables to the suggested cost estimation models.
Indian SMEs are going to play pivotal role in transforming Indian economy and achieving
double digit growth rate in near future. Performance of Indian SMEs is vital in making
India as a most preferred manufacturing destination worldwide under India’s “Make in India
Policy”. Current research was based on Indian automotive SMEs. Indian automotive SMEs
must develop significant agile capability in order to remain competitive in highly uncertain
global environment. One of the objectives of the research was to find various enablers of
agility through literature survey. Thereafter questionnaire administered exploratory factor
analysis was performed to extract various factors of agility relevant in Indian automotive
SMEs environment. Multiple regression analysis was applied to assess the relative importance
of these extracted factors. “Responsiveness” was the most important factor followed by
“Ability to reconfigure”, “Ability to collaborate”, and “Competency”. Thereafter fuzzy logic
bases algorithm was applied to assess the current level of agility of Indian automotive SMEs.
It was found as “Slightly Agile”, which was the deviation from the targeted level of agility.
Fuzzy ranking methodology facilitated the identification & criticalities of various barriers
to agility, so that necessary measures can be taken to improve the current agility level of
Indian automotive SMEs. The current research may helpful in finding; key enablers of agility,
assessing the level of agility, and ranking of the various enablers of agility to point out the
weak zone of agility so that subsequent corrective action may be taken in any industrial
environment similar to India automotive SMEs.
The prediction of rock cuttability to produce the lignite deposits in underground mining is important in excavation. Moreover, the certain geographic locations of rock masses for cuttability tests are also significant to apply and compare the rock cuttability parameters. In this study, sediment samples of two boreholes (Hole-1 and Hole-2) from the Sagdere Formation (Denizli Molasse Basin) were applied to find out the cerchar abrasivity index (CAI), rock quality designations (RQD), uniaxial compressive strengths, Brazilian tensile strengths and Shore hardnesses. The Sagdere Formation deposited in the terrestrial to shallow marine conditions consists mainly of conglomerates, sandstones, shales, lignites as well as reefal limestones coarse to fine grained. A dataset from the fine grained sediments (a part of the Sagdere Formation) have been created using rock parameters mentioned in the study. Dataset obtained were utilized to construct the best fitted statistical model for predicting CAI on the basis of multiple regression technique. Additionally, the relationships among the rock parameters were evaluated by fuzzy logic inference system whether the rock parameters used in the study can be correlated or not. When comparing the two statistical techniques, multiple regression method is more accurate and reliable than fuzzy logic inference method for the dataset in this study. Furthermore, CAI can be predicted by using UCS, BTS, SH and RQD values based on this study.